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Commit aa5859e4 authored by Chao Liu's avatar Chao Liu
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Merge remote-tracking branch 'origin/develop' into wavelet_model

parents 9bd6cc0e 5ee30459
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Showing with 1129 additions and 203 deletions
CMakeLists.txt
View file @ aa5859e4
...@@ -8,7 +8,7 @@ list(APPEND CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake") ...@@ -8,7 +8,7 @@ list(APPEND CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake")
enable_testing() enable_testing()
set(ROCM_SYMLINK_LIBS OFF) set(ROCM_SYMLINK_LIBS OFF)
find_package(ROCM 0.8 REQUIRED PATHS /opt/rocm) find_package(ROCM REQUIRED PATHS /opt/rocm)
include(ROCMInstallTargets) include(ROCMInstallTargets)
include(ROCMPackageConfigHelpers) include(ROCMPackageConfigHelpers)
...@@ -71,13 +71,6 @@ if( DEFINED CK_OVERRIDE_HIP_VERSION_PATCH ) ...@@ -71,13 +71,6 @@ if( DEFINED CK_OVERRIDE_HIP_VERSION_PATCH )
endif() endif()
message(STATUS "Build with HIP ${HIP_VERSION}") message(STATUS "Build with HIP ${HIP_VERSION}")
rocm_create_package(
NAME composablekernel
DESCRIPTION "High Performance Composable Kernel for AMD GPUs"
MAINTAINER "MIOpen Kernels Dev Team <dl.MIOpen@amd.com>"
LDCONFIG
)
## tidy ## tidy
include(EnableCompilerWarnings) include(EnableCompilerWarnings)
set(CK_TIDY_ERRORS ERRORS * -readability-inconsistent-declaration-parameter-name) set(CK_TIDY_ERRORS ERRORS * -readability-inconsistent-declaration-parameter-name)
......
Dockerfile
View file @ aa5859e4
...@@ -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
...@@ -15,7 +16,6 @@ RUN sh -c "echo deb [arch=amd64] $DEB_ROCM_REPO ubuntu main > /etc/apt/sources.l ...@@ -15,7 +16,6 @@ RUN sh -c "echo deb [arch=amd64] $DEB_ROCM_REPO ubuntu main > /etc/apt/sources.l
RUN wget --no-check-certificate -qO - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | apt-key add - RUN wget --no-check-certificate -qO - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | apt-key add -
RUN sh -c "echo deb https://apt.kitware.com/ubuntu/ bionic main | tee -a /etc/apt/sources.list" RUN sh -c "echo deb https://apt.kitware.com/ubuntu/ bionic main | tee -a /etc/apt/sources.list"
# ADD requirements.txt requirements.txt
# Install dependencies # Install dependencies
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \
apt-utils \ apt-utils \
...@@ -23,8 +23,6 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow- ...@@ -23,8 +23,6 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
cmake-data=3.15.1-0kitware1 \ cmake-data=3.15.1-0kitware1 \
cmake=3.15.1-0kitware1 \ cmake=3.15.1-0kitware1 \
curl \ curl \
g++ \
gdb \
git \ git \
hip-rocclr \ hip-rocclr \
jq \ jq \
...@@ -61,17 +59,7 @@ ENV UBSAN_OPTIONS=print_stacktrace=1 ...@@ -61,17 +59,7 @@ ENV UBSAN_OPTIONS=print_stacktrace=1
RUN wget https://github.com/Yelp/dumb-init/releases/download/v1.2.0/dumb-init_1.2.0_amd64.deb RUN wget https://github.com/Yelp/dumb-init/releases/download/v1.2.0/dumb-init_1.2.0_amd64.deb
RUN dpkg -i dumb-init_*.deb && rm dumb-init_*.deb RUN dpkg -i dumb-init_*.deb && rm dumb-init_*.deb
# Install cget
RUN pip install cget
# Install rclone
RUN pip install https://github.com/pfultz2/rclone/archive/master.tar.gz
ARG PREFIX=/opt/rocm ARG PREFIX=/opt/rocm
# Install dependencies
RUN cget install pfultz2/rocm-recipes
# Install rbuild
RUN pip3 install https://github.com/RadeonOpenCompute/rbuild/archive/6d78a0553babdaea8d2da5de15cbda7e869594b8.tar.gz
# Install packages for processing the performance results # Install packages for processing the performance results
RUN pip3 install --upgrade pip RUN pip3 install --upgrade pip
RUN pip3 install sqlalchemy RUN pip3 install sqlalchemy
...@@ -84,12 +72,26 @@ ENV UBSAN_OPTIONS=print_stacktrace=1 ...@@ -84,12 +72,26 @@ ENV UBSAN_OPTIONS=print_stacktrace=1
ENV LC_ALL=C.UTF-8 ENV LC_ALL=C.UTF-8
ENV LANG=C.UTF-8 ENV LANG=C.UTF-8
ADD rbuild.ini /rbuild.ini
ADD dev-requirements.txt dev-requirements.txt ADD dev-requirements.txt dev-requirements.txt
RUN rbuild prepare -s develop -d $PREFIX
RUN groupadd -f render RUN groupadd -f render
# Install the new rocm-cmake version # Install the new rocm-cmake version
RUN git clone -b master https://github.com/RadeonOpenCompute/rocm-cmake.git && \ RUN git clone -b master https://github.com/RadeonOpenCompute/rocm-cmake.git && \
cd rocm-cmake && mkdir build && cd build && \ cd rocm-cmake && mkdir build && cd build && \
cmake .. && cmake --build . && cmake --build . --target install cmake .. && cmake --build . && cmake --build . --target install
WORKDIR /
ENV compiler_version=$compiler_version
RUN sh -c "echo compiler version = '$compiler_version'"
RUN --mount=type=ssh if [ "$compiler_version" != "release" ]; then \
git clone -b "$compiler_version" https://github.com/RadeonOpenCompute/llvm-project.git && \
cd llvm-project && mkdir build && cd build && \
cmake -DCMAKE_INSTALL_PREFIX=/opt/rocm/llvm -DCMAKE_BUILD_TYPE=Release -DLLVM_ENABLE_ASSERTIONS=1 -DLLVM_TARGETS_TO_BUILD="AMDGPU;X86" -DLLVM_ENABLE_PROJECTS="clang;lld;compiler-rt" ../llvm && \
make -j 8 ; \
else echo "using the release compiler"; \
fi
#ENV HIP_CLANG_PATH='/llvm-project/build/bin'
#RUN sh -c "echo HIP_CLANG_PATH = '$HIP_CLANG_PATH'"
Jenkinsfile
View file @ aa5859e4
...@@ -11,6 +11,96 @@ def show_node_info() { ...@@ -11,6 +11,96 @@ def show_node_info() {
""" """
} }
def runShell(String command){
def responseCode = sh returnStatus: true, script: "${command} > tmp.txt"
def output = readFile(file: "tmp.txt")
echo "tmp.txt contents: $output"
return (output != "")
}
def getDockerImageName(){
def img = "${env.MIOPEN_IMAGE_URL}:composable_kernels_${params.COMPILER_VERSION}"
return img
}
def getDockerImage(Map conf=[:]){
env.DOCKER_BUILDKIT=1
def prefixpath = conf.get("prefixpath", "/opt/rocm") // prefix:/opt/rocm
def gpu_arch = conf.get("gpu_arch", "gfx908") // prebuilt dockers should have all the architectures enabled so one image can be used for all stages
def no_cache = conf.get("no_cache", false)
def dockerArgs = "--build-arg BUILDKIT_INLINE_CACHE=1 --build-arg PREFIX=${prefixpath} --build-arg compiler_version='${params.COMPILER_VERSION}' "
if(env.CCACHE_HOST)
{
def check_host = sh(script:"""(printf "PING\r\n";) | nc -N ${env.CCACHE_HOST} 6379 """, returnStdout: true).trim()
if(check_host == "+PONG")
{
echo "FOUND CCACHE SERVER: ${CCACHE_HOST}"
}
else
{
echo "CCACHE SERVER: ${CCACHE_HOST} NOT FOUND, got ${check_host} response"
}
dockerArgs = dockerArgs + " --build-arg CCACHE_SECONDARY_STORAGE='redis://${env.CCACHE_HOST}' --build-arg COMPILER_LAUNCHER='ccache' "
env.CCACHE_DIR = """/tmp/ccache_store"""
env.CCACHE_SECONDARY_STORAGE="""redis://${env.CCACHE_HOST}"""
}
if(no_cache)
{
dockerArgs = dockerArgs + " --no-cache "
}
echo "Docker Args: ${dockerArgs}"
def image = getDockerImageName()
//Check if image exists
def retimage
try
{
echo "Pulling down image: ${image}"
retimage = docker.image("${image}")
retimage.pull()
}
catch(Exception ex)
{
error "Unable to locate image: ${image}"
}
return [retimage, image]
}
def buildDocker(install_prefix){
show_node_info()
env.DOCKER_BUILDKIT=1
checkout scm
def image_name = getDockerImageName()
echo "Building Docker for ${image_name}"
def dockerArgs = "--build-arg BUILDKIT_INLINE_CACHE=1 --build-arg PREFIX=${install_prefix} --build-arg compiler_version='${params.COMPILER_VERSION}' "
if(env.CCACHE_HOST)
{
def check_host = sh(script:"""(printf "PING\\r\\n";) | nc -N ${env.CCACHE_HOST} 6379 """, returnStdout: true).trim()
if(check_host == "+PONG")
{
echo "FOUND CCACHE SERVER: ${CCACHE_HOST}"
}
else
{
echo "CCACHE SERVER: ${CCACHE_HOST} NOT FOUND, got ${check_host} response"
}
dockerArgs = dockerArgs + " --build-arg CCACHE_SECONDARY_STORAGE='redis://${env.CCACHE_HOST}' --build-arg COMPILER_LAUNCHER='ccache' "
env.CCACHE_DIR = """/tmp/ccache_store"""
env.CCACHE_SECONDARY_STORAGE="""redis://${env.CCACHE_HOST}"""
}
echo "Build Args: ${dockerArgs}"
try{
echo "Checking for image: ${image_name}"
sh "docker manifest inspect --insecure ${image_name}"
echo "Image: ${image_name} found!! Skipping building image"
}
catch(Exception ex){
echo "Unable to locate image: ${image_name}. Building image now"
retimage = docker.build("${image_name}", dockerArgs + ' .')
retimage.push()
}
}
def cmake_build(Map conf=[:]){ def cmake_build(Map conf=[:]){
def compiler = conf.get("compiler","/opt/rocm/bin/hipcc") def compiler = conf.get("compiler","/opt/rocm/bin/hipcc")
...@@ -60,7 +150,7 @@ def cmake_build(Map conf=[:]){ ...@@ -60,7 +150,7 @@ def cmake_build(Map conf=[:]){
""" """
def setup_cmd = conf.get("setup_cmd", "${cmake_envs} cmake ${setup_args} .. ") def setup_cmd = conf.get("setup_cmd", "${cmake_envs} cmake ${setup_args} .. ")
// reduce parallelism when compiling, clang uses too much memory // reduce parallelism when compiling, clang uses too much memory
def build_cmd = conf.get("build_cmd", "${build_envs} dumb-init make -j\$(( \$(nproc) / 1 )) ${config_targets}") def build_cmd = conf.get("build_cmd", "${build_envs} dumb-init make -j\$(( \$(nproc) / 2 )) ${config_targets}")
def execute_cmd = conf.get("execute_cmd", "") def execute_cmd = conf.get("execute_cmd", "")
def cmd = conf.get("cmd", """ def cmd = conf.get("cmd", """
...@@ -85,7 +175,7 @@ def buildHipClangJob(Map conf=[:]){ ...@@ -85,7 +175,7 @@ def buildHipClangJob(Map conf=[:]){
env.HSA_ENABLE_SDMA=0 env.HSA_ENABLE_SDMA=0
checkout scm checkout scm
def image = "composable_kernels" def image = "composable_kernels_${params.COMPILER_VERSION}"
def prefixpath = conf.get("prefixpath", "/opt/rocm") def prefixpath = conf.get("prefixpath", "/opt/rocm")
def gpu_arch = conf.get("gpu_arch", "gfx908") def gpu_arch = conf.get("gpu_arch", "gfx908")
...@@ -93,22 +183,31 @@ def buildHipClangJob(Map conf=[:]){ ...@@ -93,22 +183,31 @@ def buildHipClangJob(Map conf=[:]){
// def dockerOpts="--device=/dev/kfd --device=/dev/dri --group-add video --group-add render --cap-add=SYS_PTRACE --security-opt seccomp=unconfined" // def dockerOpts="--device=/dev/kfd --device=/dev/dri --group-add video --group-add render --cap-add=SYS_PTRACE --security-opt seccomp=unconfined"
def dockerOpts="--device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined" def dockerOpts="--device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined"
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 --env GPU_ARCH='${gpu_arch}' "
}
//def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg GPU_ARCH='${gpu_arch}' --build-arg compiler_version='${params.COMPILER_VERSION}' "
def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg compiler_version='${params.COMPILER_VERSION}' "
if (params.COMPILER_VERSION != "release"){
dockerOpts = dockerOpts + " --env HIP_CLANG_PATH='/llvm-project/build/bin' "
} }
def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg GPU_ARCH='${gpu_arch}' "
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("${image}", dockerArgs + '.')
(retimage, image) = getDockerImage(conf)
withDockerContainer(image: image, args: dockerOpts) { withDockerContainer(image: image, args: dockerOpts) {
timeout(time: 5, unit: 'MINUTES') timeout(time: 5, unit: 'MINUTES'){
{ sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo | tee clinfo.log'
sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo' if ( runShell('grep -n "Number of devices:.*. 0" clinfo.log') ){
throw new Exception ("GPU not found")
}
else{
echo "GPU is OK"
}
} }
} }
} }
...@@ -117,27 +216,23 @@ def buildHipClangJob(Map conf=[:]){ ...@@ -117,27 +216,23 @@ def buildHipClangJob(Map conf=[:]){
throw e throw e
} }
catch(Exception ex) { catch(Exception ex) {
retimage = docker.build("${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 |tee clinfo.log'
sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo' if ( runShell('grep -n "Number of devices:.*. 0" clinfo.log') ){
throw new Exception ("GPU not found")
} }
else{
echo "GPU is OK"
} }
} }
} }
else{
timeout(time: 3, unit: 'HOURS'){
retimage = docker.image('compute-artifactory.amd.com:5000/rocm-plus-docker/framework/compute-rocm-dkms-no-npi-hipclang:9110_ubuntu18.04_py3.6_pytorch_rocm5.0_internal_testing_7ff5b54').pull()
image="b56f8ac0d6ea"
sh "docker images"
}
} }
withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') { withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') {
timeout(time: 5, unit: 'HOURS') timeout(time: 5, unit: 'HOURS')
{ {
sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo'
cmake_build(conf) cmake_build(conf)
} }
} }
...@@ -149,10 +244,6 @@ def reboot(){ ...@@ -149,10 +244,6 @@ def reboot(){
build job: 'reboot-slaves', propagate: false , parameters: [string(name: 'server', value: "${env.NODE_NAME}"),] build job: 'reboot-slaves', propagate: false , parameters: [string(name: 'server', value: "${env.NODE_NAME}"),]
} }
def buildHipClangJobAndReboot(Map conf=[:]){ def buildHipClangJobAndReboot(Map conf=[:]){
try{ try{
buildHipClangJob(conf) buildHipClangJob(conf)
...@@ -169,14 +260,14 @@ def buildHipClangJobAndReboot(Map conf=[:]){ ...@@ -169,14 +260,14 @@ def buildHipClangJobAndReboot(Map conf=[:]){
} }
} }
def runCKProfiler(Map conf=[:]){ def runCKProfiler(Map conf=[:]){
show_node_info() show_node_info()
env.HSA_ENABLE_SDMA=0 env.HSA_ENABLE_SDMA=0
checkout scm checkout scm
def image = "composable_kernels"
def image = "composable_kernels_${params.COMPILER_VERSION}"
def prefixpath = conf.get("prefixpath", "/opt/rocm") def prefixpath = conf.get("prefixpath", "/opt/rocm")
def gpu_arch = conf.get("gpu_arch", "gfx908") def gpu_arch = conf.get("gpu_arch", "gfx908")
...@@ -184,22 +275,29 @@ def runCKProfiler(Map conf=[:]){ ...@@ -184,22 +275,29 @@ def runCKProfiler(Map conf=[:]){
// def dockerOpts="--device=/dev/kfd --device=/dev/dri --group-add video --group-add render --cap-add=SYS_PTRACE --security-opt seccomp=unconfined" // def dockerOpts="--device=/dev/kfd --device=/dev/dri --group-add video --group-add render --cap-add=SYS_PTRACE --security-opt seccomp=unconfined"
def dockerOpts="--device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined" def dockerOpts="--device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined"
if (conf.get("enforce_xnack_on", false)) { if (conf.get("enforce_xnack_on", false)) {
dockerOpts = dockerOpts + " --env HSA_XNACK=1" dockerOpts = dockerOpts + " --env HSA_XNACK=1 --env GPU_ARCH='${gpu_arch}' "
}
def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg compiler_version='${params.COMPILER_VERSION}' "
if (params.COMPILER_VERSION != "release"){
dockerOpts = dockerOpts + " --env HIP_CLANG_PATH='/llvm-project/build/bin' "
} }
def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg GPU_ARCH='${gpu_arch}' "
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("${image}", dockerArgs + '.')
(retimage, image) = getDockerImage(conf)
withDockerContainer(image: image, args: dockerOpts) { withDockerContainer(image: image, args: dockerOpts) {
timeout(time: 5, unit: 'MINUTES') timeout(time: 5, unit: 'MINUTES'){
{ sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo | tee clinfo.log'
sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo' if ( runShell('grep -n "Number of devices:.*. 0" clinfo.log') ){
throw new Exception ("GPU not found")
}
else{
echo "GPU is OK"
}
} }
} }
} }
...@@ -208,74 +306,61 @@ def runCKProfiler(Map conf=[:]){ ...@@ -208,74 +306,61 @@ def runCKProfiler(Map conf=[:]){
throw e throw e
} }
catch(Exception ex) { catch(Exception ex) {
retimage = docker.build("${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 | tee clinfo.log'
sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo' if ( runShell('grep -n "Number of devices:.*. 0" clinfo.log') ){
throw new Exception ("GPU not found")
} }
else{
echo "GPU is OK"
} }
} }
} }
else{
timeout(time: 3, unit: 'HOURS'){
retimage = docker.image('compute-artifactory.amd.com:5000/rocm-plus-docker/framework/compute-rocm-dkms-no-npi-hipclang:9110_ubuntu18.04_py3.6_pytorch_rocm5.0_internal_testing_7ff5b54').pull()
image="b56f8ac0d6ea"
sh "docker images"
}
} }
withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') { withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') {
timeout(time: 5, unit: 'HOURS') timeout(time: 24, unit: 'HOURS')
{ {
cmake_build(conf) cmake_build(conf)
dir("script"){ dir("script"){
//run gemm performance tests if (params.RUN_FULL_QA){
def gemm_log = "perf_gemm_${gpu_arch}.log" def qa_log = "qa_${gpu_arch}.log"
sh "rm -f ${gemm_log}" sh "./run_full_performance_tests.sh 1 QA_${params.COMPILER_VERSION} ${gpu_arch} ${env.BRANCH_NAME} ${NODE_NAME}"
sh "echo Branch name: ${env.BRANCH_NAME} > ${gemm_log}" archiveArtifacts "perf_gemm_${gpu_arch}.log"
sh "echo Node name: ${NODE_NAME} >> ${gemm_log}" archiveArtifacts "perf_resnet50_N256_${gpu_arch}.log"
sh "echo GPU_arch name: ${gpu_arch} >> ${gemm_log}" archiveArtifacts "perf_resnet50_N4_${gpu_arch}.log"
sh "rocminfo | grep 'Compute Unit:' >> ${gemm_log} " archiveArtifacts "perf_batched_gemm_${gpu_arch}.log"
sh "hipcc --version | grep -e 'HIP version' >> ${gemm_log}" archiveArtifacts "perf_grouped_gemm_${gpu_arch}.log"
sh "/opt/rocm/bin/amdclang++ --version | grep -e 'InstalledDir' >> ${gemm_log}" archiveArtifacts "perf_conv_fwd_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 0 0 0 1 0 5 | tee -a ${gemm_log}" archiveArtifacts "perf_conv_bwd_data_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 1 0 0 1 0 5 | tee -a ${gemm_log}" archiveArtifacts "perf_gemm_bilinear_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 2 0 0 1 0 5 | tee -a ${gemm_log}" archiveArtifacts "perf_reduction_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 3 0 0 1 0 5 | tee -a ${gemm_log}" // stash perf files to master
sh "./profile_gemm.sh gemm 0 1 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_gemm_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 1 1 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_resnet50_N256_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 2 1 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_resnet50_N4_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 3 1 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_batched_gemm_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 0 2 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_grouped_gemm_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 1 2 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_conv_fwd_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 2 2 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_conv_bwd_data_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 3 2 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_gemm_bilinear_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 0 3 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_reduction_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 1 3 0 1 0 5 | tee -a ${gemm_log}" //we will process results on the master node
sh "./profile_gemm.sh gemm 2 3 0 1 0 5 | tee -a ${gemm_log}" }
sh "./profile_gemm.sh gemm 3 3 0 1 0 5 | tee -a ${gemm_log}" else{
//results will be parsed, stored, and analyzed within the python script sh "./run_performance_tests.sh 0 CI_${params.COMPILER_VERSION} ${gpu_arch} ${env.BRANCH_NAME} ${NODE_NAME}"
//the script will return 0 if the performance criteria are met archiveArtifacts "perf_gemm_${gpu_arch}.log"
//or return 1 if the criteria are not met archiveArtifacts "perf_resnet50_N256_${gpu_arch}.log"
archiveArtifacts "${gemm_log}" archiveArtifacts "perf_resnet50_N4_${gpu_arch}.log"
sh "python3 parse_perf_data.py ${gemm_log} " // stash perf files to master
//run resnet50 test stash name: "perf_gemm_${gpu_arch}.log"
def resnet_log = "perf_resnet50_${gpu_arch}.log" stash name: "perf_resnet50_N256_${gpu_arch}.log"
sh "rm -f ${resnet_log}" stash name: "perf_resnet50_N4_${gpu_arch}.log"
sh "echo Branch name: ${env.BRANCH_NAME} > ${resnet_log}" //we will process the results on the master node
sh "echo Node name: ${NODE_NAME} >> ${resnet_log}" }
sh "echo GPU_arch name: ${gpu_arch} >> ${resnet_log}"
sh "rocminfo | grep 'Compute Unit:' >> ${resnet_log} "
sh "hipcc --version | grep -e 'HIP version' >> ${resnet_log}"
sh "/opt/rocm/bin/amdclang++ --version | grep -e 'InstalledDir' >> ${resnet_log}"
//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}"
//then run with N=4
sh "./profile_conv.sh conv_fwd_bias_relu 1 1 1 1 0 2 0 1 4 | tee -a ${resnet_log}"
archiveArtifacts "${resnet_log}"
//the script will put the results from N=256 and N=4 runs into separate tables
sh "python3 parse_perf_data.py ${resnet_log} "
} }
} }
} }
...@@ -283,7 +368,6 @@ def runCKProfiler(Map conf=[:]){ ...@@ -283,7 +368,6 @@ def runCKProfiler(Map conf=[:]){
return retimage return retimage
} }
def runPerfTest(Map conf=[:]){ def runPerfTest(Map conf=[:]){
try{ try{
runCKProfiler(conf) runCKProfiler(conf)
...@@ -300,16 +384,97 @@ def runPerfTest(Map conf=[:]){ ...@@ -300,16 +384,97 @@ def runPerfTest(Map conf=[:]){
} }
} }
def process_results(Map conf=[:]){
env.HSA_ENABLE_SDMA=0
checkout scm
def image = "composable_kernels_${params.COMPILER_VERSION}"
def prefixpath = "/opt/rocm"
def gpu_arch = conf.get("gpu_arch", "gfx908")
// Jenkins is complaining about the render group
def dockerOpts="--cap-add=SYS_PTRACE --security-opt seccomp=unconfined"
if (conf.get("enforce_xnack_on", false)) {
dockerOpts = dockerOpts + " --env HSA_XNACK=1 --env GPU_ARCH='${gpu_arch}' "
}
def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg compiler_version='release' "
def variant = env.STAGE_NAME
def retimage
gitStatusWrapper(credentialsId: "${status_wrapper_creds}", gitHubContext: "Jenkins - ${variant}", account: 'ROCmSoftwarePlatform', repo: 'composable_kernel') {
try {
//retimage = docker.build("${image}", dockerArgs + '.')
(retimage, image) = getDockerImage(conf)
}
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: 1, unit: 'HOURS'){
try{
dir("script"){
if (params.RUN_FULL_QA){
// unstash perf files to master
unstash "perf_gemm_${gpu_arch}.log"
unstash "perf_resnet50_N256_${gpu_arch}.log"
unstash "perf_resnet50_N4_${gpu_arch}.log"
unstash "perf_batched_gemm_${gpu_arch}.log"
unstash "perf_grouped_gemm_${gpu_arch}.log"
unstash "perf_conv_fwd_${gpu_arch}.log"
unstash "perf_conv_bwd_data_${gpu_arch}.log"
unstash "perf_gemm_bilinear_${gpu_arch}.log"
unstash "perf_reduction_${gpu_arch}.log"
sh "./process_qa_data.sh ${gpu_arch}"
}
else{
// unstash perf files to master
unstash "perf_gemm_${gpu_arch}.log"
unstash "perf_resnet50_N256_${gpu_arch}.log"
unstash "perf_resnet50_N4_${gpu_arch}.log"
sh "./process_perf_data.sh ${gpu_arch}"
}
}
}
catch(e){
echo "throwing error exception while processing performance test results"
echo 'Exception occurred: ' + e.toString()
throw e
}
}
}
}
//launch develop branch daily at 23:00 in FULL_QA mode
CRON_SETTINGS = BRANCH_NAME == "develop" ? '''0 23 * * * % RUN_FULL_QA=true''' : ""
pipeline { pipeline {
agent none agent none
triggers {
parameterizedCron(CRON_SETTINGS)
}
options { options {
parallelsAlwaysFailFast() parallelsAlwaysFailFast()
} }
parameters { parameters {
booleanParam( booleanParam(
name: "USE_DOCKERFILE", name: "BUILD_DOCKER",
defaultValue: true, defaultValue: true,
description: "") description: "Force building docker image (default: true)")
string(
name: 'COMPILER_VERSION',
defaultValue: 'ck-9110',
description: 'Specify which version of compiler to use: ck-9110 (default), release, or amd-stg-open.')
booleanParam(
name: "RUN_FULL_QA",
defaultValue: false,
description: "Select whether to run small set of performance tests (default) or full QA")
booleanParam(
name: "TEST_NODE_PERFORMANCE",
defaultValue: false,
description: "Test the node GPU performance (default: false)")
} }
environment{ environment{
dbuser = "${dbuser}" dbuser = "${dbuser}"
...@@ -319,9 +484,28 @@ pipeline { ...@@ -319,9 +484,28 @@ pipeline {
dbsshuser = "${dbsshuser}" dbsshuser = "${dbsshuser}"
dbsshpassword = "${dbsshpassword}" dbsshpassword = "${dbsshpassword}"
status_wrapper_creds = "${status_wrapper_creds}" status_wrapper_creds = "${status_wrapper_creds}"
gerrit_cred="${gerrit_cred}"
DOCKER_BUILDKIT = "1"
} }
stages{ stages{
stage("Build Docker"){
when {
expression { params.BUILD_DOCKER.toBoolean() }
}
parallel{
stage('Docker /opt/rocm'){
agent{ label rocmnode("nogpu") }
steps{
buildDocker('/opt/rocm')
}
}
}
}
stage("Static checks") { stage("Static checks") {
when {
beforeAgent true
expression { !params.TEST_NODE_PERFORMANCE.toBoolean() }
}
parallel{ parallel{
// enable after we move from hipcc to hip-clang // enable after we move from hipcc to hip-clang
// stage('Tidy') { // stage('Tidy') {
...@@ -355,6 +539,10 @@ pipeline { ...@@ -355,6 +539,10 @@ pipeline {
} }
stage("Tests") stage("Tests")
{ {
when {
beforeAgent true
expression { !params.TEST_NODE_PERFORMANCE.toBoolean() }
}
parallel parallel
{ {
stage("Run Tests: gfx908") stage("Run Tests: gfx908")
...@@ -369,6 +557,10 @@ pipeline { ...@@ -369,6 +557,10 @@ pipeline {
} }
stage("Run Tests: gfx90a") stage("Run Tests: gfx90a")
{ {
when {
beforeAgent true
expression { params.RUN_FULL_QA.toBoolean() }
}
agent{ label rocmnode("gfx90a")} agent{ label rocmnode("gfx90a")}
environment{ environment{
setup_args = """ -D CMAKE_CXX_FLAGS="--offload-arch=gfx90a -O3 " -DBUILD_DEV=On """ setup_args = """ -D CMAKE_CXX_FLAGS="--offload-arch=gfx90a -O3 " -DBUILD_DEV=On """
...@@ -381,6 +573,10 @@ pipeline { ...@@ -381,6 +573,10 @@ pipeline {
} }
stage("Client App") stage("Client App")
{ {
when {
beforeAgent true
expression { !params.TEST_NODE_PERFORMANCE.toBoolean() }
}
parallel parallel
{ {
stage("Run Client App") stage("Run Client App")
...@@ -402,6 +598,10 @@ pipeline { ...@@ -402,6 +598,10 @@ pipeline {
{ {
stage("Run ckProfiler: gfx908") stage("Run ckProfiler: gfx908")
{ {
when {
beforeAgent true
expression { !params.RUN_FULL_QA.toBoolean() && !params.TEST_NODE_PERFORMANCE.toBoolean() }
}
agent{ label rocmnode("gfx908")} agent{ label rocmnode("gfx908")}
environment{ environment{
setup_args = """ -D CMAKE_CXX_FLAGS="--offload-arch=gfx908 -O3 " -DBUILD_DEV=On """ setup_args = """ -D CMAKE_CXX_FLAGS="--offload-arch=gfx908 -O3 " -DBUILD_DEV=On """
...@@ -412,6 +612,10 @@ pipeline { ...@@ -412,6 +612,10 @@ pipeline {
} }
stage("Run ckProfiler: gfx90a") stage("Run ckProfiler: gfx90a")
{ {
when {
beforeAgent true
expression { params.RUN_FULL_QA.toBoolean() || params.TEST_NODE_PERFORMANCE.toBoolean() }
}
agent{ label rocmnode("gfx90a")} agent{ label rocmnode("gfx90a")}
environment{ environment{
setup_args = """ -D CMAKE_CXX_FLAGS="--offload-arch=gfx90a -O3 " -DBUILD_DEV=On """ setup_args = """ -D CMAKE_CXX_FLAGS="--offload-arch=gfx90a -O3 " -DBUILD_DEV=On """
...@@ -422,6 +626,33 @@ pipeline { ...@@ -422,6 +626,33 @@ pipeline {
} }
} }
} }
stage("Process Performance Test Results")
{
parallel
{
stage("Process results for gfx908"){
when {
beforeAgent true
expression { !params.RUN_FULL_QA.toBoolean() && !params.TEST_NODE_PERFORMANCE.toBoolean() }
}
agent { label 'mici' }
steps{
process_results(gpu_arch: "gfx908")
}
}
stage("Process results for gfx90a"){
when {
beforeAgent true
expression { params.RUN_FULL_QA.toBoolean() || params.TEST_NODE_PERFORMANCE.toBoolean() }
}
agent { label 'mici' }
steps{
process_results(gpu_arch: "gfx90a")
}
}
}
}
/* enable after the cmake file supports packaging /* enable after the cmake file supports packaging
stage("Packages") { stage("Packages") {
when { when {
......
README.md
View file @ aa5859e4
...@@ -10,7 +10,7 @@ rocm/tensorflow:rocm5.1-tf2.6-dev \ ...@@ -10,7 +10,7 @@ rocm/tensorflow:rocm5.1-tf2.6-dev \
/bin/bash /bin/bash
``` ```
# Install the new rocm-cmake version # Install newer version of rocm-cmake
https://github.com/RadeonOpenCompute/rocm-cmake https://github.com/RadeonOpenCompute/rocm-cmake
## Build ## Build
...@@ -26,6 +26,7 @@ cmake \ ...@@ -26,6 +26,7 @@ cmake \
-D CMAKE_CXX_FLAGS=" --offload-arch=gfx908 --offload-arch=gfx90a -O3" \ -D CMAKE_CXX_FLAGS=" --offload-arch=gfx908 --offload-arch=gfx90a -O3" \
-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \ -D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
-D CMAKE_PREFIX_PATH=/opt/rocm \ -D CMAKE_PREFIX_PATH=/opt/rocm \
-D CMAKE_INSTALL_PREFIX=${PATH_TO_CK_INSTALL_DIRECTORY} \
.. ..
``` ```
...@@ -47,6 +48,13 @@ Instructions for running each individual examples are under ```example/``` ...@@ -47,6 +48,13 @@ Instructions for running each individual examples are under ```example/```
``` ```
Instructions for running ckProfiler are under ```profiler/``` Instructions for running ckProfiler are under ```profiler/```
## Install CK
```bash
make install
```
## Using CK as pre-built kernel library
Instructions for using CK as a pre-built kernel library are under ```client_example/```
## Caveat ## Caveat
### Kernel Timing and Verification ### Kernel Timing and Verification
......
client_example/01_gemm/CMakeLists.txt 0 → 100644
View file @ aa5859e4
add_executable(client_gemm gemm.cpp)
target_link_libraries(client_gemm PRIVATE composable_kernel::device_operations)
client_example/01_gemm/gemm.cpp 0 → 100644
View file @ aa5859e4
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iomanip>
#include <vector>
#include <iostream>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/gemm.hpp"
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
using ADataType = F16;
using BDataType = F16;
using CDataType = F16;
using ALayout = Row;
using BLayout = Col;
using CLayout = Row;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
int main(int argc, char* argv[])
{
// GEMM shape
ck::index_t M = 3840;
ck::index_t N = 4096;
ck::index_t K = 4096;
ck::index_t StrideA = 4096;
ck::index_t StrideB = 4096;
ck::index_t StrideC = 4096;
if(argc == 1)
{
// use default case
}
else if(argc == 7)
{
M = std::stoi(argv[1]);
N = std::stoi(argv[2]);
K = std::stoi(argv[3]);
StrideA = std::stoi(argv[4]);
StrideB = std::stoi(argv[5]);
StrideC = std::stoi(argv[6]);
}
else
{
printf("arg1 to 6: M, N, K, StrideA, StrideB, StrideC\n");
exit(0);
}
auto f_matrix_space_size =
[](std::size_t nRow, std::size_t nCol, std::size_t stride, auto layout) {
using Layout = decltype(layout);
if(std::is_same<Layout, ck::tensor_layout::gemm::RowMajor>::value)
{
return (nRow - 1) * stride + nCol;
}
else
{
return (nCol - 1) * stride + nRow;
}
};
SimpleDeviceMem a_device_buf(sizeof(ADataType) * f_matrix_space_size(M, K, StrideA, ALayout{}));
SimpleDeviceMem b_device_buf(sizeof(BDataType) * f_matrix_space_size(K, N, StrideB, BLayout{}));
SimpleDeviceMem c_device_buf(sizeof(CDataType) * f_matrix_space_size(M, N, StrideC, CLayout{}));
using DeviceOp =
ck::tensor_operation::device::DeviceGemm<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
CDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{};
const auto c_element_op = CElementOp{};
std::string best_op_name;
bool found = false;
int best_op_id = -1;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
c_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
found = true;
best_op_id = i;
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
// run the best intance
{
auto& op_ptr = op_ptrs[best_op_id];
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
<< std::endl;
auto argument_ptr = op_ptr->MakeArgumentPointer(a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
c_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
}
std::cout << "Done" << std::endl;
}
return 0;
}
client_example/02_gemm_add_add_fastgelu/gemm_add_add_fastgelu.cpp
View file @ aa5859e4
...@@ -10,7 +10,7 @@ ...@@ -10,7 +10,7 @@
#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d.hpp" #include "ck/tensor_operation/gpu/device/device_gemm_multiple_d.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/device_gemm_add_add_fastgelu_instance.hpp" #include "ck/library/tensor_operation_instance/gpu/gemm_add_add_fastgelu.hpp"
using F16 = ck::half_t; using F16 = ck::half_t;
using F32 = float; using F32 = float;
...@@ -27,7 +27,6 @@ using CDEElementOp = AddAddFastGelu; ...@@ -27,7 +27,6 @@ using CDEElementOp = AddAddFastGelu;
using ADataType = F16; using ADataType = F16;
using BDataType = F16; using BDataType = F16;
using AccDataType = F32;
using D0DataType = F16; using D0DataType = F16;
using D1DataType = F16; using D1DataType = F16;
using EDataType = F16; using EDataType = F16;
...@@ -111,19 +110,22 @@ int main(int argc, char* argv[]) ...@@ -111,19 +110,22 @@ int main(int argc, char* argv[])
f_matrix_space_size(M, N, StrideD1, D1Layout{})); f_matrix_space_size(M, N, StrideD1, D1Layout{}));
SimpleDeviceMem e_device_buf(sizeof(EDataType) * f_matrix_space_size(M, N, StrideE, ELayout{})); SimpleDeviceMem e_device_buf(sizeof(EDataType) * f_matrix_space_size(M, N, StrideE, ELayout{}));
// add device op instances using DeviceOp = ck::tensor_operation::device::DeviceGemmMultipleD<
const auto op_ptrs = ck::tensor_operation::device::device_gemm_instance::
get_device_gemm_add_add_fastgelu_instances<ADataType,
BDataType,
AccDataType,
D0DataType,
D1DataType,
EDataType,
ALayout, ALayout,
BLayout, BLayout,
D0Layout, ck::Tuple<D0Layout, D1Layout>,
D1Layout, ELayout,
ELayout>(); ADataType,
BDataType,
ck::Tuple<D0DataType, D1DataType>,
EDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::AddAddFastGelu>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl; std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
...@@ -231,6 +233,8 @@ int main(int argc, char* argv[]) ...@@ -231,6 +233,8 @@ int main(int argc, char* argv[])
{ {
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false}); invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
} }
std::cout << "Done" << std::endl;
} }
return 0; return 0;
......
client_example/03_gemm_layernorm/CMakeLists.txt
View file @ aa5859e4
add_executable(gemm_add_add_reduce_normalize gemm_add_add_layernorm.cpp) add_executable(client_gemm_add_add_reduce_normalize gemm_add_add_layernorm.cpp)
target_link_libraries(gemm_add_add_reduce_normalize PRIVATE composable_kernel::device_operations) target_link_libraries(client_gemm_add_add_reduce_normalize PRIVATE composable_kernel::device_operations)
client_example/03_gemm_layernorm/gemm_add_add_layernorm.cpp
View file @ aa5859e4
...@@ -160,8 +160,9 @@ int main() ...@@ -160,8 +160,9 @@ int main()
ck::index_t StrideC = 1024; ck::index_t StrideC = 1024;
ck::index_t StrideD0 = 1024; ck::index_t StrideD0 = 1024;
const auto gemm_reduce_ptrs = ck::tensor_operation::device::device_gemm_instance:: const auto gemm_reduce_ptrs =
get_device_gemm_add_add_mean_squaremean_instances<ADataType, ck::tensor_operation::device::instance::get_device_gemm_add_add_mean_squaremean_instances<
ADataType,
BDataType, BDataType,
CDataType, CDataType,
ALayout, ALayout,
...@@ -169,7 +170,7 @@ int main() ...@@ -169,7 +170,7 @@ int main()
CLayout>(); CLayout>();
const auto normalize_ptrs = const auto normalize_ptrs =
ck::tensor_operation::device::get_device_normalize_from_mean_meansquare_instances< ck::tensor_operation::device::instance::get_device_normalize_from_mean_meansquare_instances<
CDataType, CDataType,
ReduceDataType, ReduceDataType,
ReduceDataType, ReduceDataType,
......
client_example/04_contraction/CMakeLists.txt 0 → 100644
View file @ aa5859e4
add_executable(client_contraction_scale contraction_scale.cpp)
target_link_libraries(client_contraction_scale PRIVATE composable_kernel::device_operations)
add_executable(client_contraction_bilinear contraction_bilinear.cpp)
target_link_libraries(client_contraction_bilinear PRIVATE composable_kernel::device_operations)
client_example/04_contraction/contraction_bilinear.cpp 0 → 100644
View file @ aa5859e4
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iomanip>
#include <numeric>
#include <vector>
#include <iostream>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_contraction_multiple_d.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/contraction_bilinear.hpp"
using F32 = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Bilinear = ck::tensor_operation::element_wise::Bilinear;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = Bilinear;
using ADataType = F32;
using BDataType = F32;
using AccDataType = F32;
using CShuffleDataType = F32;
using DDataType = F32;
using DsDataType = ck::Tuple<DDataType>;
using EDataType = F32;
static constexpr ck::index_t NumDimM = 2;
static constexpr ck::index_t NumDimN = 2;
static constexpr ck::index_t NumDimK = 2;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
int main(int argc, char* argv[])
{
// A[M0, M1, K0, K1]
std::vector<ck::index_t> a_ms_ks_lengths{30, 128, 32, 64};
std::vector<ck::index_t> a_ms_ks_strides{524288, 4096, 128, 1};
// B[N0, N1, K0, K1]
std::vector<ck::index_t> b_ns_ks_lengths{32, 64, 32, 64};
std::vector<ck::index_t> b_ns_ks_strides{524288, 4096, 128, 1};
// D[M0, M1, N0, N1]
std::vector<ck::index_t> d_ms_ns_lengths{30, 128, 32, 64};
std::vector<ck::index_t> d_ms_ns_strides{524288, 4096, 128, 1};
// E[M0, M1, N0, N1]
std::vector<ck::index_t> e_ms_ns_lengths{30, 128, 32, 64};
std::vector<ck::index_t> e_ms_ns_strides{524288, 4096, 128, 1};
float alpha = 1.f;
float beta = 1.f;
if(argc == 1)
{
// use default case
}
else if(argc == 25)
{
const ck::index_t M0 = std::stoi(argv[1]);
const ck::index_t M1 = std::stoi(argv[2]);
const ck::index_t N0 = std::stoi(argv[3]);
const ck::index_t N1 = std::stoi(argv[4]);
const ck::index_t K0 = std::stoi(argv[5]);
const ck::index_t K1 = std::stoi(argv[6]);
a_ms_ks_lengths = {M0, M1, K0, K1};
a_ms_ks_strides = {
std::stoi(argv[7]), std::stoi(argv[8]), std::stoi(argv[9]), std::stoi(argv[10])};
b_ns_ks_lengths = {N0, N1, K0, K1};
b_ns_ks_strides = {
std::stoi(argv[11]), std::stoi(argv[12]), std::stoi(argv[13]), std::stoi(argv[14])};
d_ms_ns_lengths = {M0, M1, N0, N1};
d_ms_ns_strides = {
std::stoi(argv[15]), std::stoi(argv[16]), std::stoi(argv[17]), std::stoi(argv[18])};
e_ms_ns_lengths = {M0, M1, N0, N1};
e_ms_ns_strides = {
std::stoi(argv[19]), std::stoi(argv[20]), std::stoi(argv[21]), std::stoi(argv[22])};
alpha = std::stof(argv[23]);
beta = std::stof(argv[24]);
}
else
{
printf("arg1 to 6: M0, M1, N0, N1, K0, K1\n");
printf("arg7 to 10: Stride_A_M0, Stride_A_M1, Stride_A_K0, Stride_A_K1\n");
printf("arg11 to 14: Stride_B_N0, Stride_B_N1, Stride_B_K0, Stride_B_K1\n");
printf("arg15 to 18: Stride_D_M0, Stride_D_M1, Stride_D_N0, Stride_D_N1\n");
printf("arg19 to 22: Stride_E_M0, Stride_E_M1, Stride_E_N0, Stride_E_N1\n");
printf("arg23 to 24: alpha, beta\n");
exit(0);
}
auto f_tensor_space_size = [](auto lengths, auto strides) {
std::size_t space_size = 1;
for(std::size_t i = 0; i < lengths.size(); ++i)
{
space_size += (lengths[i] - 1) * strides[i];
}
return space_size;
};
SimpleDeviceMem a_device_buf(sizeof(ADataType) *
f_tensor_space_size(a_ms_ks_lengths, a_ms_ks_strides));
SimpleDeviceMem b_device_buf(sizeof(BDataType) *
f_tensor_space_size(b_ns_ks_lengths, b_ns_ks_strides));
SimpleDeviceMem d_device_buf(sizeof(DDataType) *
f_tensor_space_size(d_ms_ns_lengths, d_ms_ns_strides));
SimpleDeviceMem e_device_buf(sizeof(EDataType) *
f_tensor_space_size(e_ms_ns_lengths, e_ms_ns_strides));
using DeviceOp = ck::tensor_operation::device::DeviceContractionMultipleD<
NumDimM,
NumDimN,
NumDimK,
ADataType,
BDataType,
ck::Tuple<DDataType>,
EDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::Bilinear>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{};
const auto cde_element_op = CDEElementOp{alpha, beta};
std::string best_op_name;
bool found = false;
int best_op_id = -1;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr =
op_ptr->MakeArgumentPointer(a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
a_ms_ks_lengths,
a_ms_ks_strides,
b_ns_ks_lengths,
b_ns_ks_strides,
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
e_ms_ns_lengths,
e_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
ck::index_t M = std::accumulate(e_ms_ns_lengths.begin(),
e_ms_ns_lengths.begin() + NumDimM,
ck::index_t{1},
std::multiplies<ck::index_t>{});
ck::index_t N = std::accumulate(e_ms_ns_lengths.begin() + NumDimM,
e_ms_ns_lengths.begin() + NumDimM + NumDimN,
ck::index_t{1},
std::multiplies<ck::index_t>{});
ck::index_t K = std::accumulate(a_ms_ks_lengths.begin() + NumDimM,
a_ms_ks_lengths.begin() + NumDimM + NumDimK,
ck::index_t{1},
std::multiplies<ck::index_t>{});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
sizeof(DDataType) * M * N + sizeof(EDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
found = true;
best_op_id = i;
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
return 0;
}
client_example/04_contraction/contraction_scale.cpp 0 → 100644
View file @ aa5859e4
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iomanip>
#include <numeric>
#include <vector>
#include <iostream>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_contraction_multiple_d.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/contraction_scale.hpp"
using F32 = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Scale = ck::tensor_operation::element_wise::Scale;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = Scale;
using ADataType = F32;
using BDataType = F32;
using AccDataType = F32;
using CShuffleDataType = F32;
using DsDataType = ck::Tuple<>;
using EDataType = F32;
static constexpr ck::index_t NumDimM = 2;
static constexpr ck::index_t NumDimN = 2;
static constexpr ck::index_t NumDimK = 2;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
int main(int argc, char* argv[])
{
// A[M0, M1, K0, K1]
std::vector<ck::index_t> a_ms_ks_lengths{30, 128, 32, 64};
std::vector<ck::index_t> a_ms_ks_strides{524288, 4096, 128, 1};
// B[N0, N1, K0, K1]
std::vector<ck::index_t> b_ns_ks_lengths{32, 64, 32, 64};
std::vector<ck::index_t> b_ns_ks_strides{524288, 4096, 128, 1};
// E[M0, M1, N0, N1]
std::vector<ck::index_t> e_ms_ns_lengths{30, 128, 32, 64};
std::vector<ck::index_t> e_ms_ns_strides{524288, 4096, 128, 1};
float scale = 1.f;
if(argc == 1)
{
// use default case
}
else if(argc == 20)
{
const ck::index_t M0 = std::stoi(argv[1]);
const ck::index_t M1 = std::stoi(argv[2]);
const ck::index_t N0 = std::stoi(argv[3]);
const ck::index_t N1 = std::stoi(argv[4]);
const ck::index_t K0 = std::stoi(argv[5]);
const ck::index_t K1 = std::stoi(argv[6]);
a_ms_ks_lengths = {M0, M1, K0, K1};
a_ms_ks_strides = {
std::stoi(argv[7]), std::stoi(argv[8]), std::stoi(argv[9]), std::stoi(argv[10])};
b_ns_ks_lengths = {N0, N1, K0, K1};
b_ns_ks_strides = {
std::stoi(argv[11]), std::stoi(argv[12]), std::stoi(argv[13]), std::stoi(argv[14])};
e_ms_ns_lengths = {M0, M1, N0, N1};
e_ms_ns_strides = {
std::stoi(argv[15]), std::stoi(argv[16]), std::stoi(argv[17]), std::stoi(argv[18])};
scale = std::stof(argv[19]);
}
else
{
printf("arg1 to 6: M0, M1, N0, N1, K0, K1\n");
printf("arg7 to 10: Stride_A_M0, Stride_A_M1, Stride_A_K0, Stride_A_K1\n");
printf("arg11 to 14: Stride_B_N0, Stride_B_N1, Stride_B_K0, Stride_B_K1\n");
printf("arg15 to 18: Stride_E_M0, Stride_E_M1, Stride_E_N0, Stride_E_N1\n");
printf("arg19: scale\n");
exit(0);
}
auto f_tensor_space_size = [](auto lengths, auto strides) {
std::size_t space_size = 1;
for(std::size_t i = 0; i < lengths.size(); ++i)
{
space_size += (lengths[i] - 1) * strides[i];
}
return space_size;
};
SimpleDeviceMem a_device_buf(sizeof(ADataType) *
f_tensor_space_size(a_ms_ks_lengths, a_ms_ks_strides));
SimpleDeviceMem b_device_buf(sizeof(BDataType) *
f_tensor_space_size(b_ns_ks_lengths, b_ns_ks_strides));
SimpleDeviceMem e_device_buf(sizeof(EDataType) *
f_tensor_space_size(e_ms_ns_lengths, e_ms_ns_strides));
using DeviceOp = ck::tensor_operation::device::DeviceContractionMultipleD<
NumDimM,
NumDimN,
NumDimK,
ADataType,
BDataType,
ck::Tuple<>,
EDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::Scale>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{};
const auto cde_element_op = CDEElementOp{scale};
std::string best_op_name;
bool found = false;
int best_op_id = -1;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
std::array<const void*, 0>{},
e_device_buf.GetDeviceBuffer(),
a_ms_ks_lengths,
a_ms_ks_strides,
b_ns_ks_lengths,
b_ns_ks_strides,
std::array<std::vector<ck::index_t>, 0>{},
std::array<std::vector<ck::index_t>, 0>{},
e_ms_ns_lengths,
e_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
ck::index_t M = std::accumulate(e_ms_ns_lengths.begin(),
e_ms_ns_lengths.begin() + NumDimM,
ck::index_t{1},
std::multiplies<ck::index_t>{});
ck::index_t N = std::accumulate(e_ms_ns_lengths.begin() + NumDimM,
e_ms_ns_lengths.begin() + NumDimM + NumDimN,
ck::index_t{1},
std::multiplies<ck::index_t>{});
ck::index_t K = std::accumulate(a_ms_ks_lengths.begin() + NumDimM,
a_ms_ks_lengths.begin() + NumDimM + NumDimK,
ck::index_t{1},
std::multiplies<ck::index_t>{});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
found = true;
best_op_id = i;
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
return 0;
}
client_example/CMakeLists.txt
View file @ aa5859e4
...@@ -6,5 +6,7 @@ find_package(composable_kernel 1.0.0 COMPONENTS device_operations) ...@@ -6,5 +6,7 @@ find_package(composable_kernel 1.0.0 COMPONENTS device_operations)
find_package(hip REQUIRED PATHS /opt/rocm) find_package(hip REQUIRED PATHS /opt/rocm)
message(STATUS "Build with HIP ${hip_VERSION}") message(STATUS "Build with HIP ${hip_VERSION}")
add_subdirectory(01_gemm)
add_subdirectory(02_gemm_add_add_fastgelu) add_subdirectory(02_gemm_add_add_fastgelu)
add_subdirectory(03_gemm_layernorm) add_subdirectory(03_gemm_layernorm)
add_subdirectory(04_contraction)
client_example/README.md
View file @ aa5859e4
## ##
Client application links to CK library, and therefore CK library needs to be installed before building client applications. Client application links to CK library, and therefore CK library needs to be installed before building client applications.
## Docker script
```bash
docker run \
-it \
--privileged \
--group-add sudo \
-w /root/workspace \
-v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace \
rocm/tensorflow:rocm5.1-tf2.6-dev \
/bin/bash
```
## Build ## Build
```bash ```bash
...@@ -22,7 +11,7 @@ cd client_example/build ...@@ -22,7 +11,7 @@ cd client_example/build
```bash ```bash
cmake \ cmake \
-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \ -D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
-D CMAKE_PREFIX_PATH=/opt/rocm \ -D CMAKE_PREFIX_PATH="/opt/rocm;${PATH_TO_CK_INSTALL_DIRECTORY}" \
.. ..
``` ```
......
cmake/googletest.cmake
View file @ aa5859e4
...@@ -20,6 +20,7 @@ list(APPEND GTEST_CMAKE_CXX_FLAGS ...@@ -20,6 +20,7 @@ list(APPEND GTEST_CMAKE_CXX_FLAGS
-Wno-unused-member-function -Wno-unused-member-function
-Wno-comma -Wno-comma
-Wno-old-style-cast -Wno-old-style-cast
-Wno-deprecated
) )
message(STATUS "Suppressing googltest warnings with flags: ${GTEST_CMAKE_CXX_FLAGS}") message(STATUS "Suppressing googltest warnings with flags: ${GTEST_CMAKE_CXX_FLAGS}")
......
example/01_gemm/CMakeLists.txt
View file @ aa5859e4
...@@ -4,5 +4,6 @@ add_example_executable(example_gemm_dl_int8 gemm_dl_int8.cpp) ...@@ -4,5 +4,6 @@ add_example_executable(example_gemm_dl_int8 gemm_dl_int8.cpp)
add_example_executable(example_gemm_xdl_fp16 gemm_xdl_fp16.cpp) add_example_executable(example_gemm_xdl_fp16 gemm_xdl_fp16.cpp)
add_example_executable(example_gemm_xdl_bf16 gemm_xdl_bf16.cpp) add_example_executable(example_gemm_xdl_bf16 gemm_xdl_bf16.cpp)
add_example_executable(example_gemm_xdl_int8 gemm_xdl_int8.cpp) add_example_executable(example_gemm_xdl_int8 gemm_xdl_int8.cpp)
add_example_executable(example_gemm_xdl_skip_b_lds_fp16 gemm_xdl_skip_b_lds_fp16.cpp)
# FIXME: re-enable this exampe as test when SWDEV-335738 is fixed # FIXME: re-enable this exampe as test when SWDEV-335738 is fixed
add_example_executable_no_testing(example_gemm_xdl_fp64 gemm_xdl_fp64.cpp) add_example_executable_no_testing(example_gemm_xdl_fp64 gemm_xdl_fp64.cpp)
example/01_gemm/gemm_dl_fp16.cpp
View file @ aa5859e4
...@@ -12,9 +12,9 @@ ...@@ -12,9 +12,9 @@
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp" #include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
template <ck::index_t... Is> template <ck::index_t... Is>
...@@ -142,9 +142,9 @@ int main(int argc, char* argv[]) ...@@ -142,9 +142,9 @@ int main(int argc, char* argv[])
b_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{}); b_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
} }
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace()); DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace()); DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace()); DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
a_m_k_device_buf.ToDevice(a_m_k.mData.data()); a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data()); b_k_n_device_buf.ToDevice(b_k_n.mData.data());
......
example/01_gemm/gemm_dl_fp32.cpp
View file @ aa5859e4
...@@ -12,9 +12,9 @@ ...@@ -12,9 +12,9 @@
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp" #include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
template <ck::index_t... Is> template <ck::index_t... Is>
...@@ -141,9 +141,9 @@ int main(int argc, char* argv[]) ...@@ -141,9 +141,9 @@ int main(int argc, char* argv[])
b_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{}); b_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
} }
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace()); DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace()); DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace()); DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
a_m_k_device_buf.ToDevice(a_m_k.mData.data()); a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data()); b_k_n_device_buf.ToDevice(b_k_n.mData.data());
......
example/01_gemm/gemm_dl_int8.cpp
View file @ aa5859e4
...@@ -12,9 +12,9 @@ ...@@ -12,9 +12,9 @@
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp" #include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
template <ck::index_t... Is> template <ck::index_t... Is>
...@@ -139,9 +139,9 @@ int main(int argc, char* argv[]) ...@@ -139,9 +139,9 @@ int main(int argc, char* argv[])
b_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{}); b_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
} }
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace()); DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace()); DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace()); DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
a_m_k_device_buf.ToDevice(a_m_k.mData.data()); a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data()); b_k_n_device_buf.ToDevice(b_k_n.mData.data());
......
example/01_gemm/gemm_xdl_bf16.cpp
View file @ aa5859e4
...@@ -11,9 +11,9 @@ ...@@ -11,9 +11,9 @@
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp" #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/library/host_tensor/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/library/utility/check_err.hpp" #include "ck/library/utility/check_err.hpp"
...@@ -170,9 +170,9 @@ int main(int argc, char* argv[]) ...@@ -170,9 +170,9 @@ int main(int argc, char* argv[])
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}); b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
} }
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace()); DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace()); DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace()); DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
a_m_k_device_buf.ToDevice(a_m_k.mData.data()); a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data()); b_k_n_device_buf.ToDevice(b_k_n.mData.data());
......
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