Skip to content

GitLab

Commit 2d92594a authored by Chao Liu's avatar Chao Liu
Browse files

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

parents ba6664b0 a11680cc
Expand all Show whitespace changes
Inline Side-by-side
Showing with 1822 additions and 331 deletions
Dockerfile
View file @ 2d92594a
...@@ -2,6 +2,7 @@ FROM ubuntu:18.04 ...@@ -2,6 +2,7 @@ FROM ubuntu:18.04
ARG ROCMVERSION=5.1 ARG ROCMVERSION=5.1
ARG OSDB_BKC_VERSION ARG OSDB_BKC_VERSION
ARG compiler_version
RUN set -xe RUN set -xe
...@@ -93,3 +94,19 @@ RUN groupadd -f render ...@@ -93,3 +94,19 @@ RUN groupadd -f render
RUN git clone -b master https://github.com/RadeonOpenCompute/rocm-cmake.git && \ RUN git clone -b master https://github.com/RadeonOpenCompute/rocm-cmake.git && \
cd rocm-cmake && mkdir build && cd build && \ cd rocm-cmake && mkdir build && cd build && \
cmake .. && cmake --build . && cmake --build . --target install cmake .. && cmake --build . && cmake --build . --target install
WORKDIR /
ENV compiler_version=$compiler_version
RUN sh -c "echo compiler version = '$compiler_version'"
RUN if [ "$compiler_version" = "9110" ]; then \
git clone -b ck-9110 https://github.com/RadeonOpenCompute/llvm-project.git && \
cd llvm-project && mkdir build && cd build && \
cmake -DCMAKE_INSTALL_PREFIX=/opt/rocm/llvm -DCMAKE_BUILD_TYPE=Release -DLLVM_ENABLE_ASSERTIONS=1 -DLLVM_TARGETS_TO_BUILD="AMDGPU;X86" -DLLVM_ENABLE_PROJECTS="clang;lld;compiler-rt" ../llvm && \
make -j 8 ; \
else echo "using the release compiler"; \
fi
#ENV HIP_CLANG_PATH='/llvm-project/build/bin'
#RUN sh -c "echo HIP_CLANG_PATH = '$HIP_CLANG_PATH'"
Jenkinsfile
View file @ 2d92594a
...@@ -95,19 +95,24 @@ def buildHipClangJob(Map conf=[:]){ ...@@ -95,19 +95,24 @@ def buildHipClangJob(Map conf=[:]){
if (conf.get("enforce_xnack_on", false)) { if (conf.get("enforce_xnack_on", false)) {
dockerOpts = dockerOpts + " --env HSA_XNACK=1" dockerOpts = dockerOpts + " --env HSA_XNACK=1"
} }
def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg GPU_ARCH='${gpu_arch}' " def dockerArgs
if (params.USE_9110){
dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg GPU_ARCH='${gpu_arch}' --build-arg compiler_version='9110' "
dockerOpts = dockerOpts + " --env HIP_CLANG_PATH='/llvm-project/build/bin' "
}
else{
dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg GPU_ARCH='${gpu_arch}' --build-arg compiler_version='release' "
}
def variant = env.STAGE_NAME def variant = env.STAGE_NAME
def retimage def retimage
gitStatusWrapper(credentialsId: "${status_wrapper_creds}", gitHubContext: "Jenkins - ${variant}", account: 'ROCmSoftwarePlatform', repo: 'composable_kernel') { gitStatusWrapper(credentialsId: "${status_wrapper_creds}", gitHubContext: "Jenkins - ${variant}", account: 'ROCmSoftwarePlatform', repo: 'composable_kernel') {
if (params.USE_DOCKERFILE){
try { try {
retimage = docker.build("${image}", dockerArgs + '.') retimage = docker.build("${image}", dockerArgs + '.')
withDockerContainer(image: image, args: dockerOpts) { withDockerContainer(image: image, args: dockerOpts) {
timeout(time: 5, unit: 'MINUTES') timeout(time: 5, unit: 'MINUTES'){
{
sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo' sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo'
} }
} }
...@@ -117,22 +122,13 @@ def buildHipClangJob(Map conf=[:]){ ...@@ -117,22 +122,13 @@ def buildHipClangJob(Map conf=[:]){
throw e throw e
} }
catch(Exception ex) { catch(Exception ex) {
retimage = docker.build("${image}", dockerArgs + "--no-cache .") retimage = docker.build("${image}", dockerArgs + " --no-cache .")
withDockerContainer(image: image, args: dockerOpts) { withDockerContainer(image: image, args: dockerOpts) {
timeout(time: 5, unit: 'MINUTES') timeout(time: 5, unit: 'MINUTES'){
{
sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo' sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo'
} }
} }
} }
}
else{
timeout(time: 3, unit: 'HOURS'){
retimage = docker.image('compute-artifactory.amd.com:5000/rocm-plus-docker/framework/compute-rocm-dkms-no-npi-hipclang:9110_ubuntu18.04_py3.6_pytorch_rocm5.0_internal_testing_7ff5b54').pull()
image="b56f8ac0d6ea"
sh "docker images"
}
}
withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') { withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') {
timeout(time: 5, unit: 'HOURS') timeout(time: 5, unit: 'HOURS')
...@@ -150,9 +146,6 @@ def reboot(){ ...@@ -150,9 +146,6 @@ def reboot(){
} }
def buildHipClangJobAndReboot(Map conf=[:]){ def buildHipClangJobAndReboot(Map conf=[:]){
try{ try{
buildHipClangJob(conf) buildHipClangJob(conf)
...@@ -186,19 +179,24 @@ def runCKProfiler(Map conf=[:]){ ...@@ -186,19 +179,24 @@ def runCKProfiler(Map conf=[:]){
if (conf.get("enforce_xnack_on", false)) { if (conf.get("enforce_xnack_on", false)) {
dockerOpts = dockerOpts + " --env HSA_XNACK=1" dockerOpts = dockerOpts + " --env HSA_XNACK=1"
} }
def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg GPU_ARCH='${gpu_arch}' " def dockerArgs
if (params.USE_9110){
dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg GPU_ARCH='${gpu_arch}' --build-arg compiler_version='9110' "
dockerOpts = dockerOpts + " --env HIP_CLANG_PATH='/llvm-project/build/bin' "
}
else{
dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg GPU_ARCH='${gpu_arch}' --build-arg compiler_version='release' "
}
def variant = env.STAGE_NAME def variant = env.STAGE_NAME
def retimage def retimage
gitStatusWrapper(credentialsId: "${status_wrapper_creds}", gitHubContext: "Jenkins - ${variant}", account: 'ROCmSoftwarePlatform', repo: 'composable_kernel') { gitStatusWrapper(credentialsId: "${status_wrapper_creds}", gitHubContext: "Jenkins - ${variant}", account: 'ROCmSoftwarePlatform', repo: 'composable_kernel') {
if (params.USE_DOCKERFILE){
try { try {
retimage = docker.build("${image}", dockerArgs + '.') retimage = docker.build("${image}", dockerArgs + '.')
withDockerContainer(image: image, args: dockerOpts) { withDockerContainer(image: image, args: dockerOpts) {
timeout(time: 5, unit: 'MINUTES') timeout(time: 5, unit: 'MINUTES'){
{
sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo' sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo'
} }
} }
...@@ -208,22 +206,13 @@ def runCKProfiler(Map conf=[:]){ ...@@ -208,22 +206,13 @@ def runCKProfiler(Map conf=[:]){
throw e throw e
} }
catch(Exception ex) { catch(Exception ex) {
retimage = docker.build("${image}", dockerArgs + "--no-cache .") retimage = docker.build("${image}", dockerArgs + " --no-cache .")
withDockerContainer(image: image, args: dockerOpts) { withDockerContainer(image: image, args: dockerOpts) {
timeout(time: 5, unit: 'MINUTES') timeout(time: 5, unit: 'MINUTES'){
{
sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo' sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo'
} }
} }
} }
}
else{
timeout(time: 3, unit: 'HOURS'){
retimage = docker.image('compute-artifactory.amd.com:5000/rocm-plus-docker/framework/compute-rocm-dkms-no-npi-hipclang:9110_ubuntu18.04_py3.6_pytorch_rocm5.0_internal_testing_7ff5b54').pull()
image="b56f8ac0d6ea"
sh "docker images"
}
}
withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') { withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') {
timeout(time: 5, unit: 'HOURS') timeout(time: 5, unit: 'HOURS')
...@@ -238,6 +227,12 @@ def runCKProfiler(Map conf=[:]){ ...@@ -238,6 +227,12 @@ def runCKProfiler(Map conf=[:]){
sh "echo GPU_arch name: ${gpu_arch} >> ${gemm_log}" sh "echo GPU_arch name: ${gpu_arch} >> ${gemm_log}"
sh "rocminfo | grep 'Compute Unit:' >> ${gemm_log} " sh "rocminfo | grep 'Compute Unit:' >> ${gemm_log} "
sh "hipcc --version | grep -e 'HIP version' >> ${gemm_log}" sh "hipcc --version | grep -e 'HIP version' >> ${gemm_log}"
if (params.USE_9110){
sh "echo Environment type: CI_9110 >> ${gemm_log}"
}
else{
sh "echo Environment type: CI_release >> ${gemm_log}"
}
sh "/opt/rocm/bin/amdclang++ --version | grep -e 'InstalledDir' >> ${gemm_log}" sh "/opt/rocm/bin/amdclang++ --version | grep -e 'InstalledDir' >> ${gemm_log}"
sh "./profile_gemm.sh gemm 0 0 0 1 0 5 | tee -a ${gemm_log}" sh "./profile_gemm.sh gemm 0 0 0 1 0 5 | tee -a ${gemm_log}"
sh "./profile_gemm.sh gemm 1 0 0 1 0 5 | tee -a ${gemm_log}" sh "./profile_gemm.sh gemm 1 0 0 1 0 5 | tee -a ${gemm_log}"
...@@ -259,23 +254,44 @@ def runCKProfiler(Map conf=[:]){ ...@@ -259,23 +254,44 @@ def runCKProfiler(Map conf=[:]){
//the script will return 0 if the performance criteria are met //the script will return 0 if the performance criteria are met
//or return 1 if the criteria are not met //or return 1 if the criteria are not met
archiveArtifacts "${gemm_log}" archiveArtifacts "${gemm_log}"
sh "python3 parse_perf_data.py ${gemm_log} " sh "python3 process_perf_data.py ${gemm_log} "
//run resnet50 test //run resnet50 test
def resnet_log = "perf_resnet50_${gpu_arch}.log" def resnet256_log = "perf_resnet50_N256_${gpu_arch}.log"
sh "rm -f ${resnet_log}" sh "rm -f ${resnet256_log}"
sh "echo Branch name: ${env.BRANCH_NAME} > ${resnet_log}" sh "echo Branch name: ${env.BRANCH_NAME} > ${resnet256_log}"
sh "echo Node name: ${NODE_NAME} >> ${resnet_log}" sh "echo Node name: ${NODE_NAME} >> ${resnet256_log}"
sh "echo GPU_arch name: ${gpu_arch} >> ${resnet_log}" sh "echo GPU_arch name: ${gpu_arch} >> ${resnet256_log}"
sh "rocminfo | grep 'Compute Unit:' >> ${resnet_log} " sh "rocminfo | grep 'Compute Unit:' >> ${resnet256_log} "
sh "hipcc --version | grep -e 'HIP version' >> ${resnet_log}" sh "hipcc --version | grep -e 'HIP version' >> ${resnet256_log}"
sh "/opt/rocm/bin/amdclang++ --version | grep -e 'InstalledDir' >> ${resnet_log}" if (params.USE_9110){
sh "echo Environment type: CI_9110 >> ${resnet256_log}"
}
else{
sh "echo Environment type: CI_release >> ${resnet256_log}"
}
sh "/opt/rocm/bin/amdclang++ --version | grep -e 'InstalledDir' >> ${resnet256_log}"
//first run tests with N=256 //first run tests with N=256
sh "./profile_conv.sh conv_fwd_bias_relu 1 1 1 1 0 2 0 1 256 | tee -a ${resnet_log}" sh "./profile_resnet50.sh conv_fwd_bias_relu 1 1 1 1 0 2 0 1 256 | tee -a ${resnet256_log}"
archiveArtifacts "${resnet256_log}"
sh "python3 process_perf_data.py ${resnet256_log} "
//then run with N=4 //then run with N=4
sh "./profile_conv.sh conv_fwd_bias_relu 1 1 1 1 0 2 0 1 4 | tee -a ${resnet_log}" def resnet4_log = "perf_resnet50_N4_${gpu_arch}.log"
archiveArtifacts "${resnet_log}" sh "rm -f ${resnet4_log}"
//the script will put the results from N=256 and N=4 runs into separate tables sh "echo Branch name: ${env.BRANCH_NAME} > ${resnet4_log}"
sh "python3 parse_perf_data.py ${resnet_log} " sh "echo Node name: ${NODE_NAME} >> ${resnet4_log}"
sh "echo GPU_arch name: ${gpu_arch} >> ${resnet4_log}"
sh "rocminfo | grep 'Compute Unit:' >> ${resnet4_log} "
sh "hipcc --version | grep -e 'HIP version' >> ${resnet4_log}"
if (params.USE_9110){
sh "echo Environment type: CI_9110 >> ${resnet4_log}"
}
else{
sh "echo Environment type: CI_release >> ${resnet4_log}"
}
sh "/opt/rocm/bin/amdclang++ --version | grep -e 'InstalledDir' >> ${resnet4_log}"
sh "./profile_resnet50.sh conv_fwd_bias_relu 1 1 1 1 0 2 0 1 4 | tee -a ${resnet4_log}"
archiveArtifacts "${resnet4_log}"
sh "python3 process_perf_data.py ${resnet4_log} "
} }
} }
} }
...@@ -307,7 +323,7 @@ pipeline { ...@@ -307,7 +323,7 @@ pipeline {
} }
parameters { parameters {
booleanParam( booleanParam(
name: "USE_DOCKERFILE", name: "USE_9110",
defaultValue: true, defaultValue: true,
description: "") description: "")
} }
......
client_example/01_gemm/gemm.cpp
View file @ 2d92594a
...@@ -63,7 +63,7 @@ int main(int argc, char* argv[]) ...@@ -63,7 +63,7 @@ int main(int argc, char* argv[])
{ {
// use default case // use default case
} }
else if(argc == 5) else if(argc == 7)
{ {
M = std::stoi(argv[1]); M = std::stoi(argv[1]);
N = std::stoi(argv[2]); N = std::stoi(argv[2]);
......
example/21_gemm_layernorm/gemm_layernorm_xdl_fp16.cpp
View file @ 2d92594a
...@@ -129,7 +129,7 @@ void host_gemm_layernorm(Tensor<LayerNormOutDataType>& out_m_n, ...@@ -129,7 +129,7 @@ void host_gemm_layernorm(Tensor<LayerNormOutDataType>& out_m_n,
const Tensor<ADataType>& a_m_k, const Tensor<ADataType>& a_m_k,
const Tensor<ADataType>& b_k_n, const Tensor<ADataType>& b_k_n,
const Tensor<GammaDataType>& gamma_n, const Tensor<GammaDataType>& gamma_n,
const Tensor<GammaDataType>& beta_n, const Tensor<BetaDataType>& beta_n,
A_functor a_element_op, A_functor a_element_op,
B_functor b_element_op, B_functor b_element_op,
C_functor c_element_op, C_functor c_element_op,
......
example/23_softmax/softmax_blockwise.cpp
View file @ 2d92594a
...@@ -212,6 +212,8 @@ int main(int argc, char* argv[]) ...@@ -212,6 +212,8 @@ int main(int argc, char* argv[])
auto device_instance = DeviceInstance{}; auto device_instance = DeviceInstance{};
std::cout << i_inLengths.size() << ", " << i_inStrides.size() << std::endl;
auto argument_ptr = device_instance.MakeArgumentPointer(i_inLengths, auto argument_ptr = device_instance.MakeArgumentPointer(i_inLengths,
i_inStrides, i_inStrides,
reduceDims, reduceDims,
......
example/27_layernorm/CMakeLists.txt 0 → 100644
View file @ 2d92594a
add_example_executable(example_layernorm_blockwise layernorm_blockwise.cpp)
\ No newline at end of file
example/27_layernorm/layernorm_blockwise.cpp 0 → 100644
View file @ 2d92594a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <getopt.h>
#include "ck/ck.hpp"
#include "ck/utility/reduction_enums.hpp"
#include "ck/tensor_operation/gpu/device/device_layernorm.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_common_util.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_layernorm.hpp"
using XDataType = ck::half_t;
using GammaDataType = ck::half_t;
using BetaDataType = ck::half_t;
using YDataType = ck::half_t;
using AccDataType = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
constexpr int Rank = 2;
constexpr int NumReduceDim = 1;
using DeviceInstance = ck::tensor_operation::device::DeviceLayernorm<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
PassThrough,
Rank,
NumReduceDim,
256, // BlockSize
8, // ClusterM
32, // ClusterK
1, // SliceM
8, // SliceK
1, // SrcVecDim (0=M, 1=K)
8, // SrcScalarPerVector
8, // GammaScalarPerVector
8, // BetaScalarPerVector
1>; // OutScalarPerVector
int main()
{
bool time_kernel = false;
ck::index_t M = 1024;
ck::index_t N = 1024;
ck::index_t Stride = N;
auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
return HostTensorDescriptor(std::vector<std::size_t>({len}),
std::vector<std::size_t>({stride}));
};
auto f_host_tensor_descriptor2d = [](std::size_t row, std::size_t col, std::size_t stride) {
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({stride, 1}));
};
Tensor<XDataType> x(f_host_tensor_descriptor2d(M, N, Stride));
Tensor<GammaDataType> gamma(f_host_tensor_descriptor1d(N, 1));
Tensor<BetaDataType> beta(f_host_tensor_descriptor1d(N, 1));
Tensor<YDataType> y(f_host_tensor_descriptor2d(M, N, Stride));
x.GenerateTensorValue(GeneratorTensor_3<XDataType>{0.0, 1.0});
gamma.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{0.0, 1.0});
beta.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{0.0, 1.0});
DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpace());
DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpace());
DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpace());
DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpace());
x_dev.ToDevice(x.mData.data());
gamma_dev.ToDevice(gamma.mData.data());
beta_dev.ToDevice(beta.mData.data());
auto device_instance = DeviceInstance{};
auto argument_ptr = device_instance.MakeArgumentPointer(
{M, N},
std::vector<ck::index_t>{x.mDesc.GetStrides().begin(), x.mDesc.GetStrides().end()},
std::vector<ck::index_t>{gamma.mDesc.GetStrides().begin(), gamma.mDesc.GetStrides().end()},
std::vector<ck::index_t>{beta.mDesc.GetStrides().begin(), beta.mDesc.GetStrides().end()},
{1},
1e-4,
x_dev.GetDeviceBuffer(),
gamma_dev.GetDeviceBuffer(),
beta_dev.GetDeviceBuffer(),
y_dev.GetDeviceBuffer(),
PassThrough{});
if(!device_instance.IsSupportedArgument(argument_ptr.get()))
{
std::cout << "The runtime parameters are not supported" << std::endl;
return 1;
};
auto invoker_ptr = device_instance.MakeInvokerPointer();
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
bool pass = true;
{
Tensor<YDataType> host_y(f_host_tensor_descriptor2d(M, N, Stride));
using ReferenceInstance = ck::tensor_operation::host::ReferenceLayernorm<XDataType,
GammaDataType,
BetaDataType,
YDataType,
AccDataType,
PassThrough,
Rank,
NumReduceDim>;
ReferenceInstance ref;
auto ref_argument =
ref.MakeArgument(x, gamma, beta, host_y, PassThrough{}, {M, N}, {1}, 1e-4);
auto ref_invoker = ref.MakeInvoker();
ref_invoker.Run(ref_argument);
y_dev.FromDevice(y.mData.data());
pass &=
ck::utils::check_err(y.mData, host_y.mData, "Error: Incorrect results d1", 1e-3, 1e-3);
}
return (pass ? 0 : 1);
}
example/CMakeLists.txt
View file @ 2d92594a
...@@ -43,3 +43,4 @@ add_subdirectory(23_softmax) ...@@ -43,3 +43,4 @@ add_subdirectory(23_softmax)
add_subdirectory(24_batched_gemm_c_permute) add_subdirectory(24_batched_gemm_c_permute)
add_subdirectory(25_gemm_bias_c_permute) add_subdirectory(25_gemm_bias_c_permute)
add_subdirectory(26_contraction) add_subdirectory(26_contraction)
add_subdirectory(27_layernorm)
include/ck/tensor_operation/gpu/device/device_layernorm.hpp 0 → 100644
View file @ 2d92594a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <sstream>
#include "ck/utility/reduction_operator.hpp"
#include "ck/tensor_operation/gpu/device/device_base.hpp"
#include "ck/tensor_operation/gpu/device/device_reduce.hpp"
#include "ck/tensor_operation/gpu/device/device_reduce_multiblock.hpp"
#include "ck/tensor_operation/gpu/device/device_reduce_common.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_layernorm.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_set_buffer_value.hpp"
#include "ck/device_utility/device_prop.hpp"
#include "ck/device_utility/kernel_launch.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
// Y = LayerNorm(X, Beta, Gamma)
template <typename XDataType,
typename GammaDataType,
typename BetaDataType,
typename AccDataType,
typename YDataType,
typename AccElementwiseOperation,
index_t Rank,
index_t NumReduceDim,
index_t BlockSize,
index_t MThreadClusterSize,
index_t KThreadClusterSize,
index_t MThreadSliceSize,
index_t KThreadSliceSize,
index_t XYSrcVectorDim,
index_t XSrcVectorSize,
index_t GammaSrcVectorSize,
index_t BetaSrcVectorSize,
index_t YDstVectorSize>
struct DeviceLayernorm : public BaseOperator
{
static_assert(
(KThreadSliceSize % GammaSrcVectorSize == 0),
"Invalid thread slice sizes and/or gamma vector sizes configuration, please check!");
static_assert(
(KThreadSliceSize % BetaSrcVectorSize == 0),
"Invalid thread slice sizes and/or beta vector sizes configuration, please check!");
using PassThrough = tensor_operation::element_wise::PassThrough;
// Used for freeloading of some handy functions from DeviceReduceMultiBlock
using Reduction = DeviceReduceMultiBlock<XDataType,
AccDataType,
YDataType,
Rank,
NumReduceDim,
reduce::Add,
PassThrough, // InElementwiseOperation
AccElementwiseOperation, // AccElementwiseOperation
InMemoryDataOperationEnum::Set,
false, // PropagateNan
false, // OutputIndex
false, // HaveIndexInputIfOutputIndex
BlockSize,
MThreadClusterSize,
KThreadClusterSize,
MThreadSliceSize,
KThreadSliceSize,
XYSrcVectorDim,
XSrcVectorSize,
1>; // YDstVectorSize
static auto MakeAffine1dDescriptor(const std::vector<index_t>& Lengths,
const std::vector<index_t>& Strides,
int blkGroupSize,
int numBlockTileIteration)
{
const auto tupleLengths = make_tuple_from_array(Lengths, Number<NumReduceDim>{});
const auto tupleStrides = make_tuple_from_array(Strides, Number<NumReduceDim>{});
auto desc = make_naive_tensor_descriptor(tupleLengths, tupleStrides);
auto grid_desc_k = transform_tensor_descriptor(
desc,
make_tuple(make_merge_transform(tupleLengths)),
make_tuple(typename arithmetic_sequence_gen<0, NumReduceDim, 1>::type{}),
make_tuple(Sequence<0>{}));
const auto reduceTotalLength = grid_desc_k.GetLength(Number<0>{});
const int reduceSizePerBlock = Reduction::K_BlockTileSize * numBlockTileIteration;
const auto Pad_K = reduceSizePerBlock * blkGroupSize - reduceTotalLength;
auto grid_desc_k_padded = transform_tensor_descriptor(
grid_desc_k,
make_tuple(make_right_pad_transform(reduceTotalLength, Pad_K)),
make_tuple(Sequence<0>{}),
make_tuple(Sequence<0>{}));
return (grid_desc_k_padded);
};
using GridDesc_M_K = decltype(Reduction::MakeSrc2dDescriptor({1}, {1}, 1, 1));
using GridDesc_K = decltype(MakeAffine1dDescriptor({1}, {1}, 1, 1));
using GridwiseReduceLayernormGeneric = GridwiseLayernorm_mk_to_mk<XDataType,
GammaDataType,
BetaDataType,
YDataType,
AccDataType,
AccElementwiseOperation,
GridDesc_M_K,
GridDesc_K,
BlockSize,
MThreadClusterSize,
KThreadClusterSize,
MThreadSliceSize,
KThreadSliceSize,
XYSrcVectorDim,
XSrcVectorSize,
GammaSrcVectorSize,
BetaSrcVectorSize,
XYSrcVectorDim,
YDstVectorSize,
false>;
using GridwiseReduceLayernormSweepOnce = GridwiseLayernorm_mk_to_mk<XDataType,
GammaDataType,
BetaDataType,
YDataType,
AccDataType,
AccElementwiseOperation,
GridDesc_M_K,
GridDesc_K,
BlockSize,
MThreadClusterSize,
KThreadClusterSize,
MThreadSliceSize,
KThreadSliceSize,
XYSrcVectorDim,
XSrcVectorSize,
GammaSrcVectorSize,
BetaSrcVectorSize,
XYSrcVectorDim,
YDstVectorSize,
true>;
struct Argument : public Reduction::Argument
{
Argument(const std::vector<index_t> lengths,
const std::vector<index_t> xStrides,
const std::vector<index_t> gammaStrides,
const std::vector<index_t> betaStrides,
const std::vector<index_t> reduceDims,
AccElementwiseOperation acc_elementwise_op,
AccDataType epsilon,
const XDataType* p_x,
const GammaDataType* p_gamma,
const BetaDataType* p_beta,
YDataType* p_y)
: Reduction::Argument(lengths,
xStrides,
{},
{},
reduceDims,
0.0f, // alpha
0.0f, // beta
p_x,
nullptr,
p_y,
nullptr,
acc_elementwise_op,
PassThrough{}),
epsilon_(epsilon),
p_gamma_(p_gamma),
p_beta_(p_beta),
gammaStrides_(gammaStrides),
betaStrides_(betaStrides)
{
reduceLength_.resize(NumReduceDim);
for(int i = 0; i < NumReduceDim; ++i)
{
reduceLength_[i] = lengths[reduceDims[i]];
}
}
AccDataType epsilon_;
const GammaDataType* p_gamma_;
const BetaDataType* p_beta_;
std::vector<index_t> reduceLength_;
std::vector<index_t> gammaStrides_;
std::vector<index_t> betaStrides_;
};
struct Invoker : public BaseInvoker
{
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
const auto x_grid_desc_m_k = Reduction::MakeSrc2dDescriptor(
arg.inLengths_, arg.inStrides_, arg.blkGroupSize, arg.numBlockTileIteration);
const auto gamma_grid_desc_k = MakeAffine1dDescriptor(
arg.reduceLength_, arg.gammaStrides_, arg.blkGroupSize, arg.numBlockTileIteration);
const auto beta_grid_desc_k = MakeAffine1dDescriptor(
arg.reduceLength_, arg.betaStrides_, arg.blkGroupSize, arg.numBlockTileIteration);
const auto y_grid_desc_m_k = Reduction::MakeSrc2dDescriptor(
arg.inLengths_, arg.inStrides_, arg.blkGroupSize, arg.numBlockTileIteration);
bool sweep_once =
x_grid_desc_m_k.GetLength(Number<1>{}) <= KThreadClusterSize * KThreadSliceSize;
const auto kernel_main = sweep_once ? kernel_layernorm<GridwiseReduceLayernormSweepOnce,
XDataType,
GammaDataType,
BetaDataType,
YDataType,
AccDataType,
AccElementwiseOperation,
GridDesc_M_K,
GridDesc_K>
: kernel_layernorm<GridwiseReduceLayernormGeneric,
XDataType,
GammaDataType,
BetaDataType,
YDataType,
AccDataType,
AccElementwiseOperation,
GridDesc_M_K,
GridDesc_K>;
float avg_time = 0;
avg_time += launch_and_time_kernel(stream_config,
kernel_main,
dim3(arg.gridSize),
dim3(BlockSize),
0,
x_grid_desc_m_k,
gamma_grid_desc_k,
beta_grid_desc_k,
y_grid_desc_m_k,
arg.numBlockTileIteration,
arg.epsilon_,
arg.in_dev_,
arg.p_gamma_,
arg.p_beta_,
arg.out_dev_,
arg.acc_elementwise_op_);
return (avg_time);
};
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
};
};
bool IsSupportedArgument(const BaseArgument* p_arg) override
{
const Argument* p_arg_ = dynamic_cast<const Argument*>(p_arg);
if(!Reduction::IsSupportedArgument(p_arg_))
{
return false;
}
if(p_arg_->inLengths_[Rank - 1] % YDstVectorSize != 0)
{
return false;
}
if(p_arg_->gammaStrides_.size() != NumReduceDim ||
p_arg_->betaStrides_.size() != NumReduceDim)
return false;
auto IsScalarPerVectorValid = [](bool isLastDimensionCoalesced, int scalarPerVector) {
bool ret = true;
if(!isLastDimensionCoalesced)
ret = scalarPerVector == 1;
else
ret = KThreadSliceSize % scalarPerVector == 0;
return ret;
};
if(!IsScalarPerVectorValid(p_arg_->gammaStrides_.back() == 1, GammaSrcVectorSize))
return false;
if(!IsScalarPerVectorValid(p_arg_->betaStrides_.back() == 1, BetaSrcVectorSize))
return false;
return true;
};
std::unique_ptr<BaseArgument> MakeArgumentPointer(const std::vector<index_t> lengths,
const std::vector<index_t> xStrides,
const std::vector<index_t> gammaStrides,
const std::vector<index_t> betaStrides,
const std::vector<index_t> reduceDims,
AccDataType epsilon,
const void* p_x,
const void* p_gamma,
const void* p_beta,
void* p_y,
AccElementwiseOperation acc_elementwise_op)
{
return std::make_unique<Argument>(lengths,
xStrides,
gammaStrides,
betaStrides,
reduceDims,
acc_elementwise_op,
epsilon,
static_cast<const XDataType*>(p_x),
static_cast<const GammaDataType*>(p_gamma),
static_cast<const BetaDataType*>(p_beta),
static_cast<YDataType*>(p_y));
};
std::unique_ptr<BaseInvoker> MakeInvokerPointer() { return std::make_unique<Invoker>(); };
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "DeviceLayernorm<" << BlockSize << ",";
str << "M_C" << MThreadClusterSize << "_S" << MThreadSliceSize << ",";
str << "K_C" << KThreadClusterSize << "_S" << KThreadSliceSize << ",";
str << "K_C" << KThreadClusterSize << "_S" << KThreadSliceSize << ",";
str << "XYSrcVectorDim_" << XYSrcVectorDim << ",";
str << "VectorSize_X" << XSrcVectorSize << "_Gamma" << GammaSrcVectorSize << "_Beta" << BetaSrcVectorSize << "_Y" << YDstVectorSize << ">";
// clang-format on
return str.str();
}
};
} // namespace device
} // namespace tensor_operation
} // namespace ck
include/ck/tensor_operation/gpu/grid/gridwise_layernorm.hpp 0 → 100644
View file @ 2d92594a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/utility/data_type.hpp"
#include "ck/utility/reduction_common.hpp"
#include "ck/utility/reduction_operator.hpp"
#include "ck/utility/reduction_functions_accumulate.hpp"
#include "ck/tensor_operation/gpu/block/reduction_functions_blockwise.hpp"
#include "ck/tensor_operation/gpu/thread/reduction_functions_threadwise.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
namespace ck {
template <typename GridwiseReduction,
typename XDataType,
typename GammaDataType,
typename BetaDataType,
typename YDataType,
typename AccDataType,
typename AccElementwiseOperation,
typename GridDesc_M_K,
typename GridDesc_K>
__global__ void kernel_layernorm(const GridDesc_M_K x_grid_desc_m_k,
const GridDesc_K gamma_grid_desc_k,
const GridDesc_K beta_grid_desc_k,
const GridDesc_M_K y_grid_desc_m_k,
index_t num_k_block_tile_iteration,
AccDataType epsilon,
const XDataType* const __restrict__ p_x_global,
const GammaDataType* const __restrict__ p_gamma_global,
const BetaDataType* const __restrict__ p_beta_global,
YDataType* const __restrict__ p_y_global,
const AccElementwiseOperation acc_elementwise_op)
{
GridwiseReduction::Run(x_grid_desc_m_k,
gamma_grid_desc_k,
beta_grid_desc_k,
y_grid_desc_m_k,
num_k_block_tile_iteration,
epsilon,
p_x_global,
p_gamma_global,
p_beta_global,
p_y_global,
acc_elementwise_op);
};
// Y = LayerNorm(X, Beta, Gamma)
template <typename XDataType,
typename GammaDataType,
typename BetaDataType,
typename YDataType,
typename AccDataType,
typename AccElementwiseOperation,
typename GridDesc_M_K,
typename GridDesc_K,
index_t BlockSize,
index_t MThreadClusterSize,
index_t KThreadClusterSize,
index_t MThreadSliceSize,
index_t KThreadSliceSize,
index_t XSrcVectorDim,
index_t XSrcVectorSize,
index_t GammaSrcVectorSize,
index_t BetaSrcVectorSize,
index_t YDstVectorDim,
index_t YDstVectorSize,
bool SweepOnce>
struct GridwiseLayernorm_mk_to_mk
{
static_assert((XSrcVectorDim == 0 && MThreadSliceSize % XSrcVectorSize == 0) ||
(XSrcVectorDim == 1 && KThreadSliceSize % XSrcVectorSize == 0),
"Invalid thread slice sizes and/or vector sizes configuration, please check!");
static_assert((YDstVectorDim == 0 && MThreadSliceSize % YDstVectorSize == 0) ||
(YDstVectorDim == 1 && KThreadSliceSize % YDstVectorSize == 0),
"Invalid thread slice sizes and/or vector sizes configuration, please check!");
static constexpr bool reorder_thread_cluster = (XSrcVectorDim == 0);
using ThreadClusterLengths_M_K = Sequence<MThreadClusterSize, KThreadClusterSize>;
using ThreadBufferDimAccessOrder =
typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
using ThreadClusterArrangeOrder =
typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
static constexpr auto thread_cluster_desc =
make_cluster_descriptor(ThreadClusterLengths_M_K{}, ThreadClusterArrangeOrder{});
using ThreadReduceSrcDesc_M_K = decltype(make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{})));
using ThreadReduceDstDesc_M =
decltype(make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{})));
using BlockwiseSumReduce = PartitionedBlockwiseReduction<AccDataType,
BlockSize,
ThreadClusterLengths_M_K,
ThreadClusterArrangeOrder,
reduce::Add,
true>;
using ThreadwiseSumReduce = ThreadwiseReduction<AccDataType,
ThreadReduceSrcDesc_M_K,
ThreadReduceDstDesc_M,
reduce::Add,
true>;
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
__device__ static void Run(const GridDesc_M_K& x_grid_desc_m_k,
const GridDesc_K& gamma_grid_desc_k,
const GridDesc_K& beta_grid_desc_k,
const GridDesc_M_K& y_grid_desc_m_k,
index_t num_k_block_tile_iteration,
AccDataType epsilon,
const XDataType* const __restrict__ p_x_global,
const GammaDataType* const __restrict__ p_gamma_global,
const BetaDataType* const __restrict__ p_beta_global,
YDataType* const __restrict__ p_y_global,
const AccElementwiseOperation acc_elementwise_op)
{
if constexpr(SweepOnce)
{
num_k_block_tile_iteration = 1;
}
// LDS
__shared__ AccDataType p_reduce_work_buffer[BlockSize];
auto y_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_y_global, y_grid_desc_m_k.GetElementSpaceSize());
auto reduce_work_buf =
make_dynamic_buffer<AddressSpaceEnum::Lds>(p_reduce_work_buffer, BlockSize);
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
x_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, KThreadSliceSize, true> gamma_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, KThreadSliceSize, true>& beta_thread_buf =
gamma_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
y_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr,
AccDataType,
MThreadSliceSize * KThreadSliceSize,
true>& x_square_thread_buf = y_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> mean_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
mean_square_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>& var_value_buf =
mean_square_thread_buf;
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
mean_thread_buf(I) = reduce::Add::template GetIdentityValue<AccDataType>();
mean_square_thread_buf(I) = reduce::Add::template GetIdentityValue<AccDataType>();
});
const index_t thread_local_id = get_thread_local_1d_id();
const index_t block_global_id = get_block_1d_id();
const auto thread_cluster_idx =
thread_cluster_desc.CalculateBottomIndex(make_multi_index(thread_local_id));
const auto thread_m_cluster_id = thread_cluster_idx[I0];
const auto thread_k_cluster_id = thread_cluster_idx[I1];
using ThreadBufferLengths_M_K = Sequence<MThreadSliceSize, KThreadSliceSize>;
using ThreadBufferLengths_K = Sequence<KThreadSliceSize>;
constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{}));
constexpr auto thread_buffer_desc_k =
make_naive_tensor_descriptor_packed(make_tuple(Number<KThreadSliceSize>{}));
auto threadwise_x_load = ThreadwiseTensorSliceTransfer_v2<XDataType,
AccDataType,
GridDesc_M_K,
decltype(thread_buffer_desc_m_k),
ThreadBufferLengths_M_K,
ThreadBufferDimAccessOrder,
XSrcVectorDim,
XSrcVectorSize,
1,
true>(
x_grid_desc_m_k,
make_multi_index(block_global_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
thread_k_cluster_id * KThreadSliceSize));
auto threadwise_gamma_load =
ThreadwiseTensorSliceTransfer_v2<GammaDataType,
AccDataType,
GridDesc_K,
decltype(thread_buffer_desc_k),
ThreadBufferLengths_K,
Sequence<0>,
0,
GammaSrcVectorSize,
1,
true>(
gamma_grid_desc_k, make_multi_index(thread_k_cluster_id * KThreadSliceSize));
auto threadwise_beta_load = ThreadwiseTensorSliceTransfer_v2<BetaDataType,
AccDataType,
GridDesc_K,
decltype(thread_buffer_desc_k),
ThreadBufferLengths_K,
Sequence<0>,
0,
BetaSrcVectorSize,
1,
true>(
beta_grid_desc_k, make_multi_index(thread_k_cluster_id * KThreadSliceSize));
auto threadwise_y_store =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
YDataType,
decltype(thread_buffer_desc_m_k),
GridDesc_M_K,
AccElementwiseOperation,
ThreadBufferLengths_M_K,
ThreadBufferDimAccessOrder,
YDstVectorDim,
YDstVectorSize,
InMemoryDataOperationEnum::Set,
1,
true>(
y_grid_desc_m_k,
make_multi_index(block_global_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
thread_k_cluster_id * KThreadSliceSize),
acc_elementwise_op);
// Copy x from Cache
// one pass: fwd, second pass: bwd
constexpr auto thread_copy_fwd_step_k = make_multi_index(SweepOnce ? 0 : K_BlockTileSize);
constexpr auto thread_copy_bwd_step_k = make_multi_index(SweepOnce ? 0 : -K_BlockTileSize);
constexpr auto thread_copy_fwd_step_m_k =
make_multi_index(0, SweepOnce ? 0 : K_BlockTileSize);
constexpr auto thread_copy_bwd_step_m_k =
make_multi_index(0, SweepOnce ? 0 : -K_BlockTileSize);
const auto x_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_x_global, x_grid_desc_m_k.GetElementSpaceSize());
const auto gamma_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_gamma_global, gamma_grid_desc_k.GetElementSpaceSize());
const auto beta_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_beta_global, beta_grid_desc_k.GetElementSpaceSize());
// E(x), E[x^2], var(x)
int reduce_length = x_grid_desc_m_k.GetTransforms()[I0].GetUpperLengths()[I1];
index_t reducedTiles = 0;
do
{
threadwise_x_load.Run(x_grid_desc_m_k,
x_global_val_buf,
thread_buffer_desc_m_k,
make_tuple(I0, I0),
x_thread_buf);
static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
static_for<0, KThreadSliceSize, 1>{}([&](auto iK) {
constexpr auto offset_m_k =
thread_buffer_desc_m_k.CalculateOffset(make_tuple(iM, iK));
x_square_thread_buf(Number<offset_m_k>{}) =
x_thread_buf(Number<offset_m_k>{}) * x_thread_buf(Number<offset_m_k>{});
});
});
ThreadwiseSumReduce::Reduce(x_thread_buf, mean_thread_buf);
ThreadwiseSumReduce::Reduce(x_square_thread_buf, mean_square_thread_buf);
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, thread_copy_fwd_step_m_k);
++reducedTiles;
} while(reducedTiles < num_k_block_tile_iteration);
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
if constexpr(I > 0)
block_sync_lds();
BlockwiseSumReduce::Reduce(reduce_work_buf, mean_thread_buf(I));
mean_thread_buf(I) = mean_thread_buf(I) / reduce_length;
block_sync_lds();
BlockwiseSumReduce::Reduce(reduce_work_buf, mean_square_thread_buf(I));
mean_square_thread_buf(I) = mean_square_thread_buf(I) / reduce_length;
// var(x) = E[x^2] - E[x]^2
var_value_buf(I) =
mean_square_thread_buf(I) - (mean_thread_buf(I) * mean_thread_buf(I));
});
// y = (x - E[x]) / sqrt(var[x] + epsilon)
auto thread_copy_tail_m_k = (num_k_block_tile_iteration - 1) * thread_copy_fwd_step_m_k;
auto thread_copy_tail_k = (num_k_block_tile_iteration - 1) * thread_copy_fwd_step_k;
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, thread_copy_bwd_step_m_k);
threadwise_gamma_load.MoveSrcSliceWindow(gamma_grid_desc_k, thread_copy_tail_k);
threadwise_beta_load.MoveSrcSliceWindow(beta_grid_desc_k, thread_copy_tail_k);
threadwise_y_store.MoveDstSliceWindow(y_grid_desc_m_k, thread_copy_tail_m_k);
reducedTiles = 0;
do
{
if constexpr(!SweepOnce)
{
threadwise_x_load.Run(x_grid_desc_m_k,
x_global_val_buf,
thread_buffer_desc_m_k,
make_tuple(I0, I0),
x_thread_buf);
}
threadwise_gamma_load.Run(gamma_grid_desc_k,
gamma_global_val_buf,
thread_buffer_desc_k,
make_tuple(I0),
gamma_thread_buf);
static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
static_for<0, KThreadSliceSize, 1>{}([&](auto iK) {
constexpr auto offset_m_k =
thread_buffer_desc_m_k.CalculateOffset(make_tuple(iM, iK));
constexpr auto offset_k = thread_buffer_desc_k.CalculateOffset(make_tuple(iK));
// normalize
y_thread_buf(Number<offset_m_k>{}) =
(x_thread_buf(Number<offset_m_k>{}) - mean_thread_buf(iM)) /
sqrt(var_value_buf(iM) + epsilon);
// gamma
y_thread_buf(Number<offset_m_k>{}) =
y_thread_buf(Number<offset_m_k>{}) * gamma_thread_buf(Number<offset_k>{});
});
});
threadwise_beta_load.Run(beta_grid_desc_k,
beta_global_val_buf,
thread_buffer_desc_k,
make_tuple(I0),
beta_thread_buf);
static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
static_for<0, KThreadSliceSize, 1>{}([&](auto iK) {
constexpr auto offset_m_k =
thread_buffer_desc_m_k.CalculateOffset(make_tuple(iM, iK));
constexpr auto offset_k = thread_buffer_desc_k.CalculateOffset(make_tuple(iK));
// beta
y_thread_buf(Number<offset_m_k>{}) =
y_thread_buf(Number<offset_m_k>{}) + beta_thread_buf(Number<offset_k>{});
});
});
threadwise_y_store.Run(thread_buffer_desc_m_k,
make_tuple(I0, I0),
y_thread_buf,
y_grid_desc_m_k,
y_global_val_buf);
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, thread_copy_bwd_step_m_k);
threadwise_gamma_load.MoveSrcSliceWindow(gamma_grid_desc_k, thread_copy_bwd_step_k);
threadwise_beta_load.MoveSrcSliceWindow(beta_grid_desc_k, thread_copy_bwd_step_k);
threadwise_y_store.MoveDstSliceWindow(y_grid_desc_m_k, thread_copy_bwd_step_m_k);
++reducedTiles;
} while(reducedTiles < num_k_block_tile_iteration);
}
};
} // namespace ck
include/ck/tensor_operation/gpu/grid/gridwise_softmax.hpp
View file @ 2d92594a
...@@ -250,8 +250,10 @@ struct GridwiseSoftmax_mk_to_mk ...@@ -250,8 +250,10 @@ struct GridwiseSoftmax_mk_to_mk
reducedTiles++; reducedTiles++;
} while(reducedTiles < num_k_block_tile_iteration); } while(reducedTiles < num_k_block_tile_iteration);
static_for<0, MThreadSliceSize, 1>{}( static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
[&](auto I) { BlockwiseMaxReduce::Reduce(reduce_work_buf, max_value_buf(I)); }); BlockwiseMaxReduce::Reduce(reduce_work_buf, max_value_buf(I));
block_sync_lds();
});
threadwise_src_load.MoveSrcSliceWindow(in_grid_desc_m_k, in_thread_copy_bwd_step); threadwise_src_load.MoveSrcSliceWindow(in_grid_desc_m_k, in_thread_copy_bwd_step);
...@@ -303,9 +305,10 @@ struct GridwiseSoftmax_mk_to_mk ...@@ -303,9 +305,10 @@ struct GridwiseSoftmax_mk_to_mk
reducedTiles++; reducedTiles++;
} while(reducedTiles < num_k_block_tile_iteration); } while(reducedTiles < num_k_block_tile_iteration);
block_sync_lds(); // wait for reading being complete before writing to LDS
static_for<0, MThreadSliceSize, 1>{}([&](auto I) { static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
BlockwiseSumReduce::Reduce(reduce_work_buf, accu_value_buf(I)); BlockwiseSumReduce::Reduce(reduce_work_buf, accu_value_buf(I));
// block_sync_lds(); block_sync_lds();
}); });
threadwise_src_load.MoveSrcSliceWindow(in_grid_desc_m_k, in_thread_copy_fwd_step); threadwise_src_load.MoveSrcSliceWindow(in_grid_desc_m_k, in_thread_copy_fwd_step);
......
library/include/ck/library/reference_tensor_operation/cpu/reference_layernorm.hpp 0 → 100644
View file @ 2d92594a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <sstream>
#include <vector>
#include <algorithm>
#include "ck/tensor_operation/gpu/device/device_base.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
namespace ck {
namespace tensor_operation {
namespace host {
template <typename XDataType,
typename GammaDataType,
typename BetaDataType,
typename YDataType,
typename AccDataType,
typename AccElementwiseOperation,
index_t Rank,
index_t NumReduceDim>
struct ReferenceLayernorm : public device::BaseOperator
{
// TODO - support generic layernorm
static_assert((Rank == 2 && NumReduceDim == 1), "Only support 2D version so far");
// Argument
struct Argument : public device::BaseArgument
{
Argument(const Tensor<XDataType>& x_m_n,
const Tensor<GammaDataType>& gamma_n,
const Tensor<BetaDataType>& beta_n,
Tensor<YDataType>& y_m_n,
AccElementwiseOperation acc_elementwise_op,
const std::vector<index_t> lengths,
const std::vector<index_t> reduceDims,
AccDataType epsilon)
: x_m_n_(x_m_n),
gamma_n_(gamma_n),
beta_n_(beta_n),
y_m_n_(y_m_n),
acc_elementwise_op_(acc_elementwise_op),
lengths_(lengths),
reduceDims_(reduceDims),
epsilon_(epsilon)
{
}
const Tensor<XDataType> x_m_n_;
const Tensor<XDataType> gamma_n_;
const Tensor<XDataType> beta_n_;
Tensor<YDataType>& y_m_n_;
AccElementwiseOperation acc_elementwise_op_;
std::vector<index_t> lengths_;
std::vector<index_t> reduceDims_;
AccDataType epsilon_;
};
// Invoker
struct Invoker : public device::BaseInvoker
{
float Run(const Argument& arg)
{
int M = arg.lengths_[0];
int N = arg.lengths_[1];
Tensor<AccDataType> mean({M});
Tensor<AccDataType> var({M});
for(int m = 0; m < M; ++m)
{
mean(m) = 0;
var(m) = 0;
for(int n = 0; n < N; ++n)
{
auto x_val = ck::type_convert<AccDataType>(arg.x_m_n_(m, n));
mean(m) += x_val;
var(m) += x_val * x_val;
}
mean(m) = mean(m) / N;
var(m) = (var(m) / N) - (mean(m) * mean(m));
}
for(int m = 0; m < M; ++m)
{
for(int n = 0; n < N; ++n)
{
auto x_val = ck::type_convert<AccDataType>(arg.x_m_n_(m, n));
auto y_val = (x_val - mean(m)) / sqrt(var(m) + arg.epsilon_);
y_val = (y_val * arg.gamma_n_(n)) + arg.beta_n_(n);
arg.y_m_n_(m, n) = ck::type_convert<YDataType>(y_val);
}
}
return 0;
}
float Run(const device::BaseArgument* p_arg,
const StreamConfig& /* stream_config */ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}
};
static constexpr bool IsValidCompilationParameter()
{
// TODO: properly implement this check
return true;
}
bool IsSupportedArgument(const device::BaseArgument* p_arg) override
{
const Argument* p_arg_ = dynamic_cast<const Argument*>(p_arg);
// TODO - support generic layernorm
if(p_arg_->lengths_.size() != 2)
return false;
if(p_arg_->reduceDims_.size() != 1)
return false;
if(p_arg_->reduceDims_[0] != 1)
return false;
return true;
}
static auto MakeArgument(const Tensor<XDataType>& x_m_n,
const Tensor<GammaDataType>& gamma_n,
const Tensor<BetaDataType>& beta_n,
Tensor<YDataType>& y_m_n,
AccElementwiseOperation acc_elementwise_op,
const std::vector<index_t> lengths,
const std::vector<index_t> reduceDims,
AccDataType epsilon)
{
return Argument{
x_m_n, gamma_n, beta_n, y_m_n, acc_elementwise_op, lengths, reduceDims, epsilon};
}
static auto MakeInvoker() { return Invoker{}; }
virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
{
return std::make_unique<Invoker>(Invoker{});
}
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "ReferenceLayernorm"
<< std::endl;
// clang-format on
return str.str();
}
};
} // namespace host
} // namespace tensor_operation
} // namespace ck
script/process_perf_data.py 0 → 100644
View file @ 2d92594a
#!/usr/bin/env python3
import os, io, argparse, datetime
#import numpy as np
import sqlalchemy
from sqlalchemy.types import NVARCHAR, Float, Integer
import pymysql
import pandas as pd
from sshtunnel import SSHTunnelForwarder
def print_to_string(*args, **kwargs):
output = io.StringIO()
print(*args, file=output, **kwargs)
contents = output.getvalue()
output.close()
return contents
def parse_args():
parser = argparse.ArgumentParser(description='Parse results from tf benchmark runs')
parser.add_argument('filename', type=str, help='Log file to prase or directory containing log files')
args = parser.parse_args()
files = []
if os.path.isdir(args.filename):
all_files = os.listdir(args.filename)
for name in all_files:
if not 'log' in name:
continue
files.append(os.path.join(args.filename, name))
else:
files = [args.filename]
args.files = files
return args
def get_log_params(logfile):
print("logfile=",logfile)
branch_name=' '
node_id=' '
gpu_arch=' '
hip_vers=' '
compute_units=0
environment=' '
rocm_vers=' '
for line in open(logfile):
if 'Branch name' in line:
lst=line.split()
branch_name=lst[2]
if 'On branch' in line:
lst=line.split()
branch_name=lst[2]
if 'Node name' in line:
lst=line.split()
node_id=lst[2]
if 'GPU_arch' in line:
lst=line.split()
gpu_arch=lst[2]
if 'HIP version' in line:
lst=line.split()
hip_vers=lst[2]
if 'Compute Unit' in line:
lst=line.split()
compute_units=lst[2]
if 'Environment type' in line:
lst=line.split()
environment=lst[2]
if 'InstalledDir' in line:
lst=line.split()
rocm_vers=lst[1][lst[1].find('/opt/rocm-')+len('/opt/rocm-'):lst[1].rfind('/llvm/bin')]
return branch_name, node_id, gpu_arch, compute_units, rocm_vers, hip_vers, environment
def parse_logfile(logfile):
glue=''
res=[]
tests=[]
kernels=[]
tflops=[]
dtype=[]
alayout=[]
blayout=[]
M=[]
N=[]
K=[]
StrideA=[]
StrideB=[]
StrideC=[]
if 'perf_gemm' in logfile:
for line in open(logfile):
if 'Best Perf' in line:
lst=line.split()
print("len(lst)=",len(lst),"lst:",lst)
if len(lst)>=37: #the line is complete
tests.append(glue.join(lst[5:30]))
kernels.append(glue.join(lst[37:]))
tflops.append(lst[33])
dtype.append(lst[5])
alayout.append(lst[8])
blayout.append(lst[11])
M.append(lst[14])
N.append(lst[17])
K.append(lst[20])
StrideA.append(lst[23])
StrideB.append(lst[26])
StrideC.append(lst[29])
elif len(lst)<37 and len(lst)>=33: #the tflops are available
tests.append(glue.join(lst[5:30]))
kernels.append("N/A")
tflops.append(lst[33])
dtype.append(lst[5])
alayout.append(lst[8])
blayout.append(lst[11])
M.append(lst[14])
N.append(lst[17])
K.append(lst[20])
StrideA.append(lst[23])
StrideB.append(lst[26])
StrideC.append(lst[29])
print("warning: incomplete line:",lst)
elif len(lst)<33: #even the tflops are not available
print("Error in ckProfiler output!")
print("warning: incomplete line=",lst)
#sort results
#sorted_tests = sorted(tests)
res = [x for _,x in sorted(zip(tests,tflops))]
#sorted_kernels = [x for _,x in sorted(zip(tests,kernels))]
test_list=list(range(1,len(tests)+1))
#parse fwd_conv performance tests:
elif 'fwd_conv' in logfile:
for line in open(logfile):
if 'tflops:' in line:
lst=line.split()
res.append(lst[1])
#parse all other performance tests:
elif 'resnet50' or 'batched_gemm' or 'grouped_gemm' or 'bwd_conv' or 'fusion' or 'reduction' in logfile:
for line in open(logfile):
if 'Best Perf' in line:
lst=line.split()
res.append(lst[4])
return res
def get_baseline(table, connection):
query = '''SELECT * from '''+table+''' WHERE Datetime = (SELECT MAX(Datetime) FROM '''+table+''' where Branch_ID='develop' );'''
return pd.read_sql_query(query, connection)
def store_new_test_result(table_name, test_results, testlist, branch_name, node_id, gpu_arch, compute_units, rocm_vers, hip_vers, environment, connection):
params=[str(branch_name),str(node_id),str(gpu_arch),compute_units,str(rocm_vers),str(hip_vers),str(environment),str(datetime.datetime.now())]
df=pd.DataFrame(data=[params],columns=['Branch_ID','Node_ID','GPU_arch','Compute Units','ROCM_version','HIP_version','Environment','Datetime'])
df_add=pd.DataFrame(data=[test_results],columns=testlist)
df=pd.concat([df,df_add],axis=1)
print("new test results dataframe:",df)
df.to_sql(table_name,connection,if_exists='append',index=False)
return 0
def compare_test_to_baseline(baseline,test,testlist):
regression=0
if not baseline.empty:
base=baseline[testlist].to_numpy(dtype='float')
base_list=base[0]
ave_perf=0
for i in range(len(base_list)):
# success criterion:
if base_list[i]>1.01*float(test[i]):
print("test # ",i,"shows regression by {:.3f}%".format(
(float(test[i])-base_list[i])/base_list[i]*100))
regression=1
ave_perf=ave_perf+float(test[i])/base_list[i]
if regression==0:
print("no regressions found")
ave_perf=ave_perf/len(base_list)
print("average performance relative to baseline:",ave_perf)
else:
print("could not find a baseline")
return regression
'''
def post_test_params(tlist,connection):
sorted_dtypes = [x for _,x in sorted(zip(tests,dtype))]
sorted_alayout = [x for _,x in sorted(zip(tests,alayout))]
sorted_blayout = [x for _,x in sorted(zip(tests,blayout))]
sorted_M = [x for _,x in sorted(zip(tests,M))]
sorted_N = [x for _,x in sorted(zip(tests,N))]
sorted_K = [x for _,x in sorted(zip(tests,K))]
sorted_StrideA = [x for _,x in sorted(zip(tests,StrideA))]
sorted_StrideB = [x for _,x in sorted(zip(tests,StrideB))]
sorted_StrideC = [x for _,x in sorted(zip(tests,StrideC))]
ck_gemm_params=[tlist,sorted_dtypes,sorted_alayout,sorted_blayout,
sorted_M,sorted_N,sorted_K,sorted_StrideA,sorted_StrideB,
sorted_StrideC]
df=pd.DataFrame(np.transpose(ck_gemm_params),columns=['Test_number','Data_type',
'Alayout','BLayout','M','N','K', 'StrideA','StrideB','StrideC'])
print(df)
dtypes = {
'Test_number': Integer(),
'Data_type': NVARCHAR(length=5),
'Alayout': NVARCHAR(length=12),
'Blayout': NVARCHAR(length=12),
'M': Integer(),
'N': Integer(),
'K': Integer(),
'StrideA': Integer(),
'StrideB': Integer(),
'StrideC': Integer()
}
df.to_sql("ck_gemm_test_params",connection,if_exists='replace',index=False, dtype=dtypes)
'''
def main():
args = parse_args()
results=[]
tflops_base=[]
testlist=[]
#parse the test parameters from the logfile
for filename in args.files:
branch_name, node_id, gpu_arch, compute_units, rocm_vers, hip_vers, environment = get_log_params(filename)
print("Branch name:",branch_name)
print("Node name:",node_id)
print("GPU_arch:",gpu_arch)
print("Compute units:",compute_units)
print("ROCM_version:",rocm_vers)
print("HIP_version:",hip_vers)
print("Environment:",environment)
#parse results, get the Tflops value for "Best Perf" kernels
results=parse_logfile(filename)
print("Number of tests:",len(results))
sql_hostname = '127.0.0.1'
sql_username = os.environ["dbuser"]
sql_password = os.environ["dbpassword"]
sql_main_database = 'miopen_perf'
sql_port = 3306
ssh_host = os.environ["dbsship"]
ssh_user = os.environ["dbsshuser"]
ssh_port = int(os.environ["dbsshport"])
ssh_pass = os.environ["dbsshpassword"]
with SSHTunnelForwarder(
(ssh_host, ssh_port),
ssh_username=ssh_user,
ssh_password=ssh_pass,
remote_bind_address=(sql_hostname, sql_port)) as tunnel:
sqlEngine = sqlalchemy.create_engine('mysql+pymysql://{0}:{1}@{2}:{3}/{4}'.
format(sql_username, sql_password, sql_hostname, tunnel.local_bind_port, sql_main_database))
conn = sqlEngine.connect()
#save gemm performance tests:
if 'perf_gemm' in filename:
#write the ck_gemm_test_params table only needed once the test set changes
#post_test_params(test_list,conn)
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
table_name="ck_gemm_tflops"
if 'batched_gemm' in filename:
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
table_name="ck_batched_gemm_tflops"
if 'grouped_gemm' in filename:
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
table_name="ck_grouped_gemm_tflops"
if 'fwd_conv' in filename:
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
table_name="ck_fwd_conv_tflops"
if 'bwd_conv' in filename:
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
table_name="ck_bwd_conv_tflops"
if 'fusion' in filename:
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
table_name="ck_fusion_tflops"
if 'reduction' in filename:
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
table_name="ck_reduction_GBps"
if 'resnet50_N4' in filename:
for i in range(1,50):
testlist.append("Layer%i"%i)
table_name="ck_resnet50_N4_tflops"
if 'resnet50_N256' in filename:
for i in range(1,50):
testlist.append("Layer%i"%i)
table_name="ck_resnet50_N256_tflops"
tflops_base = get_baseline(table_name,conn)
store_new_test_result(table_name, results, testlist, branch_name, node_id, gpu_arch, compute_units, rocm_vers, hip_vers, environment, conn)
conn.close()
#compare the results to the baseline if baseline exists
regression=0
regression=compare_test_to_baseline(tflops_base,results,testlist)
return regression
if __name__ == '__main__':
main()
\ No newline at end of file
script/profile_batched_gemm.sh 0 → 100755
View file @ 2d92594a
#!/bin/bash
## GPU visibility
export HIP_VISIBLE_DEVICES=0
DRIVER="../build/bin/ckProfiler"
OP=$1
DATATYPE=$2
LAYOUT=$3
VERIFY=$4
INIT=$5
LOG=$6
REPEAT=$7
######## op datatype layout verify init log repeat M___ N___ K___ StrideA StrideB StrideC BatchCount
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 960 1024 1024 -1 -1 -1 8
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 1920 2048 2048 -1 -1 -1 8
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 3840 4096 4096 -1 -1 -1 4
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 7680 8192 8192 -1 -1 -1 2
####### op datatype layout verify init log repeat M___ N___ K___ StrideA StrideB StrideC BatchCount
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 1024 1024 1024 1024 1024 1024 8
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 2048 2048 2048 2048 2048 2048 8
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 4096 4096 4096 4096 4096 4096 4
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 8192 8192 8192 8192 8192 8192 2
####### op datatype layout verify init log repeat M___ N___ K___ StrideA StrideB StrideC BatchCount
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 1024 1024 1024 1056 1056 1056 8
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 2048 2048 2048 2080 2080 2080 8
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 4096 4096 4096 4128 4128 4128 4
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 8192 8192 8192 8224 8224 8224 2
####### op datatype layout verify init log repeat M___ N___ K___ StrideA StrideB StrideC BatchCount
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 1024 1024 1024 1088 1088 1088 8
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 2048 2048 2048 2112 2112 2112 8
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 4096 4096 4096 4160 4160 4160 4
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 8192 8192 8192 8256 8256 8256 2
\ No newline at end of file
script/profile_conv.sh
View file @ 2d92594a
This diff is collapsed.
script/profile_gemm_bias_relu_add.sh 0 → 100755
View file @ 2d92594a
#!/bin/bash
## GPU visibility
export HIP_VISIBLE_DEVICES=0
DRIVER="../build/bin/ckProfiler"
OP=$1
DATATYPE=$2
LAYOUT=$3
VERIFY=$4
INIT=$5
LOG=$6
REPEAT=$7
######## op datatype layout verify init log repeat M___ N___ K___ StrideA StrideB StrideC StrideC1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 960 1024 1024 -1 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 1920 2048 2048 -1 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 3840 4096 4096 -1 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 7680 8192 8192 -1 -1 -1 -1
####### op datatype layout verify init log repeat M___ N___ K___ StrideA StrideB StrideC StrideC1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 1024 1024 1024 1024 1024 1024 1024
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 2048 2048 2048 2048 2048 2048 2048
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 4096 4096 4096 4096 4096 4096 4096
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 8192 8192 8192 8192 8192 8192 8192
####### op datatype layout verify init log repeat M___ N___ K___ StrideA StrideB StrideC StrideC1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 1024 1024 1024 1056 1056 1056 1056
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 2048 2048 2048 2080 2080 2080 2080
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 4096 4096 4096 4128 4128 4128 4128
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 8192 8192 8192 8224 8224 8224 8224
####### op datatype layout verify init log repeat M___ N___ K___ StrideA StrideB StrideC StrideC1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 1024 1024 1024 1088 1088 1088 1088
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 2048 2048 2048 2112 2112 2112 2112
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 4096 4096 4096 4160 4160 4160 4160
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 8192 8192 8192 8256 8256 8256 8256
\ No newline at end of file
script/profile_grouped_gemm.sh 0 → 100755
View file @ 2d92594a
#!/bin/bash
## GPU visibility
export HIP_VISIBLE_DEVICES=0
DRIVER="../build/bin/ckProfiler"
OP=$1
DATATYPE=$2
LAYOUT=$3
VERIFY=$4
INIT=$5
LOG=$6
REPEAT=$7
######## op datatype layout verify init log repeat Ms______________ Ns______________ Ks_____________ StrideAs___________ StrideBs__________ StrideCs___________
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 256,512,1024,768 128,256,384,1024 128,192,256,512 1024,1025,1044,1026 1024,1024,1024,1024 1025,1024,1028,1024
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 512,768,2048,128 128,256,384,1024 128,192,256,512 1024,1025,2053,1026 1024,1024,1024,1024 1025,1024,2054,1024
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 256,512,1024,768 512,256,768,1024 128,192,256,512 1024,1045,1034,1026 1024,1024,1024,1024 1025,1063,1028,1024
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 512,768,4096,768 128,768,512,2048 128,192,256,512 1024,1027,4096,2050 1024,1024,1024,2048 1025,1024,4099,2049
\ No newline at end of file
script/profile_reduce_no_index.sh
View file @ 2d92594a
#!/bin/bash #!/bin/bash
DRIVER="../build/bin/ckProfiler"
PRECISION= VERIFY="-v $1"
INIT=$2
NREPEAT=$3
PRECISION=$4
##PRECISION=--half ##PRECISION=--half
##PRECISION=--double ##PRECISION=--double
##PRECISION=--int8 ##PRECISION=--int8
...@@ -12,14 +15,6 @@ elif [ -n $PRECISION ] && [ "$PRECISION" = "--int8" ]; then ...@@ -12,14 +15,6 @@ elif [ -n $PRECISION ] && [ "$PRECISION" = "--int8" ]; then
ACCTYPE="-C 2" ACCTYPE="-C 2"
fi fi
driver="./bin/ckProfiler"
VERIFY="-v $1"
INIT=$2
NREPEAT=$3
#### 0 - ADD, 5 - AVG, 7 - NORM2 #### 0 - ADD, 5 - AVG, 7 - NORM2
Operations="0 5 7" Operations="0 5 7"
...@@ -32,19 +27,19 @@ fi ...@@ -32,19 +27,19 @@ fi
for op in $Operations; do for op in $Operations; do
set -x set -x
####### datatype layout reduce dims op acctype verify init repeats ####### datatype layout reduce dims op acctype verify init repeats
$driver reduce $PRECISION -D 64,4,280,82 -R 0,1,2,3 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 0,1,2,3 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 0 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 0 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 1 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 1 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 3 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 3 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 1,2,3 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 1,2,3 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 0,2,3 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 0,2,3 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 0,1,3 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 0,1,3 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,22960 -R 0 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,22960 -R 0 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,22960 -R 1 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,22960 -R 1 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 4,1469440 -R 0 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 4,1469440 -R 0 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 4,1469440 -R 1 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 4,1469440 -R 1 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
set +x set +x
done done
...@@ -55,29 +50,29 @@ Operations=5 ...@@ -55,29 +50,29 @@ Operations=5
for op in $Operations; do for op in $Operations; do
set -x set -x
####### datatype layout reduce dims op acctype verify init repeats ####### datatype layout reduce dims op acctype verify init repeats
$driver reduce $PRECISION -D 256,14,14,1024 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,14,14,1024 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,28,28,128 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,28,28,128 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,58,58,128 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,58,58,128 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,7,7,2048 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,7,7,2048 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,14,14,256 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,14,14,256 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,30,30,256 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,30,30,256 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,56,56,256 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,56,56,256 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,16,16,512 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,16,16,512 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,28,28,512 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,28,28,512 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,7,7,512 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,7,7,512 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,56,56,64 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,56,56,64 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,230,230,3 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,230,230,3 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,14,14,1024 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,14,14,1024 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,28,28,128 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,28,28,128 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,58,58,128 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,58,58,128 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,7,7,2048 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,7,7,2048 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,14,14,256 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,14,14,256 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,30,30,256 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,30,30,256 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,56,56,256 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,56,56,256 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,16,16,512 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,16,16,512 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,28,28,512 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,28,28,512 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,7,7,512 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,7,7,512 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,56,56,64 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,56,56,64 -R 0,1,2 -O $op $ACCTYPE $VERIFY $INIT $NREPEAT
set +x set +x
done done
script/profile_reduce_with_index.sh
View file @ 2d92594a
#!/bin/bash #!/bin/bash
DRIVER="../build/bin/ckProfiler"
PRECISION= VERIFY="-v $1"
INIT=$2
NREPEAT=$3
PRECISION=$4
##PRECISION=--half ##PRECISION=--half
##PRECISION=--double ##PRECISION=--double
##PRECISION=--int8 ##PRECISION=--int8
##PRECISION=--bf16 ##PRECISION=--bf16
driver="./bin/ckProfiler"
VERIFY="-v $1"
INIT=$2
NREPEAT=$3
#### 2 - MIN, 3 - MAX, 4 - AMAX #### 2 - MIN, 3 - MAX, 4 - AMAX
Operations="2 4" Operations="2 4"
...@@ -20,19 +17,19 @@ for op in $Operations; do ...@@ -20,19 +17,19 @@ for op in $Operations; do
for use_idx in 0 1; do for use_idx in 0 1; do
set -x set -x
####### datatype layout reduce dims op use index verify init repeats ####### datatype layout reduce dims op use index verify init repeats
$driver reduce $PRECISION -D 64,4,280,82 -R 0,1,2,3 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 0,1,2,3 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 0 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 0 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 1 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 1 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 3 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 3 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 1,2,3 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 1,2,3 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 0,2,3 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 0,2,3 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 64,4,280,82 -R 0,1,3 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 64,4,280,82 -R 0,1,3 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,22960 -R 0 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,22960 -R 0 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,22960 -R 1 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,22960 -R 1 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 4,1469440 -R 0 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 4,1469440 -R 0 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 4,1469440 -R 1 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 4,1469440 -R 1 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
set +x set +x
done done
done done
...@@ -44,29 +41,29 @@ for op in $Operations; do ...@@ -44,29 +41,29 @@ for op in $Operations; do
for use_idx in 0 1; do for use_idx in 0 1; do
set -x set -x
####### datatype layout reduce dims op use index verify init repeats ####### datatype layout reduce dims op use index verify init repeats
$driver reduce $PRECISION -D 256,14,14,1024 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,14,14,1024 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,28,28,128 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,28,28,128 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,58,58,128 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,58,58,128 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,7,7,2048 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,7,7,2048 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,14,14,256 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,14,14,256 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,30,30,256 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,30,30,256 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,56,56,256 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,56,56,256 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,16,16,512 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,16,16,512 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,28,28,512 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,28,28,512 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,7,7,512 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,7,7,512 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,56,56,64 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,56,56,64 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 256,230,230,3 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 256,230,230,3 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,14,14,1024 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,14,14,1024 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,28,28,128 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,28,28,128 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,58,58,128 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,58,58,128 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,7,7,2048 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,7,7,2048 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,14,14,256 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,14,14,256 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,30,30,256 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,30,30,256 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,56,56,256 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,56,56,256 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,16,16,512 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,16,16,512 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,28,28,512 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,28,28,512 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,7,7,512 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,7,7,512 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
$driver reduce $PRECISION -D 128,56,56,64 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT $DRIVER reduce $PRECISION -D 128,56,56,64 -R 0,1,2 -O $op -I $use_idx $VERIFY $INIT $NREPEAT
set +x set +x
done done
done done
......
script/profile_resnet50.sh 0 → 100755
View file @ 2d92594a
This diff is collapsed.
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment