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Unverified Commit a283cddf authored by Daniel Povey's avatar Daniel Povey Committed by GitHub
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Merge pull request #4 from pkufool/fast_rnnt 

Sync with k2 rnnt_loss
parents b5828e2b 182fe8de
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.gitignore
View file @ a283cddf
...@@ -2,3 +2,9 @@ ...@@ -2,3 +2,9 @@
.idea .idea
venv* venv*
deploy* deploy*
**/__pycache__
**/build*
Testing*
dist/*
*egg-info*/*
CMakeLists.txt 0 → 100644
View file @ a283cddf
if("x${CMAKE_SOURCE_DIR}" STREQUAL "x${CMAKE_BINARY_DIR}")
message(FATAL_ERROR "\
In-source build is not a good practice.
Please use:
mkdir build
cd build
cmake ..
to build this project"
)
endif()
cmake_minimum_required(VERSION 3.8 FATAL_ERROR)
set(languages CXX)
set(_FT_WITH_CUDA ON)
# the following settings are modified from cub/CMakeLists.txt
set(CMAKE_CXX_STANDARD 14 CACHE STRING "The C++ version to be used.")
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_CXX_EXTENSIONS OFF)
message(STATUS "C++ Standard version: ${CMAKE_CXX_STANDARD}")
find_program(FT_HAS_NVCC nvcc)
if(NOT FT_HAS_NVCC AND "$ENV{CUDACXX}" STREQUAL "")
message(STATUS "No NVCC detected. Disable CUDA support")
set(_FT_WITH_CUDA OFF)
endif()
if(APPLE OR (DEFINED FT_WITH_CUDA AND NOT FT_WITH_CUDA))
if(_FT_WITH_CUDA)
message(STATUS "Disable CUDA support")
set(_FT_WITH_CUDA OFF)
endif()
endif()
if(_FT_WITH_CUDA)
set(languages ${languages} CUDA)
if(NOT DEFINED FT_WITH_CUDA)
set(FT_WITH_CUDA ON)
endif()
endif()
message(STATUS "Enabled languages: ${languages}")
project(fast_rnnt ${languages})
set(FT_VERSION "1.0")
set(ALLOWABLE_BUILD_TYPES Debug Release RelWithDebInfo MinSizeRel)
set(DEFAULT_BUILD_TYPE "Release")
set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "${ALLOWABLE_BUILD_TYPES}")
if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
# CMAKE_CONFIGURATION_TYPES: with config type values from other generators (IDE).
message(STATUS "No CMAKE_BUILD_TYPE given, default to ${DEFAULT_BUILD_TYPE}")
set(CMAKE_BUILD_TYPE "${DEFAULT_BUILD_TYPE}")
elseif(NOT CMAKE_BUILD_TYPE IN_LIST ALLOWABLE_BUILD_TYPES)
message(FATAL_ERROR "Invalid build type: ${CMAKE_BUILD_TYPE}, \
choose one from ${ALLOWABLE_BUILD_TYPES}")
endif()
option(FT_BUILD_TESTS "Whether to build tests or not" ON)
option(BUILD_SHARED_LIBS "Whether to build shared libs" ON)
set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib")
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib")
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin")
set(CMAKE_SKIP_BUILD_RPATH FALSE)
set(BUILD_RPATH_USE_ORIGIN TRUE)
set(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE)
set(CMAKE_INSTALL_RPATH "$ORIGIN")
set(CMAKE_BUILD_RPATH "$ORIGIN")
if(FT_WITH_CUDA)
add_definitions(-DFT_WITH_CUDA)
# Force CUDA C++ standard to be the same as the C++ standard used.
#
# Now, CMake is unaligned with reality on standard versions: https://gitlab.kitware.com/cmake/cmake/issues/18597
# which means that using standard CMake methods, it's impossible to actually sync the CXX and CUDA versions for pre-11
# versions of C++; CUDA accepts 98 but translates that to 03, while CXX doesn't accept 03 (and doesn't translate that to 03).
# In case this gives You, dear user, any trouble, please escalate the above CMake bug, so we can support reality properly.
if(DEFINED CMAKE_CUDA_STANDARD)
message(WARNING "You've set CMAKE_CUDA_STANDARD; please note that this variable is ignored, and CMAKE_CXX_STANDARD"
" is used as the C++ standard version for both C++ and CUDA.")
endif()
unset(CMAKE_CUDA_STANDARD CACHE)
set(CMAKE_CUDA_STANDARD ${CMAKE_CXX_STANDARD})
include(cmake/select_compute_arch.cmake)
cuda_select_nvcc_arch_flags(FT_COMPUTE_ARCH_FLAGS)
message(STATUS "FT_COMPUTE_ARCH_FLAGS: ${FT_COMPUTE_ARCH_FLAGS}")
# set(OT_COMPUTE_ARCHS 30 32 35 50 52 53 60 61 62 70 72)
# message(WARNING "arch 62/72 are not supported for now")
# see https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/
# https://www.myzhar.com/blog/tutorials/tutorial-nvidia-gpu-cuda-compute-capability/
set(FT_COMPUTE_ARCH_CANDIDATES 35 50 60 61 70 75)
if(CUDA_VERSION VERSION_GREATER "11.0")
list(APPEND FT_COMPUTE_ARCH_CANDIDATES 80 86)
endif()
message(STATUS "FT_COMPUTE_ARCH_CANDIDATES ${FT_COMPUTE_ARCH_CANDIDATES}")
set(FT_COMPUTE_ARCHS)
foreach(COMPUTE_ARCH IN LISTS FT_COMPUTE_ARCH_CANDIDATES)
if("${FT_COMPUTE_ARCH_FLAGS}" MATCHES ${COMPUTE_ARCH})
message(STATUS "Adding arch ${COMPUTE_ARCH}")
list(APPEND FT_COMPUTE_ARCHS ${COMPUTE_ARCH})
else()
message(STATUS "Skipping arch ${COMPUTE_ARCH}")
endif()
endforeach()
if(NOT FT_COMPUTE_ARCHS)
set(FT_COMPUTE_ARCHS ${FT_COMPUTE_ARCH_CANDIDATES})
endif()
message(STATUS "FT_COMPUTE_ARCHS: ${FT_COMPUTE_ARCHS}")
foreach(COMPUTE_ARCH IN LISTS FT_COMPUTE_ARCHS)
set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-extended-lambda -gencode arch=compute_${COMPUTE_ARCH},code=sm_${COMPUTE_ARCH}")
set(CMAKE_CUDA_ARCHITECTURES "${COMPUTE_ARCH}-real;${COMPUTE_ARCH}-virtual;${CMAKE_CUDA_ARCHITECTURES}")
endforeach()
endif()
list(APPEND CMAKE_MODULE_PATH ${CMAKE_SOURCE_DIR}/cmake/Modules)
list(APPEND CMAKE_MODULE_PATH ${CMAKE_SOURCE_DIR}/cmake)
include(pybind11)
include(torch)
if(FT_WITH_CUDA AND CUDA_VERSION VERSION_LESS 11.0)
# CUB is included in CUDA toolkit 11.0 and above
include(cub)
endif()
if(FT_BUILD_TESTS)
enable_testing()
include(googletest)
endif()
add_subdirectory(fast_rnnt)
LICENSE 0 → 100644
View file @ a283cddf
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LICENSE_code deleted 100644 → 0
View file @ b5828e2b
This license file (BSD-3-Clause) covers the 'code' part of the work,
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terminate automatically.
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Section 6(a), it reinstates:
1. automatically as of the date the violation is cured, provided
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violation; or
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For the avoidance of doubt, this Section 6(b) does not affect any
right the Licensor may have to seek remedies for Your violations
of this Public License.
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Licensed Material not stated herein are separate from and
independent of the terms and conditions of this Public License.
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a. For the avoidance of doubt, this Public License does not, and
shall not be interpreted to, reduce, limit, restrict, or impose
conditions on any use of the Licensed Material that could lawfully
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=======================================================================
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MANIFEST.in
View file @ a283cddf
include requirements.txt include requirements.txt
include pyproject.toml include README.md
include LICENSE* include LICENSE*
recursive-include torch_mutual_information * include CMakeLists.txt
recursive-include doc/img * recursive-include fast_rnnt *.*
recursive-include tests * recursive-include cmake *.*
global-exclude *.pyc global-exclude *.pyc
README.md
View file @ a283cddf
This project implements a method for faster and more memory-efficient RNN-T loss computation, called `pruned rnnt`.
This contains an implementation of a particular recursion that turns out to be useful for fast RNN-T computation. Note: There is also a fast RNN-T loss implementation in [k2](https://github.com/k2-fsa/k2) project, which shares the same code here. We make `fast_rnnt` a stand-alone project in case someone wants only this rnnt loss.
We need to update this README.
\ No newline at end of file ## How does the pruned-rnnt work ?
We first obtain pruning bounds for the RNN-T recursion using a simple joiner network that is just an addition of the encoder and decoder, then we use those pruning bounds to evaluate the full, non-linear joiner network.
The picture below display the gradients (obtained by `rnnt_loss_simple` with `return_grad=true`) of lattice nodes, at each time frame, only a small set of nodes have a non-zero gradient, which justifies the pruned RNN-T loss, i.e., putting a limit on the number of symbols per frame.
<img src="https://user-images.githubusercontent.com/5284924/158116784-4dcf1107-2b84-4c0c-90c3-cb4a02f027c9.png" width="900" height="250" />
> This picture is taken from [here](https://github.com/k2-fsa/icefall/pull/251)
## Installation
You can install it via `pip`:
```
pip install fast_rnnt
```
You can also install from source:
```
git clone https://github.com/danpovey/fast_rnnt.git
cd fast_rnnt
python setup.py install
```
To check that `fast_rnnt` was installed successfully, please run
```
python3 -c "import fast_rnnt; print(fast_rnnt.__version__)"
```
which should print the version of the installed `fast_rnnt`, e.g., `1.0`.
### How to display installation log ?
Use
```
pip install --verbose fast_rnnt
```
### How to reduce installation time ?
Use
```
export FT_MAKE_ARGS="-j"
pip install --verbose fast_rnnt
```
It will pass `-j` to `make`.
### Which version of PyTorch is supported ?
It has been tested on PyTorch >= 1.5.0.
Note: The cuda version of the Pytorch should be the same as the cuda version in your environment,
or it will cause a compilation error.
### How to install a CPU version of `fast_rnnt` ?
Use
```
export FT_CMAKE_ARGS="-DCMAKE_BUILD_TYPE=Release -DFT_WITH_CUDA=OFF"
export FT_MAKE_ARGS="-j"
pip install --verbose fast_rnnt
```
It will pass `-DCMAKE_BUILD_TYPE=Release -DFT_WITH_CUDA=OFF` to `cmake`.
### Where to get help if I have problems with the installation ?
Please file an issue at <https://github.com/danpovey/fast_rnnt/issues>
and describe your problem there.
## Usage
### For rnnt_loss_simple
This is a simple case of the RNN-T loss, where the joiner network is just
addition.
Note: termination_symbol plays the role of blank in other RNN-T loss implementations, we call it termination_symbol as it terminates symbols of current frame.
```python
am = torch.randn((B, T, C), dtype=torch.float32)
lm = torch.randn((B, S + 1, C), dtype=torch.float32)
symbols = torch.randint(0, C, (B, S))
termination_symbol = 0
boundary = torch.zeros((B, 4), dtype=torch.int64)
boundary[:, 2] = target_lengths
boundary[:, 3] = num_frames
loss = fast_rnnt.rnnt_loss_simple(
lm=lm,
am=am,
symbols=symbols,
termination_symbol=termination_symbol,
boundary=boundary,
reduction="sum",
)
```
### For rnnt_loss_smoothed
The same as `rnnt_loss_simple`, except that it supports `am_only` & `lm_only` smoothing
that allows you to make the loss-function one of the form:
lm_only_scale * lm_probs +
am_only_scale * am_probs +
(1-lm_only_scale-am_only_scale) * combined_probs
where `lm_probs` and `am_probs` are the probabilities given the lm and acoustic model independently.
```python
am = torch.randn((B, T, C), dtype=torch.float32)
lm = torch.randn((B, S + 1, C), dtype=torch.float32)
symbols = torch.randint(0, C, (B, S))
termination_symbol = 0
boundary = torch.zeros((B, 4), dtype=torch.int64)
boundary[:, 2] = target_lengths
boundary[:, 3] = num_frames
loss = fast_rnnt.rnnt_loss_smoothed(
lm=lm,
am=am,
symbols=symbols,
termination_symbol=termination_symbol,
lm_only_scale=0.25,
am_only_scale=0.0
boundary=boundary,
reduction="sum",
)
```
### For rnnt_loss_pruned
`rnnt_loss_pruned` can not be used alone, it needs the gradients returned by `rnnt_loss_simple/rnnt_loss_smoothed` to get pruning bounds.
```python
am = torch.randn((B, T, C), dtype=torch.float32)
lm = torch.randn((B, S + 1, C), dtype=torch.float32)
symbols = torch.randint(0, C, (B, S))
termination_symbol = 0
boundary = torch.zeros((B, 4), dtype=torch.int64)
boundary[:, 2] = target_lengths
boundary[:, 3] = num_frames
# rnnt_loss_simple can be also rnnt_loss_smoothed
simple_loss, (px_grad, py_grad) = fast_rnnt.rnnt_loss_simple(
lm=lm,
am=am,
symbols=symbols,
termination_symbol=termination_symbol,
boundary=boundary,
reduction="sum",
return_grad=True,
)
s_range = 5 # can be other values
ranges = fast_rnnt.get_rnnt_prune_ranges(
px_grad=px_grad,
py_grad=py_grad,
boundary=boundary,
s_range=s_range,
)
am_pruned, lm_pruned = fast_rnnt.do_rnnt_pruning(am=am, lm=lm, ranges=ranges)
logits = model.joiner(am_pruned, lm_pruned)
pruned_loss = fast_rnnt.rnnt_loss_pruned(
logits=logits,
symbols=symbols,
ranges=ranges,
termination_symbol=termination_symbol,
boundary=boundary,
reduction="sum",
)
```
You can also find recipes [here](https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless) that uses `rnnt_loss_pruned` to train a model.
### For rnnt_loss
The `unprund rnnt_loss` is the same as `torchaudio rnnt_loss`, it produces same output as torchaudio for the same input.
```python
logits = torch.randn((B, S, T, C), dtype=torch.float32)
symbols = torch.randint(0, C, (B, S))
termination_symbol = 0
boundary = torch.zeros((B, 4), dtype=torch.int64)
boundary[:, 2] = target_lengths
boundary[:, 3] = num_frames
loss = fast_rnnt.rnnt_loss(
logits=logits,
symbols=symbols,
termination_symbol=termination_symbol,
boundary=boundary,
reduction="sum",
)
```
## Benchmarking
The [repo](https://github.com/csukuangfj/transducer-loss-benchmarking) compares the speed and memory usage of several transducer losses, the summary in the following table is taken from there, you can check the repository for more details.
Note: As we declared above, `fast_rnnt` is also implemented in [k2](https://github.com/k2-fsa/k2) project, so `k2` and `fast_rnnt` are equivalent in the benchmarking.
|Name |Average step time (us) | Peak memory usage (MB)|
|--------------------|-----------------------|-----------------------|
|torchaudio |601447 |12959.2 |
|fast_rnnt(unpruned) |274407 |15106.5 |
|fast_rnnt(pruned) |38112 |2647.8 |
|optimized_transducer|567684 |10903.1 |
|warprnnt_numba |229340 |13061.8 |
|warp-transducer |210772 |13061.8 |
cmake/Modules/FetchContent.cmake 0 → 100644
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This diff is collapsed.
cmake/Modules/FetchContent/CMakeLists.cmake.in 0 → 100644
View file @ a283cddf
# Distributed under the OSI-approved BSD 3-Clause License. See accompanying
# file Copyright.txt or https://cmake.org/licensing for details.
cmake_minimum_required(VERSION ${CMAKE_VERSION})
# We name the project and the target for the ExternalProject_Add() call
# to something that will highlight to the user what we are working on if
# something goes wrong and an error message is produced.
project(${contentName}-populate NONE)
include(ExternalProject)
ExternalProject_Add(${contentName}-populate
${ARG_EXTRA}
SOURCE_DIR "${ARG_SOURCE_DIR}"
BINARY_DIR "${ARG_BINARY_DIR}"
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
INSTALL_COMMAND ""
TEST_COMMAND ""
)
cmake/Modules/README.md 0 → 100644
View file @ a283cddf
## FetchContent
`FetchContent.cmake` and `FetchContent/CMakeLists.cmake.in`
are copied from `cmake/3.11.0/share/cmake-3.11/Modules`.
cmake/cub.cmake 0 → 100644
View file @ a283cddf
# Copyright 2020 Fangjun Kuang (csukuangfj@gmail.com)
# See ../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
function(download_cub)
if(CMAKE_VERSION VERSION_LESS 3.11)
list(APPEND CMAKE_MODULE_PATH ${CMAKE_SOURCE_DIR}/cmake/Modules)
endif()
include(FetchContent)
set(cub_URL "https://github.com/NVlabs/cub/archive/1.15.0.tar.gz")
set(cub_HASH "SHA256=1781ee5eb7f00acfee5bff88e3acfc67378f6b3c24281335e18ae19e1f2ff685")
FetchContent_Declare(cub
URL ${cub_URL}
URL_HASH ${cub_HASH}
)
FetchContent_GetProperties(cub)
if(NOT cub)
message(STATUS "Downloading cub")
FetchContent_Populate(cub)
endif()
message(STATUS "cub is downloaded to ${cub_SOURCE_DIR}")
add_library(cub INTERFACE)
target_include_directories(cub INTERFACE ${cub_SOURCE_DIR})
endfunction()
download_cub()
cmake/googletest.cmake 0 → 100644
View file @ a283cddf
# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
# See ../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
function(download_googltest)
if(CMAKE_VERSION VERSION_LESS 3.11)
# FetchContent is available since 3.11,
# we've copied it to ${CMAKE_SOURCE_DIR}/cmake/Modules
# so that it can be used in lower CMake versions.
list(APPEND CMAKE_MODULE_PATH ${CMAKE_SOURCE_DIR}/cmake/Modules)
endif()
include(FetchContent)
set(googletest_URL "https://github.com/google/googletest/archive/release-1.10.0.tar.gz")
set(googletest_HASH "SHA256=9dc9157a9a1551ec7a7e43daea9a694a0bb5fb8bec81235d8a1e6ef64c716dcb")
set(BUILD_GMOCK ON CACHE BOOL "" FORCE)
set(INSTALL_GTEST OFF CACHE BOOL "" FORCE)
set(gtest_disable_pthreads ON CACHE BOOL "" FORCE)
set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
FetchContent_Declare(googletest
URL ${googletest_URL}
URL_HASH ${googletest_HASH}
)
FetchContent_GetProperties(googletest)
if(NOT googletest_POPULATED)
message(STATUS "Downloading googletest")
FetchContent_Populate(googletest)
endif()
message(STATUS "googletest is downloaded to ${googletest_SOURCE_DIR}")
message(STATUS "googletest's binary dir is ${googletest_BINARY_DIR}")
if(APPLE)
set(CMAKE_MACOSX_RPATH ON) # to solve the following warning on macOS
endif()
#[==[
-- Generating done
Policy CMP0042 is not set: MACOSX_RPATH is enabled by default. Run "cmake
--help-policy CMP0042" for policy details. Use the cmake_policy command to
set the policy and suppress this warning.
MACOSX_RPATH is not specified for the following targets:
gmock
gmock_main
gtest
gtest_main
This warning is for project developers. Use -Wno-dev to suppress it.
]==]
add_subdirectory(${googletest_SOURCE_DIR} ${googletest_BINARY_DIR} EXCLUDE_FROM_ALL)
target_include_directories(gtest
INTERFACE
${googletest_SOURCE_DIR}/googletest/include
${googletest_SOURCE_DIR}/googlemock/include
)
endfunction()
download_googltest()
cmake/pybind11.cmake 0 → 100644
View file @ a283cddf
# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
# See ../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
function(download_pybind11)
if(CMAKE_VERSION VERSION_LESS 3.11)
list(APPEND CMAKE_MODULE_PATH ${CMAKE_SOURCE_DIR}/cmake/Modules)
endif()
include(FetchContent)
set(pybind11_URL "https://github.com/pybind/pybind11/archive/v2.6.0.tar.gz")
set(pybind11_HASH "SHA256=90b705137b69ee3b5fc655eaca66d0dc9862ea1759226f7ccd3098425ae69571")
set(double_quotes "\"")
set(dollar "\$")
set(semicolon "\;")
if(NOT WIN32)
FetchContent_Declare(pybind11
URL ${pybind11_URL}
URL_HASH ${pybind11_HASH}
)
else()
FetchContent_Declare(pybind11
URL ${pybind11_URL}
URL_HASH ${pybind11_HASH}
)
endif()
FetchContent_GetProperties(pybind11)
if(NOT pybind11_POPULATED)
message(STATUS "Downloading pybind11")
FetchContent_Populate(pybind11)
endif()
message(STATUS "pybind11 is downloaded to ${pybind11_SOURCE_DIR}")
add_subdirectory(${pybind11_SOURCE_DIR} ${pybind11_BINARY_DIR} EXCLUDE_FROM_ALL)
endfunction()
download_pybind11()
cmake/select_compute_arch.cmake 0 → 100644
View file @ a283cddf
#
# This file is copied from
# https://github.com/pytorch/pytorch/blob/master/cmake/Modules_CUDA_fix/upstream/FindCUDA/select_compute_arch.cmake
#
#
# Synopsis:
# CUDA_SELECT_NVCC_ARCH_FLAGS(out_variable [target_CUDA_architectures])
# -- Selects GPU arch flags for nvcc based on target_CUDA_architectures
# target_CUDA_architectures : Auto | Common | All | LIST(ARCH_AND_PTX ...)
# - "Auto" detects local machine GPU compute arch at runtime.
# - "Common" and "All" cover common and entire subsets of architectures
# ARCH_AND_PTX : NAME | NUM.NUM | NUM.NUM(NUM.NUM) | NUM.NUM+PTX
# NAME: Kepler Maxwell Kepler+Tegra Kepler+Tesla Maxwell+Tegra Pascal Volta Turing Ampere
# NUM: Any number. Only those pairs are currently accepted by NVCC though:
# 3.5 3.7 5.0 5.2 5.3 6.0 6.2 7.0 7.2 7.5 8.0
# Returns LIST of flags to be added to CUDA_NVCC_FLAGS in ${out_variable}
# Additionally, sets ${out_variable}_readable to the resulting numeric list
# Example:
# CUDA_SELECT_NVCC_ARCH_FLAGS(ARCH_FLAGS 3.0 3.5+PTX 5.2(5.0) Maxwell)
# LIST(APPEND CUDA_NVCC_FLAGS ${ARCH_FLAGS})
#
# More info on CUDA architectures: https://en.wikipedia.org/wiki/CUDA
#
if(CMAKE_CUDA_COMPILER_LOADED OR DEFINED CMAKE_CUDA_COMPILER_ID) # CUDA as a language
if(CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA"
AND CMAKE_CUDA_COMPILER_VERSION MATCHES "^([0-9]+\\.[0-9]+)")
set(CUDA_VERSION "${CMAKE_MATCH_1}")
endif()
endif()
# See: https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#gpu-feature-list
# This list will be used for CUDA_ARCH_NAME = All option
set(CUDA_KNOWN_GPU_ARCHITECTURES "Kepler" "Maxwell")
# This list will be used for CUDA_ARCH_NAME = Common option (enabled by default)
set(CUDA_COMMON_GPU_ARCHITECTURES "3.5" "5.0")
if(CUDA_VERSION VERSION_LESS "7.0")
set(CUDA_LIMIT_GPU_ARCHITECTURE "5.2")
endif()
# This list is used to filter CUDA archs when autodetecting
set(CUDA_ALL_GPU_ARCHITECTURES "3.5" "5.0")
if(CUDA_VERSION VERSION_GREATER "6.5")
list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Kepler+Tegra" "Kepler+Tesla" "Maxwell+Tegra")
list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "5.2")
if(CUDA_VERSION VERSION_LESS "8.0")
list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "5.2+PTX")
set(CUDA_LIMIT_GPU_ARCHITECTURE "6.0")
endif()
endif()
if(CUDA_VERSION VERSION_GREATER "7.5")
list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Pascal")
list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "6.0" "6.1")
list(APPEND CUDA_ALL_GPU_ARCHITECTURES "6.0" "6.1" "6.2")
if(CUDA_VERSION VERSION_LESS "9.0")
list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "6.2+PTX")
set(CUDA_LIMIT_GPU_ARCHITECTURE "7.0")
endif()
endif ()
if(CUDA_VERSION VERSION_GREATER "8.5")
list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Volta")
list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.0")
list(APPEND CUDA_ALL_GPU_ARCHITECTURES "7.0" "7.2")
if(CUDA_VERSION VERSION_LESS "10.0")
list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.2+PTX")
set(CUDA_LIMIT_GPU_ARCHITECTURE "8.0")
endif()
endif()
if(CUDA_VERSION VERSION_GREATER "9.5")
list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Turing")
list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.5")
list(APPEND CUDA_ALL_GPU_ARCHITECTURES "7.5")
if(CUDA_VERSION VERSION_LESS "11.0")
set(CUDA_LIMIT_GPU_ARCHITECTURE "8.0")
list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.5+PTX")
endif()
endif()
if(CUDA_VERSION VERSION_GREATER "10.5")
list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Ampere")
list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.0")
list(APPEND CUDA_ALL_GPU_ARCHITECTURES "8.0")
if(CUDA_VERSION VERSION_LESS "11.1")
set(CUDA_LIMIT_GPU_ARCHITECTURE "8.6")
list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.0+PTX")
endif()
endif()
if(CUDA_VERSION VERSION_GREATER "11.0")
list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.6" "8.6+PTX")
list(APPEND CUDA_ALL_GPU_ARCHITECTURES "8.6")
if(CUDA_VERSION VERSION_LESS "12.0")
set(CUDA_LIMIT_GPU_ARCHITECTURE "9.0")
endif()
endif()
################################################################################################
# A function for automatic detection of GPUs installed (if autodetection is enabled)
# Usage:
# CUDA_DETECT_INSTALLED_GPUS(OUT_VARIABLE)
#
function(CUDA_DETECT_INSTALLED_GPUS OUT_VARIABLE)
if(NOT CUDA_GPU_DETECT_OUTPUT)
if(CMAKE_CUDA_COMPILER_LOADED OR DEFINED CMAKE_CUDA_COMPILER_ID) # CUDA as a language
set(file "${PROJECT_BINARY_DIR}/detect_cuda_compute_capabilities.cu")
else()
set(file "${PROJECT_BINARY_DIR}/detect_cuda_compute_capabilities.cpp")
endif()
file(WRITE ${file} ""
"#include <cuda_runtime.h>\n"
"#include <cstdio>\n"
"int main()\n"
"{\n"
" int count = 0;\n"
" if (cudaSuccess != cudaGetDeviceCount(&count)) return -1;\n"
" if (count == 0) return -1;\n"
" for (int device = 0; device < count; ++device)\n"
" {\n"
" cudaDeviceProp prop;\n"
" if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n"
" std::printf(\"%d.%d \", prop.major, prop.minor);\n"
" }\n"
" return 0;\n"
"}\n")
if(CMAKE_CUDA_COMPILER_LOADED OR DEFINED CMAKE_CUDA_COMPILER_ID) # CUDA as a language
try_run(run_result compile_result ${PROJECT_BINARY_DIR} ${file}
RUN_OUTPUT_VARIABLE compute_capabilities)
else()
try_run(run_result compile_result ${PROJECT_BINARY_DIR} ${file}
CMAKE_FLAGS "-DINCLUDE_DIRECTORIES=${CUDA_INCLUDE_DIRS}"
LINK_LIBRARIES ${CUDA_LIBRARIES}
RUN_OUTPUT_VARIABLE compute_capabilities)
endif()
# Filter unrelated content out of the output.
string(REGEX MATCHALL "[0-9]+\\.[0-9]+" compute_capabilities "${compute_capabilities}")
if(run_result EQUAL 0)
string(REPLACE "2.1" "2.1(2.0)" compute_capabilities "${compute_capabilities}")
set(CUDA_GPU_DETECT_OUTPUT ${compute_capabilities}
CACHE INTERNAL "Returned GPU architectures from detect_gpus tool" FORCE)
endif()
endif()
if(NOT CUDA_GPU_DETECT_OUTPUT)
message(STATUS "Automatic GPU detection failed. Building for common architectures.")
set(${OUT_VARIABLE} ${CUDA_COMMON_GPU_ARCHITECTURES} PARENT_SCOPE)
else()
# Filter based on CUDA version supported archs
set(CUDA_GPU_DETECT_OUTPUT_FILTERED "")
separate_arguments(CUDA_GPU_DETECT_OUTPUT)
foreach(ITEM IN ITEMS ${CUDA_GPU_DETECT_OUTPUT})
if(CUDA_LIMIT_GPU_ARCHITECTURE AND (ITEM VERSION_GREATER CUDA_LIMIT_GPU_ARCHITECTURE OR
ITEM VERSION_EQUAL CUDA_LIMIT_GPU_ARCHITECTURE))
list(GET CUDA_COMMON_GPU_ARCHITECTURES -1 NEWITEM)
string(APPEND CUDA_GPU_DETECT_OUTPUT_FILTERED " ${NEWITEM}")
else()
string(APPEND CUDA_GPU_DETECT_OUTPUT_FILTERED " ${ITEM}")
endif()
endforeach()
set(${OUT_VARIABLE} ${CUDA_GPU_DETECT_OUTPUT_FILTERED} PARENT_SCOPE)
endif()
endfunction()
################################################################################################
# Function for selecting GPU arch flags for nvcc based on CUDA architectures from parameter list
# Usage:
# SELECT_NVCC_ARCH_FLAGS(out_variable [list of CUDA compute archs])
function(CUDA_SELECT_NVCC_ARCH_FLAGS out_variable)
set(CUDA_ARCH_LIST "${ARGN}")
if("X${CUDA_ARCH_LIST}" STREQUAL "X" )
set(CUDA_ARCH_LIST "Auto")
endif()
set(cuda_arch_bin)
set(cuda_arch_ptx)
if("${CUDA_ARCH_LIST}" STREQUAL "All")
set(CUDA_ARCH_LIST ${CUDA_KNOWN_GPU_ARCHITECTURES})
elseif("${CUDA_ARCH_LIST}" STREQUAL "Common")
set(CUDA_ARCH_LIST ${CUDA_COMMON_GPU_ARCHITECTURES})
elseif("${CUDA_ARCH_LIST}" STREQUAL "Auto")
CUDA_DETECT_INSTALLED_GPUS(CUDA_ARCH_LIST)
message(STATUS "Autodetected CUDA architecture(s): ${CUDA_ARCH_LIST}")
endif()
# Now process the list and look for names
string(REGEX REPLACE "[ \t]+" ";" CUDA_ARCH_LIST "${CUDA_ARCH_LIST}")
list(REMOVE_DUPLICATES CUDA_ARCH_LIST)
foreach(arch_name ${CUDA_ARCH_LIST})
set(arch_bin)
set(arch_ptx)
set(add_ptx FALSE)
# Check to see if we are compiling PTX
if(arch_name MATCHES "(.*)\\+PTX$")
set(add_ptx TRUE)
set(arch_name ${CMAKE_MATCH_1})
endif()
if(arch_name MATCHES "^([0-9]\\.[0-9](\\([0-9]\\.[0-9]\\))?)$")
set(arch_bin ${CMAKE_MATCH_1})
set(arch_ptx ${arch_bin})
else()
# Look for it in our list of known architectures
if(${arch_name} STREQUAL "Kepler+Tesla")
set(arch_bin 3.7)
elseif(${arch_name} STREQUAL "Kepler")
set(arch_bin 3.5)
set(arch_ptx 3.5)
elseif(${arch_name} STREQUAL "Maxwell+Tegra")
set(arch_bin 5.3)
elseif(${arch_name} STREQUAL "Maxwell")
set(arch_bin 5.0 5.2)
set(arch_ptx 5.2)
elseif(${arch_name} STREQUAL "Pascal")
set(arch_bin 6.0 6.1)
set(arch_ptx 6.1)
elseif(${arch_name} STREQUAL "Volta")
set(arch_bin 7.0 7.0)
set(arch_ptx 7.0)
elseif(${arch_name} STREQUAL "Turing")
set(arch_bin 7.5)
set(arch_ptx 7.5)
elseif(${arch_name} STREQUAL "Ampere")
set(arch_bin 8.0)
set(arch_ptx 8.0)
else()
message(SEND_ERROR "Unknown CUDA Architecture Name ${arch_name} in CUDA_SELECT_NVCC_ARCH_FLAGS")
endif()
endif()
if(NOT arch_bin)
message(SEND_ERROR "arch_bin wasn't set for some reason")
endif()
list(APPEND cuda_arch_bin ${arch_bin})
if(add_ptx)
if (NOT arch_ptx)
set(arch_ptx ${arch_bin})
endif()
list(APPEND cuda_arch_ptx ${arch_ptx})
endif()
endforeach()
# remove dots and convert to lists
string(REGEX REPLACE "\\." "" cuda_arch_bin "${cuda_arch_bin}")
string(REGEX REPLACE "\\." "" cuda_arch_ptx "${cuda_arch_ptx}")
string(REGEX MATCHALL "[0-9()]+" cuda_arch_bin "${cuda_arch_bin}")
string(REGEX MATCHALL "[0-9]+" cuda_arch_ptx "${cuda_arch_ptx}")
if(cuda_arch_bin)
list(REMOVE_DUPLICATES cuda_arch_bin)
endif()
if(cuda_arch_ptx)
list(REMOVE_DUPLICATES cuda_arch_ptx)
endif()
set(nvcc_flags "")
set(nvcc_archs_readable "")
# Tell NVCC to add binaries for the specified GPUs
foreach(arch ${cuda_arch_bin})
if(arch MATCHES "([0-9]+)\\(([0-9]+)\\)")
# User explicitly specified ARCH for the concrete CODE
list(APPEND nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1})
list(APPEND nvcc_archs_readable sm_${CMAKE_MATCH_1})
else()
# User didn't explicitly specify ARCH for the concrete CODE, we assume ARCH=CODE
list(APPEND nvcc_flags -gencode arch=compute_${arch},code=sm_${arch})
list(APPEND nvcc_archs_readable sm_${arch})
endif()
endforeach()
# Tell NVCC to add PTX intermediate code for the specified architectures
foreach(arch ${cuda_arch_ptx})
list(APPEND nvcc_flags -gencode arch=compute_${arch},code=compute_${arch})
list(APPEND nvcc_archs_readable compute_${arch})
endforeach()
string(REPLACE ";" " " nvcc_archs_readable "${nvcc_archs_readable}")
set(${out_variable} ${nvcc_flags} PARENT_SCOPE)
set(${out_variable}_readable ${nvcc_archs_readable} PARENT_SCOPE)
endfunction()
cmake/torch.cmake 0 → 100644
View file @ a283cddf
# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
# PYTHON_EXECUTABLE is set by pybind11.cmake
message(STATUS "Python executable: ${PYTHON_EXECUTABLE}")
execute_process(
COMMAND "${PYTHON_EXECUTABLE}" -c "import os; import torch; print(os.path.dirname(torch.__file__))"
OUTPUT_STRIP_TRAILING_WHITESPACE
OUTPUT_VARIABLE TORCH_DIR
)
list(APPEND CMAKE_PREFIX_PATH "${TORCH_DIR}")
find_package(Torch REQUIRED)
# set the global CMAKE_CXX_FLAGS so that
# optimized_transducer uses the same abi flag as PyTorch
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
if(OT_WITH_CUDA)
set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} ${TORCH_CXX_FLAGS}")
endif()
execute_process(
COMMAND "${PYTHON_EXECUTABLE}" -c "import torch; print(torch.__version__.split('.')[0])"
OUTPUT_STRIP_TRAILING_WHITESPACE
OUTPUT_VARIABLE OT_TORCH_VERSION_MAJOR
)
execute_process(
COMMAND "${PYTHON_EXECUTABLE}" -c "import torch; print(torch.__version__.split('.')[1])"
OUTPUT_STRIP_TRAILING_WHITESPACE
OUTPUT_VARIABLE OT_TORCH_VERSION_MINOR
)
execute_process(
COMMAND "${PYTHON_EXECUTABLE}" -c "import torch; print(torch.__version__)"
OUTPUT_STRIP_TRAILING_WHITESPACE
OUTPUT_VARIABLE TORCH_VERSION
)
message(STATUS "PyTorch version: ${TORCH_VERSION}")
if(OT_WITH_CUDA)
execute_process(
COMMAND "${PYTHON_EXECUTABLE}" -c "import torch; print(torch.version.cuda)"
OUTPUT_STRIP_TRAILING_WHITESPACE
OUTPUT_VARIABLE TORCH_CUDA_VERSION
)
message(STATUS "PyTorch cuda version: ${TORCH_CUDA_VERSION}")
if(NOT CUDA_VERSION VERSION_EQUAL TORCH_CUDA_VERSION)
message(FATAL_ERROR
"PyTorch ${TORCH_VERSION} is compiled with CUDA ${TORCH_CUDA_VERSION}.\n"
"But you are using CUDA ${CUDA_VERSION} to compile optimized_transducer.\n"
"Please try to use the same CUDA version for PyTorch and optimized_transducer.\n"
"**You can remove this check if you are sure this will not cause "
"problems**\n"
)
endif()
# Solve the following error for NVCC:
# unknown option `-Wall`
#
# It contains only some -Wno-* flags, so it is OK
# to set them to empty
set_property(TARGET torch_cuda
PROPERTY
INTERFACE_COMPILE_OPTIONS ""
)
set_property(TARGET torch_cpu
PROPERTY
INTERFACE_COMPILE_OPTIONS ""
)
endif()
cmake/transform.cmake 0 → 100644
View file @ a283cddf
# Copyright 2021 Fangjun Kuang (csukuangfj@gmail.com)
# See ../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This function is used to copy foo.cu to foo.cc
# Usage:
#
# transform(OUTPUT_VARIABLE output_variable_name SRCS foo.cu bar.cu)
#
function(transform)
set(optional_args "") # there are no optional arguments
set(one_value_arg OUTPUT_VARIABLE)
set(multi_value_args SRCS)
cmake_parse_arguments(MY "${optional_args}" "${one_value_arg}" "${multi_value_args}" ${ARGN})
foreach(src IN LISTS MY_SRCS)
get_filename_component(src_name ${src} NAME_WE)
get_filename_component(src_dir ${src} DIRECTORY)
set(dst ${CMAKE_CURRENT_BINARY_DIR}/${src_dir}/${src_name}.cc)
list(APPEND ans ${dst})
message(STATUS "Renaming ${CMAKE_CURRENT_SOURCE_DIR}/${src} to ${dst}")
configure_file(${src} ${dst})
endforeach()
set(${MY_OUTPUT_VARIABLE} ${ans} PARENT_SCOPE)
endfunction()
fast_rnnt/CMakeLists.txt 0 → 100644
View file @ a283cddf
add_subdirectory(csrc)
add_subdirectory(python)
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