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Unverified Commit 8b49f207 authored by Max Podkorytov's avatar Max Podkorytov Committed by GitHub
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Merge branch 'develop' into fa-h512

parents 0d59f474 a6b761c3
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.github/CODEOWNERS
View file @ 8b49f207
* @junliume @illsilin @carlushuang @qianfengz @aosewski @poyenc @geyyer @bartekxk @andriy-ca
* @junliume @illsilin @carlushuang @qianfengz @aosewski @poyenc @geyyer @bartekxk @andriy-ca @afagaj
# Documentation files
docs/ @ROCm/rocm-documentation @junliume @illsilin @carlushuang @qianfengz @aosewski @poyenc @geyyer @bartekxk @andriy-ca
*.md @ROCm/rocm-documentation @junliume @illsilin @carlushuang @qianfengz @aosewski @poyenc @geyyer @bartekxk @andriy-ca
*.rst @ROCm/rocm-documentation @junliume @illsilin @carlushuang @qianfengz @aosewski @poyenc @geyyer @bartekxk @andriy-ca
.readthedocs.yaml @ROCm/rocm-documentation @junliume @illsilin @carlushuang @qianfengz @aosewski @poyenc @geyyer @bartekxk @andriy-ca
docs/ @ROCm/rocm-documentation @junliume @illsilin @carlushuang @qianfengz @aosewski @poyenc @geyyer @bartekxk @andriy-ca @afagaj
*.md @ROCm/rocm-documentation @junliume @illsilin @carlushuang @qianfengz @aosewski @poyenc @geyyer @bartekxk @andriy-ca @afagaj
*.rst @ROCm/rocm-documentation @junliume @illsilin @carlushuang @qianfengz @aosewski @poyenc @geyyer @bartekxk @andriy-ca @afagaj
.readthedocs.yaml @ROCm/rocm-documentation @junliume @illsilin @carlushuang @qianfengz @aosewski @poyenc @geyyer @bartekxk @andriy-ca @afagaj
# Header directory for Doxygen documentation
library/include/ @ROCm/rocm-documentation @junliume @illsilin @carlushuang @qianfengz @aosewski @poyenc @geyyer @bartekxk @andriy-ca
library/include/ @ROCm/rocm-documentation @junliume @illsilin @carlushuang @qianfengz @aosewski @poyenc @geyyer @bartekxk @andriy-ca @afagaj
.github/CONTRIBUTING.md 0 → 100644
View file @ 8b49f207
We'd love for you to contribute to our source code!
Some helpful links:
- [Code of Conduct guidelines](https://www.contributor-covenant.org/version/2/1/code_of_conduct/code_of_conduct.txt)
- [New issue guidelines](https://github.com/rocm/composable_kernel/blob/develop/.github/ISSUE_TEMPLATE.md)
- [Submitting a pull request guidelines](https://github.com/rocm/composable_kernel/blob/develop/.github/PULL_REQUEST_TEMPLATE.md)
- [Maintainers](https://github.com/rocm/composable_kernel/blob/develop/CONTRIBUTORS.md)
- [General information](https://github.com/rocm/composable_kernel/blob/develop/README.md)
- [ROCm documentation](https://rocm.docs.amd.com/en/latest/how-to/llm-fine-tuning-optimization/optimizing-with-composable-kernel.html)
\ No newline at end of file
.github/ISSUE_TEMPLATE.md 0 → 100644
View file @ 8b49f207
When creating an issue, please check if a similar issue already exists.
### When reporting a bug, please include:
- [ ] A descriptive title
- [ ] An isolated way to reproduce the behavior (preferably a docker container with a repro)
- [ ] ROCm version, clang version, Composable Kernel commit pin
- [ ] Environment variables
- [ ] The behavior you expect to see, and the behavior you actually see
### When requesting a feature, please include:
- [ ] A descriptive title
- [ ] A detailed description of the problem you are trying to solve
- [ ] An overview of the suggested solution
- [ ] Explanation why the solution is an improvement
\ No newline at end of file
.github/PULL_REQUEST_TEMPLATE.md 0 → 100644
View file @ 8b49f207
## Proposed changes
Please describe the motivation behind the pull request, whether it enables a new feature or fixes a bug. If there are associated pull requests or issues, please link them to the pull request.
## Checklist
Please put an `x` into the boxes that apply. You can also fill these out after creating the PR. If you're not sure, please don't hesitate to ask.
- [ ] I have added tests relevant to the introduced functionality, and the unit tests are passing locally
- [ ] I have added inline documentation which enables the maintainers with understanding the motivation
- [ ] I have removed the stale documentation which is no longer relevant after this pull request
- [ ] (If this change is user-facing) I have added release notes which provide the end users with a brief summary of the improvement from this pull request
- [ ] I have run `clang-format` on all changed files
- [ ] Any dependent changes have been merged
## Discussion
If this is a relatively large or complex change, feel free to start a discussion by explaining why you chose the solution you did and what alternatives you considered
CMakeLists.txt
View file @ 8b49f207
......@@ -183,14 +183,17 @@ message("Building CK for the following targets: ${SUPPORTED_GPU_TARGETS}")
if (SUPPORTED_GPU_TARGETS MATCHES "gfx9")
message("Enabling XDL instances")
add_definitions(-DCK_USE_XDL)
set(CK_USE_XDL "ON")
endif()
if (SUPPORTED_GPU_TARGETS MATCHES "gfx94")
message("Enabling FP8 gemms on native architectures")
add_definitions(-DCK_USE_GFX94)
set(CK_USE_GFX94 "ON")
endif()
if (SUPPORTED_GPU_TARGETS MATCHES "gfx11" OR SUPPORTED_GPU_TARGETS MATCHES "gfx12")
message("Enabling WMMA instances")
add_definitions(-DCK_USE_WMMA)
set(CK_USE_WMMA "ON")
endif()
if (SUPPORTED_GPU_TARGETS MATCHES "gfx12")
add_definitions(-DCK_USE_OCP_FP8)
......@@ -204,6 +207,7 @@ endif()
option(CK_USE_FP8_ON_UNSUPPORTED_ARCH "Enable FP8 GEMM instances on older architectures" OFF)
if(CK_USE_FP8_ON_UNSUPPORTED_ARCH AND (SUPPORTED_GPU_TARGETS MATCHES "gfx90a" OR SUPPORTED_GPU_TARGETS MATCHES "gfx908"))
add_definitions(-DCK_USE_FP8_ON_UNSUPPORTED_ARCH)
set(CK_USE_FP8_ON_UNSUPPORTED_ARCH "ON")
endif()
# CK config file to record supported datatypes, etc.
......@@ -581,7 +585,7 @@ if(NOT GPU_ARCHS AND USER_GPU_TARGETS)
)
add_subdirectory(example)
if(BUILD_TESTING)
add_subdirectory(test)
add_subdirectory(test)
endif()
endif()
......
Dockerfile
View file @ 8b49f207
FROM ubuntu:20.04
FROM ubuntu:22.04
ARG DEBIAN_FRONTEND=noninteractive
ARG ROCMVERSION=6.3
ARG compiler_version=""
......@@ -48,6 +48,7 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
libnuma-dev \
libpthread-stubs0-dev \
llvm-amdgpu \
mpich \
net-tools \
pkg-config \
python \
......@@ -63,6 +64,7 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
nano \
zlib1g-dev \
zip \
libzstd-dev \
openssh-server \
clang-format-12 \
kmod && \
......@@ -70,7 +72,7 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
rm -rf /var/lib/apt/lists/* && \
rm -rf amdgpu-install* && \
# Remove unnecessary rocm components that take a lot of space
apt-get remove -y rocblas rocfft rocsparse composablekernel-dev
apt-get remove -y rocblas rocfft rocsparse composablekernel-dev hipblaslt
# Update the cmake to version 3.27.5
RUN pip install --upgrade cmake==3.27.5 && \
......@@ -92,7 +94,7 @@ RUN pip install --upgrade cmake==3.27.5 && \
dpkg -i dumb-init_*.deb && rm dumb-init_*.deb && \
# Install packages for processing the performance results
pip3 install --upgrade pip && \
pip3 install sqlalchemy==1.4.46 pymysql pandas==2.0.3 setuptools-rust sshtunnel==0.4.0 && \
pip3 install sqlalchemy==2.0.36 pymysql pandas==2.2.3 setuptools-rust sshtunnel==0.4.0 && \
# Add render group
groupadd -f render && \
# Install the new rocm-cmake version
......
Dockerfile.compiler
View file @ 8b49f207
ARG BASE_DOCKER="rocm/composable_kernel:ck_ub20.04_rocm6.3"
ARG BASE_DOCKER="rocm/composable_kernel:ck_ub22.04_rocm6.3"
FROM $BASE_DOCKER
ARG compiler_version=""
ARG compiler_commit=""
......
Jenkinsfile
View file @ 8b49f207
......@@ -40,10 +40,10 @@ def getBaseDockerImageName(){
else{
def ROCM_numeric = "${params.ROCMVERSION}" as float
if ( ROCM_numeric < 6.4 ){
img = "${env.CK_DOCKERHUB}:ck_ub20.04_rocm${params.ROCMVERSION}"
img = "${env.CK_DOCKERHUB}:ck_ub22.04_rocm${params.ROCMVERSION}"
}
else{
img = "${env.CK_DOCKERHUB_PRIVATE}:ck_ub20.04_rocm${params.ROCMVERSION}"
img = "${env.CK_DOCKERHUB_PRIVATE}:ck_ub22.04_rocm${params.ROCMVERSION}"
}
}
return img
......@@ -330,10 +330,8 @@ def cmake_build(Map conf=[:]){
try{
archiveArtifacts "perf_fmha_fwd_*.log"
archiveArtifacts "perf_fmha_bwd_*.log"
stash name: "perf_fmha_fwd_gfx942.log"
stash name: "perf_fmha_bwd_gfx942.log"
stash name: "perf_fmha_fwd_gfx90a.log"
stash name: "perf_fmha_bwd_gfx90a.log"
stash includes: "perf_fmha_**_gfx942.log", name: "perf_fmha_log_gfx942"
stash includes: "perf_fmha_**_gfx90a.log", name: "perf_fmha_log_gfx90a"
}
catch(Exception err){
echo "could not locate the requested artifacts: ${err.getMessage()}. will skip the stashing."
......@@ -359,7 +357,7 @@ def buildHipClangJob(Map conf=[:]){
def prefixpath = conf.get("prefixpath", "/opt/rocm")
// Jenkins is complaining about the render group
def dockerOpts="--device=/dev/kfd --device=/dev/dri --group-add video --group-add render --cap-add=SYS_PTRACE --security-opt seccomp=unconfined"
def dockerOpts="-u root --device=/dev/kfd --device=/dev/dri --group-add video --group-add render --cap-add=SYS_PTRACE --security-opt seccomp=unconfined"
if (conf.get("enforce_xnack_on", false)) {
dockerOpts = dockerOpts + " --env HSA_XNACK=1 "
}
......@@ -379,7 +377,7 @@ def buildHipClangJob(Map conf=[:]){
gitStatusWrapper(credentialsId: "${env.ck_git_creds}", gitHubContext: "Jenkins - ${variant}", account: 'ROCm', repo: 'composable_kernel') {
withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') {
timeout(time: 48, unit: 'HOURS')
timeout(time: 20, unit: 'HOURS')
{
cmake_build(conf)
}
......@@ -408,128 +406,6 @@ def buildHipClangJobAndReboot(Map conf=[:]){
}
}
def runCKProfiler(Map conf=[:]){
show_node_info()
env.HSA_ENABLE_SDMA=0
checkout scm
def image = getDockerImageName()
def prefixpath = conf.get("prefixpath", "/opt/rocm")
// Jenkins is complaining about the render group
def dockerOpts="--device=/dev/kfd --device=/dev/dri --group-add video --group-add render --cap-add=SYS_PTRACE --security-opt seccomp=unconfined"
if (conf.get("enforce_xnack_on", false)) {
dockerOpts = dockerOpts + " --env HSA_XNACK=1 "
}
def video_id = sh(returnStdout: true, script: 'getent group video | cut -d: -f3')
def render_id = sh(returnStdout: true, script: 'getent group render | cut -d: -f3')
dockerOpts = dockerOpts + " --group-add=${video_id} --group-add=${render_id} "
echo "Docker flags: ${dockerOpts}"
def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg compiler_version='${params.COMPILER_VERSION}' --build-arg compiler_commit='${params.COMPILER_COMMIT}' --build-arg ROCMVERSION='${params.ROCMVERSION}' "
def variant = env.STAGE_NAME
def retimage
gitStatusWrapper(credentialsId: "${env.ck_git_creds}", gitHubContext: "Jenkins - ${variant}", account: 'ROCm', repo: 'composable_kernel') {
try {
(retimage, image) = getDockerImage(conf)
withDockerContainer(image: image, args: dockerOpts) {
timeout(time: 5, unit: 'MINUTES'){
sh 'rocminfo | tee rocminfo.log'
if ( !runShell('grep -n "gfx" rocminfo.log') ){
throw new Exception ("GPU not found")
}
else{
echo "GPU is OK"
}
}
}
}
catch (org.jenkinsci.plugins.workflow.steps.FlowInterruptedException e){
echo "The job was cancelled or aborted"
throw e
}
withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') {
timeout(time: 24, unit: 'HOURS')
{
sh """
rm -rf build
mkdir build
"""
dir("build"){
unstash 'ckProfiler.tar.gz'
sh 'tar -xvf ckProfiler.tar.gz'
}
dir("script"){
if (params.RUN_FULL_QA){
sh "./run_full_performance_tests.sh 0 QA_${params.COMPILER_VERSION} ${env.BRANCH_NAME} ${NODE_NAME}"
archiveArtifacts "perf_gemm.log"
archiveArtifacts "perf_resnet50_N256.log"
archiveArtifacts "perf_resnet50_N4.log"
archiveArtifacts "perf_batched_gemm.log"
archiveArtifacts "perf_grouped_gemm.log"
archiveArtifacts "perf_grouped_conv_fwd.log"
archiveArtifacts "perf_grouped_conv_bwd_data.log"
archiveArtifacts "perf_grouped_conv_bwd_weight.log"
archiveArtifacts "perf_gemm_bilinear.log"
archiveArtifacts "perf_reduction.log"
archiveArtifacts "perf_splitK_gemm.log"
archiveArtifacts "perf_onnx_gemm.log"
archiveArtifacts "perf_mixed_gemm.log"
// stash perf files to master
stash name: "perf_gemm.log"
stash name: "perf_resnet50_N256.log"
stash name: "perf_resnet50_N4.log"
stash name: "perf_batched_gemm.log"
stash name: "perf_grouped_gemm.log"
stash name: "perf_grouped_conv_fwd.log"
stash name: "perf_grouped_conv_bwd_data.log"
stash name: "perf_grouped_conv_bwd_weight.log"
stash name: "perf_gemm_bilinear.log"
stash name: "perf_reduction.log"
stash name: "perf_splitK_gemm.log"
stash name: "perf_onnx_gemm.log"
stash name: "perf_mixed_gemm.log"
//we will process results on the master node
}
else{
sh "./run_performance_tests.sh 0 CI_${params.COMPILER_VERSION} ${env.BRANCH_NAME} ${NODE_NAME}"
archiveArtifacts "perf_gemm.log"
archiveArtifacts "perf_resnet50_N256.log"
archiveArtifacts "perf_resnet50_N4.log"
// stash perf files to master
stash name: "perf_gemm.log"
stash name: "perf_resnet50_N256.log"
stash name: "perf_resnet50_N4.log"
//we will process the results on the master node
}
}
}
}
}
return retimage
}
def runPerfTest(Map conf=[:]){
try{
runCKProfiler(conf)
}
catch(e){
echo "throwing error exception in performance tests"
echo 'Exception occurred: ' + e.toString()
throw e
}
finally{
if (!conf.get("no_reboot", false)) {
reboot()
}
}
}
def Build_CK(Map conf=[:]){
show_node_info()
......@@ -550,7 +426,7 @@ def Build_CK(Map conf=[:]){
def prefixpath = conf.get("prefixpath", "/opt/rocm")
// Jenkins is complaining about the render group
def dockerOpts="--device=/dev/kfd --device=/dev/dri --group-add video --group-add render --cap-add=SYS_PTRACE --security-opt seccomp=unconfined"
def dockerOpts="-u root --device=/dev/kfd --device=/dev/dri --group-add video --group-add render --cap-add=SYS_PTRACE --security-opt seccomp=unconfined"
if (conf.get("enforce_xnack_on", false)) {
dockerOpts = dockerOpts + " --env HSA_XNACK=1 "
}
......@@ -573,7 +449,7 @@ def Build_CK(Map conf=[:]){
try {
(retimage, image) = getDockerImage(conf)
withDockerContainer(image: image, args: dockerOpts) {
timeout(time: 5, unit: 'MINUTES'){
timeout(time: 2, unit: 'MINUTES'){
sh 'rocminfo | tee rocminfo.log'
if ( !runShell('grep -n "gfx" rocminfo.log') ){
throw new Exception ("GPU not found")
......@@ -589,36 +465,95 @@ def Build_CK(Map conf=[:]){
throw e
}
withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') {
timeout(time: 24, unit: 'HOURS')
timeout(time: 20, unit: 'HOURS')
{
//check whether to run performance tests on this node
def do_perf_tests = 0
def arch_type = 0
sh 'rocminfo | tee rocminfo.log'
if ( runShell('grep -n "gfx1030" rocminfo.log') || runShell('grep -n "gfx1101" rocminfo.log') || runShell('grep -n "gfx1201" rocminfo.log') || runShell('grep -n "gfx942" rocminfo.log') ){
do_perf_tests = 1
echo "Stash profiler and run performance tests"
if ( runShell('grep -n "gfx90a" rocminfo.log') ){
arch_type = 1
}
else if ( runShell('grep -n "gfx942" rocminfo.log') ) {
arch_type = 2
}
else if ( runShell('grep -n "gfx1030" rocminfo.log') ) {
arch_type = 3
}
else if ( runShell('grep -n "gfx1101" rocminfo.log') ) {
arch_type = 4
}
else if ( runShell('grep -n "gfx1201" rocminfo.log') ) {
arch_type = 5
}
cmake_build(conf)
dir("build"){
//run tests and examples
//sh 'make -j check'
if (params.RUN_PERFORMANCE_TESTS && do_perf_tests == 0 ){
//we only need the ckProfiler to run the performance tests, so we pack and stash it
//do not stash profiler on nodes where we don't need to run performance tests
sh 'tar -zcvf ckProfiler.tar.gz bin/ckProfiler'
stash name: "ckProfiler.tar.gz"
}
if (params.RUN_FULL_QA && do_perf_tests == 0 ){
// build deb packages for all gfx9 targets and prepare to export
if (params.RUN_FULL_QA && arch_type == 1 ){
// build deb packages for all gfx9 targets on gfx90a system and prepare to export
echo "Build ckProfiler package"
sh 'make -j package'
archiveArtifacts artifacts: 'composablekernel-ckprofiler_*.deb'
archiveArtifacts artifacts: 'composablekernel-tests_*.deb'
sh 'mv composablekernel-ckprofiler_*.deb ckprofiler_0.2.0_amd64.deb'
stash name: "ckprofiler_0.2.0_amd64.deb"
stash includes: "ckprofiler_0.2.0_amd64.deb", name: "ckprofiler_0.2.0_amd64.deb"
}
}
if (params.hipTensor_test && do_perf_tests == 0 ){
//build and test hipTensor
// run performance tests, stash the logs, results will be processed on the master node
dir("script"){
if (params.RUN_PERFORMANCE_TESTS){
if (params.RUN_FULL_QA && arch_type == 1){
// run full tests on gfx90a
echo "Run full performance tests"
sh "./run_full_performance_tests.sh 0 QA_${params.COMPILER_VERSION} ${env.BRANCH_NAME} ${NODE_NAME}"
archiveArtifacts "perf_gemm.log"
archiveArtifacts "perf_resnet50_N256.log"
archiveArtifacts "perf_resnet50_N4.log"
archiveArtifacts "perf_batched_gemm.log"
archiveArtifacts "perf_grouped_gemm.log"
archiveArtifacts "perf_grouped_conv_fwd.log"
archiveArtifacts "perf_grouped_conv_bwd_data.log"
archiveArtifacts "perf_grouped_conv_bwd_weight.log"
archiveArtifacts "perf_gemm_bilinear.log"
archiveArtifacts "perf_reduction.log"
archiveArtifacts "perf_splitK_gemm.log"
archiveArtifacts "perf_onnx_gemm.log"
archiveArtifacts "perf_mixed_gemm.log"
stash includes: "perf_**.log", name: "perf_log"
}
else if ( arch_type == 1 ){
// run standard tests on gfx90a
echo "Run performance tests"
sh "./run_performance_tests.sh 0 CI_${params.COMPILER_VERSION} ${env.BRANCH_NAME} ${NODE_NAME}"
archiveArtifacts "perf_gemm.log"
archiveArtifacts "perf_onnx_gemm.log"
archiveArtifacts "perf_resnet50_N256.log"
archiveArtifacts "perf_resnet50_N4.log"
stash includes: "perf_**.log", name: "perf_log"
}
// disable performance tests on gfx1030 for now.
//else if ( arch_type == 3){
// run basic tests on gfx1030
// echo "Run gemm performance tests"
// sh "./run_gemm_performance_tests.sh 0 CI_${params.COMPILER_VERSION} ${env.BRANCH_NAME} ${NODE_NAME} gfx10"
// archiveArtifacts "perf_onnx_gemm_gfx10.log"
// stash includes: "perf_onnx_gemm_gfx10.log", name: "perf_log_gfx10"
//}
else if ( arch_type == 4){
// run basic tests on gfx11
echo "Run gemm performance tests"
sh "./run_gemm_performance_tests.sh 0 CI_${params.COMPILER_VERSION} ${env.BRANCH_NAME} ${NODE_NAME} gfx11"
archiveArtifacts "perf_onnx_gemm_gfx11.log"
stash includes: "perf_onnx_gemm_gfx11.log", name: "perf_log_gfx11"
}
else if ( arch_type == 5 ){
// run basic tests on gfx12
echo "Run gemm performance tests"
sh "./run_gemm_performance_tests.sh 0 CI_${params.COMPILER_VERSION} ${env.BRANCH_NAME} ${NODE_NAME} gfx12"
archiveArtifacts "perf_onnx_gemm_gfx12.log"
stash includes: "perf_onnx_gemm_gfx12.log", name: "perf_log_gfx12"
}
}
}
if (params.hipTensor_test && arch_type == 1 ){
// build and test hipTensor on gfx90a node
sh """#!/bin/bash
rm -rf "${params.hipTensor_branch}".zip
rm -rf hipTensor-"${params.hipTensor_branch}"
......@@ -631,11 +566,9 @@ def Build_CK(Map conf=[:]){
ls -ltr
CC=hipcc CXX=hipcc cmake -Bbuild . -D CMAKE_PREFIX_PATH="${env.WORKSPACE}/install"
cmake --build build -- -j
ctest --test-dir build
"""
}
dir("hipTensor-${params.hipTensor_branch}/build"){
sh 'ctest'
}
}
}
}
......@@ -685,15 +618,13 @@ def process_results(Map conf=[:]){
}
withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') {
timeout(time: 1, unit: 'HOURS'){
timeout(time: 15, unit: 'MINUTES'){
try{
dir("script"){
if (params.RUN_CK_TILE_FMHA_TESTS){
try{
unstash "perf_fmha_fwd_gfx942.log"
unstash "perf_fmha_bwd_gfx942.log"
unstash "perf_fmha_fwd_gfx90a.log"
unstash "perf_fmha_bwd_gfx90a.log"
unstash "perf_fmha_log_gfx942"
unstash "perf_fmha_log_gfx90a"
}
catch(Exception err){
echo "could not locate the FMHA performance logs: ${err.getMessage()}."
......@@ -703,26 +634,26 @@ def process_results(Map conf=[:]){
// unstash perf files to master
unstash "ckprofiler_0.2.0_amd64.deb"
sh "sshpass -p ${env.ck_deb_pw} scp -o StrictHostKeyChecking=no ckprofiler_0.2.0_amd64.deb ${env.ck_deb_user}@${env.ck_deb_ip}:/var/www/html/composable_kernel/"
unstash "perf_gemm.log"
unstash "perf_resnet50_N256.log"
unstash "perf_resnet50_N4.log"
unstash "perf_batched_gemm.log"
unstash "perf_grouped_gemm.log"
unstash "perf_grouped_conv_fwd.log"
unstash "perf_grouped_conv_bwd_data.log"
unstash "perf_grouped_conv_bwd_weight.log"
unstash "perf_gemm_bilinear.log"
unstash "perf_reduction.log"
unstash "perf_splitK_gemm.log"
unstash "perf_onnx_gemm.log"
unstash "perf_mixed_gemm.log"
unstash "perf_log"
try{
unstash "perf_log_gfx11"
unstash "perf_log_gfx12"
}
catch(Exception err){
echo "could not locate the GEMM gfx11/gfx12 performance logs: ${err.getMessage()}."
}
sh "./process_qa_data.sh"
}
else{
// unstash perf files to master
unstash "perf_gemm.log"
unstash "perf_resnet50_N256.log"
unstash "perf_resnet50_N4.log"
unstash "perf_log"
try{
unstash "perf_log_gfx11"
unstash "perf_log_gfx12"
}
catch(Exception err){
echo "could not locate the GEMM gfx11/gfx12 performance logs: ${err.getMessage()}."
}
sh "./process_perf_data.sh"
}
}
......@@ -742,8 +673,8 @@ def process_results(Map conf=[:]){
//launch develop branch daily at 23:00 UT in FULL_QA mode and at 19:00 UT with latest staging compiler version
CRON_SETTINGS = BRANCH_NAME == "develop" ? '''0 23 * * * % RUN_FULL_QA=true;ROCMVERSION=6.3;RUN_CK_TILE_FMHA_TESTS=true;RUN_CK_TILE_GEMM_TESTS=true
0 21 * * * % ROCMVERSION=6.3;hipTensor_test=true;RUN_CODEGEN_TESTS=true
0 19 * * * % BUILD_DOCKER=true;DL_KERNELS=true;COMPILER_VERSION=amd-staging;BUILD_COMPILER=/llvm-project/build/bin/clang++;BUILD_GFX12=true;USE_SCCACHE=false;NINJA_BUILD_TRACE=true
0 17 * * * % BUILD_DOCKER=true;DL_KERNELS=true;COMPILER_VERSION=amd-mainline;BUILD_COMPILER=/llvm-project/build/bin/clang++;BUILD_GFX12=true;USE_SCCACHE=false;NINJA_BUILD_TRACE=true
0 19 * * * % BUILD_DOCKER=true;DL_KERNELS=true;COMPILER_VERSION=amd-staging;BUILD_COMPILER=/llvm-project/build/bin/clang++;USE_SCCACHE=false;NINJA_BUILD_TRACE=true
0 17 * * * % BUILD_DOCKER=true;DL_KERNELS=true;COMPILER_VERSION=amd-mainline;BUILD_COMPILER=/llvm-project/build/bin/clang++;USE_SCCACHE=false;NINJA_BUILD_TRACE=true
0 15 * * * % BUILD_INSTANCES_ONLY=true;RUN_PERFORMANCE_TESTS=false;USE_SCCACHE=false
0 13 * * * % BUILD_LEGACY_OS=true''' : ""
......@@ -830,8 +761,8 @@ pipeline {
description: "Test building instances for various architectures simultaneously (default: OFF)")
booleanParam(
name: "BUILD_GFX12",
defaultValue: false,
description: "Build CK and run tests on gfx12 (default: OFF)")
defaultValue: true,
description: "Build CK and run tests on gfx12 (default: ON)")
booleanParam(
name: "NINJA_BUILD_TRACE",
defaultValue: false,
......@@ -1241,29 +1172,6 @@ pipeline {
}
}
}
stage("Performance Tests")
{
parallel
{
stage("Run ckProfiler: gfx90a")
{
when {
beforeAgent true
expression { params.RUN_PERFORMANCE_TESTS.toBoolean() && !params.BUILD_LEGACY_OS.toBoolean() }
}
options { retry(1) }
agent{ label rocmnode("gfx90a")}
environment{
setup_args = "NO_CK_BUILD"
}
steps{
runPerfTest(setup_args:setup_args, config_targets: "ckProfiler", no_reboot:true, build_type: 'Release')
cleanWs()
}
}
}
}
stage("Process Performance Test Results")
{
parallel
......
LICENSE
View file @ 8b49f207
......@@ -7,7 +7,7 @@ Copyright (c) 2020 , Advanced Micro Devices, Inc. (Xiaoyan Zhou)
Copyright (c) 2021-2022, Advanced Micro Devices, Inc. (Jianfeng Yan)
SPDX-License-Identifier: MIT
Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
......
cmake/EnableCompilerWarnings.cmake
View file @ 8b49f207
......@@ -66,7 +66,7 @@ else()
-Wunreachable-code
-Wunused
-Wno-reserved-identifier
-Werror
-Werror
-Wno-option-ignored
-Wsign-compare
-Wno-extra-semi-stmt
......
codegen/test/rtc/include/rtc/hip.hpp
View file @ 8b49f207
......@@ -4,6 +4,7 @@
#include <hip/hip_runtime_api.h>
#include <memory>
#include <string>
#include <stdexcept>
namespace rtc {
......
docs/sphinx/requirements.in
View file @ 8b49f207
rocm-docs-core==1.11.0
rocm-docs-core==1.13.0
sphinxcontrib-bibtex==2.6.3
docs/sphinx/requirements.txt
View file @ 8b49f207
......@@ -103,7 +103,7 @@ requests==2.32.3
# via
# pygithub
# sphinx
rocm-docs-core==1.11.0
rocm-docs-core==1.13.0
# via -r requirements.in
six==1.16.0
# via pybtex
......
example/01_gemm/CMakeLists.txt 100644 → 100755
View file @ 8b49f207
......@@ -29,10 +29,16 @@ add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_v3)
add_example_executable(example_gemm_xdl_fp8_v3 gemm_xdl_fp8_v3.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp8_v3)
add_example_executable(example_gemm_xdl_fp16_fp8_v3 gemm_xdl_fp16_fp8_v3.cpp)
add_example_executable(example_gemm_xdl_fp16_pk_i4_v3 gemm_xdl_fp16_pk_i4_v3.cpp)
add_example_executable(example_gemm_xdl_fp16_pk_i4_v3_b_scale gemm_xdl_fp16_pk_i4_v3_b_scale.cpp)
add_example_executable(example_gemm_xdl_bf16_pk_i4_v3 gemm_xdl_bf16_pk_i4_v3.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_fp8_v3)
add_example_executable(example_gemm_xdl_bf16_v3 gemm_xdl_bf16_v3.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_bf16_v3)
add_example_executable(example_gemm_xdl_bf16_streamk_v3 gemm_xdl_bf16_streamk_v3.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_bf16_streamk_v3)
add_example_executable(example_gemm_xdl_wavelet_fp16 gemm_xdl_wavelet_fp16.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_wavelet_fp16)
......
example/01_gemm/common.hpp
View file @ 8b49f207
......@@ -287,3 +287,85 @@ bool parse_cmd_args<ProblemSizeSplitK>(int argc,
return true;
}
template <typename DataType>
inline __host__ __device__ constexpr double get_rtol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 1e-1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 1.5e-1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename DataType>
inline __host__ __device__ constexpr double get_atol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 16.1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 8192.1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
example/01_gemm/gemm_xdl_bf16.cpp 100644 → 100755
View file @ 8b49f207
File mode changed from 100644 to 100755
example/01_gemm/gemm_xdl_bf16_pk_i4_v3.cpp 0 → 100644
View file @ 8b49f207
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3.hpp"
using ADataType = ck::bhalf_t;
using BDataType = ck::pk_i4_t;
using AccDataType = float;
using CShuffleDataType = ck::bhalf_t;
using CDataType = ck::bhalf_t;
using ALayout = Row;
using BLayout = Col;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
static constexpr bool PermuteA = false;
static constexpr bool PermuteB = true;
static constexpr ck::index_t KPerBlock = 128;
// clang-format off
using DeviceGemmV2Instance =
ck::tensor_operation::device::DeviceGemm_Xdl_CShuffleV3<
ALayout, BLayout, CLayout,
ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,
AElementOp, BElementOp, CElementOp, GemmDefault,
128,
16, 64,
KPerBlock, 8, 32,
16, 16,
1, 2,
S<16, 8, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 8, 8, 0,
S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 32, 32, 0,
1, 1, S<1, 16, 1, 8>, 4,
ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v2, ADataType, ADataType, PermuteA, PermuteB>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
CDataType,
AccDataType,
PassThrough,
PassThrough,
PassThrough>;
template <typename ProblemType>
bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
{
using namespace ck::literals;
auto M = problem_size.M;
auto N = problem_size.N;
auto K = problem_size.K;
auto StrideA = problem_size.StrideA;
auto StrideB = problem_size.StrideB;
auto StrideC = problem_size.StrideC;
auto KBatch = problem_size.KBatch;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
auto f_get_default_stride =
[](std::size_t row, std::size_t col, ck::index_t stride, auto layout) {
if(stride == -1)
{
// give a chance if stride is -1, return a default packed stride
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return static_cast<std::size_t>(col);
}
else
{
return static_cast<std::size_t>(row);
}
}
else
return static_cast<std::size_t>(stride);
};
StrideA = f_get_default_stride(M, K, StrideA, ALayout{});
StrideB = f_get_default_stride(K, N, StrideB, BLayout{});
StrideC = f_get_default_stride(M, N, StrideC, CLayout{});
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_permute(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
switch(config.init_method)
{
case 0:
a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
break;
case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
break;
case 2:
a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
break;
case 3:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
break;
default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
}
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n_permute.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
// weight permute
if constexpr(PermuteB)
{
int K1 = KPerBlock;
int K0 = K / KPerBlock;
// int K0, N, K1
for(int j = 0; j < K0; j++)
{
for(int i = 0; i < N; i++)
{
for(int jj = 0; jj < K1; jj++)
{
b_k_n_permute(j * N * K1 + i * K1 + jj) = b_k_n(i * K + (j * K1 + jj));
}
}
}
}
else
{
for(int i = 0; i < N; i++)
{
for(int j = 0; j < K; j++)
{
b_k_n_permute(i * K + j) = b_k_n(i * K + j);
}
}
}
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n_permute.mData.data());
DeviceMem workspace;
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto c_element_op = CElementOp{};
// do GEMM
auto gemm = DeviceGemmV2Instance{};
auto invoker = gemm.MakeInvoker();
float ave_time = 0;
auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
KBatch,
a_element_op,
b_element_op,
c_element_op);
if(!gemm.IsSupportedArgument(argument))
{
std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
return true;
}
bool pass = true;
if(config.do_verification)
{
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_host_result, PassThrough{}, PassThrough{}, PassThrough{});
ref_invoker.Run(ref_argument);
ave_time = invoker.Run(argument, StreamConfig{nullptr, false, 0});
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
pass &= ck::utils::check_err(c_m_n_device_result,
c_m_n_host_result,
"Error: Incorrect results!",
get_rtol<CDataType>(),
get_atol<CDataType>());
}
if(config.time_kernel)
{
ave_time =
invoker.Run(argument, StreamConfig{nullptr, config.time_kernel, 0, 20, 50, true, 50});
std::size_t flop = 2_uz * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K +
sizeof(BDataType) * K * N /
(ck::is_same_v<ck::remove_cvref_t<BDataType>, ck::pk_i4_t> ? 2 : 1) +
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: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
<< " GB/s, " << gemm.GetTypeString() << std::endl;
}
return pass;
}
bool run_gemm_splitk_example(int argc, char* argv[])
{
ProblemSizeSplitK problem_size;
ExecutionConfig config;
return parse_cmd_args(argc, argv, problem_size, config) && run_gemm(problem_size, config);
}
int main(int argc, char* argv[]) { return !run_gemm_splitk_example(argc, argv); }
example/01_gemm/gemm_xdl_bf16_streamk_v3.cpp 0 → 100755
View file @ 8b49f207
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_streamk_v3.hpp"
using ADataType = ck::bhalf_t;
using BDataType = ck::bhalf_t;
using CDataType = ck::bhalf_t;
using AccDataType = float;
using CShuffleDataType = ck::bhalf_t;
using ALayout = Row;
using BLayout = Col;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// clang-format off
using DeviceGemmV2_Streamk_Instance =
ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle_Streamk_V3<
ALayout, BLayout, CLayout,
ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,
PassThrough, PassThrough, PassThrough, GemmDefault,
256,
128, 128,
64, 8, 8,
16, 16,
4, 4,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 8, 8, 0,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 8, 8, 0,
1, 2, S<1, 32, 1, 8>, 8,
ck::BlockGemmPipelineScheduler::Intrawave,ck::BlockGemmPipelineVersion::v3>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
using ReferenceGemmInstanceGPU = ck::tensor_operation::device::ReferenceGemm<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp>;
#include "run_gemm_example_streamk_v2.inc"
int main(int argc, char* argv[]) { return !run_gemm_universal_streamk_example(argc, argv); }
example/01_gemm/gemm_xdl_fp16_fp8_v3.cpp
View file @ 8b49f207
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3.hpp"
using ADataType = ck::f8_t;
using BDataType = ck::half_t;
using ADataType = ck::half_t;
using BDataType = ck::f8_t;
using AccDataType = float;
using CShuffleDataType = ck::half_t;
using CDataType = ck::half_t;
......@@ -29,15 +29,15 @@ using DeviceGemmV2Instance =
AElementOp, BElementOp, CElementOp, GemmDefault,
64,
16, 16,
64, 16, 8,
256, 8, 16,
16, 16,
1, 1,
S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 16, 16, 0,
S<8, 8, 1>, S<1, 0, 2>, S<1, 0, 2>,
S<32, 2, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 8, 8, 0,
S<16, 4, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 16, 16, 0,
1, 1, S<1, 16, 1, 4>, 4,
ck::BlockGemmPipelineScheduler::Intrawave,ck::BlockGemmPipelineVersion::v1>;
ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v1>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
......
example/01_gemm/gemm_xdl_fp16_pk_i4_v3.cpp 0 → 100644
View file @ 8b49f207
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3.hpp"
using ADataType = ck::half_t;
using BDataType = ck::pk_i4_t;
using AccDataType = float;
using CShuffleDataType = ck::half_t;
using CDataType = ck::half_t;
using ALayout = Row;
using BLayout = Col;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
static constexpr bool PermuteA = false;
static constexpr bool PermuteB = true;
static constexpr ck::index_t KPerBlock = 128;
// clang-format off
using DeviceGemmV2Instance =
ck::tensor_operation::device::DeviceGemm_Xdl_CShuffleV3<
ALayout, BLayout, CLayout,
ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,
AElementOp, BElementOp, CElementOp, GemmDefault,
128,
16, 128,
KPerBlock, 8, 32,
16, 16,
1, 4,
S<16, 8, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 8, 8, 0,
S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 32, 32, 0,
1, 1, S<1, 16, 1, 8>, 4,
ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v2, ADataType, ADataType, PermuteA, PermuteB>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
CDataType,
AccDataType,
PassThrough,
PassThrough,
PassThrough>;
template <typename ProblemType>
bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
{
using namespace ck::literals;
auto M = problem_size.M;
auto N = problem_size.N;
auto K = problem_size.K;
auto StrideA = problem_size.StrideA;
auto StrideB = problem_size.StrideB;
auto StrideC = problem_size.StrideC;
auto KBatch = problem_size.KBatch;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
auto f_get_default_stride =
[](std::size_t row, std::size_t col, ck::index_t stride, auto layout) {
if(stride == -1)
{
// give a chance if stride is -1, return a default packed stride
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return static_cast<std::size_t>(col);
}
else
{
return static_cast<std::size_t>(row);
}
}
else
return static_cast<std::size_t>(stride);
};
StrideA = f_get_default_stride(M, K, StrideA, ALayout{});
StrideB = f_get_default_stride(K, N, StrideB, BLayout{});
StrideC = f_get_default_stride(M, N, StrideC, CLayout{});
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_permute(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
switch(config.init_method)
{
case 0:
a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
break;
case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
break;
case 2:
a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
break;
case 3:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
break;
default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
}
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n_permute.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
// weight permute
if constexpr(PermuteB)
{
int K1 = KPerBlock;
int K0 = K / KPerBlock;
// int K0, N, K1
for(int j = 0; j < K0; j++)
{
for(int i = 0; i < N; i++)
{
for(int jj = 0; jj < K1; jj++)
{
b_k_n_permute(j * N * K1 + i * K1 + jj) = b_k_n(i * K + (j * K1 + jj));
}
}
}
}
else
{
for(int i = 0; i < N; i++)
{
for(int j = 0; j < K; j++)
{
b_k_n_permute(i * K + j) = b_k_n(i * K + j);
}
}
}
// vector pk_i4x4 permute
for(int i = 0; i < N; i++)
{
for(int j = 0; j < K; j += 8)
{
int input[8];
for(int k = 0; k < 4; k++)
{
int i4x2 = b_k_n_permute(j + k * 2, i).data;
input[k * 2 + 0] = (i4x2 >> 4) & 0xf;
input[k * 2 + 1] = (i4x2 >> 0) & 0xf;
}
// permute 01234567->20643175
{
int hi = input[2];
int lo = input[0];
int i4x2 = (hi << 4) | lo;
b_k_n_permute(j + 0, i) = i4x2;
}
{
int hi = input[6];
int lo = input[4];
int i4x2 = (hi << 4) | lo;
b_k_n_permute(j + 2, i) = i4x2;
}
{
int hi = input[3];
int lo = input[1];
int i4x2 = (hi << 4) | lo;
b_k_n_permute(j + 4, i) = i4x2;
}
{
int hi = input[7];
int lo = input[5];
int i4x2 = (hi << 4) | lo;
b_k_n_permute(j + 6, i) = i4x2;
}
}
}
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n_permute.mData.data());
DeviceMem workspace;
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto c_element_op = CElementOp{};
// do GEMM
auto gemm = DeviceGemmV2Instance{};
auto invoker = gemm.MakeInvoker();
float ave_time = 0;
auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
KBatch,
a_element_op,
b_element_op,
c_element_op);
if(!gemm.IsSupportedArgument(argument))
{
std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
return true;
}
bool pass = true;
if(config.do_verification)
{
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_host_result, PassThrough{}, PassThrough{}, PassThrough{});
ref_invoker.Run(ref_argument);
ave_time = invoker.Run(argument, StreamConfig{nullptr, false, 0});
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
pass &= ck::utils::check_err(c_m_n_device_result,
c_m_n_host_result,
"Error: Incorrect results!",
get_rtol<CDataType>(),
get_atol<CDataType>());
}
if(config.time_kernel)
{
ave_time =
invoker.Run(argument, StreamConfig{nullptr, config.time_kernel, 0, 20, 50, true, 50});
std::size_t flop = 2_uz * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K +
sizeof(BDataType) * K * N /
(ck::is_same_v<ck::remove_cvref_t<BDataType>, ck::pk_i4_t> ? 2 : 1) +
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: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
<< " GB/s, " << gemm.GetTypeString() << std::endl;
}
return pass;
}
bool run_gemm_splitk_example(int argc, char* argv[])
{
ProblemSizeSplitK problem_size;
ExecutionConfig config;
return parse_cmd_args(argc, argv, problem_size, config) && run_gemm(problem_size, config);
}
int main(int argc, char* argv[]) { return !run_gemm_splitk_example(argc, argv); }
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