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"...text-generation-inference.git" did not exist on "94d243b3d7916879f1735d4a6f231e915765c1c4"
Commit 6ef4e211 authored by Chao Liu's avatar Chao Liu
Browse files

Merge remote-tracking branch 'origin/develop' into contraction

parents b0a2afb9 9e4429f9
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.gitignore
View file @ 6ef4e211
...@@ -45,4 +45,4 @@ build* ...@@ -45,4 +45,4 @@ build*
*~ *~
# GDB temporary files # GDB temporary files
.gdb_history .gdb_history
\ No newline at end of file
CMakeLists.txt
View file @ 6ef4e211
...@@ -7,7 +7,8 @@ list(APPEND CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake") ...@@ -7,7 +7,8 @@ list(APPEND CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake")
enable_testing() enable_testing()
find_package(ROCM REQUIRED PATHS /opt/rocm) set(ROCM_SYMLINK_LIBS OFF)
find_package(ROCM 0.8 REQUIRED PATHS /opt/rocm)
include(ROCMInstallTargets) include(ROCMInstallTargets)
include(ROCMPackageConfigHelpers) include(ROCMPackageConfigHelpers)
...@@ -16,7 +17,7 @@ include(ROCMInstallSymlinks) ...@@ -16,7 +17,7 @@ include(ROCMInstallSymlinks)
include(ROCMCreatePackage) include(ROCMCreatePackage)
include(CheckCXXCompilerFlag) include(CheckCXXCompilerFlag)
rocm_setup_version(VERSION 1.0.0) rocm_setup_version(VERSION 0.2.0)
include(TargetFlags) include(TargetFlags)
list(APPEND CMAKE_PREFIX_PATH ${CMAKE_INSTALL_PREFIX} ${CMAKE_INSTALL_PREFIX}/llvm ${CMAKE_INSTALL_PREFIX}/hip /opt/rocm /opt/rocm/llvm /opt/rocm/hip) list(APPEND CMAKE_PREFIX_PATH ${CMAKE_INSTALL_PREFIX} ${CMAKE_INSTALL_PREFIX}/llvm ${CMAKE_INSTALL_PREFIX}/hip /opt/rocm /opt/rocm/llvm /opt/rocm/hip)
...@@ -70,14 +71,6 @@ if( DEFINED CK_OVERRIDE_HIP_VERSION_PATCH ) ...@@ -70,14 +71,6 @@ if( DEFINED CK_OVERRIDE_HIP_VERSION_PATCH )
endif() endif()
message(STATUS "Build with HIP ${HIP_VERSION}") message(STATUS "Build with HIP ${HIP_VERSION}")
rocm_create_package(
NAME composablekernel
DESCRIPTION "High Performance Composable Kernel for AMD GPUs"
MAINTAINER "MIOpen Kernels Dev Team <dl.MIOpen@amd.com>"
LDCONFIG
)
## tidy ## tidy
include(EnableCompilerWarnings) include(EnableCompilerWarnings)
set(CK_TIDY_ERRORS ERRORS * -readability-inconsistent-declaration-parameter-name) set(CK_TIDY_ERRORS ERRORS * -readability-inconsistent-declaration-parameter-name)
...@@ -238,6 +231,11 @@ message("CMAKE_CXX_FLAGS: ${CMAKE_CXX_FLAGS}") ...@@ -238,6 +231,11 @@ message("CMAKE_CXX_FLAGS: ${CMAKE_CXX_FLAGS}")
add_custom_target(check COMMAND ${CMAKE_CTEST_COMMAND} --output-on-failure -C ${CMAKE_CFG_INTDIR}) add_custom_target(check COMMAND ${CMAKE_CTEST_COMMAND} --output-on-failure -C ${CMAKE_CFG_INTDIR})
rocm_package_setup_component(tests
LIBRARY_NAME composablekernel
PACKAGE_NAME tests # Prevent -static suffix on package name
)
add_subdirectory(library) add_subdirectory(library)
add_subdirectory(example) add_subdirectory(example)
add_subdirectory(test) add_subdirectory(test)
...@@ -259,8 +257,19 @@ configure_package_config_file(${CMAKE_CURRENT_SOURCE_DIR}/Config.cmake.in ...@@ -259,8 +257,19 @@ configure_package_config_file(${CMAKE_CURRENT_SOURCE_DIR}/Config.cmake.in
NO_CHECK_REQUIRED_COMPONENTS_MACRO NO_CHECK_REQUIRED_COMPONENTS_MACRO
) )
install(FILES rocm_install(FILES
"${CMAKE_CURRENT_BINARY_DIR}/composable_kernelConfig.cmake" "${CMAKE_CURRENT_BINARY_DIR}/composable_kernelConfig.cmake"
"${CMAKE_CURRENT_BINARY_DIR}/composable_kernelConfigVersion.cmake" "${CMAKE_CURRENT_BINARY_DIR}/composable_kernelConfigVersion.cmake"
DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/composable_kernel DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/composable_kernel
) )
set(CPACK_RESOURCE_FILE_LICENSE "${CMAKE_CURRENT_SOURCE_DIR}/LICENSE")
set(CPACK_RPM_PACKAGE_LICENSE "MIT")
rocm_create_package(
NAME composablekernel
DESCRIPTION "High Performance Composable Kernel for AMD GPUs"
MAINTAINER "MIOpen Kernels Dev Team <dl.MIOpen@amd.com>"
LDCONFIG
HEADER_ONLY
)
Dockerfile
View file @ 6ef4e211
...@@ -88,3 +88,8 @@ ADD rbuild.ini /rbuild.ini ...@@ -88,3 +88,8 @@ ADD rbuild.ini /rbuild.ini
ADD dev-requirements.txt dev-requirements.txt ADD dev-requirements.txt dev-requirements.txt
RUN rbuild prepare -s develop -d $PREFIX RUN rbuild prepare -s develop -d $PREFIX
RUN groupadd -f render RUN groupadd -f render
# Install the new rocm-cmake version
RUN git clone -b master https://github.com/RadeonOpenCompute/rocm-cmake.git && \
cd rocm-cmake && mkdir build && cd build && \
cmake .. && cmake --build . && cmake --build . --target install
Jenkinsfile
View file @ 6ef4e211
...@@ -379,23 +379,23 @@ pipeline { ...@@ -379,23 +379,23 @@ pipeline {
} }
} }
} }
//stage("Client App") stage("Client App")
//{ {
// parallel parallel
// { {
// stage("Run Client App") stage("Run Client App")
// { {
// agent{ label rocmnode("gfx908")} agent{ label rocmnode("gfx908")}
// environment{ environment{
// setup_args = """ -D -DBUILD_DEV=Off -DCMAKE_INSTALL_PREFIX=../install CMAKE_CXX_FLAGS="--offload-arch=gfx908 -O3 " """ setup_args = """ -D -DBUILD_DEV=Off -DCMAKE_INSTALL_PREFIX=../install CMAKE_CXX_FLAGS="--offload-arch=gfx908 -O3 " """
// execute_args = """ cd ../test/client_app && rm -rf build && mkdir build && cd build && cmake -DCMAKE_PREFIX_PATH="${env.WORKSPACE}/install;/opt/rocm" .. && make """ execute_args = """ cd ../client_example && rm -rf build && mkdir build && cd build && cmake -DCMAKE_PREFIX_PATH="${env.WORKSPACE}/install;/opt/rocm" -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc .. && make -j """
// } }
// steps{ steps{
// buildHipClangJobAndReboot(setup_args: setup_args, config_targets: "install", no_reboot:true, build_type: 'Release', execute_cmd: execute_args, prefixpath: '/usr/local') buildHipClangJobAndReboot(setup_args: setup_args, config_targets: "install", no_reboot:true, build_type: 'Release', execute_cmd: execute_args, prefixpath: '/usr/local')
// } }
// } }
// } }
//} }
stage("Performance Tests") stage("Performance Tests")
{ {
parallel parallel
......
README.md
View file @ 6ef4e211
...@@ -10,6 +10,9 @@ rocm/tensorflow:rocm5.1-tf2.6-dev \ ...@@ -10,6 +10,9 @@ rocm/tensorflow:rocm5.1-tf2.6-dev \
/bin/bash /bin/bash
``` ```
# Install the new rocm-cmake version
https://github.com/RadeonOpenCompute/rocm-cmake
## Build ## Build
```bash ```bash
mkdir build && cd build mkdir build && cd build
...@@ -23,6 +26,7 @@ cmake \ ...@@ -23,6 +26,7 @@ cmake \
-D CMAKE_CXX_FLAGS=" --offload-arch=gfx908 --offload-arch=gfx90a -O3" \ -D CMAKE_CXX_FLAGS=" --offload-arch=gfx908 --offload-arch=gfx90a -O3" \
-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \ -D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
-D CMAKE_PREFIX_PATH=/opt/rocm \ -D CMAKE_PREFIX_PATH=/opt/rocm \
-D CMAKE_INSTALL_PREFIX=${PATH_TO_CK_INSTALL_DIRECTORY} \
.. ..
``` ```
...@@ -34,7 +38,7 @@ Instructions for running each individual examples are under ```example/``` ...@@ -34,7 +38,7 @@ Instructions for running each individual examples are under ```example/```
## Tests ## Tests
```bash ```bash
make -j tests make -j examples tests
make test make test
``` ```
...@@ -44,6 +48,12 @@ Instructions for running each individual examples are under ```example/``` ...@@ -44,6 +48,12 @@ Instructions for running each individual examples are under ```example/```
``` ```
Instructions for running ckProfiler are under ```profiler/``` Instructions for running ckProfiler are under ```profiler/```
## Install CK
```bash
make install
```
## Using CK as pre-built kernel library
## Caveat ## Caveat
### Kernel Timing and Verification ### Kernel Timing and Verification
......
client_example/01_gemm/CMakeLists.txt 0 → 100644
View file @ 6ef4e211
add_executable(client_gemm gemm.cpp)
target_link_libraries(client_gemm PRIVATE composable_kernel::device_operations)
client_example/01_gemm/gemm.cpp 0 → 100644
View file @ 6ef4e211
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iomanip>
#include <vector>
#include <iostream>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/gemm.hpp"
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
using ADataType = F16;
using BDataType = F16;
using CDataType = F16;
using ALayout = Row;
using BLayout = Col;
using CLayout = Row;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
int main(int argc, char* argv[])
{
// GEMM shape
ck::index_t M = 3840;
ck::index_t N = 4096;
ck::index_t K = 4096;
ck::index_t StrideA = 4096;
ck::index_t StrideB = 4096;
ck::index_t StrideC = 4096;
if(argc == 1)
{
// use default case
}
else if(argc == 5)
{
M = std::stoi(argv[1]);
N = std::stoi(argv[2]);
K = std::stoi(argv[3]);
StrideA = std::stoi(argv[4]);
StrideB = std::stoi(argv[5]);
StrideC = std::stoi(argv[6]);
}
else
{
printf("arg1 to 6: M, N, K, StrideA, StrideB, StrideC\n");
exit(0);
}
auto f_matrix_space_size =
[](std::size_t nRow, std::size_t nCol, std::size_t stride, auto layout) {
using Layout = decltype(layout);
if(std::is_same<Layout, ck::tensor_layout::gemm::RowMajor>::value)
{
return (nRow - 1) * stride + nCol;
}
else
{
return (nCol - 1) * stride + nRow;
}
};
SimpleDeviceMem a_device_buf(sizeof(ADataType) * f_matrix_space_size(M, K, StrideA, ALayout{}));
SimpleDeviceMem b_device_buf(sizeof(BDataType) * f_matrix_space_size(K, N, StrideB, BLayout{}));
SimpleDeviceMem c_device_buf(sizeof(CDataType) * f_matrix_space_size(M, N, StrideC, CLayout{}));
using DeviceOp =
ck::tensor_operation::device::DeviceGemm<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
CDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{};
const auto c_element_op = CElementOp{};
std::string best_op_name;
bool found = false;
int best_op_id = -1;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
c_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
found = true;
best_op_id = i;
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
// run the best intance
{
auto& op_ptr = op_ptrs[best_op_id];
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
<< std::endl;
auto argument_ptr = op_ptr->MakeArgumentPointer(a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
c_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
}
std::cout << "Done" << std::endl;
}
return 0;
}
client_example/02_gemm_add_add_fastgelu/CMakeLists.txt 0 → 100644
View file @ 6ef4e211
add_executable(client_gemm_add_add_fastgelu gemm_add_add_fastgelu.cpp)
target_link_libraries(client_gemm_add_add_fastgelu PRIVATE composable_kernel::device_operations)
client_example/02_gemm_add_add_fastgelu/gemm_add_add_fastgelu.cpp 0 → 100644
View file @ 6ef4e211
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iomanip>
#include <vector>
#include <iostream>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/gemm_add_add_fastgelu.hpp"
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AddAddFastGelu = ck::tensor_operation::element_wise::AddAddFastGelu;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = AddAddFastGelu;
using ADataType = F16;
using BDataType = F16;
using D0DataType = F16;
using D1DataType = F16;
using EDataType = F16;
using ALayout = Row;
using BLayout = Col;
using DDELayout = Row;
using DDELayout = Row;
using DELayout = Row;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
int main(int argc, char* argv[])
{
// GEMM shape
ck::index_t M = 3840;
ck::index_t N = 4096;
ck::index_t K = 4096;
ck::index_t StrideA = 4096;
ck::index_t StrideB = 4096;
ck::index_t StrideD0 = 0;
ck::index_t StrideD1 = 4096;
ck::index_t StrideE = 4096;
if(argc == 1)
{
// use default case
}
else if(argc == 9)
{
M = std::stoi(argv[1]);
N = std::stoi(argv[2]);
K = std::stoi(argv[3]);
StrideA = std::stoi(argv[4]);
StrideB = std::stoi(argv[5]);
StrideD0 = std::stoi(argv[6]);
StrideD1 = std::stoi(argv[7]);
StrideE = std::stoi(argv[8]);
}
else
{
printf("arg1 to 8: M, N, K, StrideA, StrideB, StrideD0, StrideD1, StrideE\n");
exit(0);
}
auto f_matrix_space_size =
[](std::size_t nRow, std::size_t nCol, std::size_t stride, auto layout) {
using Layout = decltype(layout);
if(std::is_same<Layout, ck::tensor_layout::gemm::RowMajor>::value)
{
return (nRow - 1) * stride + nCol;
}
else
{
return (nCol - 1) * stride + nRow;
}
};
SimpleDeviceMem a_device_buf(sizeof(ADataType) * f_matrix_space_size(M, K, StrideA, ALayout{}));
SimpleDeviceMem b_device_buf(sizeof(BDataType) * f_matrix_space_size(K, N, StrideB, BLayout{}));
SimpleDeviceMem d0_m_n_device_buf(sizeof(D0DataType) *
f_matrix_space_size(M, N, StrideD0, DDELayout{}));
SimpleDeviceMem d1_m_n_device_buf(sizeof(D1DataType) *
f_matrix_space_size(M, N, StrideD1, DDELayout{}));
SimpleDeviceMem e_device_buf(sizeof(EDataType) *
f_matrix_space_size(M, N, StrideE, DELayout{}));
using DeviceOp = ck::tensor_operation::device::DeviceGemmMultipleD<
ALayout,
BLayout,
DDELayout,
ADataType,
BDataType,
ck::Tuple<D0DataType, D1DataType>,
EDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::AddAddFastGelu>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{};
const auto cde_element_op = CDEElementOp{};
std::string best_op_name;
bool found = false;
int best_op_id = -1;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(
a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
std::array<const void*, 2>{d0_m_n_device_buf.GetDeviceBuffer(),
d1_m_n_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
std::array<ck::index_t, 2>{StrideD0, StrideD1},
StrideE,
a_element_op,
b_element_op,
cde_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
found = true;
best_op_id = i;
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
// run the best intance
{
auto& op_ptr = op_ptrs[best_op_id];
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
<< std::endl;
auto argument_ptr = op_ptr->MakeArgumentPointer(
a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
std::array<const void*, 2>{d0_m_n_device_buf.GetDeviceBuffer(),
d1_m_n_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
std::array<ck::index_t, 2>{StrideD0, StrideD1},
StrideE,
a_element_op,
b_element_op,
cde_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
}
std::cout << "Done" << std::endl;
}
return 0;
}
client_example/03_gemm_layernorm/CMakeLists.txt 0 → 100644
View file @ 6ef4e211
add_executable(client_gemm_add_add_reduce_normalize gemm_add_add_layernorm.cpp)
target_link_libraries(client_gemm_add_add_reduce_normalize PRIVATE composable_kernel::device_operations)
client_example/03_gemm_layernorm/gemm_add_add_layernorm.cpp 0 → 100644
View file @ 6ef4e211
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iomanip>
#include <vector>
#include <iostream>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm_reduce.hpp"
#include "ck/tensor_operation/gpu/device/device_elementwise.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/device_elementwise_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/device_gemm_mean_squaremean_instance.hpp"
using F16 = ck::half_t;
using F32 = float;
using ADataType = F16;
using BDataType = F16;
using BiasDataType = F32;
using CDataType = F16;
using D0DataType = F16;
using ReduceDataType = F32;
using GammaDataType = F16;
using BetaDataType = F16;
using LayerNormOutDataType = F16;
using ALayout = ck::tensor_layout::gemm::RowMajor;
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
using CLayout = ck::tensor_layout::gemm::RowMajor;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
template <typename gemm_reduce_op_ptr>
bool RunDeviceGemmMeanSquareMean(gemm_reduce_op_ptr& p_op,
const void* p_a,
const void* p_b,
const void* p_bias,
const void* p_d0,
void* p_c,
void* p_mean,
void* p_square_mean,
int M,
int N,
int K,
int StrideA,
int StrideB,
int StrideC,
int StrideD0,
bool time_kernel)
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using UnaryDivElementOp = ck::tensor_operation::element_wise::UnaryDivide;
using UnarySquareElementOp = ck::tensor_operation::element_wise::UnarySquare;
auto passOp = PassThrough{};
auto squareOp = UnarySquareElementOp{};
auto divOp = UnaryDivElementOp{N};
auto argument_ptr =
p_op->MakeArgumentPointer(p_a,
p_b,
p_bias,
{p_d0},
p_c,
{p_mean, p_square_mean},
M,
N,
K,
StrideA,
StrideB,
StrideC,
{StrideD0},
{&passOp, &passOp, &passOp}, // functor for a, b, c
{&passOp}, // functor for d0
{&passOp, &squareOp}, // functor for inputs of reduction
{&divOp, &divOp}); // functor for outputs of reduction
if(p_op->IsSupportedArgument(argument_ptr.get()))
{
auto invoker_ptr = p_op->MakeInvokerPointer();
// If we evaluate running time of gemm_reduce. The output may wrong.
// Because we need to initialize the reduction tensor before runing the kernel.
// However we run kernel many times for time_kernel = trie without reinitialize the out
// of reduction tensor.
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
if(time_kernel)
std::cout << "Gemm + reduce Perf: " << std::setw(10) << ave_time << " ms" << std::endl;
return true;
}
return false;
}
template <typename normalize_op_ptr>
bool RunDeviceNormalize2D(normalize_op_ptr& p_op,
const void* p_x,
const void* p_mean,
const void* p_square_mean,
const void* p_gamma,
const void* p_beta,
void* p_y,
int M,
int N,
int StrideX,
bool time_kernel)
{
std::array<const void*, 5> input = {p_x, p_mean, p_square_mean, p_gamma, p_beta};
std::array<void*, 1> output = {p_y};
auto normalize_functor = ck::tensor_operation::element_wise::Normalize{};
auto argument_ptr = p_op->MakeArgumentPointer(input,
output,
{M, N},
{{StrideX, 1}, {1, 0}, {1, 0}, {0, 1}, {0, 1}},
{{StrideX, 1}},
ck::tensor_operation::element_wise::Normalize{});
if(p_op->IsSupportedArgument(argument_ptr.get()))
{
auto invoker_ptr = p_op->MakeInvokerPointer();
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
if(time_kernel)
std::cout << "Normalize Perf: " << std::setw(10) << ave_time << " ms" << std::endl;
return true;
}
return false;
}
int main()
{
ck::index_t M = 1024;
ck::index_t N = 1024;
ck::index_t K = 1024;
ck::index_t StrideA = 1024;
ck::index_t StrideB = 1024;
ck::index_t StrideC = 1024;
ck::index_t StrideD0 = 1024;
const auto gemm_reduce_ptrs =
ck::tensor_operation::device::instance::get_device_gemm_add_add_mean_squaremean_instances<
ADataType,
BDataType,
CDataType,
ALayout,
BLayout,
CLayout>();
const auto normalize_ptrs =
ck::tensor_operation::device::instance::get_device_normalize_from_mean_meansquare_instances<
CDataType,
ReduceDataType,
ReduceDataType,
GammaDataType,
BetaDataType,
LayerNormOutDataType>();
std::cout << "found " << gemm_reduce_ptrs.size()
<< " gemm_reduceMean_reduceSquareMean instances" << std::endl;
std::cout << "found " << normalize_ptrs.size() << " normalize instances" << std::endl;
auto f_matrix_space_size =
[](std::size_t nRow, std::size_t nCol, std::size_t stride, auto layout) {
using Layout = decltype(layout);
if(std::is_same<Layout, ck::tensor_layout::gemm::RowMajor>::value)
{
return (nRow - 1) * stride + nCol;
}
else
{
return (nCol - 1) * stride + nRow;
}
};
SimpleDeviceMem a_device_buf(sizeof(ADataType) * f_matrix_space_size(M, K, StrideA, ALayout{}));
SimpleDeviceMem b_device_buf(sizeof(BDataType) * f_matrix_space_size(K, N, StrideB, BLayout{}));
SimpleDeviceMem bias_device_buf(sizeof(BiasDataType) * N);
SimpleDeviceMem c_device_buf(sizeof(CDataType) * f_matrix_space_size(M, N, StrideC, CLayout{}));
SimpleDeviceMem d0_device_buf(sizeof(D0DataType) *
f_matrix_space_size(M, N, StrideD0, CLayout{}));
SimpleDeviceMem reduceMean_device_buf(sizeof(ReduceDataType) * M);
SimpleDeviceMem reduceMeanSquare_device_buf(sizeof(ReduceDataType) * M);
SimpleDeviceMem gamma_device_buf(sizeof(GammaDataType) * N);
SimpleDeviceMem beta_device_buf(sizeof(BetaDataType) * N);
SimpleDeviceMem layerNorm_device_buf(sizeof(LayerNormOutDataType) * M * N);
bool b_time_kernel = true;
bool b_only_run_first_kernel = true;
// layernorm => (1) + (2)
// (1). c = gemm(a, b), reduce_mean(c), reduce_square_mean(c)
// (2). normalize(c, mean, square_mean, gamma, beta)
for(auto& gemm_reduce_ptr : gemm_reduce_ptrs)
{
// run first available kernel
if(RunDeviceGemmMeanSquareMean(gemm_reduce_ptr,
a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
bias_device_buf.GetDeviceBuffer(),
d0_device_buf.GetDeviceBuffer(),
c_device_buf.GetDeviceBuffer(),
reduceMean_device_buf.GetDeviceBuffer(),
reduceMeanSquare_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
StrideC,
StrideD0,
b_time_kernel))
{
if(b_only_run_first_kernel)
break;
}
else
{
std::cout << gemm_reduce_ptr->GetTypeString() << " does not support this problem"
<< std::endl;
}
}
for(auto& normalize_ptr : normalize_ptrs)
{
if(RunDeviceNormalize2D(normalize_ptr,
c_device_buf.GetDeviceBuffer(),
reduceMean_device_buf.GetDeviceBuffer(),
reduceMeanSquare_device_buf.GetDeviceBuffer(),
gamma_device_buf.GetDeviceBuffer(),
beta_device_buf.GetDeviceBuffer(),
layerNorm_device_buf.GetDeviceBuffer(),
M,
N,
StrideC,
b_time_kernel))
{
if(b_only_run_first_kernel)
break;
}
else
{
std::cout << normalize_ptr->GetTypeString() << " does not support this problem"
<< std::endl;
}
}
}
client_example/CMakeLists.txt 0 → 100644
View file @ 6ef4e211
cmake_minimum_required(VERSION 3.15)
project(ck_app)
add_compile_options(-std=c++17)
find_package(composable_kernel 1.0.0 COMPONENTS device_operations)
find_package(hip REQUIRED PATHS /opt/rocm)
message(STATUS "Build with HIP ${hip_VERSION}")
add_subdirectory(01_gemm)
add_subdirectory(02_gemm_add_add_fastgelu)
add_subdirectory(03_gemm_layernorm)
client_example/README.md 0 → 100644
View file @ 6ef4e211
##
Client application links to CK library, and therefore CK library needs to be installed before building client applications.
## Build
```bash
mkdir -p client_example/build
cd client_example/build
```
```bash
cmake \
-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
-D CMAKE_PREFIX_PATH="/opt/rocm;${PATH_TO_CK_INSTALL_DIRECTORY}" \
..
```
### Build client example
```bash
make -j
```
cmake/googletest.cmake
View file @ 6ef4e211
...@@ -8,7 +8,7 @@ endif() ...@@ -8,7 +8,7 @@ endif()
message(STATUS "Fetching GoogleTest") message(STATUS "Fetching GoogleTest")
list(APPEND GTEST_CMAKE_CXX_FLAGS list(APPEND GTEST_CMAKE_CXX_FLAGS
-Wno-undef -Wno-undef
-Wno-reserved-identifier -Wno-reserved-identifier
-Wno-global-constructors -Wno-global-constructors
...@@ -31,7 +31,11 @@ FetchContent_Declare( ...@@ -31,7 +31,11 @@ FetchContent_Declare(
# Will be necessary for windows build # Will be necessary for windows build
# set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) # set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
FetchContent_MakeAvailable(googletest) FetchContent_GetProperties(googletest)
if(NOT googletest_POPULATED)
FetchContent_Populate(googletest)
add_subdirectory(${googletest_SOURCE_DIR} ${googletest_BINARY_DIR} EXCLUDE_FROM_ALL)
endif()
target_compile_options(gtest PRIVATE ${GTEST_CMAKE_CXX_FLAGS}) target_compile_options(gtest PRIVATE ${GTEST_CMAKE_CXX_FLAGS})
target_compile_options(gtest_main PRIVATE ${GTEST_CMAKE_CXX_FLAGS}) target_compile_options(gtest_main PRIVATE ${GTEST_CMAKE_CXX_FLAGS})
......
example/01_gemm/gemm_xdl_bf16.cpp
View file @ 6ef4e211
...@@ -84,8 +84,13 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle ...@@ -84,8 +84,13 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
8>; // index_t CShuffleBlockTransferScalarPerVector_NPerBlock 8>; // index_t CShuffleBlockTransferScalarPerVector_NPerBlock
// clang-format on // clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host:: using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
ReferenceGemm<float, float, float, float, PassThrough, PassThrough, PassThrough>; BDataType,
CDataType,
AccDataType,
PassThrough,
PassThrough,
PassThrough>;
int main(int argc, char* argv[]) int main(int argc, char* argv[])
{ {
...@@ -216,24 +221,17 @@ int main(int argc, char* argv[]) ...@@ -216,24 +221,17 @@ int main(int argc, char* argv[])
if(do_verification) if(do_verification)
{ {
Tensor<float> a_f32_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{})); Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<float> b_f32_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<float> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<float> c_m_n_device_f32_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
bf16_to_f32_(a_m_k, a_f32_m_k);
bf16_to_f32_(b_k_n, b_f32_k_n);
bf16_to_f32_(c_m_n_device_result, c_m_n_device_f32_result);
auto ref_gemm = ReferenceGemmInstance{}; auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker(); auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument( auto ref_argument = ref_gemm.MakeArgument(
a_f32_m_k, b_f32_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op); a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
ref_invoker.Run(ref_argument); ref_invoker.Run(ref_argument);
return ck::utils::check_err(c_m_n_device_f32_result.mData, c_m_n_host_result.mData) ? 0 : 1; return ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData) ? 0 : 1;
} }
return 0; return 0;
......
example/02_gemm_alpha_beta/CMakeLists.txt deleted 100644 → 0
View file @ b0a2afb9
add_example_executable(example_gemm_xdl_alpha_beta gemm_xdl_alpha_beta.cpp)
example/02_gemm_bilinear/CMakeLists.txt 0 → 100644
View file @ 6ef4e211
add_example_executable(example_gemm_bilinear_xdl_fp16 gemm_bilinear_xdl_fp16.cpp)
example/02_gemm_alpha_beta/README.md → example/02_gemm_bilinear/README.md
View file @ 6ef4e211
# Instructions for ```example_gemm_xdl_alpha_beta``` # Instructions for ```example_gemm_bilinear_xdl_fp16```
## Run ```example_gemm_xdl_alpha_beta``` ## Run ```example_gemm_bilinear_xdl_fp16```
```bash ```bash
#arg1: verification (0=no, 1=yes) #arg1: verification (0=no, 1=yes)
#arg2: initialization (0=no init, 1=integer value, 2=decimal value) #arg2: initialization (0=no init, 1=integer value, 2=decimal value)
#arg3: run kernel # of times (>1) #arg3: time kernel (0=no, 1=yes)
./bin/example_gemm_xdl_alpha_beta 1 1 1 0.5 0.5 #arg4 to 10: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE
#arg11 to 12: alpha, beta
./bin/example_gemm_bilinear_xdl_fp16 1 1 1 3840 4096 4096 4096 4096 4096 4096 0.5 0.5
``` ```
Result (MI100 @ 1502Mhz, 184.6TFlops peak FP16) Result (MI100 @ 1502Mhz, 184.6TFlops peak FP16)
``` ```
......
example/02_gemm_alpha_beta/gemm_xdl_alpha_beta.cpp → example/02_gemm_bilinear/gemm_bilinear_xdl_fp16.cpp
View file @ 6ef4e211
...@@ -8,80 +8,105 @@ ...@@ -8,80 +8,105 @@
#include "ck/ck.hpp" #include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp" #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm_xdl_c_shuffle_bias_2d.hpp" #include "ck/tensor_operation/gpu/device/device_gemm_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp" #include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp" #include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp" #include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm_bias_2d.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/library/utility/check_err.hpp"
struct AlphaBetaAdd
{
AlphaBetaAdd(float alpha, float beta) : alpha_(alpha), beta_(beta){};
template <typename E, typename C, typename D>
__host__ __device__ constexpr void operator()(E& e, const C& c, const D& d) const;
template <>
__host__ __device__ constexpr void operator()<ck::half_t, float, ck::half_t>(
ck::half_t& e, const float& c, const ck::half_t& d) const
{
e = ck::type_convert<ck::half_t>(alpha_ * c + beta_ * ck::type_convert<float>(d));
};
float alpha_;
float beta_;
};
template <ck::index_t... Is> template <ck::index_t... Is>
using S = ck::Sequence<Is...>; using S = ck::Sequence<Is...>;
using ADataType = ck::half_t; using F16 = ck::half_t;
using BDataType = ck::half_t; using F32 = float;
using CDataType = ck::half_t;
using AccDataType = float; using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using ALayout = ck::tensor_layout::gemm::RowMajor;
using BLayout = ck::tensor_layout::gemm::ColumnMajor; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using CLayout = ck::tensor_layout::gemm::RowMajor;
using ADataType = F16;
using AElementOp = ck::tensor_operation::element_wise::PassThrough; using BDataType = F16;
using BElementOp = ck::tensor_operation::element_wise::PassThrough; using AccDataType = F32;
using CElementOp = ck::tensor_operation::element_wise::AlphaBetaAdd; using CShuffleDataType = F32;
using DDataType = F16;
// clang-format off using DsDataType = ck::Tuple<DDataType>;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl_C_Shuffle_Bias_2d< using EDataType = F16;
ADataType, // ADataType
BDataType, // BDataType using ALayout = Row;
CDataType, // CDataType using BLayout = Col;
AccDataType, // AccDataType using DELayout = Row;
ALayout, // ALayout
BLayout, // BLayout using AElementOp = PassThrough;
CLayout, // CLayout using BElementOp = PassThrough;
AElementOp, // AElementwiseOperation using CDEElementOp = AlphaBetaAdd;
BElementOp, // BElementwiseOperation
CElementOp, // CElementwiseOperation static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
256, // BlockSize
256, // MPerBlock using DeviceOpInstance =
128, // NPerBlock ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_CShuffle<ALayout,
4, // K0PerBlock BLayout,
8, // K1 DELayout,
32, // MPerXDL ADataType,
32, // NPerXDL BDataType,
4, // MXdlPerWave AccDataType,
2, // NXdlPerWave CShuffleDataType,
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_K0_M_K1 DsDataType,
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder EDataType,
S<1, 0, 2>, // ABlockTransferSrcAccessOrder AElementOp,
2, // ABlockTransferSrcVectorDim BElementOp,
8, // ABlockTransferSrcScalarPerVector CDEElementOp,
8, // ABlockTransferDstScalarPerVector_K1 GemmDefault,
true, // ABlockLdsAddExtraM 1,
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_K0_N_K1 256,
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder 256,
S<1, 0, 2>, // BBlockTransferSrcAccessOrder 128,
2, // BBlockTransferSrcVectorDim 32,
8, // BBlockTransferSrcScalarPerVector 8,
8, // BBlockTransferDstScalarPerVector_K1 8,
true, // BBlockLdsAddExtraN 32,
1, // CShuffleMXdlPerWavePerShuffle 32,
1, // CShuffleNXdlPerWavePerShuffle 4,
S<1, 1, 32, 1, 1, 8>, // CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl 2,
8>; // CBlockTransferScalarPerVector_NWaveNPerXdl S<4, 64, 1>,
// clang-format on S<1, 0, 2>,
S<1, 0, 2>,
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemmBias2D<ADataType, 2,
BDataType, 8,
CDataType, 8,
CDataType, 1,
AccDataType, S<4, 64, 1>,
AElementOp, S<1, 0, 2>,
BElementOp, S<1, 0, 2>,
CElementOp>; 2,
8,
8,
1,
1,
1,
S<1, 32, 1, 8>,
8>;
int main(int argc, char* argv[]) int main(int argc, char* argv[])
{ {
...@@ -96,12 +121,17 @@ int main(int argc, char* argv[]) ...@@ -96,12 +121,17 @@ int main(int argc, char* argv[])
ck::index_t StrideA = 4096; ck::index_t StrideA = 4096;
ck::index_t StrideB = 4096; ck::index_t StrideB = 4096;
ck::index_t StrideC = 4096; ck::index_t StrideD = 4096;
ck::index_t StrideE = 4096;
float alpha = 1.0f; float alpha = 1.0f;
float beta = 1.0f; float beta = 1.0f;
if(argc == 4) if(argc == 1)
{
// use default case
}
else if(argc == 4)
{ {
do_verification = std::stoi(argv[1]); do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]); init_method = std::stoi(argv[2]);
...@@ -116,7 +146,7 @@ int main(int argc, char* argv[]) ...@@ -116,7 +146,7 @@ int main(int argc, char* argv[])
alpha = std::stof(argv[4]); alpha = std::stof(argv[4]);
beta = std::stof(argv[5]); beta = std::stof(argv[5]);
} }
else if(argc == 12) else if(argc == 13)
{ {
do_verification = std::stoi(argv[1]); do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]); init_method = std::stoi(argv[2]);
...@@ -128,17 +158,19 @@ int main(int argc, char* argv[]) ...@@ -128,17 +158,19 @@ int main(int argc, char* argv[])
StrideA = std::stoi(argv[7]); StrideA = std::stoi(argv[7]);
StrideB = std::stoi(argv[8]); StrideB = std::stoi(argv[8]);
StrideC = std::stoi(argv[9]); StrideD = std::stoi(argv[9]);
StrideE = std::stoi(argv[10]);
alpha = std::stof(argv[10]); alpha = std::stof(argv[11]);
beta = std::stof(argv[11]); beta = std::stof(argv[12]);
} }
else else
{ {
printf("arg1: verification (0=no, 1=yes)\n"); printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"); printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n"); printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC, alpha, beta\n"); printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE, alpha, "
"beta\n");
exit(0); exit(0);
} }
...@@ -158,14 +190,14 @@ int main(int argc, char* argv[]) ...@@ -158,14 +190,14 @@ int main(int argc, char* argv[])
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{})); Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{})); Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<CDataType> c0_m_n(f_host_tensor_descriptor(M, N, StrideC, CLayout{})); Tensor<DDataType> d_m_n(f_host_tensor_descriptor(M, N, StrideD, DELayout{}));
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{})); Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, DELayout{}));
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{})); Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, DELayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl; std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl; std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "c0_m_n: " << c0_m_n.mDesc << std::endl; std::cout << "d_m_n: " << d_m_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl; std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;
switch(init_method) switch(init_method)
{ {
...@@ -173,42 +205,48 @@ int main(int argc, char* argv[]) ...@@ -173,42 +205,48 @@ int main(int argc, char* argv[])
case 1: case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}); a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}); b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
c0_m_n.GenerateTensorValue(GeneratorTensor_2<CDataType>{-5, 5}); d_m_n.GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5});
break; break;
default: default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}); a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}); b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
c0_m_n.GenerateTensorValue(GeneratorTensor_3<CDataType>{-0.5, 0.5}); d_m_n.GenerateTensorValue(GeneratorTensor_3<DDataType>{-0.5, 0.5});
} }
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace()); DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace()); DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
DeviceMem c0_m_n_device_buf(sizeof(CDataType) * c0_m_n.mDesc.GetElementSpace()); DeviceMem d_m_n_device_buf(sizeof(DDataType) * d_m_n.mDesc.GetElementSpace());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace()); DeviceMem e_m_n_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpace());
a_m_k_device_buf.ToDevice(a_m_k.mData.data()); a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data()); b_k_n_device_buf.ToDevice(b_k_n.mData.data());
c0_m_n_device_buf.ToDevice(c0_m_n.mData.data()); d_m_n_device_buf.ToDevice(d_m_n.mData.data());
c_m_n_device_buf.ToDevice(c_m_n_device_result.mData.data()); e_m_n_device_buf.ToDevice(e_m_n_device_result.mData.data());
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{alpha, beta};
// do GEMM // do GEMM
auto gemm = DeviceGemmInstance{}; auto device_op = DeviceOpInstance{};
auto invoker = gemm.MakeInvoker(); auto invoker = device_op.MakeInvoker();
auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()), auto argument =
static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()), device_op.MakeArgument(a_m_k_device_buf.GetDeviceBuffer(),
static_cast<CDataType*>(c0_m_n_device_buf.GetDeviceBuffer()), b_k_n_device_buf.GetDeviceBuffer(),
static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()), std::array<const void*, 1>{d_m_n_device_buf.GetDeviceBuffer()},
M, e_m_n_device_buf.GetDeviceBuffer(),
N, M,
K, N,
StrideA, K,
StrideB, StrideA,
StrideC, StrideB,
AElementOp{}, std::array<ck::index_t, 1>{StrideD},
BElementOp{}, StrideE,
CElementOp{alpha, beta}); a_element_op,
b_element_op,
if(!gemm.IsSupportedArgument(argument)) cde_element_op);
if(!device_op.IsSupportedArgument(argument))
{ {
throw std::runtime_error( throw std::runtime_error(
"wrong! device_gemm with the specified compilation parameters does " "wrong! device_gemm with the specified compilation parameters does "
...@@ -219,7 +257,7 @@ int main(int argc, char* argv[]) ...@@ -219,7 +257,7 @@ int main(int argc, char* argv[])
std::size_t flop = std::size_t(2) * M * N * K; std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype = std::size_t num_btype =
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N; sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time; float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
...@@ -228,24 +266,39 @@ int main(int argc, char* argv[]) ...@@ -228,24 +266,39 @@ int main(int argc, char* argv[])
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s" std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
<< std::endl; << std::endl;
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data()); e_m_n_device_buf.FromDevice(e_m_n_device_result.mData.data());
if(do_verification) if(do_verification)
{ {
auto ref_gemm = ReferenceGemmInstance{}; Tensor<CShuffleDataType> c_m_n(HostTensorDescriptor(
auto ref_invoker = ref_gemm.MakeInvoker(); std::vector<std::size_t>{static_cast<std::size_t>(M), static_cast<std::size_t>(N)}));
auto ref_argument = ref_gemm.MakeArgument(a_m_k, using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
b_k_n, BDataType,
c0_m_n, CShuffleDataType,
c_m_n_host_result, AccDataType,
AElementOp{}, AElementOp,
BElementOp{}, BElementOp,
CElementOp{alpha, beta}); PassThrough>;
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument =
ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
ref_invoker.Run(ref_argument); ref_invoker.Run(ref_argument);
return ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData) ? 0 : 1; for(int m = 0; m < M; ++m)
{
for(int n = 0; n < N; ++n)
{
cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d_m_n(m, n));
}
}
e_m_n_device_buf.FromDevice(e_m_n_device_result.mData.data());
return ck::utils::check_err(e_m_n_device_result.mData, e_m_n_host_result.mData) ? 0 : 1;
} }
return 0; return 0;
......
example/03_gemm_bias_relu/CMakeLists.txt
View file @ 6ef4e211
add_example_executable(example_gemm_xdl_bias_relu gemm_xdl_bias_relu.cpp) add_example_executable(example_gemm_bias_relu_xdl_fp16 gemm_bias_relu_xdl_fp16.cpp)
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