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Commit 0c823497 authored by muozturk's avatar muozturk
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merge

parents 334cfe1c 68f2b5e7
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Showing with 470 additions and 635 deletions
script/redis-cli.conf 0 → 100644
View file @ 0c823497
fips = no
setuid = root
setgid = root
pid = /var/run/stunnel.pid
debug = 7
options = NO_SSLv2
options = NO_SSLv3
[redis-cli]
client = yes
accept = 127.0.0.1:6379
script/sccache_wrapper.sh 0 → 100755
View file @ 0c823497
#!/bin/bash
set -e
COMPILERS_HASH_DIR=${COMPILERS_HASH_DIR:-"/tmp/.sccache"}
SCCACHE_EXTRAFILES=${SCCACHE_EXTRAFILES:-"${COMPILERS_HASH_DIR}/rocm_compilers_hash_file"}
SCCACHE_BIN=${SCCACHE_BIN:-"${SCCACHE_INSTALL_LOCATION}/sccache"}
ENFORCE_REDIS="false"
while [ "$1" != "" ];
do
case $1 in
--enforce_redis )
shift; ENFORCE_REDIS="true" ;;
--no-hipcc )
shift ;;
*)
break ;;
esac
done
setup_rocm_compilers_hash_file() {
mkdir -p "$COMPILERS_HASH_DIR"
HIPCC_MD5="$(md5sum "${ROCM_PATH}/bin/hipcc")"
pushd "${ROCM_PATH}/amdgcn/bitcode"
DEVICELIBS_BITCODES_MD5="$(find . -type f -exec md5sum {} \; | sort | md5sum)"
popd
HIPCC_HASH_VALUE="${HIPCC_MD5%% *}"
DEVICELIBS_BITCODES_HASH_VALUE="${DEVICELIBS_BITCODES_MD5%% *}"
# MD5 checksums of clang and clang-offload-bundler cannot be used since they will keep changing
# if the ROCM_PATH changes, ie; for every mainline build.
# This is because ROCM_PATH gets encoded into the clang/clang-offload-bundler binaries as part
# of RPATH.
# The versions themselves contain the commit hash of the compiler repo at the time of building.
# Hence, this should be a viable alternative to using the binary checksum itself.
CLANG_VERSION="$("${ROCM_PATH}/llvm/bin/clang" --version | head -n 1)"
CLANG_OFFLOAD_BUNDLER_VERSION="$("${ROCM_PATH}/llvm/bin/clang-offload-bundler" --version | head -n 1)"
printf '%s: %s\n' 'clang version' "${CLANG_VERSION}" | tee -a "$SCCACHE_EXTRAFILES"
printf '%s: %s\n' 'clang-offload-bundler version' "${CLANG_OFFLOAD_BUNDLER_VERSION}" | tee -a "$SCCACHE_EXTRAFILES"
printf '%s: %s\n' 'hipcc md5sum' "${HIPCC_HASH_VALUE}" | tee -a "$SCCACHE_EXTRAFILES"
printf '%s: %s\n' 'devicelibs bitcode md5sum' "${DEVICELIBS_BITCODES_HASH_VALUE}" | tee -a "$SCCACHE_EXTRAFILES"
echo "sccache-wrapper: compilers hash file set up at ${SCCACHE_EXTRAFILES}"
cat "$SCCACHE_EXTRAFILES"
}
if [ "${ENFORCE_REDIS}" == "true" ]; then
if [ -z "${SCCACHE_REDIS}" ]; then
echo "SCCACHE_REDIS not set. Not wrapping compilers with sccache."
exit 10
else
response=$(redis-cli -u ${SCCACHE_REDIS} ping) || true
if [ "${response}" != "PONG" ]; then
echo "Redis server unreachable. Not wrapping compilers with sccache."
exit 20
fi
fi
fi
setup_rocm_compilers_hash_file
$SCCACHE_BIN --version
$SCCACHE_BIN --start-server
test/CMakeLists.txt
View file @ 0c823497
......@@ -11,40 +11,36 @@ function(add_test_executable TEST_NAME)
message("adding test ${TEST_NAME}")
set(result 1)
if(DEFINED DTYPES)
foreach(source IN LISTS ARGN)
set(test 0)
foreach(type IN LISTS DTYPES)
if(type MATCHES "fp16")
set(type1 "_f16")
elseif(type MATCHES "fp32")
set(type1 "_f32")
elseif(type MATCHES "fp8")
set(type1 "_f8")
elseif(type MATCHES "bf16")
set(type1 "_b16")
elseif(type MATCHES "fp64")
set(type1 "_f64")
elseif(type MATCHES "int8")
set(type1 "_i8")
endif()
if("${source}" MATCHES "${type}" OR "${source}" MATCHES "${type1}")
#if filename matches any selected type, exit type loop and do no exclude the file from the list
set(test 0)
break()
elseif((source MATCHES "fp8" OR source MATCHES "fp32" OR source MATCHES "fp64" OR source MATCHES "bf16" OR source MATCHES "int8" OR source MATCHES "fp16" OR
source MATCHES "_f8" OR source MATCHES "_f32" OR source MATCHES "_f64" OR source MATCHES "_i8" OR source MATCHES "_f16" OR source MATCHES "_b16") AND
NOT(source MATCHES type OR source MATCHES type1))
#if filename contains a type which doesn't match any selected type, mark it for removal
set(test 1)
foreach(source IN LISTS ARGN)
set(test 0)
if((source MATCHES "_fp16" OR source MATCHES "_f16") AND NOT "fp16" IN_LIST DTYPES)
set(test 1)
endif()
if((source MATCHES "_fp32" OR source MATCHES "_f32") AND NOT "fp32" IN_LIST DTYPES)
set(test 1)
endif()
if((source MATCHES "_fp64" OR source MATCHES "_f64") AND NOT "fp64" IN_LIST DTYPES)
set(test 1)
endif()
if((source MATCHES "_fp8" OR source MATCHES "_f8") AND NOT "fp8" IN_LIST DTYPES)
set(test 1)
endif()
if((source MATCHES "_bf8" OR source MATCHES "_bf8") AND NOT "bf8" IN_LIST DTYPES)
set(test 1)
endif()
if((source MATCHES "_bf16" OR source MATCHES "_b16") AND NOT "bf16" IN_LIST DTYPES)
set(test 1)
endif()
if((source MATCHES "_int8" OR source MATCHES "_i8") AND NOT "int8" IN_LIST DTYPES)
set(test 1)
endif()
if(test EQUAL 1)
message("removing test ${source} ")
list(REMOVE_ITEM ARGN "${source}")
endif()
endforeach()
if(test EQUAL 1)
message("removing test ${source} ")
list(REMOVE_ITEM ARGN "${source}")
endif()
endforeach()
endif()
foreach(source IN LISTS ARGN)
endif()
foreach(source IN LISTS ARGN)
if(NOT DEFINED DL_KERNELS AND source MATCHES "_dl")
message("removing dl test ${source} ")
list(REMOVE_ITEM ARGN "${source}")
......@@ -70,38 +66,34 @@ function(add_gtest_executable TEST_NAME)
message("adding gtest ${TEST_NAME}")
set(result 1)
if(DEFINED DTYPES)
foreach(source IN LISTS ARGN)
set(test 0)
foreach(type IN LISTS DTYPES)
if(type MATCHES "fp16")
set(type1 "_f16")
elseif(type MATCHES "fp32")
set(type1 "_f32")
elseif(type MATCHES "fp8")
set(type1 "_f8")
elseif(type MATCHES "bf16")
set(type1 "_b16")
elseif(type MATCHES "fp64")
set(type1 "_f64")
elseif(type MATCHES "int8")
set(type1 "_i8")
endif()
if("${source}" MATCHES "${type}" OR "${source}" MATCHES "${type1}")
#if filename matches any selected type, exit type loop and do no exclude the file from the list
set(test 0)
break()
elseif((source MATCHES "fp8" OR source MATCHES "fp32" OR source MATCHES "fp64" OR source MATCHES "bf16" OR source MATCHES "int8" OR source MATCHES "fp16" OR
source MATCHES "_f8" OR source MATCHES "_f32" OR source MATCHES "_f64" OR source MATCHES "_i8" OR source MATCHES "_f16" OR source MATCHES "_b16") AND
NOT(source MATCHES type OR source MATCHES type1))
#if filename contains a type which doesn't match any selected type, mark it for removal
set(test 1)
foreach(source IN LISTS ARGN)
set(test 0)
if((source MATCHES "_fp16" OR source MATCHES "_f16") AND NOT "fp16" IN_LIST DTYPES)
set(test 1)
endif()
if((source MATCHES "_fp32" OR source MATCHES "_f32") AND NOT "fp32" IN_LIST DTYPES)
set(test 1)
endif()
if((source MATCHES "_fp64" OR source MATCHES "_f64") AND NOT "fp64" IN_LIST DTYPES)
set(test 1)
endif()
if((source MATCHES "_fp8" OR source MATCHES "_f8") AND NOT "fp8" IN_LIST DTYPES)
set(test 1)
endif()
if((source MATCHES "_bf8" OR source MATCHES "_bf8") AND NOT "bf8" IN_LIST DTYPES)
set(test 1)
endif()
if((source MATCHES "_bf16" OR source MATCHES "_b16") AND NOT "bf16" IN_LIST DTYPES)
set(test 1)
endif()
if((source MATCHES "_int8" OR source MATCHES "_i8") AND NOT "int8" IN_LIST DTYPES)
set(test 1)
endif()
if(test EQUAL 1)
message("removing gtest ${source} ")
list(REMOVE_ITEM ARGN "${source}")
endif()
endforeach()
if(test EQUAL 1)
message("removing gtest ${source} ")
list(REMOVE_ITEM ARGN "${source}")
endif()
endforeach()
endif()
foreach(source IN LISTS ARGN)
if(NOT DEFINED DL_KERNELS AND source MATCHES "_dl")
......@@ -147,7 +139,7 @@ add_subdirectory(grouped_convnd_fwd)
add_subdirectory(grouped_convnd_bwd_weight)
add_subdirectory(block_to_ctile_map)
add_subdirectory(softmax)
add_subdirectory(normalization)
add_subdirectory(normalization_fwd)
add_subdirectory(data_type)
add_subdirectory(elementwise_normalization)
add_subdirectory(batchnorm)
......@@ -156,6 +148,7 @@ add_subdirectory(pool)
add_subdirectory(batched_gemm_multi_d)
add_subdirectory(grouped_convnd_bwd_data)
add_subdirectory(conv_tensor_rearrange)
add_subdirectory(transpose)
if(GPU_TARGETS MATCHES "gfx11")
add_subdirectory(wmma_op)
endif()
test/batched_gemm/CMakeLists.txt
View file @ 0c823497
......@@ -2,22 +2,8 @@ list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
set(target 0)
foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0)
add_test_executable(test_batched_gemm_fp16 batched_gemm_fp16.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_fp16 PRIVATE utility device_batched_gemm_instance)
endif()
add_test_executable(test_batched_gemm_fp32 batched_gemm_fp32.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_fp32 PRIVATE utility device_batched_gemm_instance)
endif()
add_test_executable(test_batched_gemm_bf16 batched_gemm_bf16.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_bf16 PRIVATE utility device_batched_gemm_instance)
endif()
add_test_executable(test_batched_gemm_int8 batched_gemm_int8.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_int8 PRIVATE utility device_batched_gemm_instance)
endif()
add_gtest_executable(test_batched_gemm test_batched_gemm.cpp)
target_link_libraries(test_batched_gemm PRIVATE utility device_batched_gemm_instance)
set(target 1)
endif()
endforeach()
\ No newline at end of file
test/batched_gemm/batched_gemm_bf16.cpp deleted 100644 → 0
View file @ 334cfe1c
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include "profiler/profile_batched_gemm_impl.hpp"
#include "ck/library/tensor_operation_instance/gpu/batched_gemm.hpp"
namespace {
using ADataType = ck::bhalf_t;
using BDataType = ck::bhalf_t;
using CDataType = ck::bhalf_t;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
} // namespace
int main()
{
int M = 256;
int N = 256;
int K = 128;
int BatchCount = 3;
bool pass = true;
using namespace ck::tensor_operation::device;
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, K, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, K, N, M * K, K * N, M * N, BatchCount);
std::cout << "test BatchedGEMM bf16: " << (pass ? "Pass" : "Fail") << std::endl;
return pass ? 0 : 1;
}
test/batched_gemm/batched_gemm_fp16.cpp deleted 100644 → 0
View file @ 334cfe1c
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include "profiler/profile_batched_gemm_impl.hpp"
#include "ck/library/tensor_operation_instance/gpu/batched_gemm.hpp"
namespace {
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
} // namespace
int main()
{
int M = 512;
int N = 256;
int K = 128;
int BatchCount = 3;
bool pass = true;
using namespace ck::tensor_operation::device;
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, K, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, K, N, M * K, K * N, M * N, BatchCount);
std::cout << "test BatchedGEMM fp16: " << (pass ? "Pass" : "Fail") << std::endl;
return pass ? 0 : 1;
}
test/batched_gemm/batched_gemm_fp32.cpp deleted 100644 → 0
View file @ 334cfe1c
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include "profiler/profile_batched_gemm_impl.hpp"
#include "ck/library/tensor_operation_instance/gpu/batched_gemm.hpp"
namespace {
using ADataType = float;
using BDataType = float;
using CDataType = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
} // namespace
int main()
{
int M = 256;
int N = 256;
int K = 128;
int BatchCount = 3;
bool pass = true;
using namespace ck::tensor_operation::device;
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, K, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, K, N, M * K, K * N, M * N, BatchCount);
std::cout << "test BatchedGEMM fp32: " << (pass ? "Pass" : "Fail") << std::endl;
return pass ? 0 : 1;
}
test/batched_gemm/batched_gemm_int8.cpp deleted 100644 → 0
View file @ 334cfe1c
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include "profiler/profile_batched_gemm_impl.hpp"
#include "ck/library/tensor_operation_instance/gpu/batched_gemm.hpp"
namespace {
using ADataType = int8_t;
using BDataType = int8_t;
using CDataType = int8_t;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
} // namespace
int main()
{
int M = 256;
int N = 256;
int K = 128;
int BatchCount = 3;
bool pass = true;
using namespace ck::tensor_operation::device;
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, K, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, K, N, M * K, K * N, M * N, BatchCount);
std::cout << "test BatchedGEMM int8: " << (pass ? "Pass" : "Fail") << std::endl;
return pass ? 0 : 1;
}
test/batched_gemm/test_batched_gemm.cpp 0 → 100644
View file @ 0c823497
// SPDX-License-Identifier: MIT
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include <iostream>
#include <initializer_list>
#include <tuple>
#include <vector>
#include <gtest/gtest.h>
#include "profiler/profile_batched_gemm_impl.hpp"
#include "ck/library/tensor_operation_instance/gpu/batched_gemm.hpp"
struct GemmParams
{
ck::index_t M;
ck::index_t N;
ck::index_t K;
ck::index_t BatchCount;
};
class TestBatchedGemm : public ::testing::Test
{
protected:
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
std::vector<GemmParams> params;
template <typename DataType>
void Run()
{
using namespace ck::tensor_operation::device;
bool pass = true;
for(auto& param : params)
{
const auto M = param.M;
const auto N = param.N;
const auto K = param.K;
const auto BatchCount = param.BatchCount;
pass =
pass && ck::profiler::profile_batched_gemm_impl<DataType,
DataType,
DataType,
Row,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Row,
Row,
DataType,
DataType,
DataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, N, N, M * K, K * N, M * N, BatchCount);
pass =
pass && ck::profiler::profile_batched_gemm_impl<DataType,
DataType,
DataType,
Row,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Col,
Row,
DataType,
DataType,
DataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, K, N, M * K, K * N, M * N, BatchCount);
pass =
pass && ck::profiler::profile_batched_gemm_impl<DataType,
DataType,
DataType,
Col,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Row,
Row,
DataType,
DataType,
DataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, N, N, M * K, K * N, M * N, BatchCount);
pass =
pass && ck::profiler::profile_batched_gemm_impl<DataType,
DataType,
DataType,
Col,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Col,
Row,
DataType,
DataType,
DataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, K, N, M * K, K * N, M * N, BatchCount);
}
EXPECT_TRUE(pass);
}
};
#ifdef CK_ENABLE_INT8
TEST_F(TestBatchedGemm, i8)
{
this->params.push_back({64, 64, 64, 2});
this->params.push_back({64, 64, 64, 1});
this->params.push_back({60, 60, 60, 2});
this->params.push_back({68, 68, 68, 2});
this->params.push_back({40, 40, 40, 2});
this->params.push_back({256, 256, 128, 3});
this->template Run<int8_t>();
}
#endif
#ifdef CK_ENABLE_BF16
TEST_F(TestBatchedGemm, bf16)
{
this->params.push_back({64, 64, 64, 2});
this->params.push_back({64, 64, 64, 1});
this->params.push_back({60, 60, 60, 2});
this->params.push_back({68, 68, 68, 2});
this->params.push_back({40, 40, 40, 2});
this->params.push_back({256, 256, 128, 3});
this->template Run<ck::bhalf_t>();
}
#endif
#ifdef CK_ENABLE_FP16
TEST_F(TestBatchedGemm, fp16)
{
this->params.push_back({64, 64, 64, 2});
this->params.push_back({64, 64, 64, 1});
this->params.push_back({60, 60, 60, 2});
this->params.push_back({68, 68, 68, 2});
this->params.push_back({40, 40, 40, 2});
this->params.push_back({256, 256, 128, 3});
this->template Run<ck::half_t>();
}
#endif
#ifdef CK_ENABLE_FP32
TEST_F(TestBatchedGemm, fp32)
{
this->params.push_back({64, 64, 64, 2});
this->params.push_back({64, 64, 64, 1});
this->params.push_back({60, 60, 60, 2});
this->params.push_back({68, 68, 68, 2});
this->params.push_back({40, 40, 40, 2});
this->params.push_back({256, 256, 128, 3});
this->template Run<float>();
}
#endif
test/contraction/CMakeLists.txt
View file @ 0c823497
add_gtest_executable(test_contraction test_contraction.cpp)
target_link_libraries(test_contraction PRIVATE utility device_contraction_bilinear_instance device_contraction_scale_instance)
list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
set(target 0)
foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0)
add_gtest_executable(test_contraction_interface test_contraction_interface.cpp)
target_link_libraries(test_contraction_interface PRIVATE utility device_contraction_bilinear_instance device_contraction_scale_instance)
set(target 1)
endif()
if(gpu IN_LIST gpu_list AND target EQUAL 0)
if((DTYPES MATCHES "fp32" OR DTYPES MATCHES "fp64") OR NOT DEFINED DTYPES)
add_gtest_executable(test_contraction test_contraction.cpp)
target_link_libraries(test_contraction PRIVATE utility device_contraction_bilinear_instance device_contraction_scale_instance)
add_gtest_executable(test_contraction_interface test_contraction_interface.cpp)
target_link_libraries(test_contraction_interface PRIVATE utility device_contraction_bilinear_instance device_contraction_scale_instance)
set(target 1)
endif()
endif()
endforeach()
test/contraction/test_contraction.cpp
View file @ 0c823497
......@@ -10,9 +10,12 @@
#include <gtest/gtest.h>
#include "profiler/profile_contraction_impl.hpp"
#include "profiler/profile_contraction_utils.hpp"
using F32 = float;
using F64 = double;
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using F32 = float;
using F64 = double;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
......@@ -20,49 +23,49 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
using Bilinear = ck::tensor_operation::element_wise::Bilinear;
using Scale = ck::tensor_operation::element_wise::Scale;
struct MemoryParams
struct Dimensions
{
std::vector<ck::index_t> M;
std::vector<ck::index_t> N;
std::vector<ck::index_t> K;
std::vector<ck::index_t> StridesA;
std::vector<ck::index_t> StridesB;
std::vector<ck::index_t> StridesC;
std::vector<ck::index_t> StridesD;
};
template <typename Tuple>
class TestContraction : public ::testing::Test
{
protected:
using ALayout = std::tuple_element_t<0, Tuple>;
using BLayout = std::tuple_element_t<1, Tuple>;
using CDLayout = std::tuple_element_t<2, Tuple>;
using DataType = std::tuple_element_t<3, Tuple>;
using DTupleDataType = std::tuple_element_t<4, Tuple>;
using CDElementOp = std::tuple_element_t<5, Tuple>;
std::vector<MemoryParams> list_of_memory_params = {{{32, 32},
{32, 32},
{32, 32},
{32768, 1024, 32, 1},
{32768, 1024, 32, 1},
{32768, 1024, 32, 1},
{32768, 1024, 32, 1}},
{{16, 16},
{32, 32},
{16, 16},
{4096, 256, 16, 1},
{16, 1, 8192, 256},
{16384, 1024, 32, 1},
{16384, 1024, 32, 1}}};
std::vector<ck::index_t> init_methods = {0, 1, 2};
using ALayout = std::tuple_element_t<0, Tuple>;
using BLayout = std::tuple_element_t<1, Tuple>;
using CDLayout = std::tuple_element_t<2, Tuple>;
using DataType = std::tuple_element_t<3, Tuple>;
using DTupleDataType = std::tuple_element_t<4, Tuple>;
using ComputeDataType = std::tuple_element_t<5, Tuple>;
using CDElementOp = std::tuple_element_t<6, Tuple>;
std::vector<Dimensions> dimension_list = {{{32, 32}, {32, 32}, {32, 32}},
{{16, 16}, {32, 32}, {16, 16}}};
std::vector<ck::index_t> init_methods = {1, 2};
std::unique_ptr<CDElementOp> p_cd_element_op;
void Run()
{
for(auto& memory_params : list_of_memory_params)
for(auto& dimension_params : dimension_list)
{
std::vector<ck::index_t> StridesA;
std::vector<ck::index_t> StridesB;
std::vector<ck::index_t> StridesC;
std::vector<ck::index_t> StridesD;
const auto& M = dimension_params.M;
const auto& N = dimension_params.N;
const auto& K = dimension_params.K;
assign_default_strides(ALayout{}, StridesA, {M[0], M[1], K[0], K[1]});
assign_default_strides(BLayout{}, StridesB, {N[0], N[1], K[0], K[1]});
assign_default_strides(CDLayout{}, StridesC, {M[0], M[1], N[0], N[1]});
assign_default_strides(CDLayout{}, StridesD, {M[0], M[1], N[0], N[1]});
for(const ck::index_t init_method : init_methods)
{
bool pass =
......@@ -70,19 +73,20 @@ class TestContraction : public ::testing::Test
BLayout,
CDLayout,
DataType,
ComputeDataType,
DTupleDataType,
CDElementOp>(true /*do_verification*/,
init_method,
false /*do_logs*/,
false /*time_kernel*/,
*p_cd_element_op,
memory_params.M,
memory_params.N,
memory_params.K,
memory_params.StridesA,
memory_params.StridesB,
memory_params.StridesC,
memory_params.StridesD);
dimension_params.M,
dimension_params.N,
dimension_params.K,
StridesA,
StridesB,
StridesC,
StridesD);
EXPECT_TRUE(pass);
}
}
......@@ -99,24 +103,18 @@ class TestContractionBilinear : public TestContraction<Tuple>
{
};
#define ALL_LAYOUT_COMBINATIONS(dt, tuple_dt, compute_dt, op) \
std::tuple<Row, Row, Row, dt, tuple_dt, compute_dt, op>, \
std::tuple<Row, Col, Row, dt, tuple_dt, compute_dt, op>, \
std::tuple<Col, Row, Row, dt, tuple_dt, compute_dt, op>, \
std::tuple<Col, Col, Row, dt, tuple_dt, compute_dt, op>
using BilinearKernelTypes =
::testing::Types<std::tuple<Row, Row, Row, F32, ck::Tuple<F32>, Bilinear>,
std::tuple<Row, Col, Row, F32, ck::Tuple<F32>, Bilinear>,
std::tuple<Col, Row, Row, F32, ck::Tuple<F32>, Bilinear>,
std::tuple<Col, Col, Row, F32, ck::Tuple<F32>, Bilinear>,
std::tuple<Row, Row, Row, F64, ck::Tuple<F32>, Bilinear>,
std::tuple<Row, Col, Row, F64, ck::Tuple<F32>, Bilinear>,
std::tuple<Col, Row, Row, F64, ck::Tuple<F32>, Bilinear>,
std::tuple<Col, Col, Row, F64, ck::Tuple<F32>, Bilinear>>;
using ScaleKernelTypes = ::testing::Types<std::tuple<Row, Row, Row, F32, ck::Tuple<>, Scale>,
std::tuple<Row, Col, Row, F32, ck::Tuple<>, Scale>,
std::tuple<Col, Row, Row, F32, ck::Tuple<>, Scale>,
std::tuple<Col, Col, Row, F32, ck::Tuple<>, Scale>,
std::tuple<Row, Row, Row, F64, ck::Tuple<>, Scale>,
std::tuple<Row, Col, Row, F64, ck::Tuple<>, Scale>,
std::tuple<Col, Row, Row, F64, ck::Tuple<>, Scale>,
std::tuple<Col, Col, Row, F64, ck::Tuple<>, Scale>>;
::testing::Types<ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<F32>, F32, Bilinear),
ALL_LAYOUT_COMBINATIONS(F64, ck::Tuple<F64>, F64, Bilinear)>;
using ScaleKernelTypes = ::testing::Types<ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<>, F32, Scale),
ALL_LAYOUT_COMBINATIONS(F64, ck::Tuple<>, F64, Scale)>;
TYPED_TEST_SUITE(TestContractionBilinear, BilinearKernelTypes);
TYPED_TEST_SUITE(TestContractionScale, ScaleKernelTypes);
......@@ -136,3 +134,46 @@ TYPED_TEST(TestContractionScale, scale)
this->p_cd_element_op = std::make_unique<Scale>(0.5f);
this->Run();
}
template <typename Tuple>
class TestContractionScaleMixedPrecision : public TestContraction<Tuple>
{
};
template <typename Tuple>
class TestContractionBilinearMixedPrecision : public TestContraction<Tuple>
{
};
using BilinearKernelTypesMixedPrecision =
::testing::Types<ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<F32>, F16, Bilinear),
ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<F32>, BF16, Bilinear),
ALL_LAYOUT_COMBINATIONS(F64, ck::Tuple<F64>, F32, Bilinear),
ALL_LAYOUT_COMBINATIONS(F16, ck::Tuple<F16>, F32, Bilinear),
ALL_LAYOUT_COMBINATIONS(BF16, ck::Tuple<BF16>, F32, Bilinear)>;
using ScaleKernelTypesMixedPrecision =
::testing::Types<ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<>, F16, Scale),
ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<>, BF16, Scale),
ALL_LAYOUT_COMBINATIONS(F64, ck::Tuple<>, F32, Scale),
ALL_LAYOUT_COMBINATIONS(F16, ck::Tuple<>, F32, Scale),
ALL_LAYOUT_COMBINATIONS(BF16, ck::Tuple<>, F32, Scale)>;
TYPED_TEST_SUITE(TestContractionBilinearMixedPrecision, BilinearKernelTypesMixedPrecision);
TYPED_TEST_SUITE(TestContractionScaleMixedPrecision, ScaleKernelTypesMixedPrecision);
TYPED_TEST(TestContractionBilinearMixedPrecision, bilinear)
{
this->p_cd_element_op = std::make_unique<Bilinear>(1.f, 1.f);
this->Run();
this->p_cd_element_op = std::make_unique<Bilinear>(-0.5f, 0.5f);
this->Run();
}
TYPED_TEST(TestContractionScaleMixedPrecision, scale)
{
this->p_cd_element_op = std::make_unique<Scale>(1.f);
this->Run();
this->p_cd_element_op = std::make_unique<Scale>(0.5f);
this->Run();
}
test/contraction/test_contraction_interface.cpp
View file @ 0c823497
......@@ -34,11 +34,11 @@ class ContractionInstanceWrapper
static constexpr ck::index_t NumDim = 2;
// clang-format off
using ContractionDeviceInstance = ck::tensor_operation::device::
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceContractionMultipleD_Xdl_CShuffle< NumDim, NumDim, NumDim, F32, F32, F32, F32, ck::Tuple<F32>, F32, Pass, Pass, Bilinear, GemmSpec, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, ABlockTransferSrcVectorDim, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, BBlockTransferSrcVectorDim, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, CDEBlockTransferScalarPerVector>;
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| Compute|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| Data|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| Type|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceContractionMultipleD_Xdl_CShuffle< NumDim, NumDim, NumDim, F32, F32, F32, F32, ck::Tuple<F32>, F32, Pass, Pass, Bilinear, GemmSpec, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, ABlockTransferSrcVectorDim, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, BBlockTransferSrcVectorDim, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, CDEBlockTransferScalarPerVector, F32>;
// clang-format on
bool isSupported(std::vector<ck::index_t>& ADims,
......
test/conv_tensor_rearrange/CMakeLists.txt
View file @ 0c823497
add_gtest_executable(test_conv_tensor_rearrange test_conv_tensor_rearrange.cpp)
target_link_libraries(test_conv_tensor_rearrange PRIVATE utility device_image_to_column_instance device_column_to_image_instance)
add_gtest_executable(test_conv_tensor_rearrange_interface test_conv_tensor_rearrange_interface.cpp)
target_link_libraries(test_conv_tensor_rearrange_interface PRIVATE utility)
test/conv_tensor_rearrange/test_conv_tensor_rearrange.cpp
View file @ 0c823497
......@@ -45,14 +45,20 @@ class TestConvTensorRearrange : public ::testing::Test
using namespace ck::tensor_layout::convolution;
using namespace ck::conv_tensor_rearrange_op;
using KernelTypes1d =
::testing::Types<std::tuple<GNWC, ImageToColumn>, std::tuple<GNWC, ColumnToImage>>;
using KernelTypes1d = ::testing::Types<std::tuple<GNWC, ImageToColumn>,
std::tuple<GNWC, ColumnToImage>,
std::tuple<NWGC, ImageToColumn>,
std::tuple<NWGC, ColumnToImage>>;
using KernelTypes2d =
::testing::Types<std::tuple<GNHWC, ImageToColumn>, std::tuple<GNHWC, ColumnToImage>>;
using KernelTypes2d = ::testing::Types<std::tuple<GNHWC, ImageToColumn>,
std::tuple<GNHWC, ColumnToImage>,
std::tuple<NHWGC, ImageToColumn>,
std::tuple<NHWGC, ColumnToImage>>;
using KernelTypes3d =
::testing::Types<std::tuple<GNDHWC, ImageToColumn>, std::tuple<GNDHWC, ColumnToImage>>;
using KernelTypes3d = ::testing::Types<std::tuple<GNDHWC, ImageToColumn>,
std::tuple<GNDHWC, ColumnToImage>,
std::tuple<NDHWGC, ImageToColumn>,
std::tuple<NDHWGC, ColumnToImage>>;
template <typename Tuple>
class TestConvTensorRearrange1d : public TestConvTensorRearrange<Tuple>
......@@ -77,16 +83,16 @@ TYPED_TEST(TestConvTensorRearrange1d, Test1D)
{
this->conv_params.clear();
this->conv_params.push_back({1, 1, 4, 1, 192, {3}, {28}, {1}, {1}, {1}, {1}});
this->conv_params.push_back({1, 1, 64, 1, 64, {3}, {14}, {1}, {1}, {1}, {1}});
this->conv_params.push_back({1, 1, 64, 1, 64, {1}, {7}, {3}, {1}, {0}, {0}});
this->conv_params.push_back({1, 1, 64, 1, 64, {1}, {3}, {1}, {1}, {0}, {0}});
this->conv_params.push_back({1, 2, 4, 1, 192, {3}, {28}, {1}, {1}, {1}, {1}});
this->conv_params.push_back({1, 2, 64, 1, 64, {3}, {14}, {1}, {1}, {1}, {1}});
this->conv_params.push_back({1, 2, 64, 1, 64, {1}, {7}, {3}, {1}, {0}, {0}});
this->conv_params.push_back({1, 2, 64, 1, 64, {1}, {3}, {1}, {1}, {0}, {0}});
// ScalarPerVector should be 1
this->conv_params.push_back({1, 1, 4, 1, 1, {3}, {28}, {1}, {1}, {1}, {1}});
this->conv_params.push_back({1, 2, 4, 1, 1, {3}, {28}, {1}, {1}, {1}, {1}});
// stride != 1
this->conv_params.push_back({1, 1, 1, 1, 4, {3}, {28}, {2}, {1}, {1}, {1}});
this->conv_params.push_back({1, 2, 1, 1, 4, {3}, {28}, {2}, {1}, {1}, {1}});
// dilation != 1
this->conv_params.push_back({1, 1, 1, 1, 4, {3}, {28}, {1}, {2}, {1}, {1}});
this->conv_params.push_back({1, 2, 1, 1, 4, {3}, {28}, {1}, {2}, {1}, {1}});
#ifdef CK_ENABLE_FP32
this->template Run<1, float, float>();
#endif
......@@ -106,13 +112,13 @@ TYPED_TEST(TestConvTensorRearrange2d, Test2D)
this->conv_params.clear();
this->conv_params.push_back(
{2, 1, 4, 1, 192, {3, 3}, {28, 28}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
{2, 2, 4, 1, 192, {3, 3}, {28, 28}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
this->conv_params.push_back(
{2, 1, 64, 1, 64, {3, 3}, {14, 14}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
{2, 2, 64, 1, 64, {3, 3}, {14, 14}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
this->conv_params.push_back({2, 1, 64, 1, 64, {1, 1}, {7, 7}, {3, 3}, {1, 1}, {0, 0}, {0, 0}});
this->conv_params.push_back({2, 1, 64, 1, 64, {1, 1}, {3, 3}, {1, 1}, {1, 1}, {0, 0}, {0, 0}});
this->conv_params.push_back(
{2, 1, 64, 1, 64, {3, 3}, {28, 28}, {2, 2}, {2, 2}, {1, 1}, {1, 1}});
{2, 2, 64, 1, 64, {3, 3}, {28, 28}, {2, 2}, {2, 2}, {1, 1}, {1, 1}});
#ifdef CK_ENABLE_FP32
this->template Run<2, float, float>();
#endif
......@@ -131,13 +137,13 @@ TYPED_TEST(TestConvTensorRearrange3d, Test3D)
{
this->conv_params.clear();
this->conv_params.push_back(
{3, 1, 16, 1, 64, {1, 1, 1}, {7, 7, 7}, {2, 2, 2}, {3, 3, 3}, {0, 0, 0}, {0, 0, 0}});
{3, 2, 16, 1, 64, {1, 1, 1}, {7, 7, 7}, {2, 2, 2}, {3, 3, 3}, {0, 0, 0}, {0, 0, 0}});
this->conv_params.push_back(
{3, 1, 2, 1, 64, {3, 3, 3}, {14, 14, 3}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
{3, 2, 2, 1, 64, {3, 3, 3}, {14, 14, 3}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
this->conv_params.push_back(
{3, 1, 32, 1, 64, {1, 1, 1}, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}});
{3, 2, 32, 1, 64, {1, 1, 1}, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}});
this->conv_params.push_back(
{3, 1, 64, 1, 64, {3, 3, 3}, {14, 14, 14}, {2, 2, 2}, {2, 2, 2}, {1, 1, 1}, {1, 1, 1}});
{3, 2, 64, 1, 64, {3, 3, 3}, {14, 14, 14}, {2, 2, 2}, {2, 2, 2}, {1, 1, 1}, {1, 1, 1}});
#ifdef CK_ENABLE_FP32
this->template Run<3, float, float>();
#endif
......
test/conv_tensor_rearrange/test_conv_tensor_rearrange_interface.cpp
View file @ 0c823497
......@@ -53,7 +53,7 @@ class TestConvTensorRearrangeInterface : public ::testing::Test
template <typename ConvTensorRearrangeOp>
bool Run()
{
const auto G = conv_param.G_;
const auto N = conv_param.N_;
const auto C = conv_param.C_;
const auto FakeC =
......@@ -71,13 +71,13 @@ class TestConvTensorRearrangeInterface : public ::testing::Test
const auto image_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<ImLayout>(
conv_param);
const auto gemm_desc = HostTensorDescriptor({NDoHoWo, CZYX});
const auto gemm_desc = HostTensorDescriptor({G, NDoHoWo, CZYX});
std::array<ck::index_t, NDimSpatial> input_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> output_spatial_lengths{};
std::array<ck::index_t, NDimSpatial + 3> input_g_n_c_wis_strides{};
std::array<ck::index_t, 2> output_m_k_strides{};
std::array<ck::index_t, 3> output_g_m_k_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
std::array<ck::index_t, NDimSpatial> input_left_pads{};
......@@ -89,7 +89,7 @@ class TestConvTensorRearrangeInterface : public ::testing::Test
copy(conv_param.filter_spatial_lengths_, filter_spatial_lengths);
copy(conv_param.output_spatial_lengths_, output_spatial_lengths);
copy(image_desc.GetStrides(), input_g_n_c_wis_strides);
copy(gemm_desc.GetStrides(), output_m_k_strides);
copy(gemm_desc.GetStrides(), output_g_m_k_strides);
copy(conv_param.conv_filter_strides_, conv_filter_strides);
copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
copy(conv_param.input_left_pads_, input_left_pads);
......@@ -100,13 +100,14 @@ class TestConvTensorRearrangeInterface : public ::testing::Test
auto img2col = DeviceImgToColInstance{};
auto argument = img2col.MakeArgument(nullptr,
nullptr,
G,
N,
IsCPacked ? C : FakeC,
input_spatial_lengths,
filter_spatial_lengths,
output_spatial_lengths,
input_g_n_c_wis_strides,
output_m_k_strides,
output_g_m_k_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
......@@ -119,13 +120,14 @@ class TestConvTensorRearrangeInterface : public ::testing::Test
auto col2img = DeviceColToimgInstance{};
auto argument = col2img.MakeArgument(nullptr,
nullptr,
G,
N,
IsCPacked ? C : FakeC,
input_spatial_lengths,
filter_spatial_lengths,
output_spatial_lengths,
input_g_n_c_wis_strides,
output_m_k_strides,
output_g_m_k_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
......
test/data_type/CMakeLists.txt
View file @ 0c823497
if (USE_BITINT_EXTENSION_INT4)
add_gtest_executable(test_int4 int4.cpp)
add_gtest_executable(test_int4 test_int4.cpp)
if(result EQUAL 0)
target_link_libraries(test_int4 PRIVATE utility)
endif()
endif()
add_gtest_executable(test_fp8 fp8.cpp)
add_gtest_executable(test_fp8 test_fp8.cpp)
if(result EQUAL 0)
target_link_libraries(test_fp8 PRIVATE utility)
endif()
add_gtest_executable(test_bf8 bf8.cpp)
add_gtest_executable(test_bf8 test_bf8.cpp)
if(result EQUAL 0)
target_link_libraries(test_bf8 PRIVATE utility)
endif()
......
test/grouped_convnd_bwd_weight/CMakeLists.txt
View file @ 0c823497
list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
list(APPEND gpu_list_xdl gfx908 gfx90a gfx940 gfx941 gfx942)
list(APPEND gpu_list_wmma gfx1100 gfx1101 gfx1102)
set(target 0)
foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0)
if(gpu IN_LIST gpu_list_xdl AND target EQUAL 0)
add_gtest_executable(test_grouped_convnd_bwd_weight test_grouped_convnd_bwd_weight.cpp)
target_link_libraries(test_grouped_convnd_bwd_weight PRIVATE utility device_grouped_conv1d_bwd_weight_instance device_grouped_conv2d_bwd_weight_instance device_grouped_conv3d_bwd_weight_instance)
add_gtest_executable(test_grouped_convnd_bwd_weight_interface test_grouped_convnd_bwd_weight_interface.cpp)
target_link_libraries(test_grouped_convnd_bwd_weight_interface PRIVATE utility device_grouped_conv1d_bwd_weight_instance device_grouped_conv2d_bwd_weight_instance device_grouped_conv3d_bwd_weight_instance)
add_gtest_executable(test_grouped_convnd_bwd_weight_interface test_grouped_convnd_bwd_weight_interface_xdl.cpp)
target_link_libraries(test_grouped_convnd_bwd_weight_interface PRIVATE utility)
set(target 1)
endif()
if(gpu IN_LIST gpu_list_wmma AND target EQUAL 0)
add_gtest_executable(test_grouped_convnd_bwd_weight test_grouped_convnd_bwd_weight.cpp)
target_link_libraries(test_grouped_convnd_bwd_weight PRIVATE utility device_grouped_conv1d_bwd_weight_instance device_grouped_conv2d_bwd_weight_instance device_grouped_conv3d_bwd_weight_instance)
add_gtest_executable(test_grouped_convnd_bwd_weight_interface test_grouped_convnd_bwd_weight_interface_wmma.cpp)
target_link_libraries(test_grouped_convnd_bwd_weight_interface PRIVATE utility)
set(target 1)
endif()
endforeach()
\ No newline at end of file
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