ROCm/composable_kernel
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Add host API (#220)

Browse Source 
* Add host API

* manually rebase on develop

* clean

* manually rebase on develop

* exclude tests from all target

* address review comments

* update client app name

* fix missing lib name

* clang-format update

* refactor

* refactor

* refactor

* refactor

* refactor

* fix test issue

* refactor

* refactor

* refactor

* upate cmake and readme

Co-authored-by: Chao Liu <chao.liu2@amd.com>
This commit is contained in:
JD
2022-05-12 09:21:01 -05:00
committed by GitHub
parent 0f912e205e
commit cec69bc3bc
131 changed files with 1666 additions and 1089 deletions
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30
CMakeLists.txt
View File

@@ -27,6 +27,8 @@ set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_CXX_EXTENSIONS OFF)
message("CMAKE_CXX_COMPILER_ID: ${CMAKE_CXX_COMPILER_ID}")
option(CK_TIME_KERNEL "Turning off will disable kernel timing globally" ON)
## OpenMP
if(CMAKE_CXX_COMPILER_ID MATCHES "Clang")
# workaround issue hipcc in rocm3.5 cannot find openmp
@@ -72,8 +74,9 @@ message(STATUS "Build with HIP ${HIP_VERSION}")
rocm_create_package(
NAME CK-${CK_BACKEND}
NAME composablekernel
DESCRIPTION "High Performance Composable Kernel for AMD GPUs"
MAINTAINER "MIOpen Kernels Dev Team <dl.MIOpen@amd.com>"
LDCONFIG
)
@@ -226,7 +229,7 @@ set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/lib)
set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/lib)
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/bin)
configure_file("${PROJECT_SOURCE_DIR}/include/ck/hip_version.hpp.in" "${PROJECT_BINARY_DIR}/include/ck/hip_version.hpp")
configure_file("${PROJECT_SOURCE_DIR}/include/ck/options.hpp.in" "${PROJECT_BINARY_DIR}/include/ck/options.hpp")
include_directories(BEFORE
${PROJECT_SOURCE_DIR}/include
@@ -234,7 +237,6 @@ include_directories(BEFORE
${PROJECT_SOURCE_DIR}/library/include
)
include(googletest)
SET(BUILD_DEV ON CACHE BOOL "BUILD_DEV")
if(BUILD_DEV)
@@ -247,3 +249,25 @@ add_subdirectory(library)
add_subdirectory(example)
add_subdirectory(test)
add_subdirectory(profiler)
#Create an interface target for the include only files and call it "composablekernels"
include(CMakePackageConfigHelpers)
set(version 1.0.0)
write_basic_package_version_file(
"${CMAKE_CURRENT_BINARY_DIR}/composable_kernelConfigVersion.cmake"
VERSION "${version}"
COMPATIBILITY AnyNewerVersion
)
configure_package_config_file(${CMAKE_CURRENT_SOURCE_DIR}/Config.cmake.in
"${CMAKE_CURRENT_BINARY_DIR}/composable_kernelConfig.cmake"
INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/composable_kernel
NO_CHECK_REQUIRED_COMPONENTS_MACRO
)
install(FILES
"${CMAKE_CURRENT_BINARY_DIR}/composable_kernelConfig.cmake"
"${CMAKE_CURRENT_BINARY_DIR}/composable_kernelConfigVersion.cmake"
DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/composable_kernel
)

11
Config.cmake.in Normal file
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@@ -0,0 +1,11 @@
@PACKAGE_INIT@
set(_composable_kernel_supported_components device_operations host_tensor)
foreach(_comp ${composable_kernel_FIND_COMPONENTS})
if(NOT _comp IN_LIST _composable_kernel_supported_components)
set(composable_kernel_FOUND False)
set(composable_kernel_NOT_FOUND_MESSAGE "Unsupported component: ${_comp}")
endif()
include("${CMAKE_CURRENT_LIST_DIR}/composable_kernel${_comp}Targets.cmake")
endforeach()

15
Dockerfile
View File

@@ -11,13 +11,7 @@ ARG DEB_ROCM_REPO=http://repo.radeon.com/rocm/apt/.apt_$ROCMVERSION/
RUN apt-get update
RUN apt-get install -y wget gnupg
RUN wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add -
RUN if ! [ -z $OSDB_BKC_VERSION ]; then \
echo "Using BKC VERISION: $OSDB_BKC_VERSION";\
sh -c "echo deb [arch=amd64 trusted=yes] http://compute-artifactory.amd.com/artifactory/list/rocm-osdb-deb/ compute-rocm-dkms-no-npi-hipclang ${OSDB_BKC_VERSION} > /etc/apt/sources.list.d/rocm.list" ;\
cat /etc/apt/sources.list.d/rocm.list;\
else \
sh -c "echo deb [arch=amd64] $DEB_ROCM_REPO ubuntu main > /etc/apt/sources.list.d/rocm.list" ;\
fi
RUN sh -c "echo deb [arch=amd64] $DEB_ROCM_REPO ubuntu main > /etc/apt/sources.list.d/rocm.list"
RUN wget --no-check-certificate -qO - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | apt-key add -
RUN sh -c "echo deb https://apt.kitware.com/ubuntu/ bionic main | tee -a /etc/apt/sources.list"
@@ -25,18 +19,15 @@ RUN sh -c "echo deb https://apt.kitware.com/ubuntu/ bionic main | tee -a /etc/ap
# Install dependencies
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \
apt-utils \
sshpass \
build-essential \
cmake-data=3.15.1-0kitware1 \
cmake=3.15.1-0kitware1 \
curl \
doxygen \
g++ \
gdb \
git \
hip-rocclr \
jq \
lcov \
libelf-dev \
libncurses5-dev \
libnuma-dev \
@@ -62,8 +53,6 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# RUN pip3 install --default-timeout=100000 -r requirements.txt
# Setup ubsan environment to printstacktrace
RUN ln -s /usr/bin/llvm-symbolizer-3.8 /usr/local/bin/llvm-symbolizer
ENV UBSAN_OPTIONS=print_stacktrace=1
@@ -92,5 +81,3 @@ ADD rbuild.ini /rbuild.ini
ADD dev-requirements.txt dev-requirements.txt
RUN rbuild prepare -s develop -d $PREFIX
RUN groupadd -f render
# RUN cget install -f min-requirements.txt
# RUN CXXFLAGS='-isystem $PREFIX/include' cget install -f ./mlir-requirements.txt

19
Jenkinsfile vendored
View File

@@ -320,7 +320,7 @@ pipeline {
{
agent{ label rocmnode("gfx908")}
environment{
setup_args = """ -D CMAKE_CXX_FLAGS="--offload-arch=gfx908 -O3 " -DBUILD_DEV=On """
setup_args = """ -D CMAKE_CXX_FLAGS=" --offload-arch=gfx900 --offload-arch=gfx906 --offload-arch=gfx908 --offload-arch=gfx90a -O3 " -DBUILD_DEV=On """
}
steps{
buildHipClangJobAndReboot(setup_args:setup_args, config_targets: "check", no_reboot:true, build_type: 'Release')
@@ -341,6 +341,23 @@ pipeline {
}
}
stage("Client App")
{
parallel
{
stage("Run Client App")
{
agent{ label rocmnode("gfx908")}
environment{
setup_args = """ -D -DBUILD_DEV=Off -DCMAKE_INSTALL_PREFIX=../install CMAKE_CXX_FLAGS="--offload-arch=gfx908 -O3 " """
execute_args = """ cd ../test/client_app && rm -rf build && mkdir build && cd build && cmake -DCMAKE_PREFIX_PATH="${env.WORKSPACE}/install;/opt/rocm" .. && make """
}
steps{
buildHipClangJobAndReboot(setup_args: setup_args, config_targets: "install", no_reboot:true, build_type: 'Release', execute_cmd: execute_args, prefixpath: '/usr/local')
}
}
}
}
stage("Performance Tests")
{
parallel

10
README.md
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@@ -43,3 +43,13 @@ Instructions for running each individual examples are under ```example/```
make -j ckProfiler
```
Instructions for running ckProfiler are under ```profiler/```
## Caveat
### Kernel Timing and Verification
CK's own kernel timer will warn up kernel once, and then run it multiple times
to get average kernel time. For some kernels that use atomic add, this will cause
output buffer to be accumulated multiple times, causing verfication failure.
To work around it, do not use CK's own timer and do verification at the same time.
CK's own timer and verification in each example and ckProfiler can be enabled or
disabled from command line.

3
cmake/googletest.cmake
View File

@@ -19,6 +19,7 @@ list(APPEND GTEST_CMAKE_CXX_FLAGS
-Wno-zero-as-null-pointer-constant
-Wno-unused-member-function
-Wno-comma
-Wno-old-style-cast
)
message(STATUS "Suppressing googltest warnings with flags: ${GTEST_CMAKE_CXX_FLAGS}")
@@ -35,4 +36,4 @@ FetchContent_MakeAvailable(googletest)
target_compile_options(gtest PRIVATE ${GTEST_CMAKE_CXX_FLAGS})
target_compile_options(gtest_main PRIVATE ${GTEST_CMAKE_CXX_FLAGS})
target_compile_options(gmock PRIVATE ${GTEST_CMAKE_CXX_FLAGS})
target_compile_options(gmock_main PRIVATE ${GTEST_CMAKE_CXX_FLAGS})

14
example/01_gemm/gemm_xdl_bf16.cpp
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@@ -88,9 +88,9 @@ using ReferenceGemmInstance = ck::tensor_operation::host::
int main(int argc, char* argv[])
{
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 3840;
@@ -105,13 +105,13 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 10)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
@@ -125,7 +125,7 @@ int main(int argc, char* argv[])
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
exit(0);
}
@@ -198,7 +198,7 @@ int main(int argc, char* argv[])
"not support this GEMM problem");
}
float ave_time = invoker.Run(argument, nrepeat);
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =

14
example/01_gemm/gemm_xdl_fp16.cpp
View File

@@ -56,9 +56,9 @@ using ReferenceGemmInstance = ck::tensor_operation::host::
int main(int argc, char* argv[])
{
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 3840;
@@ -73,13 +73,13 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 10)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
@@ -93,7 +93,7 @@ int main(int argc, char* argv[])
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
exit(0);
}
@@ -171,7 +171,7 @@ int main(int argc, char* argv[])
"not support this GEMM problem");
}
float ave_time = invoker.Run(argument, nrepeat);
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =

14
example/01_gemm/gemm_xdl_int8.cpp
View File

@@ -83,9 +83,9 @@ using ReferenceGemmInstance = ck::tensor_operation::host::
int main(int argc, char* argv[])
{
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 3840;
@@ -100,13 +100,13 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 10)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
@@ -120,7 +120,7 @@ int main(int argc, char* argv[])
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
exit(0);
}
@@ -194,7 +194,7 @@ int main(int argc, char* argv[])
"not support this GEMM problem");
}
float ave_time = invoker.Run(argument, nrepeat);
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =

16
example/02_gemm_alpha_beta/gemm_xdl_alpha_beta.cpp
View File

@@ -86,9 +86,9 @@ using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemmBias2D<AD
int main(int argc, char* argv[])
{
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 3840;
@@ -106,13 +106,13 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 6)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
alpha = std::stof(argv[4]);
beta = std::stof(argv[5]);
@@ -121,7 +121,7 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
@@ -138,7 +138,7 @@ int main(int argc, char* argv[])
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC, alpha, beta\n");
exit(0);
}
@@ -216,7 +216,7 @@ int main(int argc, char* argv[])
"not support this GEMM problem");
}
float ave_time = invoker.Run(argument, nrepeat);
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =

14
example/03_gemm_bias_relu/gemm_xdl_bias_relu.cpp
View File

@@ -83,9 +83,9 @@ using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemmBiasActiv
int main(int argc, char* argv[])
{
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 3840;
@@ -100,13 +100,13 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 10)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
@@ -120,7 +120,7 @@ int main(int argc, char* argv[])
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
exit(0);
}
@@ -206,7 +206,7 @@ int main(int argc, char* argv[])
"not support this GEMM problem");
}
float ave_time = invoker.Run(argument, nrepeat);
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * M * N * K;

14
example/04_gemm_bias_relu_add/gemm_xdl_bias_relu_add.cpp
View File

@@ -83,9 +83,9 @@ using ReferenceGemmInstance =
CElementOp>;
int main(int argc, char* argv[])
{
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 3840;
@@ -101,13 +101,13 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 11)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
@@ -122,7 +122,7 @@ int main(int argc, char* argv[])
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg4 to 10: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC, StrideC1\n");
exit(0);
}
@@ -218,7 +218,7 @@ int main(int argc, char* argv[])
"not support this GEMM problem");
}
float ave_time = invoker.Run(argument, nrepeat);
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * M +

12
example/06_conv2d_fwd_bias_relu/conv2d_fwd_xdl_bias_relu.cpp
View File

@@ -93,7 +93,7 @@ void PrintUseMsg()
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: run kernel # of times (>1)\n"
<< "arg3: time kernel (0=n0, 1=yes)\n"
<< "Following arguments:\n"
<< " N, K, C, \n"
<< " <filter spatial dimensions>, (ie Y, X for 2D)\n"
@@ -165,9 +165,9 @@ int main(int argc, char* argv[])
{
using namespace ck::utils::conv;
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
const int num_dim_spatial = 2;
ck::utils::conv::ConvParams params;
@@ -176,7 +176,7 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
}
if(argc >= 5)
@@ -269,7 +269,7 @@ int main(int argc, char* argv[])
"not support this problem");
}
float ave_time = invoker.Run(argument, nrepeat);
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = get_flops(
params.N_, params.C_, params.K_, params.filter_spatial_lengths_, output_spatial_lengths);

12
example/07_conv2d_fwd_bias_relu_add/conv2d_fwd_xdl_bias_relu_add.cpp
View File

@@ -90,7 +90,7 @@ void PrintUseMsg()
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: run kernel # of times (>1)\n"
<< "arg3: time kernel (0=n0, 1=yes)\n"
<< "Following arguments:\n"
<< " N, K, C, \n"
<< " <filter spatial dimensions>, (ie Y, X for 2D)\n"
@@ -162,9 +162,9 @@ int main(int argc, char* argv[])
{
using namespace ck::utils::conv;
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
const int num_dim_spatial = 2;
ck::utils::conv::ConvParams params;
@@ -173,7 +173,7 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
}
if(argc >= 5)
@@ -280,7 +280,7 @@ int main(int argc, char* argv[])
"not support this problem");
}
float ave_time = invoker.Run(argument, nrepeat);
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = get_flops(
params.N_, params.C_, params.K_, params.filter_spatial_lengths_, output_spatial_lengths);

12
example/09_convnd_fwd/convnd_fwd_xdl.cpp
View File

@@ -107,7 +107,7 @@ void print_use_msg()
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: run kernel # of times (>1)\n"
<< "arg3: time kernel (0=n0, 1=yes)\n"
<< "arg4: N spatial dimensions (default 2)\n"
<< "Following arguments (depending on number of spatial dims):\n"
<< " N, K, C, \n"
@@ -179,9 +179,9 @@ int main(int argc, char* argv[])
{
using namespace ck::utils::conv;
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
int num_dim_spatial = 2;
ck::utils::conv::ConvParams params;
@@ -190,7 +190,7 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
num_dim_spatial = std::stoi(argv[4]);
}
@@ -276,7 +276,7 @@ int main(int argc, char* argv[])
"not support this Conv problem");
}
float ave_time = invoker->Run(argument.get(), nrepeat);
float ave_time = invoker->Run(argument.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = get_flops(
params.N_, params.C_, params.K_, params.filter_spatial_lengths_, output_spatial_lengths);

12
example/09_convnd_fwd/convnd_fwd_xdl_fp16.cpp
View File

@@ -110,7 +110,7 @@ void print_use_msg()
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: run kernel # of times (>1)\n"
<< "arg3: time kernel (0=n0, 1=yes)\n"
<< "arg4: N spatial dimensions (default 2)\n"
<< "Following arguments (depending on number of spatial dims):\n"
<< " N, K, C, \n"
@@ -182,9 +182,9 @@ int main(int argc, char* argv[])
{
using namespace ck::utils::conv;
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
int num_dim_spatial = 2;
ck::utils::conv::ConvParams params;
@@ -193,7 +193,7 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
num_dim_spatial = std::stoi(argv[4]);
}
@@ -277,7 +277,7 @@ int main(int argc, char* argv[])
"not support this Conv problem");
}
float ave_time = invoker->Run(argument.get(), nrepeat);
float ave_time = invoker->Run(argument.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = get_flops(
params.N_, params.C_, params.K_, params.filter_spatial_lengths_, output_spatial_lengths);

12
example/09_convnd_fwd/convnd_fwd_xdl_int8.cpp
View File

@@ -112,7 +112,7 @@ void print_use_msg()
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: run kernel # of times (>1)\n"
<< "arg3: time kernel (0=n0, 1=yes)\n"
<< "arg4: N spatial dimensions (default 2)\n"
<< "Following arguments (depending on number of spatial dims):\n"
<< " N, K, C, \n"
@@ -184,9 +184,9 @@ int main(int argc, char* argv[])
{
using namespace ck::utils::conv;
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
int num_dim_spatial = 2;
ck::utils::conv::ConvParams params;
@@ -195,7 +195,7 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
num_dim_spatial = std::stoi(argv[4]);
}
@@ -279,7 +279,7 @@ int main(int argc, char* argv[])
"not support this Conv problem");
}
float ave_time = invoker->Run(argument.get(), nrepeat);
float ave_time = invoker->Run(argument.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = get_flops(
params.N_, params.C_, params.K_, params.filter_spatial_lengths_, output_spatial_lengths);

14
example/10_conv2d_bwd_data/conv2d_bwd_data_xdl.cpp
View File

@@ -77,9 +77,9 @@ using ReferenceConvBwdInstance = ck::tensor_operation::host::ReferenceConvBwdDat
int main(int argc, char* argv[])
{
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// Conv shape
ck::index_t N = 128;
@@ -102,13 +102,13 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 19)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
N = std::stoi(argv[4]);
K = std::stoi(argv[5]);
@@ -130,7 +130,7 @@ int main(int argc, char* argv[])
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg4 to 18: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
"RightPx\n");
exit(0);
@@ -214,7 +214,7 @@ int main(int argc, char* argv[])
"not support this Conv problem");
}
float ave_time = invoker.Run(argument, nrepeat);
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;

14
example/11_conv2d_bwd_weight/conv2d_bwd_weight_xdl.cpp
View File

@@ -82,9 +82,9 @@ using ReferenceConvBwdWeightInstance =
int main(int argc, char* argv[])
{
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
int do_log = 0;
int split_k = 4;
@@ -109,7 +109,7 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
do_log = std::stoi(argv[4]);
split_k = std::stoi(argv[5]);
}
@@ -117,7 +117,7 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
do_log = std::stoi(argv[4]);
split_k = std::stoi(argv[5]);
@@ -141,7 +141,7 @@ int main(int argc, char* argv[])
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg4: is show log (0=no, 1=yes)\n");
printf("arg5: split-k \n");
printf("arg6 to 19: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
@@ -246,7 +246,7 @@ int main(int argc, char* argv[])
return 1;
}
float ave_time = invoker.Run(argument, nrepeat);
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;

13
example/12_reduce/reduce_blockwise.cpp
View File

@@ -116,10 +116,9 @@ class SimpleAppArgs
std::vector<size_t> inLengths;
std::vector<float> scales;
bool do_verification = false;
int init_method = 1;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
public:
void show_usage(const char* cmd)
@@ -135,7 +134,7 @@ class SimpleAppArgs
std::cout << "Arg1 -- init method (0=no init, 1=single integer value, 2=scope integer "
"value, 3=decimal value)"
<< std::endl;
std::cout << "Arg2 -- number of repeats to run the kernel" << std::endl;
std::cout << "Arg2 -- time kernel (0=n0, 1=yes)" << std::endl;
};
int processArgs(int argc, char* argv[])
@@ -182,7 +181,7 @@ class SimpleAppArgs
throw std::runtime_error("Invalid cmd-line arguments, more argumetns are needed!");
init_method = std::atoi(argv[optind++]);
nrepeat = std::atoi(argv[optind]);
time_kernel = std::atoi(argv[optind]);
if(scales.empty())
{
@@ -352,7 +351,7 @@ int main(int argc, char* argv[])
auto invoker_ptr = reduce.MakeInvokerPointer();
float avg_time = invoker_ptr->Run(argument_ptr.get(), args.nrepeat);
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, args.time_kernel});
std::size_t num_bytes = invariant_total_length * reduce_total_length * sizeof(InDataType) +
invariant_total_length * sizeof(OutDataType);

14
example/13_pool2d_fwd/pool2d_fwd.cpp
View File

@@ -149,9 +149,9 @@ int main(int argc, char* argv[])
{
using namespace ck::host_reduce;
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// Pool shape
ck::index_t N = 128;
@@ -171,13 +171,13 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 16)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
N = std::stoi(argv[4]);
C = std::stoi(argv[5]);
@@ -196,7 +196,7 @@ int main(int argc, char* argv[])
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg4 to 15: N, C, Y, X, Hi, Wi, Sy, Sx, LeftPy, LeftPx, RightPy, "
"RightPx\n");
exit(0);
@@ -271,7 +271,7 @@ int main(int argc, char* argv[])
"not support this problem");
}
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * N * C * Ho * Wo * Y * X;

14
example/14_gemm_xdl_requant_relu_requant/gemm_xdl_requant_relu_requant_int8.cpp
View File

@@ -105,9 +105,9 @@ using ReferenceGemmInstance = ck::tensor_operation::host::
int main(int argc, char* argv[])
{
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 3840;
@@ -125,13 +125,13 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 10)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
@@ -145,7 +145,7 @@ int main(int argc, char* argv[])
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
exit(0);
}
@@ -219,7 +219,7 @@ int main(int argc, char* argv[])
"not support this GEMM problem");
}
float ave_time = invoker.Run(argument, nrepeat);
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =

12
example/15_grouped_gemm/grouped_gemm_xdl_fp16.cpp
View File

@@ -60,21 +60,21 @@ using ReferenceGemmInstance = ck::tensor_operation::host::
int main(int argc, char* argv[])
{
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
exit(0);
}
@@ -202,7 +202,7 @@ int main(int argc, char* argv[])
"not support this GEMM problem");
}
float ave_time = invoker.Run(argument, nrepeat);
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;

39
example/16_gemm_reduce/gemm_reduce_xdl_fp16.cpp
View File

@@ -58,9 +58,9 @@ using ReferenceGemmInstance = ck::tensor_operation::host::
int main(int argc, char* argv[])
{
bool do_verification = 1;
bool do_verification = true;
int init_method = 1;
int nrepeat = 5;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 3840;
@@ -79,13 +79,13 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 10)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
@@ -99,7 +99,7 @@ int main(int argc, char* argv[])
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
exit(0);
}
@@ -192,30 +192,13 @@ int main(int argc, char* argv[])
"not support this GEMM problem");
}
// warm up
invoker.Run(argument);
// init DO, D1 to 0
d0_device_buf.SetZero();
d1_device_buf.SetZero();
// timing
float total_time = 0;
for(int i = 0; i < nrepeat; ++i)
{
// init DO, D1 to 0
d0_device_buf.SetZero();
d1_device_buf.SetZero();
KernelTimer timer;
timer.Start();
invoker.Run(argument);
timer.End();
total_time += timer.GetElapsedTime();
}
float ave_time = total_time / nrepeat;
// if time_kernel == true, kernel will run multiple times. This kernel use atomic-add so result
// will not be correct. need to set time_kernel = false for correctness test
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =

14
example/17_convnd_bwd_data_xdl/convnd_bwd_data_xdl.cpp
View File

@@ -87,7 +87,7 @@ void print_use_msg()
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=random value, 2= init to 1 )\n"
<< "arg3: run kernel # of times (>1)\n"
<< "arg3: time kernel (0=n0, 1=yes)\n"
<< "arg4: N spatial dimensions (default 2)\n"
<< "Following arguments (depending on number of spatial dims):\n"
<< " N, K, C, \n"
@@ -165,9 +165,9 @@ DeviceConvBwdDataBasePtr get_conv_instance(int num_dim_spatial)
int main(int argc, char* argv[])
{
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
int num_dim_spatial = 2;
ck::utils::conv::ConvParams params;
@@ -177,13 +177,13 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
}
else if(argc > 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
num_dim_spatial = std::stoi(argv[4]);
// check args number
int conv_args = 3 + num_dim_spatial * 6;
@@ -284,7 +284,7 @@ int main(int argc, char* argv[])
"not support this Conv problem");
}
float ave_time = invoker->Run(argument.get(), nrepeat);
float ave_time = invoker->Run(argument.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = ck::utils::conv::get_flops(
params.N_, params.C_, params.K_, params.filter_spatial_lengths_, output_spatial_lengths);

39
example/18_batched_gemm_reduce/batched_gemm_reduce_xdl_fp16.cpp
View File

@@ -57,9 +57,9 @@ using ReferenceBatchedGemmInstance = ck::tensor_operation::host::
int main(int argc, char* argv[])
{
bool do_verification = 1;
bool do_verification = true;
int init_method = 1;
int nrepeat = 5;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 3840;
@@ -80,13 +80,13 @@ int main(int argc, char* argv[])
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 11)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
@@ -102,7 +102,7 @@ int main(int argc, char* argv[])
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg4 to 10: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC, BatchCount\n");
exit(0);
}
@@ -204,30 +204,13 @@ int main(int argc, char* argv[])
"not support this GEMM problem");
}
// warm up
invoker.Run(argument);
// init DO, D1 to 0
d0_device_buf.SetZero();
d1_device_buf.SetZero();
// timing
float total_time = 0;
for(int i = 0; i < nrepeat; ++i)
{
// init DO, D1 to 0
d0_device_buf.SetZero();
d1_device_buf.SetZero();
KernelTimer timer;
timer.Start();
invoker.Run(argument);
timer.End();
total_time += timer.GetElapsedTime();
}
float ave_time = total_time / nrepeat;
// if time_kernel == true, kernel will run multiple times. This kernel use atomic-add so result
// will not be correct. need to set time_kernel = false for correctness test
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * BatchCount * M * N * K;
std::size_t num_btype = sizeof(ADataType) * BatchCount * M * K +

28
include/ck/hip_version.hpp.in
View File

@@ -1,28 +0,0 @@
#pragma once
// "_PACKAGE_" to avoid name contentions: the macros like
// HIP_VERSION_MAJOR are defined in HIP_VERSION.h.
// clang-format off
#define CK_HIP_PACKAGE_VERSION_MAJOR @CK_HIP_VERSION_MAJOR@
#define CK_HIP_PACKAGE_VERSION_MINOR @CK_HIP_VERSION_MINOR@
#define CK_HIP_PACKAGE_VERSION_PATCH @CK_HIP_VERSION_PATCH@
// clang-format on
#ifndef CK_HIP_PACKAGE_VERSION_MAJOR
#define CK_HIP_PACKAGE_VERSION_MAJOR 0
#endif
#ifndef CK_HIP_PACKAGE_VERSION_MINOR
#define CK_HIP_PACKAGE_VERSION_MINOR 0
#endif
#ifndef CK_HIP_PACKAGE_VERSION_PATCH
#define CK_HIP_PACKAGE_VERSION_PATCH 0
#endif
// 3 decimal digits for major and minor, 6 digits for patch number.
// Max number is 999,999,999999 == 0xE8,D4A5,0FFF that fits into 64-bit math.
#if CK_HIP_PACKAGE_VERSION_MAJOR > 999 || CK_HIP_PACKAGE_VERSION_MAJOR > 999 || \
CK_HIP_PACKAGE_VERSION_PATCH > 999999
#error "Too big HIP version number(s)"
#endif
#define CK_HIP_PACKAGE_VERSION_FLAT \
((CK_HIP_PACKAGE_VERSION_MAJOR * 1000ULL + CK_HIP_PACKAGE_VERSION_MINOR) * 1000000 + \
CK_HIP_PACKAGE_VERSION_PATCH)

3
include/ck/options.hpp.in Normal file
View File

@@ -0,0 +1,3 @@
#pragma once
#cmakedefine01 CK_TIME_KERNEL

10
include/ck/stream_config.hpp Normal file
View File

@@ -0,0 +1,10 @@
#pragma once
#include <hip/hip_runtime.h>
#include <hip/hip_fp16.h>
struct StreamConfig
{
hipStream_t stream_id_ = nullptr;
bool time_kernel_ = false;
};

15
include/ck/tensor_operation/gpu/device/device_base.hpp
View File

@@ -1,8 +1,9 @@
#ifndef DEVICE_BASE_HPP
#define DEVICE_BASE_HPP
#pragma once
#include <string>
#include "stream_config.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
@@ -22,7 +23,10 @@ struct BaseInvoker
BaseInvoker(const BaseInvoker&) = default;
BaseInvoker& operator=(const BaseInvoker&) = default;
virtual float Run(const BaseArgument*, int = 1) = 0;
virtual float Run(const BaseArgument*, const StreamConfig& = StreamConfig{})
{
return float{0};
}
virtual ~BaseInvoker() {}
};
@@ -33,8 +37,8 @@ struct BaseOperator
BaseOperator(const BaseOperator&) = default;
BaseOperator& operator=(const BaseOperator&) = default;
virtual bool IsSupportedArgument(const BaseArgument*) = 0;
virtual std::string GetTypeString() const = 0;
virtual bool IsSupportedArgument(const BaseArgument*) { return false; }
virtual std::string GetTypeString() const { return ""; }
virtual ~BaseOperator() {}
};
@@ -42,4 +46,3 @@ struct BaseOperator
} // namespace device
} // namespace tensor_operation
} // namespace ck
#endif

94
include/ck/tensor_operation/gpu/device/device_batched_gemm_reduce_xdl_cshuffle.hpp
View File

@@ -693,7 +693,7 @@ struct DeviceBatchedGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<AElementwi
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, int /* nrepeat */ = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
#if 0
{
@@ -729,6 +729,7 @@ struct DeviceBatchedGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<AElementwi
const auto K =
arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);
float elapsed_time = 0.0f;
if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
{
const auto kernel = kernel_batched_gemm_reduce_xdl_cshuffle_v1<
@@ -748,26 +749,28 @@ struct DeviceBatchedGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<AElementwi
remove_reference_t<Block2CTileMap>,
true>;
launch_kernel(kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.p_d0_grid_,
arg.p_d1_grid_,
arg.BatchCount_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.d1_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.d_grid_desc_mblock_mperblock_,
arg.compute_base_ptr_of_batch_,
arg.block_2_ctile_map_);
elapsed_time =
launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.p_d0_grid_,
arg.p_d1_grid_,
arg.BatchCount_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.d1_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.d_grid_desc_mblock_mperblock_,
arg.compute_base_ptr_of_batch_,
arg.block_2_ctile_map_);
}
else
{
@@ -788,35 +791,38 @@ struct DeviceBatchedGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<AElementwi
remove_reference_t<Block2CTileMap>,
false>;
launch_kernel(kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.p_d0_grid_,
arg.p_d1_grid_,
arg.BatchCount_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.d1_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.d_grid_desc_mblock_mperblock_,
arg.compute_base_ptr_of_batch_,
arg.block_2_ctile_map_);
elapsed_time =
launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.p_d0_grid_,
arg.p_d1_grid_,
arg.BatchCount_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.d1_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.d_grid_desc_mblock_mperblock_,
arg.compute_base_ptr_of_batch_,
arg.block_2_ctile_map_);
}
return 0;
return elapsed_time;
}
// polymorphic
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

15
include/ck/tensor_operation/gpu/device/device_batched_gemm_xdl.hpp
View File

@@ -428,7 +428,7 @@ struct DeviceBatchedGemmXdl
{
using Argument = DeviceBatchedGemmXdl::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
{
std::cout << "arg.a_grid_desc_k0_m_k1_{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
@@ -477,8 +477,8 @@ struct DeviceBatchedGemmXdl
remove_reference_t<Block2CTileMap>,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
ave_time = launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -511,8 +511,8 @@ struct DeviceBatchedGemmXdl
remove_reference_t<Block2CTileMap>,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
ave_time = launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -534,9 +534,10 @@ struct DeviceBatchedGemmXdl
}
// polymorphic
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

70
include/ck/tensor_operation/gpu/device/device_conv2d_backward_weight_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp
View File

@@ -415,9 +415,10 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
<< arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
}
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
ShowInfo(arg);
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_m_n_,
@@ -437,49 +438,27 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
float ave_time = 0;
const auto Run = [&](const auto& kernel) {
if(nrepeat > 0)
{
ave_time =
launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.block_2_ctile_map_);
}
hipGetErrorString(hipMemset(
arg.p_c_grid_,
0,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_.GetElementSpaceSize() *
sizeof(CDataType)));
if(kbatch > 1 || nrepeat <= 0)
{
hipGetErrorString(hipMemset(
arg.p_c_grid_,
0,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_.GetElementSpaceSize() *
sizeof(CDataType)));
launch_kernel(kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.block_2_ctile_map_);
}
launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.block_2_ctile_map_);
};
if(has_main_k0_block_loop)
@@ -560,9 +539,10 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
return ave_time;
}
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

11
include/ck/tensor_operation/gpu/device/device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk.hpp
View File

@@ -531,7 +531,7 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
float ave_time = 0;
for(size_t i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
@@ -602,8 +602,8 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
true>;
ave_time += launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -635,8 +635,8 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
false>;
ave_time += launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -655,9 +655,10 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
return ave_time;
}
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

11
include/ck/tensor_operation/gpu/device/device_conv2d_fwd_xdl_c_shuffle_bias_activation_add_nhwc_kyxc_nhwk.hpp
View File

@@ -642,7 +642,7 @@ struct
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
#if 0
{
@@ -727,8 +727,8 @@ struct
true>;
ave_time = launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -771,8 +771,8 @@ struct
false>;
ave_time = launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -795,9 +795,10 @@ struct
return ave_time;
}
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

11
include/ck/tensor_operation/gpu/device/device_conv2d_fwd_xdl_c_shuffle_bias_activation_nhwc_kyxc_nhwk.hpp
View File

@@ -605,7 +605,7 @@ struct DeviceConv2dFwdXdl_C_Shuffle_Bias_Activation_Input_N_Hi_Wi_C_Weight_K_Y_X
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
#if 0
{
@@ -684,8 +684,8 @@ struct DeviceConv2dFwdXdl_C_Shuffle_Bias_Activation_Input_N_Hi_Wi_C_Weight_K_Y_X
true>;
ave_time = launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -723,8 +723,8 @@ struct DeviceConv2dFwdXdl_C_Shuffle_Bias_Activation_Input_N_Hi_Wi_C_Weight_K_Y_X
false>;
ave_time = launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -745,9 +745,10 @@ struct DeviceConv2dFwdXdl_C_Shuffle_Bias_Activation_Input_N_Hi_Wi_C_Weight_K_Y_X
return ave_time;
}
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

11
include/ck/tensor_operation/gpu/device/device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp
View File

@@ -568,7 +568,7 @@ struct DeviceConv2dFwdXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_W
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
#if 0
{
@@ -663,8 +663,8 @@ struct DeviceConv2dFwdXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_W
true>;
ave_time = launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -697,8 +697,8 @@ struct DeviceConv2dFwdXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_W
false>;
ave_time = launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -717,9 +717,10 @@ struct DeviceConv2dFwdXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_W
return ave_time;
}
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

15
include/ck/tensor_operation/gpu/device/device_conv2d_fwd_xdl_nhwc_kyxc_nhwk.hpp
View File

@@ -450,7 +450,7 @@ struct DeviceConv2dFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
#if 0
{
@@ -498,8 +498,8 @@ struct DeviceConv2dFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
ave_time = launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -529,8 +529,8 @@ struct DeviceConv2dFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
ave_time = launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -549,9 +549,10 @@ struct DeviceConv2dFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
return ave_time;
}
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

11
include/ck/tensor_operation/gpu/device/device_conv3d_fwd_naive_ndhwc_kzyxc_ndhwk.hpp
View File

@@ -92,7 +92,7 @@ struct DeviceConv3dFwdNaive_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_W
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
const auto naive_conv3d_fwd =
ref::naive_conv_fwd_ndhwc_kzyxc_ndhwk<InDataType,
@@ -103,8 +103,8 @@ struct DeviceConv3dFwdNaive_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_W
WeiElementwiseOperation,
OutElementwiseOperation>;
float ave_time = launch_and_time_kernel(naive_conv3d_fwd,
nrepeat,
float ave_time = launch_and_time_kernel(stream_config,
naive_conv3d_fwd,
dim3(256),
dim3(256),
0,
@@ -137,9 +137,10 @@ struct DeviceConv3dFwdNaive_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_W
}
// polymorphic
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

15
include/ck/tensor_operation/gpu/device/device_conv3d_fwd_xdl_ndhwc_kzyxc_ndhwk.hpp
View File

@@ -438,7 +438,7 @@ struct DeviceConv3dFwdXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
{
std::cout << "num_batches_of_GEMM = " << arg.num_subbatches_ << std::endl;
@@ -487,8 +487,8 @@ struct DeviceConv3dFwdXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_
OutElementwiseOperation,
remove_reference_t<Block2CTileMap>,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
ave_time = launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -522,8 +522,8 @@ struct DeviceConv3dFwdXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_
remove_reference_t<Block2CTileMap>,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
ave_time = launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -547,9 +547,10 @@ struct DeviceConv3dFwdXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_
}
// polymorphic
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

11
include/ck/tensor_operation/gpu/device/device_convnd_bwd_data_xdl_ndhwc_kzyxc_ndhwk.hpp
View File

@@ -1241,7 +1241,7 @@ struct DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
float ave_time = 0;
for(size_t i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
@@ -1316,8 +1316,8 @@ struct DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho
true>;
ave_time += launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -1349,8 +1349,8 @@ struct DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho
false>;
ave_time += launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -1369,9 +1369,10 @@ struct DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho
return ave_time;
}
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

15
include/ck/tensor_operation/gpu/device/device_convnd_fwd_xdl_nhwc_kyxc_nhwk.hpp
View File

@@ -747,7 +747,7 @@ struct DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
#if 0
{
@@ -795,8 +795,8 @@ struct DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
ave_time = launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -826,8 +826,8 @@ struct DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
ave_time = launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -846,9 +846,10 @@ struct DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
return ave_time;
}
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

86
include/ck/tensor_operation/gpu/device/device_gemm_reduce_xdl_cshuffle.hpp
View File

@@ -503,7 +503,7 @@ struct DeviceGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<AElementwiseOpera
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, int /* nrepeat */ = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
#if 0
{
@@ -536,6 +536,7 @@ struct DeviceGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<AElementwiseOpera
const auto K =
arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);
float elapsed_time = 0.0f;
if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
{
const auto kernel = kernel_gemm_reduce_xdl_cshuffle_v1<
@@ -554,24 +555,26 @@ struct DeviceGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<AElementwiseOpera
typename GridwiseGemm::DefaultBlock2CTileMap,
true>;
launch_kernel(kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.p_d0_grid_,
arg.p_d1_grid_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.d1_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.d_grid_desc_mblock_mperblock_,
arg.block_2_ctile_map_);
elapsed_time =
launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.p_d0_grid_,
arg.p_d1_grid_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.d1_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.d_grid_desc_mblock_mperblock_,
arg.block_2_ctile_map_);
}
else
{
@@ -591,33 +594,36 @@ struct DeviceGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<AElementwiseOpera
typename GridwiseGemm::DefaultBlock2CTileMap,
false>;
launch_kernel(kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.p_d0_grid_,
arg.p_d1_grid_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.d1_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.d_grid_desc_mblock_mperblock_,
arg.block_2_ctile_map_);
elapsed_time =
launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.p_d0_grid_,
arg.p_d1_grid_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.d1_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.d_grid_desc_mblock_mperblock_,
arg.block_2_ctile_map_);
}
return 0;
return elapsed_time;
}
// polymorphic
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

15
include/ck/tensor_operation/gpu/device/device_gemm_xdl.hpp
View File

@@ -290,7 +290,7 @@ struct DeviceGemmXdl
{
using Argument = DeviceGemmXdl::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
#if 0
{
@@ -339,8 +339,8 @@ struct DeviceGemmXdl
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
ave_time = launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -370,8 +370,8 @@ struct DeviceGemmXdl
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
ave_time = launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -391,9 +391,10 @@ struct DeviceGemmXdl
}
// polymorphic
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

11
include/ck/tensor_operation/gpu/device/device_gemm_xdl_c_shuffle_bias_2d.hpp
View File

@@ -264,7 +264,7 @@ struct DeviceGemmXdl_C_Shuffle_Bias_2d
{
using Argument = DeviceGemmXdl_C_Shuffle_Bias_2d::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
{
std::cout << "arg.a_grid_desc_k0_m_k1_{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
@@ -320,8 +320,8 @@ struct DeviceGemmXdl_C_Shuffle_Bias_2d
true>;
ave_time = launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -359,8 +359,8 @@ struct DeviceGemmXdl_C_Shuffle_Bias_2d
false>;
ave_time = launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -382,9 +382,10 @@ struct DeviceGemmXdl_C_Shuffle_Bias_2d
}
// polymorphic
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

11
include/ck/tensor_operation/gpu/device/device_gemm_xdl_c_shuffle_bias_activation.hpp
View File

@@ -273,7 +273,7 @@ struct DeviceGemmXdl_C_Shuffle_Bias_Activation
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
{
std::cout << "arg.a_grid_desc_k0_m_k1_{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
@@ -329,8 +329,8 @@ struct DeviceGemmXdl_C_Shuffle_Bias_Activation
true>;
ave_time = launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -368,8 +368,8 @@ struct DeviceGemmXdl_C_Shuffle_Bias_Activation
false>;
ave_time = launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -391,9 +391,10 @@ struct DeviceGemmXdl_C_Shuffle_Bias_Activation
}
// polymorphic
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

11
include/ck/tensor_operation/gpu/device/device_gemm_xdl_c_shuffle_bias_activation_add.hpp
View File

@@ -312,7 +312,7 @@ struct DeviceGemmXdl_C_Shuffle_Bias_Activation_Add
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
{
std::cout << "arg.a_grid_desc_k0_m_k1_{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
@@ -374,8 +374,8 @@ struct DeviceGemmXdl_C_Shuffle_Bias_Activation_Add
true>;
ave_time = launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -418,8 +418,8 @@ struct DeviceGemmXdl_C_Shuffle_Bias_Activation_Add
false>;
ave_time = launch_and_time_kernel(
stream_config,
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -443,9 +443,10 @@ struct DeviceGemmXdl_C_Shuffle_Bias_Activation_Add
}
// polymorphic
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

112
include/ck/tensor_operation/gpu/device/device_gemm_xdl_cshuffle.hpp
View File

@@ -440,7 +440,7 @@ struct DeviceGemm_Xdl_CShuffle
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
#if 0
{
@@ -487,42 +487,22 @@ struct DeviceGemm_Xdl_CShuffle
typename GridwiseGemm::DefaultBlock2CTileMap,
true>;
if(nrepeat == 0)
{
launch_kernel(kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.block_2_ctile_map_);
}
else
{
ave_time =
launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.block_2_ctile_map_);
}
ave_time =
launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.block_2_ctile_map_);
}
else
{
@@ -538,52 +518,32 @@ struct DeviceGemm_Xdl_CShuffle
typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
typename GridwiseGemm::DefaultBlock2CTileMap,
false>;
if(nrepeat == 0)
{
launch_kernel(kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.block_2_ctile_map_);
}
else
{
ave_time =
launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.block_2_ctile_map_);
}
ave_time =
launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.block_2_ctile_map_);
}
return ave_time;
}
// polymorphic
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

74
include/ck/tensor_operation/gpu/device/device_gemm_xdl_splitk.hpp
View File

@@ -385,8 +385,11 @@ struct DeviceGemmXdlSplitK
std::cout << "arg.c_grid_desc_m_n_{ " << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
<< arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
}
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
ShowInfo(arg);
const auto kbatch = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I0);
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
@@ -408,50 +411,30 @@ struct DeviceGemmXdlSplitK
float ave_time = 0;
const auto Run = [&](const auto& kernel) {
if(nrepeat > 0)
{
ShowInfo(arg);
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.block_2_ctile_map_);
}
// FIXME: this should be moved outside of DeviceOp
hipGetErrorString(
hipMemset(arg.p_c_grid_,
0,
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_.GetElementSpaceSize() *
sizeof(CDataType)));
if(kbatch > 1 || nrepeat <= 0)
{
hipGetErrorString(
hipMemset(arg.p_c_grid_,
0,
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_.GetElementSpaceSize() *
sizeof(CDataType)));
launch_kernel(kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.block_2_ctile_map_);
}
ave_time = launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.block_2_ctile_map_);
};
if(has_main_k0_block_loop)
{
if(kbatch == 1)
@@ -531,9 +514,10 @@ struct DeviceGemmXdlSplitK
}
// polymorphic
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

74
include/ck/tensor_operation/gpu/device/device_gemm_xdl_splitk_c_shuffle.hpp
View File

@@ -391,8 +391,11 @@ struct DeviceGemmXdlSplitKCShuffle
std::cout << "arg.c_grid_desc_m_n_{ " << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
<< arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
}
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
ShowInfo(arg);
const auto kbatch = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I0);
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
@@ -414,51 +417,29 @@ struct DeviceGemmXdlSplitKCShuffle
float ave_time = 0;
const auto Run = [&](const auto& kernel) {
if(nrepeat > 0)
{
ShowInfo(arg);
ave_time =
launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.block_2_ctile_map_);
}
hipGetErrorString(hipMemset(
arg.p_c_grid_,
0,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_.GetElementSpaceSize() *
sizeof(CDataType)));
if(kbatch > 1 || nrepeat <= 0)
{
hipGetErrorString(hipMemset(
arg.p_c_grid_,
0,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_.GetElementSpaceSize() *
sizeof(CDataType)));
launch_kernel(kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.block_2_ctile_map_);
}
launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.block_2_ctile_map_);
};
if(has_main_k0_block_loop)
{
if(kbatch == 1)
@@ -542,9 +523,10 @@ struct DeviceGemmXdlSplitKCShuffle
}
// polymorphic
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

15
include/ck/tensor_operation/gpu/device/device_grouped_gemm_xdl.hpp
View File

@@ -449,7 +449,7 @@ struct DeviceGroupedGemmXdl
{
using Argument = DeviceGroupedGemmXdl::Argument;
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
StaticallyIndexedArray<GemmDescKernelArg, MaxGroupCount> gemm_desc_kernel_args;
@@ -510,8 +510,8 @@ struct DeviceGroupedGemmXdl
true,
MaxGroupCount>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
ave_time = launch_and_time_kernel(stream_config,
kernel,
dim3(arg.grid_size_),
dim3(BlockSize),
0,
@@ -534,8 +534,8 @@ struct DeviceGroupedGemmXdl
false,
MaxGroupCount>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
ave_time = launch_and_time_kernel(stream_config,
kernel,
dim3(arg.grid_size_),
dim3(BlockSize),
0,
@@ -550,9 +550,10 @@ struct DeviceGroupedGemmXdl
}
// polymorphic
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

11
include/ck/tensor_operation/gpu/device/device_pool2d_fwd_nhwc_nhwc.hpp
View File

@@ -204,7 +204,7 @@ struct DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C : public DevicePool2dFwd
struct Invoker : public BaseInvoker
{
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
using gridwise_reduce = GridwiseReduction_mk_to_m_threadwise<InDataType,
OutDataType,
@@ -241,8 +241,8 @@ struct DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C : public DevicePool2dFwd
const index_t grid_size = (ReduceM / ReduceM_BlockTileSize);
return launch_and_time_kernel(kernel,
nrepeat,
return launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
@@ -257,9 +257,10 @@ struct DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C : public DevicePool2dFwd
arg.p_out_indices_dev_);
}
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};

11
include/ck/tensor_operation/gpu/device/device_reduce_blockwise.hpp
View File

@@ -211,7 +211,7 @@ struct DeviceReduceBlockWise : public DeviceReduce<InElementwiseOperation, AccEl
struct Invoker : public BaseInvoker
{
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
const auto in_grid_desc_m_k =
DeviceReduceBlockWise::MakeSrc2dDescriptor(arg.inLengths_, arg.inStrides_);
@@ -253,8 +253,8 @@ struct DeviceReduceBlockWise : public DeviceReduce<InElementwiseOperation, AccEl
InElementwiseOperation,
AccElementwiseOperation>;
avg_time = launch_and_time_kernel(kernel,
nrepeat,
avg_time = launch_and_time_kernel(stream_config,
kernel,
dim3(arg.gridSize),
dim3(BlockSize),
0,
@@ -272,9 +272,10 @@ struct DeviceReduceBlockWise : public DeviceReduce<InElementwiseOperation, AccEl
return (avg_time);
};
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
};
};

13
include/ck/tensor_operation/gpu/device/device_reduce_blockwise_second_call.hpp
View File

@@ -182,7 +182,7 @@ struct DeviceReduceBlockWiseSecondCall
struct Invoker : public BaseInvoker
{
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
const auto in_grid_desc_m_k = DeviceReduceBlockWiseSecondCall::MakeSrc2dDescriptor(
arg.inLengths_, arg.inStrides_);
@@ -224,8 +224,8 @@ struct DeviceReduceBlockWiseSecondCall
InElementwiseOperation,
AccElementwiseOperation>;
avg_time = launch_and_time_kernel(kernel,
nrepeat,
avg_time = launch_and_time_kernel(stream_config,
kernel,
dim3(arg.gridSize),
dim3(BlockSize),
0,
@@ -243,10 +243,11 @@ struct DeviceReduceBlockWiseSecondCall
return (avg_time);
};
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
};
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};
bool IsSupportedArgument(const BaseArgument* p_arg) override

72
include/ck/tensor_operation/gpu/device/device_reduce_multiblock_atomic_add.hpp
View File

@@ -245,7 +245,7 @@ struct DeviceReduceMultiBlockAtomicAdd
struct Invoker : public BaseInvoker
{
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
const auto in_grid_desc_m_k = DeviceReduceMultiBlockAtomicAdd::MakeSrc2dDescriptor(
arg.inLengths_, arg.inStrides_, arg.blkGroupSize, arg.kBlockTileIterations);
@@ -275,8 +275,6 @@ struct DeviceReduceMultiBlockAtomicAdd
float avg_time = 0;
KernelTimer timer;
const auto kernel_pre = kernel_buffer_set_value<BlockSize, OutDataType, OutGridDesc_M>;
const auto kernel_main = kernel_reduce_multiblock_atocmi_add<GridwiseReduce,
InDataType,
@@ -287,50 +285,38 @@ struct DeviceReduceMultiBlockAtomicAdd
InElementwiseOperation,
AccElementwiseOperation>;
printf("launch_and_time_kernel: grid_dim {%ld, 1, 1}, block_dim {%d, 1, 1} \n",
arg.gridSize,
BlockSize);
printf("Warm up\n");
avg_time += launch_and_time_kernel(stream_config,
kernel_pre,
dim3(arg.gridSize_pre),
dim3(BlockSize),
0,
out_grid_desc_m,
arg.out_dev_,
static_cast<OutDataType>(0.0f));
for(int i = 0; i < nrepeat + 1; i++)
{
if(i == 1)
timer.Start();
avg_time += launch_and_time_kernel(stream_config,
kernel_main,
dim3(arg.gridSize),
dim3(BlockSize),
0,
in_grid_desc_m_k,
out_grid_desc_m,
arg.in_elementwise_op_,
arg.acc_elementwise_op_,
arg.blkGroupSize,
arg.kBlockTileIterations,
arg.alpha_,
arg.in_dev_,
arg.out_dev_);
launch_kernel(kernel_pre,
dim3(arg.gridSize_pre),
dim3(BlockSize),
0,
out_grid_desc_m,
arg.out_dev_,
static_cast<OutDataType>(0.0f));
return avg_time;
}
launch_kernel(kernel_main,
dim3(arg.gridSize),
dim3(BlockSize),
0,
in_grid_desc_m_k,
out_grid_desc_m,
arg.in_elementwise_op_,
arg.acc_elementwise_op_,
arg.blkGroupSize,
arg.kBlockTileIterations,
arg.alpha_,
arg.in_dev_,
arg.out_dev_);
};
timer.End();
avg_time = timer.GetElapsedTime() / nrepeat;
return (avg_time);
};
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
};
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};
bool IsSupportedArgument(const BaseArgument* p_arg) override

13
include/ck/tensor_operation/gpu/device/device_reduce_multiblock_partial_reduce.hpp
View File

@@ -273,7 +273,7 @@ struct DeviceReduceMultiBlockPartialReduce
struct Invoker : public BaseInvoker
{
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
const auto in_grid_desc_m_k = DeviceReduceMultiBlockPartialReduce::MakeSrc2dDescriptor(
arg.inLengths_, arg.inStrides_, arg.blkGroupSize, arg.kBlockTileIterations);
@@ -313,8 +313,8 @@ struct DeviceReduceMultiBlockPartialReduce
InElementwiseOperation,
AccElementwiseOperation>;
avg_time = launch_and_time_kernel(kernel,
nrepeat,
avg_time = launch_and_time_kernel(stream_config,
kernel,
dim3(arg.gridSize),
dim3(BlockSize),
0,
@@ -331,10 +331,11 @@ struct DeviceReduceMultiBlockPartialReduce
return (avg_time);
};
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
};
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};
bool IsSupportedArgument(const BaseArgument* p_arg) override

13
include/ck/tensor_operation/gpu/device/device_reduce_threadwise.hpp
View File

@@ -212,7 +212,7 @@ struct DeviceReduceThreadWise : public DeviceReduce<InElementwiseOperation, OutE
struct Invoker : public BaseInvoker
{
float Run(const Argument& arg, int nrepeat = 1)
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
const auto in_grid_desc_m_k =
DeviceReduceThreadWise::MakeSrc2dDescriptor(arg.inLengths_, arg.inStrides_);
@@ -254,8 +254,8 @@ struct DeviceReduceThreadWise : public DeviceReduce<InElementwiseOperation, OutE
InElementwiseOperation,
OutElementwiseOperation>;
avg_time = launch_and_time_kernel(kernel,
nrepeat,
avg_time = launch_and_time_kernel(stream_config,
kernel,
dim3(arg.gridSize),
dim3(BlockSize),
0,
@@ -272,10 +272,11 @@ struct DeviceReduceThreadWise : public DeviceReduce<InElementwiseOperation, OutE
return (avg_time);
};
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
};
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};
bool IsSupportedArgument(const BaseArgument* p_arg) override

54
library/include/ck/library/host/host_interface.hpp Normal file
View File

@@ -0,0 +1,54 @@
#pragma once
#include <memory>
#include <string>
#include "stream_config.hpp"
#include "config.hpp"
#include "device_base.hpp"
struct DeviceConvFwdPtr_t
{
using BaseArgument = ck::tensor_operation::device::BaseArgument;
using BaseInvoker = ck::tensor_operation::device::BaseInvoker;
struct DeviceConvFwdPtrImpl;
std::unique_ptr<DeviceConvFwdPtrImpl> pImpl;
DeviceConvFwdPtr_t();
~DeviceConvFwdPtr_t();
DeviceConvFwdPtr_t(DeviceConvFwdPtr_t&&);
DeviceConvFwdPtr_t(DeviceConvFwdPtrImpl&);
DeviceConvFwdPtr_t& operator=(DeviceConvFwdPtr_t&) = delete;
DeviceConvFwdPtr_t& operator=(const DeviceConvFwdPtr_t&) = delete;
std::unique_ptr<BaseArgument>
MakeArgumentPointer(void* in_ptr,
void* wei_ptr,
void* out_ptr,
size_t N,
size_t K,
size_t C,
std::vector<ck::index_t> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads)
const; // in,wei and out element ops are ignored for now since even if we change them, they
// cant be linked
std::unique_ptr<BaseInvoker>
MakeInvokerPointer() const; // requires including BaseInvoker headers
std::string GetTypeString();
bool IsSupportedArgument(const BaseArgument* arg_ptr);
};
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f32_instances_t(
std::vector<DeviceConvFwdPtr_t>& instances);
void add_device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk_f16_instances_t(
std::vector<DeviceConvFwdPtr_t>& instances);
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_bf16_instances_t(
std::vector<DeviceConvFwdPtr_t>& instances);
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f16_instances_t(
std::vector<DeviceConvFwdPtr_t>& instances);
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_int8_instances_t(
std::vector<DeviceConvFwdPtr_t>& instances);

109
library/include/ck/library/host_tensor/device.hpp
View File

@@ -1,12 +1,25 @@
#ifndef DEVICE_HPP
#define DEVICE_HPP
#pragma once
#include <memory>
#include <functional>
#include <thread>
#include <chrono>
#include "hip/hip_runtime.h"
#include "hip/hip_fp16.h"
#include <hip/hip_runtime.h>
#include <hip/hip_fp16.h>
#include "stream_config.hpp"
#include "ck/options.hpp"
inline void hip_check_error(hipError_t x)
{
if(x != hipSuccess)
{
std::ostringstream ss;
ss << "HIP runtime error: " << hipGetErrorString(x) << ". " << __FILE__ << ": " << __LINE__
<< "in function: " << __func__;
throw std::runtime_error(ss.str());
}
}
struct DeviceMem
{
@@ -36,49 +49,59 @@ struct KernelTimer
std::unique_ptr<KernelTimerImpl> impl;
};
using device_stream_t = hipStream_t;
template <typename... Args, typename F>
void launch_kernel(F kernel, dim3 grid_dim, dim3 block_dim, std::size_t lds_byte, Args... args)
float launch_and_time_kernel(const StreamConfig& stream_config,
F kernel,
dim3 grid_dim,
dim3 block_dim,
std::size_t lds_byte,
Args... args)
{
hipStream_t stream_id = nullptr;
hipLaunchKernelGGL(kernel, grid_dim, block_dim, lds_byte, stream_id, args...);
}
template <typename... Args, typename F>
float launch_and_time_kernel(
F kernel, int nrepeat, dim3 grid_dim, dim3 block_dim, std::size_t lds_byte, Args... args)
{
KernelTimer timer;
printf("%s: grid_dim {%d, %d, %d}, block_dim {%d, %d, %d} \n",
__func__,
grid_dim.x,
grid_dim.y,
grid_dim.z,
block_dim.x,
block_dim.y,
block_dim.z);
printf("Warm up\n");
hipStream_t stream_id = nullptr;
// warm up
hipLaunchKernelGGL(kernel, grid_dim, block_dim, lds_byte, stream_id, args...);
printf("Start running %d times...\n", nrepeat);
timer.Start();
for(int i = 0; i < nrepeat; ++i)
#if CK_TIME_KERNEL
if(stream_config.time_kernel_)
{
hipLaunchKernelGGL(kernel, grid_dim, block_dim, lds_byte, stream_id, args...);
printf("%s: grid_dim {%d, %d, %d}, block_dim {%d, %d, %d} \n",
__func__,
grid_dim.x,
grid_dim.y,
grid_dim.z,
block_dim.x,
block_dim.y,
block_dim.z);
const int nrepeat = 10;
printf("Warm up 1 time\n");
// warm up
hipLaunchKernelGGL(
kernel, grid_dim, block_dim, lds_byte, stream_config.stream_id_, args...);
printf("Start running %d times...\n", nrepeat);
KernelTimer timer;
timer.Start();
for(int i = 0; i < nrepeat; ++i)
{
hipLaunchKernelGGL(
kernel, grid_dim, block_dim, lds_byte, stream_config.stream_id_, args...);
}
timer.End();
return timer.GetElapsedTime() / nrepeat;
}
else
{
hipLaunchKernelGGL(
kernel, grid_dim, block_dim, lds_byte, stream_config.stream_id_, args...);
timer.End();
return 0;
}
#else
hipLaunchKernelGGL(kernel, grid_dim, block_dim, lds_byte, stream_config.stream_id_, args...);
return timer.GetElapsedTime() / nrepeat;
}
return 0;
#endif
}

3
library/include/ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp
View File

@@ -84,7 +84,8 @@ struct ReferenceBatchedGemm : public device::BaseOperator
return 0;
}
float Run(const device::BaseArgument* p_arg, int) override
float Run(const device::BaseArgument* p_arg,
const StreamConfig& /* stream_config */ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}

3
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_backward_weight.hpp
View File

@@ -121,7 +121,8 @@ struct ReferenceConvBwdWeight : public device::BaseOperator
return 0;
}
float Run(const device::BaseArgument* p_arg, int) override
float Run(const device::BaseArgument* p_arg,
const StreamConfig& /*stream_config*/ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}

3
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_bwd_data.hpp
View File

@@ -291,7 +291,8 @@ struct ReferenceConvBwdData : public device::BaseOperator
}
}
float Run(const device::BaseArgument* p_arg, int) override
float Run(const device::BaseArgument* p_arg,
const StreamConfig& /* stream_config */ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}

9
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp
View File

@@ -1,9 +1,10 @@
#ifndef REFERENCE_CONV_FWD_HPP
#define REFERENCE_CONV_FWD_HPP
#pragma once
#include <iostream>
#include <type_traits>
#include <sstream>
#include "stream_config.hpp"
#include "device_base.hpp"
#include "host_tensor.hpp"
@@ -251,7 +252,8 @@ struct ReferenceConvFwd : public device::BaseOperator
}
}
float Run(const device::BaseArgument* p_arg, int) override
float Run(const device::BaseArgument* p_arg,
const StreamConfig& /*stream_config*/ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}
@@ -311,4 +313,3 @@ struct ReferenceConvFwd : public device::BaseOperator
} // namespace host
} // namespace tensor_operation
} // namespace ck
#endif

3
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_fwd_bias_activation.hpp
View File

@@ -124,7 +124,8 @@ struct ReferenceConvFwd_Bias_Activation : public device::BaseOperator
return 0;
}
float Run(const device::BaseArgument* p_arg, int) override
float Run(const device::BaseArgument* p_arg,
const StreamConfig& /* stream_config */ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}

3
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_fwd_bias_activation_add.hpp
View File

@@ -130,7 +130,8 @@ struct ReferenceConvFwd_Bias_Activation_Add : public device::BaseOperator
return 0;
}
float Run(const device::BaseArgument* p_arg, int) override
float Run(const device::BaseArgument* p_arg,
const StreamConfig& /*stream_config*/ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}

3
library/include/ck/library/reference_tensor_operation/cpu/reference_gemm.hpp
View File

@@ -80,7 +80,8 @@ struct ReferenceGemm : public device::BaseOperator
return 0;
}
float Run(const device::BaseArgument* p_arg, int) override
float Run(const device::BaseArgument* p_arg,
const StreamConfig& /* stream_config */ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}

3
library/include/ck/library/reference_tensor_operation/cpu/reference_gemm_bias_2d.hpp
View File

@@ -82,7 +82,8 @@ struct ReferenceGemmBias2D : public device::BaseOperator
return 0;
}
float Run(const device::BaseArgument* p_arg, int) override
float Run(const device::BaseArgument* p_arg,
const StreamConfig& /* stream_config */ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}

3
library/include/ck/library/reference_tensor_operation/cpu/reference_gemm_bias_activation.hpp
View File

@@ -85,7 +85,8 @@ struct ReferenceGemmBiasActivation : public device::BaseOperator
return 0;
}
float Run(const device::BaseArgument* p_arg, int) override
float Run(const device::BaseArgument* p_arg,
const StreamConfig& /* stream_config */ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}

3
library/include/ck/library/reference_tensor_operation/cpu/reference_gemm_bias_activation_add.hpp
View File

@@ -91,7 +91,8 @@ struct ReferenceGemmBiasActivationAdd : public device::BaseOperator
return 0;
}
float Run(const device::BaseArgument* p_arg, int) override
float Run(const device::BaseArgument* p_arg,
const StreamConfig& /* stream_config */ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}

4
library/include/ck/library/utility/op_instance_engine.hpp
View File

@@ -128,7 +128,7 @@ class OpInstanceRunEngine
template <typename OpInstancePtr>
ProfileBestConfig Profile(const std::vector<OpInstancePtr>& op_ptrs,
int nrepeat = 100,
bool time_kernel = false,
bool do_verification = false,
bool do_log = false)
{
@@ -143,7 +143,7 @@ class OpInstanceRunEngine
if(op_ptr->IsSupportedArgument(argument.get()))
{
std::string op_name = op_ptr->GetTypeString();
float avg_time = invoker->Run(argument.get(), nrepeat);
float avg_time = invoker->Run(argument.get(), StreamConfig{nullptr, time_kernel});
std::size_t flops = op_instance_.GetFlops();
std::size_t num_btype = op_instance_.GetBtype();

25
library/src/host_tensor/CMakeLists.txt
View File

@@ -10,10 +10,31 @@ set(HOST_TENSOR_SOURCE
host_tensor.cpp
)
add_library(host_tensor SHARED ${HOST_TENSOR_SOURCE})
add_library(host_tensor STATIC ${HOST_TENSOR_SOURCE})
add_library(composable_kernel::host_tensor ALIAS host_tensor)
target_compile_features(host_tensor PUBLIC)
set_target_properties(host_tensor PROPERTIES POSITION_INDEPENDENT_CODE ON)
target_include_directories(host_tensor SYSTEM PUBLIC $<BUILD_INTERFACE:${HALF_INCLUDE_DIR}>)
install(TARGETS host_tensor LIBRARY DESTINATION lib)
target_include_directories(host_tensor PUBLIC
"$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck>"
"$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/utility>"
"$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/library/host_tensor>"
)
install(TARGETS host_tensor
EXPORT host_tensorTargets
LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
INCLUDES DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
)
install(EXPORT host_tensorTargets
FILE composable_kernelhost_tensorTargets.cmake
NAMESPACE composable_kernel::
DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/composable_kernel
)
clang_tidy_check(host_tensor)

29
library/src/host_tensor/device.cpp
View File

@@ -2,7 +2,7 @@
DeviceMem::DeviceMem(std::size_t mem_size) : mMemSize(mem_size)
{
hipGetErrorString(hipMalloc(static_cast<void**>(&mpDeviceBuf), mMemSize));
hip_check_error(hipMalloc(static_cast<void**>(&mpDeviceBuf), mMemSize));
}
void* DeviceMem::GetDeviceBuffer() { return mpDeviceBuf; }
@@ -11,49 +11,48 @@ std::size_t DeviceMem::GetBufferSize() { return mMemSize; }
void DeviceMem::ToDevice(const void* p)
{
hipGetErrorString(
hipMemcpy(mpDeviceBuf, const_cast<void*>(p), mMemSize, hipMemcpyHostToDevice));
hip_check_error(hipMemcpy(mpDeviceBuf, const_cast<void*>(p), mMemSize, hipMemcpyHostToDevice));
}
void DeviceMem::FromDevice(void* p)
{
hipGetErrorString(hipMemcpy(p, mpDeviceBuf, mMemSize, hipMemcpyDeviceToHost));
hip_check_error(hipMemcpy(p, mpDeviceBuf, mMemSize, hipMemcpyDeviceToHost));
}
void DeviceMem::SetZero() { hipGetErrorString(hipMemset(mpDeviceBuf, 0, mMemSize)); }
void DeviceMem::SetZero() { hip_check_error(hipMemset(mpDeviceBuf, 0, mMemSize)); }
DeviceMem::~DeviceMem() { hipGetErrorString(hipFree(mpDeviceBuf)); }
DeviceMem::~DeviceMem() { hip_check_error(hipFree(mpDeviceBuf)); }
struct KernelTimerImpl
{
KernelTimerImpl()
{
hipGetErrorString(hipEventCreate(&mStart));
hipGetErrorString(hipEventCreate(&mEnd));
hip_check_error(hipEventCreate(&mStart));
hip_check_error(hipEventCreate(&mEnd));
}
~KernelTimerImpl()
{
hipGetErrorString(hipEventDestroy(mStart));
hipGetErrorString(hipEventDestroy(mEnd));
hip_check_error(hipEventDestroy(mStart));
hip_check_error(hipEventDestroy(mEnd));
}
void Start()
{
hipGetErrorString(hipDeviceSynchronize());
hipGetErrorString(hipEventRecord(mStart, nullptr));
hip_check_error(hipDeviceSynchronize());
hip_check_error(hipEventRecord(mStart, nullptr));
}
void End()
{
hipGetErrorString(hipEventRecord(mEnd, nullptr));
hipGetErrorString(hipEventSynchronize(mEnd));
hip_check_error(hipEventRecord(mEnd, nullptr));
hip_check_error(hipEventSynchronize(mEnd));
}
float GetElapsedTime() const
{
float time;
hipGetErrorString(hipEventElapsedTime(&time, mStart, mEnd));
hip_check_error(hipEventElapsedTime(&time, mStart, mEnd));
return time;
}

73
library/src/tensor_operation_instance/gpu/CMakeLists.txt
View File

@@ -11,6 +11,7 @@ include_directories(BEFORE
${PROJECT_SOURCE_DIR}/include/ck/tensor_operation/gpu/thread
${PROJECT_SOURCE_DIR}/include/ck/tensor_operation/gpu/element
${PROJECT_SOURCE_DIR}/library/include/ck/library/host_tensor
${PROJECT_SOURCE_DIR}/library/include/ck/library/host
${PROJECT_SOURCE_DIR}/library/include/ck/library/tensor_operation_instance
${PROJECT_SOURCE_DIR}/library/include/ck/library/tensor_operation_instance/gpu/reduce
${PROJECT_SOURCE_DIR}/external/include/half
@@ -18,7 +19,7 @@ include_directories(BEFORE
function(add_instance_library INSTANCE_NAME)
message("adding instance ${INSTANCE_NAME}")
add_library(${INSTANCE_NAME} SHARED ${ARGN})
add_library(${INSTANCE_NAME} OBJECT ${ARGN})
target_compile_features(${INSTANCE_NAME} PUBLIC)
set_target_properties(${INSTANCE_NAME} PROPERTIES POSITION_INDEPENDENT_CODE ON)
endfunction(add_instance_library INSTANCE_NAME)
@@ -41,3 +42,73 @@ add_subdirectory(convnd_bwd_data)
add_subdirectory(grouped_gemm)
add_subdirectory(conv2d_bwd_weight)
add_subdirectory(batched_gemm_reduce)
add_library(device_operations STATIC
$<TARGET_OBJECTS:device_conv1d_fwd_instance>
$<TARGET_OBJECTS:device_batched_gemm_instance>
$<TARGET_OBJECTS:device_conv2d_bwd_data_instance>
$<TARGET_OBJECTS:device_conv2d_fwd_instance>
$<TARGET_OBJECTS:device_conv2d_fwd_bias_relu_instance>
$<TARGET_OBJECTS:device_conv2d_fwd_bias_relu_add_instance>
$<TARGET_OBJECTS:device_conv2d_fwd_bias_relu_atomic_add_instance>
$<TARGET_OBJECTS:device_gemm_instance>
$<TARGET_OBJECTS:device_gemm_bias_relu_instance>
$<TARGET_OBJECTS:device_gemm_bias_relu_add_instance>
$<TARGET_OBJECTS:device_gemm_bias2d_instance>
$<TARGET_OBJECTS:device_reduce_instance>
$<TARGET_OBJECTS:device_convnd_bwd_data_instance>
$<TARGET_OBJECTS:device_grouped_gemm_instance>
$<TARGET_OBJECTS:device_conv2d_bwd_weight_instance>
$<TARGET_OBJECTS:device_batched_gemm_reduce_instance>
$<TARGET_OBJECTS:device_conv3d_fwd_instance>
device_conv2d.cpp
)
add_library(composablekernels::device_operations ALIAS device_operations)
set(DEV_OPS_INC_DIRS
${PROJECT_SOURCE_DIR}/include/ck/
${PROJECT_SOURCE_DIR}/library/include/ck/
${PROJECT_SOURCE_DIR}/external/include/
)
target_compile_features(device_operations PUBLIC)
set_target_properties(device_operations PROPERTIES POSITION_INDEPENDENT_CODE ON)
target_include_directories(device_operations PUBLIC
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/utility>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/tensor_description>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/tensor>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/problem_transform>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/tensor_operation/gpu/device>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/tensor_operation/gpu/grid>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/tensor_operation/gpu/block>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/tensor_operation/gpu/warp>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/tensor_operation/gpu/thread>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/tensor_operation/gpu/element>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/library/host_tensor>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/library/host>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/library/tensor_operation_instance>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/library/tensor_operation_instance/gpu/reduce>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ck/half>
)
#once new arches are enabled make this an option on the main cmake file
# and pass down here to be exported
target_compile_options(device_operations
PRIVATE --offload-arch=gfx908
)
# install(TARGETS device_operations LIBRARY DESTINATION lib)
install(TARGETS device_operations
EXPORT device_operationsTargets
LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
INCLUDES DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
)
install(DIRECTORY ${DEV_OPS_INC_DIRS} DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/ck)
install(EXPORT device_operationsTargets
FILE composable_kerneldevice_operationsTargets.cmake
NAMESPACE composable_kernel::
DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/composable_kernel
)

6
library/src/tensor_operation_instance/gpu/batched_gemm/CMakeLists.txt
View File

@@ -18,9 +18,9 @@ set(DEVICE_BATCHED_GEMM_INSTANCE_SOURCE
device_batched_gemm_xdl_int8_int8_int8_gkm_gnk_gmn_instance.cpp;
)
add_library(device_batched_gemm_instance SHARED ${DEVICE_BATCHED_GEMM_INSTANCE_SOURCE})
target_compile_features(device_batched_gemm_instance PUBLIC)
add_library(device_batched_gemm_instance OBJECT ${DEVICE_BATCHED_GEMM_INSTANCE_SOURCE})
# target_compile_features(device_batched_gemm_instance PUBLIC)
set_target_properties(device_batched_gemm_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_batched_gemm_instance LIBRARY DESTINATION lib)
# install(TARGETS device_batched_gemm_instance LIBRARY DESTINATION lib)
clang_tidy_check(device_batched_gemm_instance)

5
library/src/tensor_operation_instance/gpu/batched_gemm_reduce/CMakeLists.txt
View File

@@ -5,7 +5,8 @@ set(DEVICE_BATCHED_GEMM_REDUCE_INSTANCE_SOURCE
device_batched_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_gkm_gnk_gmn_instance.cpp
)
add_instance_library(device_batched_gemm_reduce_instance ${DEVICE_BATCHED_GEMM_REDUCE_INSTANCE_SOURCE})
install(TARGETS device_batched_gemm_reduce_instance LIBRARY DESTINATION lib)
add_instance_library(device_batched_gemm_reduce_instance OBJECT ${DEVICE_BATCHED_GEMM_REDUCE_INSTANCE_SOURCE})
target_compile_features(device_batched_gemm_reduce_instance PUBLIC)
set_target_properties(device_batched_gemm_reduce_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
clang_tidy_check(device_batched_gemm_reduce_instance)

6
library/src/tensor_operation_instance/gpu/conv1d_fwd/CMakeLists.txt
View File

@@ -6,9 +6,9 @@ set(DEVICE_CONV1D_FWD_INSTANCE_SOURCE
device_conv1d_fwd_xdl_nwc_kxc_nwk_int8_instance.cpp;
)
add_library(device_conv1d_fwd_instance SHARED ${DEVICE_CONV1D_FWD_INSTANCE_SOURCE})
target_compile_features(device_conv1d_fwd_instance PUBLIC)
add_library(device_conv1d_fwd_instance OBJECT ${DEVICE_CONV1D_FWD_INSTANCE_SOURCE})
# target_compile_features(device_conv1d_fwd_instance PUBLIC)
set_target_properties(device_conv1d_fwd_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_conv1d_fwd_instance LIBRARY DESTINATION lib)
# install(TARGETS device_conv1d_fwd_instance LIBRARY DESTINATION lib)
clang_tidy_check(device_conv1d_fwd_instance)

4
library/src/tensor_operation_instance/gpu/conv2d_bwd_data/CMakeLists.txt
View File

@@ -6,9 +6,7 @@ set(DEVICE_CONV2D_BWD_DATA_INSTANCE_SOURCE
device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_int8_instance.cpp;
)
add_library(device_conv2d_bwd_data_instance SHARED ${DEVICE_CONV2D_BWD_DATA_INSTANCE_SOURCE})
target_compile_features(device_conv2d_bwd_data_instance PUBLIC)
add_library(device_conv2d_bwd_data_instance OBJECT ${DEVICE_CONV2D_BWD_DATA_INSTANCE_SOURCE})
set_target_properties(device_conv2d_bwd_data_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_conv2d_bwd_data_instance LIBRARY DESTINATION lib)
clang_tidy_check(device_conv2d_bwd_data_instance)

2
library/src/tensor_operation_instance/gpu/conv2d_bwd_weight/CMakeLists.txt
View File

@@ -3,7 +3,7 @@ set(DEVICE_CONV2D_BWD_WEIGHT_INSTANCE_SOURCE
device_conv2d_bwd_weight_xdl_nhwc_kyxc_nhwk_f16_instance.cpp;
device_conv2d_bwd_weight_xdl_nhwc_kyxc_nhwk_f32_instance.cpp;
)
add_library(device_conv2d_bwd_weight_instance SHARED ${DEVICE_CONV2D_BWD_WEIGHT_INSTANCE_SOURCE})
add_library(device_conv2d_bwd_weight_instance OBJECT ${DEVICE_CONV2D_BWD_WEIGHT_INSTANCE_SOURCE})
target_compile_features(device_conv2d_bwd_weight_instance PUBLIC)
set_target_properties(device_conv2d_bwd_weight_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_conv2d_bwd_weight_instance LIBRARY DESTINATION lib)

4
library/src/tensor_operation_instance/gpu/conv2d_fwd/CMakeLists.txt
View File

@@ -6,9 +6,7 @@ set(DEVICE_CONV2D_FWD_INSTANCE_SOURCE
device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_int8_instance.cpp;
device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk_f16_instance.cpp;
)
add_library(device_conv2d_fwd_instance SHARED ${DEVICE_CONV2D_FWD_INSTANCE_SOURCE})
target_compile_features(device_conv2d_fwd_instance PUBLIC)
add_library(device_conv2d_fwd_instance OBJECT ${DEVICE_CONV2D_FWD_INSTANCE_SOURCE})
set_target_properties(device_conv2d_fwd_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_conv2d_fwd_instance LIBRARY DESTINATION lib)
clang_tidy_check(device_conv2d_fwd_instance)

4
library/src/tensor_operation_instance/gpu/conv2d_fwd_bias_relu/CMakeLists.txt
View File

@@ -2,9 +2,7 @@
set(DEVICE_CONV2D_FWD_BIAS_RELU_INSTANCE_SOURCE
device_conv2d_fwd_xdl_c_shuffle_bias_relu_nhwc_kyxc_nhwk_f16_instance.cpp;
)
add_library(device_conv2d_fwd_bias_relu_instance SHARED ${DEVICE_CONV2D_FWD_BIAS_RELU_INSTANCE_SOURCE})
target_compile_features(device_conv2d_fwd_bias_relu_instance PUBLIC)
add_library(device_conv2d_fwd_bias_relu_instance OBJECT ${DEVICE_CONV2D_FWD_BIAS_RELU_INSTANCE_SOURCE})
set_target_properties(device_conv2d_fwd_bias_relu_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_conv2d_fwd_bias_relu_instance LIBRARY DESTINATION lib)
clang_tidy_check(device_conv2d_fwd_bias_relu_instance)

4
library/src/tensor_operation_instance/gpu/conv2d_fwd_bias_relu_add/CMakeLists.txt
View File

@@ -2,9 +2,7 @@
set(DEVICE_CONV2D_FWD_BIAS_RELU_ADD_INSTANCE_SOURCE
device_conv2d_fwd_xdl_c_shuffle_bias_relu_add_nhwc_kyxc_nhwk_f16_instance.cpp;
)
add_library(device_conv2d_fwd_bias_relu_add_instance SHARED ${DEVICE_CONV2D_FWD_BIAS_RELU_ADD_INSTANCE_SOURCE})
target_compile_features(device_conv2d_fwd_bias_relu_add_instance PUBLIC)
add_library(device_conv2d_fwd_bias_relu_add_instance OBJECT ${DEVICE_CONV2D_FWD_BIAS_RELU_ADD_INSTANCE_SOURCE})
set_target_properties(device_conv2d_fwd_bias_relu_add_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_conv2d_fwd_bias_relu_add_instance LIBRARY DESTINATION lib)
clang_tidy_check(device_conv2d_fwd_bias_relu_add_instance)

4
library/src/tensor_operation_instance/gpu/conv2d_fwd_bias_relu_atomic_add/CMakeLists.txt
View File

@@ -3,9 +3,7 @@ set(DEVICE_CONV2D_FWD_BIAS_RELU_ATOMIC_ADD_INSTANCE_SOURCE
device_conv2d_fwd_xdl_c_shuffle_bias_relu_atomic_add_nhwc_kyxc_nhwk_f16_instance.cpp;
)
add_library(device_conv2d_fwd_bias_relu_atomic_add_instance SHARED ${DEVICE_CONV2D_FWD_BIAS_RELU_ATOMIC_ADD_INSTANCE_SOURCE})
target_compile_features(device_conv2d_fwd_bias_relu_atomic_add_instance PUBLIC)
add_library(device_conv2d_fwd_bias_relu_atomic_add_instance OBJECT ${DEVICE_CONV2D_FWD_BIAS_RELU_ATOMIC_ADD_INSTANCE_SOURCE})
set_target_properties(device_conv2d_fwd_bias_relu_atomic_add_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_conv2d_fwd_bias_relu_atomic_add_instance LIBRARY DESTINATION lib)
clang_tidy_check(device_conv2d_fwd_bias_relu_atomic_add_instance)

3
library/src/tensor_operation_instance/gpu/conv3d_fwd/CMakeLists.txt
View File

@@ -5,9 +5,8 @@ set(DEVICE_CONV3D_FWD_INSTANCE_SOURCE
device_conv3d_fwd_xdl_ndhwc_kzyxc_ndhwk_bf16_instance.cpp;
device_conv3d_fwd_xdl_ndhwc_kzyxc_ndhwk_int8_instance.cpp;
)
add_library(device_conv3d_fwd_instance SHARED ${DEVICE_CONV3D_FWD_INSTANCE_SOURCE})
add_library(device_conv3d_fwd_instance OBJECT ${DEVICE_CONV3D_FWD_INSTANCE_SOURCE})
target_compile_features(device_conv3d_fwd_instance PUBLIC)
set_target_properties(device_conv3d_fwd_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_conv3d_fwd_instance LIBRARY DESTINATION lib)
clang_tidy_check(device_conv3d_fwd_instance)

2
library/src/tensor_operation_instance/gpu/convnd_bwd_data/CMakeLists.txt
View File

@@ -14,7 +14,7 @@ set(DEVICE_CONVND_BWD_DATA_INSTANCE_SOURCE
device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_int8_instance.cpp;
)
add_library(device_convnd_bwd_data_instance SHARED ${DEVICE_CONVND_BWD_DATA_INSTANCE_SOURCE})
add_library(device_convnd_bwd_data_instance OBJECT ${DEVICE_CONVND_BWD_DATA_INSTANCE_SOURCE})
target_compile_features(device_convnd_bwd_data_instance PUBLIC)
set_target_properties(device_convnd_bwd_data_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_convnd_bwd_data_instance LIBRARY DESTINATION lib)

201
library/src/tensor_operation_instance/gpu/device_conv2d.cpp Normal file
View File

@@ -0,0 +1,201 @@
#include <stdlib.h>
#include "config.hpp"
#include "device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp"
#include "element_wise_operation.hpp"
#include "device_operation_instance.hpp"
#include "host_interface.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_conv2d_fwd_instance {
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
void add_device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk_f16_instances(
std::vector<DeviceConvFwdPtr<PassThrough, PassThrough, PassThrough>>& instances);
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f32_instances(
std::vector<DeviceConvFwdPtr<PassThrough, PassThrough, PassThrough>>& instances);
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_bf16_instances(
std::vector<DeviceConvFwdPtr<PassThrough, PassThrough, PassThrough>>& instances);
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f16_instances(
std::vector<DeviceConvFwdPtr<PassThrough, PassThrough, PassThrough>>& instances);
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_int8_instances(
std::vector<DeviceConvFwdPtr<PassThrough, PassThrough, PassThrough>>& instances);
} // namespace device_conv2d_fwd_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
struct DeviceConvFwdPtr_t::DeviceConvFwdPtrImpl
{
std::unique_ptr<DeviceConvFwdPtr_t::BaseArgument>
MakeArgumentPointer(void* in_ptr,
void* wei_ptr,
void* out_ptr,
size_t N,
size_t K,
size_t C,
std::vector<ck::index_t> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads) const
{
return el->MakeArgumentPointer(in_ptr,
wei_ptr,
out_ptr,
N,
K,
C,
input_spatial_lengths,
filter_spatial_lengths,
output_spatial_lengths,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
PassThrough{},
PassThrough{},
PassThrough{});
}
std::unique_ptr<DeviceConvFwdPtr_t::BaseInvoker> MakeInvokerPointer() const
{
return el->MakeInvokerPointer();
}
std::string GetTypeString() { return el->GetTypeString(); }
bool IsSupportedArgument(const DeviceConvFwdPtr_t::BaseArgument* arg)
{
return el->IsSupportedArgument(arg);
}
ck::tensor_operation::device::DeviceConvFwdPtr<PassThrough, PassThrough, PassThrough> el;
};
DeviceConvFwdPtr_t::DeviceConvFwdPtr_t() : pImpl(nullptr) {}
DeviceConvFwdPtr_t::~DeviceConvFwdPtr_t() = default;
DeviceConvFwdPtr_t::DeviceConvFwdPtr_t(DeviceConvFwdPtr_t&&) = default;
DeviceConvFwdPtr_t::DeviceConvFwdPtr_t(DeviceConvFwdPtr_t::DeviceConvFwdPtrImpl& other)
: pImpl(std::make_unique<DeviceConvFwdPtr_t::DeviceConvFwdPtrImpl>(std::move(other)))
{
}
std::unique_ptr<DeviceConvFwdPtr_t::BaseArgument>
DeviceConvFwdPtr_t::MakeArgumentPointer(void* in_ptr,
void* wei_ptr,
void* out_ptr,
size_t N,
size_t K,
size_t C,
std::vector<ck::index_t> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads) const
{
return pImpl->MakeArgumentPointer(in_ptr,
wei_ptr,
out_ptr,
N,
K,
C,
input_spatial_lengths,
filter_spatial_lengths,
output_spatial_lengths,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads);
}
std::unique_ptr<DeviceConvFwdPtr_t::BaseInvoker> DeviceConvFwdPtr_t::MakeInvokerPointer() const
{
return pImpl->MakeInvokerPointer();
}
std::string DeviceConvFwdPtr_t::GetTypeString() { return pImpl->GetTypeString(); }
bool DeviceConvFwdPtr_t::IsSupportedArgument(const DeviceConvFwdPtr_t::BaseArgument* arg_ptr)
{
return pImpl->IsSupportedArgument(arg_ptr);
}
using namespace ck::tensor_operation::device::device_conv2d_fwd_instance;
void add_device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk_f16_instances_t(
std::vector<DeviceConvFwdPtr_t>& instances)
{
std::vector<
ck::tensor_operation::device::DeviceConvFwdPtr<PassThrough, PassThrough, PassThrough>>
local_instances;
add_device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk_f16_instances(local_instances);
for(auto& kinder : local_instances)
{
DeviceConvFwdPtr_t::DeviceConvFwdPtrImpl tmp{std::move(kinder)};
instances.emplace_back(tmp);
}
return;
}
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f32_instances_t(
std::vector<DeviceConvFwdPtr_t>& instances)
{
std::vector<
ck::tensor_operation::device::DeviceConvFwdPtr<PassThrough, PassThrough, PassThrough>>
local_instances;
add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f32_instances(local_instances);
for(auto& kinder : local_instances)
{
DeviceConvFwdPtr_t::DeviceConvFwdPtrImpl tmp{std::move(kinder)};
instances.emplace_back(tmp); // Perhaps we can do better
}
return;
}
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_bf16_instances_t(
std::vector<DeviceConvFwdPtr_t>& instances)
{
std::vector<
ck::tensor_operation::device::DeviceConvFwdPtr<PassThrough, PassThrough, PassThrough>>
local_instances;
add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_bf16_instances(local_instances);
for(auto& kinder : local_instances)
{
DeviceConvFwdPtr_t::DeviceConvFwdPtrImpl tmp{std::move(kinder)};
instances.emplace_back(tmp); // Perhaps we can do better
}
return;
}
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f16_instances_t(
std::vector<DeviceConvFwdPtr_t>& instances)
{
std::vector<
ck::tensor_operation::device::DeviceConvFwdPtr<PassThrough, PassThrough, PassThrough>>
local_instances;
add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f16_instances(local_instances);
for(auto& kinder : local_instances)
{
DeviceConvFwdPtr_t::DeviceConvFwdPtrImpl tmp{std::move(kinder)};
instances.emplace_back(tmp); // Perhaps we can do better
}
return;
}
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_int8_instances_t(
std::vector<DeviceConvFwdPtr_t>& instances)
{
std::vector<
ck::tensor_operation::device::DeviceConvFwdPtr<PassThrough, PassThrough, PassThrough>>
local_instances;
add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_int8_instances(local_instances);
for(auto& kinder : local_instances)
{
DeviceConvFwdPtr_t::DeviceConvFwdPtrImpl tmp{std::move(kinder)};
instances.emplace_back(tmp);
}
return;
}

3
library/src/tensor_operation_instance/gpu/gemm/CMakeLists.txt
View File

@@ -35,10 +35,9 @@ set(DEVICE_GEMM_INSTANCE_SOURCE
device_gemm_xdl_splitk_f16_f16_f16_km_nk_mn_instance.cpp;
)
add_library(device_gemm_instance SHARED ${DEVICE_GEMM_INSTANCE_SOURCE})
add_library(device_gemm_instance OBJECT ${DEVICE_GEMM_INSTANCE_SOURCE})
target_compile_features(device_gemm_instance PUBLIC)
set_target_properties(device_gemm_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_gemm_instance LIBRARY DESTINATION lib)
clang_tidy_check(device_gemm_instance)

4
library/src/tensor_operation_instance/gpu/gemm_bias2d/CMakeLists.txt
View File

@@ -10,9 +10,7 @@ set(DEVICE_GEMM_BIAS2D_INSTANCE_SOURCE
device_gemm_xdl_c_shuffle_bias_2d_f16_f16_f16_mk_nk_mn_instance.cpp;
)
add_library(device_gemm_bias2d_instance SHARED ${DEVICE_GEMM_BIAS2D_INSTANCE_SOURCE})
target_compile_features(device_gemm_bias2d_instance PUBLIC)
add_library(device_gemm_bias2d_instance OBJECT ${DEVICE_GEMM_BIAS2D_INSTANCE_SOURCE})
set_target_properties(device_gemm_bias2d_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_gemm_bias2d_instance LIBRARY DESTINATION lib)
clang_tidy_check(device_gemm_bias2d_instance)

4
library/src/tensor_operation_instance/gpu/gemm_bias_relu/CMakeLists.txt
View File

@@ -6,9 +6,7 @@ set(DEVICE_GEMM_BIAS_RELU_INSTANCE_SOURCE
device_gemm_xdl_c_shuffle_bias_relu_f16_f16_f16_km_nk_mn_instance.cpp;
)
add_library(device_gemm_bias_relu_instance SHARED ${DEVICE_GEMM_BIAS_RELU_INSTANCE_SOURCE})
target_compile_features(device_gemm_bias_relu_instance PUBLIC)
add_library(device_gemm_bias_relu_instance OBJECT ${DEVICE_GEMM_BIAS_RELU_INSTANCE_SOURCE})
set_target_properties(device_gemm_bias_relu_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_gemm_bias_relu_instance LIBRARY DESTINATION lib)
clang_tidy_check(device_gemm_bias_relu_instance)

4
library/src/tensor_operation_instance/gpu/gemm_bias_relu_add/CMakeLists.txt
View File

@@ -6,9 +6,7 @@ set(DEVICE_GEMM_BIAS_RELU_ADD_INSTANCE_SOURCE
device_gemm_xdl_c_shuffle_bias_relu_add_f16_f16_f16_km_nk_mn_instance.cpp;
)
add_library(device_gemm_bias_relu_add_instance SHARED ${DEVICE_GEMM_BIAS_RELU_ADD_INSTANCE_SOURCE})
target_compile_features(device_gemm_bias_relu_add_instance PUBLIC)
add_library(device_gemm_bias_relu_add_instance OBJECT ${DEVICE_GEMM_BIAS_RELU_ADD_INSTANCE_SOURCE})
set_target_properties(device_gemm_bias_relu_add_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_gemm_bias_relu_add_instance LIBRARY DESTINATION lib)
clang_tidy_check(device_gemm_bias_relu_add_instance)

2
library/src/tensor_operation_instance/gpu/grouped_gemm/CMakeLists.txt
View File

@@ -6,7 +6,7 @@ set(DEVICE_GROUPED_GEMM_INSTANCE_SOURCE
device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instance.cpp;
)
add_library(device_grouped_gemm_instance SHARED ${DEVICE_GROUPED_GEMM_INSTANCE_SOURCE})
add_library(device_grouped_gemm_instance OBJECT ${DEVICE_GROUPED_GEMM_INSTANCE_SOURCE})
target_compile_features(device_grouped_gemm_instance PUBLIC)
set_target_properties(device_grouped_gemm_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)

4
library/src/tensor_operation_instance/gpu/reduce/CMakeLists.txt
View File

@@ -38,9 +38,7 @@ set(DEVICE_REDUCE_INSTANCE_SOURCE
device_reduce_instance_multiblock_partial_reduce_b16_f32_b16.cpp;
)
add_library(device_reduce_instance SHARED ${DEVICE_REDUCE_INSTANCE_SOURCE})
target_compile_features(device_reduce_instance PUBLIC)
add_library(device_reduce_instance OBJECT ${DEVICE_REDUCE_INSTANCE_SOURCE})
set_target_properties(device_reduce_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_reduce_instance LIBRARY DESTINATION lib)
clang_tidy_check(device_reduce_instance)

7
profiler/include/profile_batched_gemm_impl.hpp
View File

@@ -63,7 +63,7 @@ template <typename ADataType,
bool profile_batched_gemm_impl(int do_verification,
int init_method,
bool do_log,
int nrepeat,
bool time_kernel,
int M,
int N,
int K,
@@ -356,11 +356,12 @@ bool profile_batched_gemm_impl(int do_verification,
{
std::string gemm_name = gemm_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * BatchCount * M * N * K;
std::size_t num_btype = (sizeof(ADataType) * M * K + sizeof(BDataType) * K * M +
std::size_t num_btype = (sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
sizeof(CDataType) * M * N) *
BatchCount;

30
profiler/include/profile_batched_gemm_reduce_impl.hpp
View File

@@ -53,7 +53,7 @@ template <typename ADataType,
bool profile_batched_gemm_reduce_impl(int do_verification,
int init_method,
bool do_log,
int nrepeat,
bool time_kernel,
int M,
int N,
int K,
@@ -259,30 +259,12 @@ bool profile_batched_gemm_reduce_impl(int do_verification,
if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
{
// warm up
invoker_ptr->Run(argument_ptr.get());
// init DO, D1 to 0
d0_device_buf.SetZero();
d1_device_buf.SetZero();
// timing
float total_time = 0;
for(int i = 0; i < nrepeat; ++i)
{
// init DO, D1 to 0
d0_device_buf.SetZero();
d1_device_buf.SetZero();
KernelTimer timer;
timer.Start();
invoker_ptr->Run(argument_ptr.get());
timer.End();
total_time += timer.GetElapsedTime();
}
float ave_time = total_time / nrepeat;
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::string gemm_name = gemm_ptr->GetTypeString();

5
profiler/include/profile_conv_bwd_data_impl.hpp
View File

@@ -51,7 +51,7 @@ template <int NDimSpatial,
void profile_conv_bwd_data_impl(int do_verification,
int init_method,
bool do_log,
int nrepeat,
bool time_kernel,
ck::index_t N,
ck::index_t K,
ck::index_t C,
@@ -228,7 +228,8 @@ void profile_conv_bwd_data_impl(int do_verification,
{
std::string conv_name = conv_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamControl{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;

10
profiler/include/profile_conv_bwd_weight_impl.hpp
View File

@@ -1,4 +1,6 @@
#pragma once
#include "stream_config.hpp"
#include "config.hpp"
#include "device.hpp"
#include "host_tensor.hpp"
@@ -43,7 +45,7 @@ template <int NDimSpatial,
bool profile_conv_bwd_weight_impl(int do_verification,
int init_method,
bool do_log,
int nrepeat,
bool time_kernel,
ck::index_t N,
ck::index_t K,
ck::index_t C,
@@ -182,6 +184,7 @@ bool profile_conv_bwd_weight_impl(int do_verification,
// profile device Conv instances
bool pass = true;
for(auto& conv_ptr : conv_ptrs)
{
// using atomic, so need to reset input
@@ -189,6 +192,7 @@ bool profile_conv_bwd_weight_impl(int do_verification,
{
wei_device_buf.SetZero();
}
auto argument_ptr = conv_ptr->MakeArgumentPointer(
static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
@@ -214,7 +218,8 @@ bool profile_conv_bwd_weight_impl(int do_verification,
{
std::string conv_name = conv_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;
@@ -242,6 +247,7 @@ bool profile_conv_bwd_weight_impl(int do_verification,
wei_device_buf.FromDevice(wei_k_c_y_x_device_result.mData.data());
float max_error = check_error(wei_k_c_y_x_host_result, wei_k_c_y_x_device_result);
if(max_error > 8)
{
pass = false;

5
profiler/include/profile_conv_fwd_bias_relu_add_impl.hpp
View File

@@ -42,7 +42,7 @@ template <int NDimSpatial,
void profile_conv_fwd_bias_relu_add_impl(int do_verification,
int init_method,
bool do_log,
int nrepeat,
bool time_kernel,
ck::index_t N,
ck::index_t K,
ck::index_t C,
@@ -219,7 +219,8 @@ void profile_conv_fwd_bias_relu_add_impl(int do_verification,
{
std::string conv_name = op_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;

5
profiler/include/profile_conv_fwd_bias_relu_atomic_add_impl.hpp
View File

@@ -119,7 +119,7 @@ template <int NDimSpatial,
void profile_conv_fwd_bias_relu_atomic_add_impl(int do_verification,
int init_method,
bool do_log,
int nrepeat,
bool time_kernel,
ck::index_t N,
ck::index_t K,
ck::index_t C,
@@ -275,7 +275,8 @@ void profile_conv_fwd_bias_relu_atomic_add_impl(int do_verification,
{
std::string conv_name = op_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;

5
profiler/include/profile_conv_fwd_bias_relu_impl.hpp
View File

@@ -41,7 +41,7 @@ template <int NDimSpatial,
void profile_conv_fwd_bias_relu_impl(int do_verification,
int init_method,
bool do_log,
int nrepeat,
bool time_kernel,
ck::index_t N,
ck::index_t K,
ck::index_t C,
@@ -207,7 +207,8 @@ void profile_conv_fwd_bias_relu_impl(int do_verification,
{
std::string conv_name = op_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;

5
profiler/include/profile_convnd_bwd_data_impl.hpp
View File

@@ -269,7 +269,7 @@ template <int NDimSpatial,
bool profile_convnd_bwd_data_impl(int do_verification,
int init_method,
bool do_log,
int nrepeat,
bool time_kernel,
ck::index_t N,
ck::index_t K,
ck::index_t C,
@@ -410,7 +410,8 @@ bool profile_convnd_bwd_data_impl(int do_verification,
{
std::string conv_name = conv_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop =
ck::utils::conv::get_flops(N, C, K, filter_spatial_lengths, output_spatial_lengths);

5
profiler/include/profile_gemm_bias_2d_impl.hpp
View File

@@ -65,7 +65,7 @@ template <typename ADataType,
void profile_gemm_bias_2d_impl(int do_verification,
int init_method,
bool do_log,
int nrepeat,
bool time_kernel,
int M,
int N,
int K,
@@ -259,7 +259,8 @@ void profile_gemm_bias_2d_impl(int do_verification,
{
std::string gemm_name = gemm_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * M * N * K;

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