merge develop branch and add gridwise pipeline v3

CMakeLists.txt

-cmake_minimum_required(VERSION 3.5)
+cmake_minimum_required(VERSION 3.14)
 
 # Check support for CUDA/HIP in Cmake
 project(composable_kernel)
@@ -72,8 +72,9 @@ message(STATUS "Build with HIP ${HIP_VERSION}")
 
 
 rocm_create_package(
-    NAME CK-${CK_BACKEND}
+    NAME composablekernel
     DESCRIPTION "High Performance Composable Kernel for AMD GPUs"
+    MAINTAINER "MIOpen Kernels Dev Team <dl.MIOpen@amd.com>"
     LDCONFIG
 )
 
@@ -226,14 +227,13 @@ set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/lib)
 set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/lib)
 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/bin)
 
-configure_file("${PROJECT_SOURCE_DIR}/include/ck/hip_version.hpp.in" "${PROJECT_BINARY_DIR}/include/ck/hip_version.hpp")
-
 include_directories(BEFORE
     ${PROJECT_SOURCE_DIR}/include
     ${PROJECT_BINARY_DIR}/include
     ${PROJECT_SOURCE_DIR}/library/include
 )
 
+
 SET(BUILD_DEV ON CACHE BOOL "BUILD_DEV")
 if(BUILD_DEV)
     add_compile_options(-Werror)
@@ -241,7 +241,31 @@ if(BUILD_DEV)
 endif()
 message("CMAKE_CXX_FLAGS: ${CMAKE_CXX_FLAGS}")
 
+add_custom_target(check COMMAND ${CMAKE_CTEST_COMMAND} --output-on-failure -C ${CMAKE_CFG_INTDIR})
+
 add_subdirectory(library)
 add_subdirectory(example)
 add_subdirectory(test)
 add_subdirectory(profiler)
+
+#Create an interface target for the include only files and call it "composablekernels"
+include(CMakePackageConfigHelpers)
+
+set(version 1.0.0)
+write_basic_package_version_file(
+    "${CMAKE_CURRENT_BINARY_DIR}/composable_kernelConfigVersion.cmake"
+    VERSION "${version}"
+    COMPATIBILITY AnyNewerVersion
+)
+
+configure_package_config_file(${CMAKE_CURRENT_SOURCE_DIR}/Config.cmake.in
+        "${CMAKE_CURRENT_BINARY_DIR}/composable_kernelConfig.cmake"
+        INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/composable_kernel
+        NO_CHECK_REQUIRED_COMPONENTS_MACRO
+)
+
+install(FILES
+    "${CMAKE_CURRENT_BINARY_DIR}/composable_kernelConfig.cmake"
+    "${CMAKE_CURRENT_BINARY_DIR}/composable_kernelConfigVersion.cmake"
+    DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/composable_kernel
+)

Config.cmake.in

+@PACKAGE_INIT@
+
+set(_composable_kernel_supported_components device_operations host_tensor)
+
+foreach(_comp ${composable_kernel_FIND_COMPONENTS})
+	if(NOT _comp IN_LIST _composable_kernel_supported_components)
+		set(composable_kernel_FOUND False)
+		set(composable_kernel_NOT_FOUND_MESSAGE "Unsupported component: ${_comp}")
+	endif()
+	include("${CMAKE_CURRENT_LIST_DIR}/composable_kernel${_comp}Targets.cmake")
+endforeach()

Dockerfile

@@ -11,13 +11,7 @@ ARG DEB_ROCM_REPO=http://repo.radeon.com/rocm/apt/.apt_$ROCMVERSION/
 RUN apt-get update
 RUN apt-get install -y wget gnupg
 RUN wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add -
-RUN if ! [ -z $OSDB_BKC_VERSION ]; then \
-       echo "Using BKC VERISION: $OSDB_BKC_VERSION";\
-       sh -c "echo deb [arch=amd64 trusted=yes] http://compute-artifactory.amd.com/artifactory/list/rocm-osdb-deb/ compute-rocm-dkms-no-npi-hipclang ${OSDB_BKC_VERSION} > /etc/apt/sources.list.d/rocm.list" ;\
-       cat  /etc/apt/sources.list.d/rocm.list;\
-    else \
-       sh -c "echo deb [arch=amd64] $DEB_ROCM_REPO ubuntu main > /etc/apt/sources.list.d/rocm.list" ;\
-    fi
+RUN sh -c "echo deb [arch=amd64] $DEB_ROCM_REPO ubuntu main > /etc/apt/sources.list.d/rocm.list"
 RUN wget --no-check-certificate -qO - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | apt-key add -
 RUN sh -c "echo deb https://apt.kitware.com/ubuntu/ bionic main | tee -a /etc/apt/sources.list"
 
@@ -25,18 +19,15 @@ RUN sh -c "echo deb https://apt.kitware.com/ubuntu/ bionic main | tee -a /etc/ap
 # Install dependencies
 RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \
     apt-utils \
-    sshpass \
     build-essential \
     cmake-data=3.15.1-0kitware1 \
     cmake=3.15.1-0kitware1 \
     curl \
-    doxygen \
     g++ \
     gdb \
     git \
     hip-rocclr \
     jq \
-    lcov \
     libelf-dev \
     libncurses5-dev \
     libnuma-dev \
@@ -44,7 +35,7 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
     llvm-amdgpu \
     pkg-config \
     python \
-    python3 \
+    python3.8 \
     python-dev \
     python3-dev \
     python-pip \
@@ -62,8 +53,6 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
 
-# RUN pip3 install --default-timeout=100000 -r requirements.txt
-
 # Setup ubsan environment to printstacktrace
 RUN ln -s /usr/bin/llvm-symbolizer-3.8 /usr/local/bin/llvm-symbolizer
 ENV UBSAN_OPTIONS=print_stacktrace=1
@@ -83,6 +72,13 @@ ARG PREFIX=/opt/rocm
 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
+RUN pip3 install --upgrade pip
+RUN pip3 install sqlalchemy
+RUN pip3 install pymysql
+RUN pip3 install pandas
+RUN pip3 install setuptools-rust
+RUN pip3 install sshtunnel
 # Setup ubsan environment to printstacktrace
 ENV UBSAN_OPTIONS=print_stacktrace=1
 
@@ -92,5 +88,3 @@ ADD rbuild.ini /rbuild.ini
 ADD dev-requirements.txt dev-requirements.txt
 RUN rbuild prepare -s develop -d $PREFIX
 RUN groupadd -f render
-# RUN cget install -f min-requirements.txt
-# RUN CXXFLAGS='-isystem $PREFIX/include' cget install -f ./mlir-requirements.txt

Jenkinsfile

@@ -140,6 +140,10 @@ def reboot(){
     build job: 'reboot-slaves', propagate: false , parameters: [string(name: 'server', value: "${env.NODE_NAME}"),]
 }
 
+
+
+
+
 def buildHipClangJobAndReboot(Map conf=[:]){
     try{
         buildHipClangJob(conf)
@@ -156,6 +160,126 @@ def buildHipClangJobAndReboot(Map conf=[:]){
     }
 }
 
+
+def runCKProfiler(Map conf=[:]){
+        show_node_info()
+
+        env.HSA_ENABLE_SDMA=0
+        checkout scm
+
+        def image = "composable_kernels"
+        def prefixpath = conf.get("prefixpath", "/opt/rocm")
+        def gpu_arch = conf.get("gpu_arch", "gfx908")
+
+        // 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="--device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined"
+        if (conf.get("enforce_xnack_on", false)) {
+            dockerOpts = dockerOpts + " --env HSA_XNACK=1"
+        }
+        def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg GPU_ARCH='${gpu_arch}' "
+
+        def variant = env.STAGE_NAME
+
+
+        def retimage
+        gitStatusWrapper(credentialsId: '7126e5fe-eb51-4576-b52b-9aaf1de8f0fd', gitHubContext: "Jenkins - ${variant}", account: 'ROCmSoftwarePlatform', repo: 'composable_kernel') {
+            try {
+                retimage = docker.build("${image}", dockerArgs + '.')
+                withDockerContainer(image: image, args: dockerOpts) {
+                    timeout(time: 5, unit: 'MINUTES')
+                    {
+                        sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo'
+                    }
+                }
+            }
+            catch (org.jenkinsci.plugins.workflow.steps.FlowInterruptedException e){
+                echo "The job was cancelled or aborted"
+                throw e
+            }
+            catch(Exception ex) {
+                retimage = docker.build("${image}", dockerArgs + "--no-cache .")
+                withDockerContainer(image: image, args: dockerOpts) {
+                    timeout(time: 5, unit: 'MINUTES')
+                    {
+                        sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo'
+                    }
+                }
+            }
+
+            withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') {
+                timeout(time: 5, unit: 'HOURS')
+                {
+                    cmake_build(conf)
+					dir("script"){
+                        //run gemm performance tests
+                        def gemm_log = "perf_gemm_${gpu_arch}.log"
+                        sh "rm -f ${gemm_log}"
+                        sh "echo Branch name: ${env.BRANCH_NAME} > ${gemm_log}"
+                        sh "echo Node name: ${NODE_NAME} >> ${gemm_log}"
+                        sh "echo GPU_arch: ${gpu_arch}  >> ${gemm_log}"
+                        sh "hipcc --version | grep -e 'HIP version'  >> ${gemm_log}"
+                        sh "/opt/rocm/bin/amdclang++ --version | grep -e 'InstalledDir' >> ${gemm_log}"
+                        sh "./profile_gemm.sh gemm 0 0 0 1 0 5 | tee -a ${gemm_log}"
+                        sh "./profile_gemm.sh gemm 1 0 0 1 0 5 | tee -a ${gemm_log}"
+                        sh "./profile_gemm.sh gemm 2 0 0 1 0 5 | tee -a ${gemm_log}"
+                        sh "./profile_gemm.sh gemm 3 0 0 1 0 5 | tee -a ${gemm_log}"
+                        sh "./profile_gemm.sh gemm 0 1 0 1 0 5 | tee -a ${gemm_log}"
+                        sh "./profile_gemm.sh gemm 1 1 0 1 0 5 | tee -a ${gemm_log}"
+                        sh "./profile_gemm.sh gemm 2 1 0 1 0 5 | tee -a ${gemm_log}"
+                        sh "./profile_gemm.sh gemm 3 1 0 1 0 5 | tee -a ${gemm_log}"
+                        sh "./profile_gemm.sh gemm 0 2 0 1 0 5 | tee -a ${gemm_log}"
+                        sh "./profile_gemm.sh gemm 1 2 0 1 0 5 | tee -a ${gemm_log}"
+                        sh "./profile_gemm.sh gemm 2 2 0 1 0 5 | tee -a ${gemm_log}"
+                        sh "./profile_gemm.sh gemm 3 2 0 1 0 5 | tee -a ${gemm_log}"
+                        sh "./profile_gemm.sh gemm 0 3 0 1 0 5 | tee -a ${gemm_log}"
+                        sh "./profile_gemm.sh gemm 1 3 0 1 0 5 | tee -a ${gemm_log}"
+                        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 ${gemm_log}"
+                        //results will be parsed, stored, and analyzed within the python script
+                        //the script will return 0 if the performance criteria are met
+                        //or return 1 if the criteria are not met
+                        archiveArtifacts  "${gemm_log}"
+                        sh "python3 parse_perf_data.py ${gemm_log} "
+                        //run resnet50 test
+                        def resnet_log = "perf_resnet50_${gpu_arch}.log"
+                        sh "rm -f ${resnet_log}"
+                        sh "echo Branch name: ${env.BRANCH_NAME} > ${resnet_log}"
+                        sh "echo Node name: ${NODE_NAME} >> ${resnet_log}"
+                        sh "echo GPU_arch: ${gpu_arch}  >> ${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} "
+					}
+                }
+            }
+        }
+        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()
+        }
+    }
+}
+
 pipeline {
     agent none
     options {
@@ -178,29 +302,30 @@ pipeline {
                 //         buildHipClangJobAndReboot(build_cmd: build_cmd, no_reboot:true, prefixpath: '/opt/rocm', build_type: 'debug')
                 //     }
                 // }
-                stage('Build Profiler: Release, gfx908')
-                {
-                    agent { label rocmnode("nogpu")}
-                    environment{
-                        setup_args = """ -D CMAKE_CXX_FLAGS="--offload-arch=gfx908 -O3 " -DBUILD_DEV=On """
-                    }
-                    steps{
-                        buildHipClangJobAndReboot(setup_args:setup_args, config_targets: "ckProfiler", no_reboot:true, build_type: 'Release')
-                    }
-                }
-                stage('Build Profiler: Debug, gfx908')
-                {
-                    agent { label rocmnode("nogpu")}
-                    environment{
-                        setup_args = """ -D CMAKE_CXX_FLAGS="--offload-arch=gfx908 -O3 " -DBUILD_DEV=On """
-                    }
-                    steps{
-                        // until we stabilize debug build due to compiler crashes
-                        catchError(buildResult: 'SUCCESS', stageResult: 'FAILURE') {
-                            buildHipClangJobAndReboot(setup_args:setup_args, config_targets: "ckProfiler", no_reboot:true, build_type: 'Debug')
-                        }
-                    }
-                }
+				// we will build and run ckProfiler release version later, during the performance test stage
+                //stage('Build Profiler: Release, gfx908')
+                //{
+                //    agent { label rocmnode("nogpu")}
+                //    environment{
+                //        setup_args = """ -D CMAKE_CXX_FLAGS="--offload-arch=gfx908 -O3 " -DBUILD_DEV=On """
+                //    }
+                //    steps{
+                //        buildHipClangJobAndReboot(setup_args:setup_args, config_targets: "ckProfiler", no_reboot:true, build_type: 'Release')
+                //    }
+                //}
+                //stage('Build Profiler: Debug, gfx908')
+				//{
+                //    agent { label rocmnode("nogpu")}
+                //    environment{
+                //        setup_args = """ -D CMAKE_CXX_FLAGS="--offload-arch=gfx908 -O3 " -DBUILD_DEV=On """
+                //    }
+                //    steps{
+                //        // until we stabilize debug build due to compiler crashes
+                //        catchError(buildResult: 'SUCCESS', stageResult: 'FAILURE') {
+                //            buildHipClangJobAndReboot(setup_args:setup_args, config_targets: "ckProfiler", no_reboot:true, build_type: 'Debug')
+                //        }
+                //    }
+                //}
                 stage('Clang Format') {
                     agent{ label rocmnode("nogpu") }
                     environment{
@@ -220,7 +345,7 @@ pipeline {
                 }
             }
         }
-        stage("Tests")
+		stage("Tests")
         {
             parallel
             {
@@ -228,7 +353,7 @@ pipeline {
                 {
                     agent{ label rocmnode("gfx908")}
                     environment{
-                        setup_args = """ -D CMAKE_CXX_FLAGS="--offload-arch=gfx908 -O3 " -DBUILD_DEV=On """
+                        setup_args = """ -D CMAKE_CXX_FLAGS=" --offload-arch=gfx900 --offload-arch=gfx906  --offload-arch=gfx908 --offload-arch=gfx90a -O3 " -DBUILD_DEV=On """
                     }
                     steps{
                         buildHipClangJobAndReboot(setup_args:setup_args, config_targets: "check", no_reboot:true, build_type: 'Release')
@@ -249,6 +374,46 @@ pipeline {
 
             }
         }
+        stage("Client App")
+        {
+            parallel
+            {
+                stage("Run Client App")
+                {
+                    agent{ label rocmnode("gfx908")}
+                    environment{
+                        setup_args = """ -D  -DBUILD_DEV=Off -DCMAKE_INSTALL_PREFIX=../install CMAKE_CXX_FLAGS="--offload-arch=gfx908 -O3 " """
+                        execute_args = """ cd ../test/client_app && rm -rf build && mkdir build && cd build && cmake -DCMAKE_PREFIX_PATH="${env.WORKSPACE}/install;/opt/rocm" .. && make  """ 
+                    }
+                    steps{
+                        buildHipClangJobAndReboot(setup_args: setup_args, config_targets: "install", no_reboot:true, build_type: 'Release', execute_cmd: execute_args, prefixpath: '/usr/local')
+                    }
+                }
+            }
+        }
+        stage("Performance Tests")
+        {
+            parallel
+            {
+                stage("Run ckProfiler: gfx908")
+                {
+                    agent{ label rocmnode("gfx908")}
+                    environment{
+                        setup_args = """ -D CMAKE_CXX_FLAGS="--offload-arch=gfx908 -O3 " -DBUILD_DEV=On """
+                        dbuser = "${dbuser}"
+                        dbpassword = "${dbpassword}"
+                        dbsship = "${dbsship}"
+                        dbsshport = "${dbsshport}"
+                        dbsshuser = "${dbsshuser}"
+                        dbsshpassword = "${dbsshpassword}"
+                   }
+                    steps{
+                        runPerfTest(setup_args:setup_args, config_targets: "ckProfiler", no_reboot:true, build_type: 'Release')
+                    }
+                }
+            }
+        }
+
         // enable after the cmake file supports packaging
         // stage("Packages") {
         //     when {

README.md

@@ -20,7 +20,7 @@ mkdir build && cd build
 cmake                                                                 \
 -D BUILD_DEV=OFF                                                      \
 -D CMAKE_BUILD_TYPE=Release                                           \
--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_PREFIX_PATH=/opt/rocm                                        \
 ..
@@ -43,3 +43,13 @@ Instructions for running each individual examples are under ```example/```
  make -j ckProfiler
 ```
 Instructions for running ckProfiler are under ```profiler/```
+
+
+## Caveat
+### Kernel Timing and Verification
+CK's own kernel timer will warn up kernel once, and then run it multiple times
+to get average kernel time. For some kernels that use atomic add, this will cause
+output buffer to be accumulated multiple times, causing verfication failure.
+To work around it, do not use CK's own timer and do verification at the same time.
+CK's own timer and verification in each example and ckProfiler can be enabled or
+disabled from command line.

cmake/EnableCompilerWarnings.cmake

@@ -66,7 +66,7 @@ else()
             -Wunreachable-code
             -Wunused
 
-            -Wno-sign-compare
+            -Wsign-compare
             -Wno-extra-semi-stmt
         )
         if (CMAKE_${COMPILER}_COMPILER_ID MATCHES "Clang")

cmake/googletest.cmake

+include(FetchContent)
+
+set(GOOGLETEST_DIR "" CACHE STRING "Location of local GoogleTest repo to build against")
+
+if(GOOGLETEST_DIR)
+  set(FETCHCONTENT_SOURCE_DIR_GOOGLETEST ${GOOGLETEST_DIR} CACHE STRING "GoogleTest source directory override")
+endif()
+
+message(STATUS "Fetching GoogleTest")
+
+list(APPEND GTEST_CMAKE_CXX_FLAGS 
+     -Wno-undef
+     -Wno-reserved-identifier
+     -Wno-global-constructors
+     -Wno-missing-noreturn
+     -Wno-disabled-macro-expansion
+     -Wno-used-but-marked-unused
+     -Wno-switch-enum
+     -Wno-zero-as-null-pointer-constant
+     -Wno-unused-member-function
+     -Wno-comma
+     -Wno-old-style-cast
+)
+message(STATUS "Suppressing googltest warnings with flags: ${GTEST_CMAKE_CXX_FLAGS}")
+
+FetchContent_Declare(
+  googletest
+  GIT_REPOSITORY https://github.com/google/googletest.git
+  GIT_TAG        b85864c64758dec007208e56af933fc3f52044ee
+)
+
+# Will be necessary for windows build
+# set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
+FetchContent_MakeAvailable(googletest)
+
+target_compile_options(gtest PRIVATE ${GTEST_CMAKE_CXX_FLAGS})
+target_compile_options(gtest_main PRIVATE ${GTEST_CMAKE_CXX_FLAGS})
+target_compile_options(gmock PRIVATE ${GTEST_CMAKE_CXX_FLAGS})
+target_compile_options(gmock_main PRIVATE ${GTEST_CMAKE_CXX_FLAGS})

example/01_gemm/CMakeLists.txt

+add_example_executable(example_gemm_dl_fp32 gemm_dl_fp32.cpp)
+add_example_executable(example_gemm_dl_fp16 gemm_dl_fp16.cpp)
+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_bf16 gemm_xdl_bf16.cpp)
 add_example_executable(example_gemm_xdl_int8 gemm_xdl_int8.cpp)
+# 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)

example/01_gemm/gemm_dl_fp16.cpp

+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include <half.hpp>
+
+#include "check_err.hpp"
+#include "config.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "device_tensor.hpp"
+#include "device_gemm_dl.hpp"
+#include "element_wise_operation.hpp"
+#include "reference_gemm.hpp"
+#include "gemm_specialization.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+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 ADataType   = ck::half_t;
+using BDataType   = ck::half_t;
+using CDataType   = ck::half_t;
+using AccDataType = float;
+
+using ALayout = Col;
+using BLayout = Row;
+using CLayout = Row;
+
+using AElementOp = ck::tensor_operation::element_wise::PassThrough;
+using BElementOp = ck::tensor_operation::element_wise::PassThrough;
+using CElementOp = ck::tensor_operation::element_wise::PassThrough;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+
+// clang-format off
+using DeviceGemmInstance = ck::tensor_operation::device::
+        //  ########| AData| BData| CData| AccData| ALayout| BLayout| CLayout|           A|           B|           C|           GEMM| Block|  MPer|  NPer| K0Per| K1|      M1Per|      N1Per|   KPer|  M11N11Thread|  M11N11Thread|     ABlockTransfer|       ABlockTransfer| ABlockTransfer| ABlockTransfer|      ABlockTransfer|     ABlockTransfer|       ABlockTransfer|     BBlockTransfer|       BBlockTransfer| BBlockTransfer| BBlockTransfer|      BBlockTransfer|     BBlockTransfer|       BBlockTransfer|     CThreadTransfer|  CThreadTransfer|    CThreadTransfer|
+        //  ########|  Type|  Type|  Type|    Type|        |        |        | Elementwise| Elementwise| Elementwise| Spacialization|  Size| Block| Block| Block|   | ThreadM111| ThreadN111| Thread| ClusterM110Xs| ClusterN110Xs| ThreadSliceLengths| ThreadClusterLengths|  ThreadCluster|      SrcAccess|     SrcVectorTensor|    SrcVectorTensor|      DstVectorTensor| ThreadSliceLengths| ThreadClusterLengths|  ThreadCluster|      SrcAccess|     SrcVectorTensor|    SrcVectorTensor|      DstVectorTensor|        SrcDstAccess|  SrcDstVectorDim| DstScalarPerVector|
+        //  ########|      |      |      |        |        |        |        |   Operation|   Operation|   Operation|               |      |      |      |      |   |           |           |       |              |              |        K0_M0_M1_K1|          K0_M0_M1_K1|   ArrangeOrder|          Order| Lengths_K0_M0_M1_K1| ContiguousDimOrder|  Lengths_K0_M0_M1_K1|        K0_N0_N1_K1|          K0_N0_N1_K1|   ArrangeOrder|          Order| Lengths_K0_N0_N1_K1| ContiguousDimOrder|  Lengths_K0_N0_N1_K1|               Order|                 |                   |
+        //  ########|      |      |      |        |        |        |        |            |            |            |               |      |      |      |      |   |           |           |       |              |              |                   |                     |               |               |                    |                   |                     |                   |                     |               |               |                    |                   |                     |                    |                 |                   |
+        DeviceGemmDl<   F16,   F16,   F16,     F32,     Col,     Row,     Row, PassThrough, PassThrough, PassThrough,    GemmDefault,   256,   128,   128,    16,  2,          4,          4,      1,       S<8, 2>,       S<8, 2>,      S<2, 1, 4, 2>,      S<8, 1,  32, 1>,  S<0, 3, 1, 2>,  S<0, 3, 1, 2>,       S<1, 1, 4, 1>,      S<0, 3, 1, 2>,        S<1, 1, 4, 2>,      S<2, 1, 4, 2>,      S<8, 1,  32, 1>,  S<0, 3, 1, 2>,  S<0, 3, 1, 2>,       S<1, 1, 4, 1>,      S<0, 3, 1, 2>,        S<1, 1, 4, 2>, S<0, 1, 2, 3, 4, 5>,                5,                  4>;
+// clang-format on
+
+using ReferenceGemmInstance = ck::tensor_operation::host::
+    ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
+
+    // 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)
+    {
+        // do nothing
+    }
+    else if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+    }
+    else if(argc == 10)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+
+        M = std::stoi(argv[4]);
+        N = std::stoi(argv[5]);
+        K = std::stoi(argv[6]);
+
+        StrideA = std::stoi(argv[7]);
+        StrideB = std::stoi(argv[8]);
+        StrideC = std::stoi(argv[9]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: time kernel (0=n0, 1=yes)\n");
+        printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
+        exit(1);
+    }
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({stride, 1}));
+            }
+            else
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({1, stride}));
+            }
+        };
+
+    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<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;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+        break;
+    case 2:
+        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+        break;
+    default:
+        a_m_k.GenerateTensorValue(GeneratorTensor_Sequential<0>{});
+        b_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+    }
+
+    DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
+    DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
+    DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());
+
+    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
+    b_k_n_device_buf.ToDevice(b_k_n.mData.data());
+
+    auto a_element_op = AElementOp{};
+    auto b_element_op = BElementOp{};
+    auto c_element_op = CElementOp{};
+
+    // do GEMM
+    auto gemm     = DeviceGemmInstance{};
+    auto invoker  = gemm.MakeInvoker();
+    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,
+                                      a_element_op,
+                                      b_element_op,
+                                      c_element_op);
+
+    if(!gemm.IsSupportedArgument(argument))
+    {
+        std::cout << gemm.GetTypeString() << " does not support this problem" << std::endl;
+
+        return 0;
+    }
+
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop = std::size_t(2) * M * N * K;
+    std::size_t num_btype =
+        sizeof(ADataType) * M * K + sizeof(BDataType) * K * 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: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
+              << gemm.GetTypeString() << std::endl;
+
+    c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
+
+    bool pass = true;
+
+    if(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, a_element_op, b_element_op, c_element_op);
+
+        ref_invoker.Run(ref_argument);
+
+        pass = ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
+    }
+
+    return pass ? 0 : 1;
+}

example/01_gemm/gemm_dl_fp32.cpp

+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include <half.hpp>
+
+#include "check_err.hpp"
+#include "config.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "device_tensor.hpp"
+#include "device_gemm_dl.hpp"
+#include "element_wise_operation.hpp"
+#include "reference_gemm.hpp"
+#include "gemm_specialization.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+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 ADataType   = float;
+using BDataType   = float;
+using CDataType   = float;
+using AccDataType = float;
+
+using ALayout = Col;
+using BLayout = Row;
+using CLayout = Row;
+
+using AElementOp = ck::tensor_operation::element_wise::PassThrough;
+using BElementOp = ck::tensor_operation::element_wise::PassThrough;
+using CElementOp = ck::tensor_operation::element_wise::PassThrough;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+
+// clang-format off
+using DeviceGemmInstance = ck::tensor_operation::device::
+        //  ########| AData| BData| CData| AccData| ALayout| BLayout| CLayout|           A|           B|           C|           GEMM| Block|  MPer|  NPer| K0Per| K1|      M1Per|      N1Per|   KPer|  M11N11Thread|  M11N11Thread|     ABlockTransfer|       ABlockTransfer| ABlockTransfer| ABlockTransfer|      ABlockTransfer|     ABlockTransfer|       ABlockTransfer|     BBlockTransfer|       BBlockTransfer| BBlockTransfer| BBlockTransfer|      BBlockTransfer|     BBlockTransfer|       BBlockTransfer|     CThreadTransfer|  CThreadTransfer|    CThreadTransfer|
+        //  ########|  Type|  Type|  Type|    Type|        |        |        | Elementwise| Elementwise| Elementwise| Spacialization|  Size| Block| Block| Block|   | ThreadM111| ThreadN111| Thread| ClusterM110Xs| ClusterN110Xs| ThreadSliceLengths| ThreadClusterLengths|  ThreadCluster|      SrcAccess|     SrcVectorTensor|    SrcVectorTensor|      DstVectorTensor| ThreadSliceLengths| ThreadClusterLengths|  ThreadCluster|      SrcAccess|     SrcVectorTensor|    SrcVectorTensor|      DstVectorTensor|        SrcDstAccess|  SrcDstVectorDim| DstScalarPerVector|
+        //  ########|      |      |      |        |        |        |        |   Operation|   Operation|   Operation|               |      |      |      |      |   |           |           |       |              |              |        K0_M0_M1_K1|          K0_M0_M1_K1|   ArrangeOrder|          Order| Lengths_K0_M0_M1_K1| ContiguousDimOrder|  Lengths_K0_M0_M1_K1|        K0_N0_N1_K1|          K0_N0_N1_K1|   ArrangeOrder|          Order| Lengths_K0_N0_N1_K1| ContiguousDimOrder|  Lengths_K0_N0_N1_K1|               Order|                 |                   |
+        //  ########|      |      |      |        |        |        |        |            |            |            |               |      |      |      |      |   |           |           |       |              |              |                   |                     |               |               |                    |                   |                     |                   |                     |               |               |                    |                   |                     |                    |                 |                   |
+        DeviceGemmDl<   F32,   F32,   F32,     F32,     Col,     Row,     Row, PassThrough, PassThrough, PassThrough,    GemmDefault,   256,   128,   128,    16,  1,          4,          4,      1,       S<8, 2>,       S<8, 2>,      S<2, 1, 4, 1>,      S<8, 1,  32, 1>,  S<0, 3, 1, 2>,  S<0, 3, 1, 2>,       S<1, 1, 4, 1>,      S<0, 3, 1, 2>,        S<1, 1, 4, 1>,      S<2, 1, 4, 1>,      S<8, 1,  32, 1>,  S<0, 3, 1, 2>,  S<0, 3, 1, 2>,       S<1, 1, 4, 1>,      S<0, 3, 1, 2>,        S<1, 1, 4, 1>, S<0, 1, 2, 3, 4, 5>,                5,                  4>;
+// clang-format on
+
+using ReferenceGemmInstance = ck::tensor_operation::host::
+    ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
+
+    // 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)
+    {
+        // do nothing
+    }
+    else if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+    }
+    else if(argc == 10)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+
+        M = std::stoi(argv[4]);
+        N = std::stoi(argv[5]);
+        K = std::stoi(argv[6]);
+
+        StrideA = std::stoi(argv[7]);
+        StrideB = std::stoi(argv[8]);
+        StrideC = std::stoi(argv[9]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: time kernel (0=n0, 1=yes)\n");
+        printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
+        exit(1);
+    }
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({stride, 1}));
+            }
+            else
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({1, stride}));
+            }
+        };
+
+    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<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;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+        break;
+    case 2:
+        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+        break;
+    default:
+        a_m_k.GenerateTensorValue(GeneratorTensor_Sequential<0>{});
+        b_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+    }
+
+    DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
+    DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
+    DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());
+
+    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
+    b_k_n_device_buf.ToDevice(b_k_n.mData.data());
+
+    auto a_element_op = AElementOp{};
+    auto b_element_op = BElementOp{};
+    auto c_element_op = CElementOp{};
+
+    // do GEMM
+    auto gemm     = DeviceGemmInstance{};
+    auto invoker  = gemm.MakeInvoker();
+    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,
+                                      a_element_op,
+                                      b_element_op,
+                                      c_element_op);
+
+    if(!gemm.IsSupportedArgument(argument))
+    {
+        std::cout << gemm.GetTypeString() << " does not support this problem" << std::endl;
+
+        return 0;
+    }
+
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop = std::size_t(2) * M * N * K;
+    std::size_t num_btype =
+        sizeof(ADataType) * M * K + sizeof(BDataType) * K * 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: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
+              << gemm.GetTypeString() << std::endl;
+
+    c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
+
+    bool pass = true;
+
+    if(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, a_element_op, b_element_op, c_element_op);
+
+        ref_invoker.Run(ref_argument);
+
+        pass = ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
+    }
+
+    return pass ? 0 : 1;
+}

example/01_gemm/gemm_dl_int8.cpp

+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include <half.hpp>
+
+#include "check_err.hpp"
+#include "config.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "device_tensor.hpp"
+#include "device_gemm_dl.hpp"
+#include "element_wise_operation.hpp"
+#include "reference_gemm.hpp"
+#include "gemm_specialization.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using ADataType   = int8_t;
+using BDataType   = int8_t;
+using CDataType   = int8_t;
+using AccDataType = int32_t;
+
+using ALayout = Col;
+using BLayout = Row;
+using CLayout = Row;
+
+using AElementOp = ck::tensor_operation::element_wise::PassThrough;
+using BElementOp = ck::tensor_operation::element_wise::PassThrough;
+using CElementOp = ck::tensor_operation::element_wise::PassThrough;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+
+// clang-format off
+using DeviceGemmInstance = ck::tensor_operation::device::
+        // #########|  AData|   BData|   CData|    AccData| ALayout| BLayout| CLayout|           A|           B|           C|           GEMM| Block|  MPer|  NPer| K0Per| K1|      M1Per|      N1Per|   KPer|  M11N11Thread|  M11N11Thread|     ABlockTransfer|       ABlockTransfer| ABlockTransfer| ABlockTransfer|      ABlockTransfer|     ABlockTransfer|       ABlockTransfer|     BBlockTransfer|       BBlockTransfer| BBlockTransfer| BBlockTransfer|      BBlockTransfer|     BBlockTransfer|      BBlockTransfer|     CThreadTransfer| CThreadTransfer|    CThreadTransfer|
+        // #########|   Type|    Type|    Type|       Type|        |        |        | Elementwise| Elementwise| Elementwise| Spacialization|  Size| Block| Block| Block|   | ThreadM111| ThreadN111| Thread| ClusterM110Xs| ClusterN110Xs| ThreadSliceLengths| ThreadClusterLengths|  ThreadCluster|      SrcAccess|     SrcVectorTensor|    SrcVectorTensor|      DstVectorTensor| ThreadSliceLengths| ThreadClusterLengths|  ThreadCluster|      SrcAccess|     SrcVectorTensor|    SrcVectorTensor|     DstVectorTensor|        SrcDstAccess| SrcDstVectorDim| DstScalarPerVector|
+        // #########|       |        |        |           |        |        |        |   Operation|   Operation|   Operation|               |      |      |      |      |   |           |           |       |              |              |        K0_M0_M1_K1|          K0_M0_M1_K1|   ArrangeOrder|          Order| Lengths_K0_M0_M1_K1| ContiguousDimOrder|  Lengths_K0_M0_M1_K1|        K0_N0_N1_K1|          K0_N0_N1_K1|   ArrangeOrder|          Order| Lengths_K0_N0_N1_K1| ContiguousDimOrder| Lengths_K0_N0_N1_K1|               Order|                |                   |
+        // #########|       |        |        |           |        |        |        |            |            |            |               |      |      |      |      |   |           |           |       |              |              |                   |                     |               |               |                    |                   |                     |                   |                     |               |               |                    |                   |                    |                    |                |                   |
+        DeviceGemmDl< int8_t,  int8_t,  int8_t,    int32_t,     Col,     Row,     Row, PassThrough, PassThrough, PassThrough,    GemmDefault,   256,   128,   128,    16,  4,          4,          4,      1,       S<8, 2>,       S<8, 2>,      S<2, 1, 4, 4>,      S<8, 1,  32, 1>,  S<0, 3, 1, 2>,  S<0, 3, 1, 2>,       S<1, 1, 4, 1>,      S<0, 3, 1, 2>,        S<1, 1, 4, 4>,      S<2, 1, 4, 4>,      S<8, 1,  32, 1>,  S<0, 3, 1, 2>,  S<0, 3, 1, 2>,       S<1, 1, 4, 1>,      S<0, 3, 1, 2>,       S<1, 1, 4, 4>, S<0, 1, 2, 3, 4, 5>,               5,                  4>;
+// clang-format on
+
+using ReferenceGemmInstance = ck::tensor_operation::host::
+    ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
+
+    // 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)
+    {
+        // do nothing
+    }
+    else if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+    }
+    else if(argc == 10)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+
+        M = std::stoi(argv[4]);
+        N = std::stoi(argv[5]);
+        K = std::stoi(argv[6]);
+
+        StrideA = std::stoi(argv[7]);
+        StrideB = std::stoi(argv[8]);
+        StrideC = std::stoi(argv[9]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: time kernel (0=n0, 1=yes)\n");
+        printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
+        exit(1);
+    }
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({stride, 1}));
+            }
+            else
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({1, stride}));
+            }
+        };
+
+    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<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;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+        break;
+    case 2:
+        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+        break;
+    default:
+        a_m_k.GenerateTensorValue(GeneratorTensor_Sequential<0>{});
+        b_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+    }
+
+    DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
+    DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
+    DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());
+
+    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
+    b_k_n_device_buf.ToDevice(b_k_n.mData.data());
+
+    auto a_element_op = AElementOp{};
+    auto b_element_op = BElementOp{};
+    auto c_element_op = CElementOp{};
+
+    // do GEMM
+    auto gemm     = DeviceGemmInstance{};
+    auto invoker  = gemm.MakeInvoker();
+    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,
+                                      a_element_op,
+                                      b_element_op,
+                                      c_element_op);
+
+    if(!gemm.IsSupportedArgument(argument))
+    {
+        std::cout << gemm.GetTypeString() << " does not support this problem" << std::endl;
+
+        return 0;
+    }
+
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop = std::size_t(2) * M * N * K;
+    std::size_t num_btype =
+        sizeof(ADataType) * M * K + sizeof(BDataType) * K * 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: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
+              << gemm.GetTypeString() << std::endl;
+
+    c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
+
+    bool pass = true;
+
+    if(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, a_element_op, b_element_op, c_element_op);
+
+        ref_invoker.Run(ref_argument);
+
+        pass = ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
+    }
+
+    return pass ? 0 : 1;
+}

example/01_gemm/gemm_xdl_bf16.cpp

@@ -11,8 +11,7 @@
 #include "host_tensor.hpp"
 #include "host_tensor_generator.hpp"
 #include "device_tensor.hpp"
-#include "device_gemm_xdl.hpp"
-#include "device_gemm_xdl_c_shuffle.hpp"
+#include "device_gemm_xdl_cshuffle.hpp"
 #include "element_wise_operation.hpp"
 #include "reference_gemm.hpp"
 #include "gemm_specialization.hpp"
@@ -37,57 +36,61 @@ using ALayout = ck::tensor_layout::gemm::RowMajor;
 using BLayout = ck::tensor_layout::gemm::ColumnMajor;
 using CLayout = ck::tensor_layout::gemm::RowMajor;
 
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+
 // clang-format off
-using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl_C_Shuffle<
-    ADataType,              // ADataType
-    BDataType,              // BDataType
-    CDataType,              // CDataType
-    AccDataType,            // AccDataType
-    CDataType,              // CShuffleDataType
-    ALayout,                // ALayout
-    BLayout,                // BLayout
-    CLayout,                // CLayout
-    PassThrough,            // AElementwiseOperation
-    PassThrough,            // BElementwiseOperation
-    PassThrough,            // CElementwiseOperation
-    256,                    // BlockSize
-    256,                    // MPerBlock
-    128,                    // NPerBlock
-    32,                     // KPerBlock
-    8,                      // AK1
-    8,                      // BK1
-    32,                     // MPerXDL
-    32,                     // NPerXDL
-    4,                      // MXdlPerWave
-    2,                      // NXdlPerWave
-    S<4, 64, 1>,            // ABlockTransferThreadClusterLengths_K0_M_K1
-    S<1, 0, 2>,             // ABlockTransferThreadClusterArrangeOrder
-    S<1, 0, 2>,             // ABlockTransferSrcAccessOrder
-    2,                      // ABlockTransferSrcVectorDim
-    8,                      // ABlockTransferSrcScalarPerVector
-    8,                      // ABlockTransferDstScalarPerVector_K1
-    true,                   // ABlockLdsAddExtraM
-    S<4, 64, 1>,            // BBlockTransferThreadClusterLengths_K0_N_K1
-    S<1, 0, 2>,             // BBlockTransferThreadClusterArrangeOrder
-    S<1, 0, 2>,             // BBlockTransferSrcAccessOrder
-    2,                      // BBlockTransferSrcVectorDim
-    8,                      // BBlockTransferSrcScalarPerVector
-    8,                      // BBlockTransferDstScalarPerVector_K1
-    true,                   // BBlockLdsAddExtraN
-    1,                      // CShuffleMXdlPerWavePerShuffle
-    1,                      // CShuffleNXdlPerWavePerShuffle
-    S<1, 1, 32, 1, 1, 8>,   // CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
-    8>;                     // CBlockTransferScalarPerVector_NWaveNPerXdl
+using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
+    <ALayout,                    // typename ALayout
+     BLayout,                    // typename BLayout
+     CLayout,                    // typename CLayout
+     ADataType,                  // typename ADataType
+     BDataType,                  // typename BDataType
+     CDataType,                  // typename CDataType
+     AccDataType,                // typename GemmAccDataType
+     CDataType,                  // typename CShuffleDataType
+     PassThrough,                // typename AElementwiseOperation
+     PassThrough,                // typename BElementwiseOperation
+     PassThrough,                // typename CElementwiseOperation
+     GemmDefault,                // GemmSpecialization GemmSpec
+     1,                          // index_t NumGemmKPrefetchStage
+     256,                        // index_t BlockSize
+     256,                        // index_t MPerBlock
+     128,                        // index_t NPerBlock
+     32,                         // index_t KPerBlock
+     8,                          // index_t AK1
+     8,                          // index_t BK1
+     32,                         // index_t MPerXDL
+     32,                         // index_t NPerXDL
+     4,                          // index_t MXdlPerWave
+     2,                          // index_t NXdlPerWave
+     S<4, 64, 1>,                // typename ABlockTransferThreadClusterLengths_AK0_M_AK1
+     S<1, 0, 2>,                 // typename ABlockTransferThreadClusterArrangeOrder
+     S<1, 0, 2>,                 // typename ABlockTransferSrcAccessOrder
+     2,                          // index_t ABlockTransferSrcVectorDim
+     8,                          // index_t ABlockTransferSrcScalarPerVector
+     8,                          // index_t ABlockTransferDstScalarPerVector_AK1
+     1,                          // index_t ABlockLdsExtraM
+     S<4, 64, 1>,                // typename BBlockTransferThreadClusterLengths_BK0_N_BK1
+     S<1, 0, 2>,                 // typename BBlockTransferThreadClusterArrangeOrder
+     S<1, 0, 2>,                 // typename BBlockTransferSrcAccessOrder
+     2,                          // index_t BBlockTransferSrcVectorDim
+     8,                          // index_t BBlockTransferSrcScalarPerVector
+     8,                          // index_t BBlockTransferDstScalarPerVector_BK1
+     1,                          // index_t BBlockLdsExtraN
+     1,                          // index_t CShuffleMXdlPerWavePerShuffle
+     1,                          // index_t CShuffleNXdlPerWavePerShuffle
+     S<1, 32, 1, 8>,             // typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
+     8>;                         // index_t CShuffleBlockTransferScalarPerVector_NPerBlock
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::
-    ReferenceGemm<float, float, float, PassThrough, PassThrough, PassThrough>;
+    ReferenceGemm<float, float, float, float, PassThrough, PassThrough, PassThrough>;
 
 int main(int argc, char* argv[])
 {
-    bool do_verification = 0;
-    int init_method      = 0;
-    int nrepeat          = 5;
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
 
     // GEMM shape
     ck::index_t M = 3840;
@@ -102,13 +105,13 @@ int main(int argc, char* argv[])
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
-        nrepeat         = std::stoi(argv[3]);
+        time_kernel     = std::stoi(argv[3]);
     }
     else if(argc == 10)
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
-        nrepeat         = std::stoi(argv[3]);
+        time_kernel     = std::stoi(argv[3]);
 
         M = std::stoi(argv[4]);
         N = std::stoi(argv[5]);
@@ -122,7 +125,7 @@ int main(int argc, char* argv[])
     {
         printf("arg1: verification (0=no, 1=yes)\n");
         printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
-        printf("arg3: run kernel # of times (>1)\n");
+        printf("arg3: time kernel (0=n0, 1=yes)\n");
         printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
         exit(0);
     }
@@ -190,12 +193,12 @@ int main(int argc, char* argv[])
 
     if(!gemm.IsSupportedArgument(argument))
     {
-        throw std::runtime_error(
-            "wrong! device_gemm with the specified compilation parameters does "
-            "not support this GEMM problem");
+        std::cout << gemm.GetTypeString() << " does not support this problem" << std::endl;
+
+        return 0;
     }
 
-    float ave_time = invoker.Run(argument, nrepeat);
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
 
     std::size_t flop = std::size_t(2) * M * N * K;
     std::size_t num_btype =
@@ -229,7 +232,7 @@ int main(int argc, char* argv[])
 
         ref_invoker.Run(ref_argument);
 
-        ck::utils::check_err(c_m_n_device_f32_result.mData, c_m_n_host_result.mData);
+        return ck::utils::check_err(c_m_n_device_f32_result.mData, c_m_n_host_result.mData) ? 0 : 1;
     }
 
     return 0;

example/01_gemm/gemm_xdl_fp16.cpp

@@ -4,7 +4,6 @@
 #include <cstdlib>
 #include <stdlib.h>
 #include <half.hpp>
-
 #include "check_err.hpp"
 #include "config.hpp"
 #include "device.hpp"
@@ -12,7 +11,6 @@
 #include "host_tensor_generator.hpp"
 #include "device_tensor.hpp"
 #include "device_gemm_xdl.hpp"
-#include "device_gemm_xdl_c_shuffle.hpp"
 #include "device_gemm_xdl_cshuffle.hpp"
 #include "element_wise_operation.hpp"
 #include "reference_gemm.hpp"
@@ -46,21 +44,21 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa
 
 // clang-format off
 using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
-//######| ALayout| BLayout| CLayout|AData| BData| CData|  GemmAcc| CShuffle|           A|           B|           C|           GEMM| NumGemmK| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
-//######|        |        |        | Type|  Type|  Type| DataType| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar|    ExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar|    ExtraN| MXdlPerWave| NXdlPerWave|            _MBlock_MPerBlock| ScalarPerVector|
-//######|        |        |        |     |      |      |         |         |   Operation|   Operation|   Operation|               |    Stage|      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|            _NBlock_NPerBlock|      _NPerBlock|
-//######|        |        |        |     |      |      |         |         |            |            |            |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
-        <     Row,     Col,     Row,  F16,   F16,   F16,      F32,      F32,  AElementOp,  BElementOp,  CElementOp,    GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,               8>;
+//######| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle|           A|           B|           C|           GEMM| NumGemmK| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
+//######|        |        |        |  Type|  Type|  Type|    Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
+//######|        |        |        |      |      |      |        |         |   Operation|   Operation|   Operation|               |    Stage|      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
+//######|        |        |        |      |      |      |        |         |            |            |            |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
+        <     Row,     Col,     Row,   F16,   F16,   F16,     F32,      F32,  AElementOp,  BElementOp,  CElementOp,    GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,               8>;
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::
-    ReferenceGemm<ADataType, BDataType, CDataType, AElementOp, BElementOp, CElementOp>;
+    ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
 
 int main(int argc, char* argv[])
 {
-    bool do_verification = 0;
-    int init_method      = 0;
-    int nrepeat          = 5;
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
 
     // GEMM shape
     ck::index_t M = 3840;
@@ -75,13 +73,13 @@ int main(int argc, char* argv[])
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
-        nrepeat         = std::stoi(argv[3]);
+        time_kernel     = std::stoi(argv[3]);
     }
     else if(argc == 10)
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
-        nrepeat         = std::stoi(argv[3]);
+        time_kernel     = std::stoi(argv[3]);
 
         M = std::stoi(argv[4]);
         N = std::stoi(argv[5]);
@@ -95,7 +93,7 @@ int main(int argc, char* argv[])
     {
         printf("arg1: verification (0=no, 1=yes)\n");
         printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
-        printf("arg3: run kernel # of times (>1)\n");
+        printf("arg3: time kernel (0=n0, 1=yes)\n");
         printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
         exit(0);
     }
@@ -168,12 +166,12 @@ int main(int argc, char* argv[])
 
     if(!gemm.IsSupportedArgument(argument))
     {
-        throw std::runtime_error(
-            "wrong! device_gemm with the specified compilation parameters does "
-            "not support this GEMM problem");
+        std::cout << gemm.GetTypeString() << " does not support this problem" << std::endl;
+
+        return 0;
     }
 
-    float ave_time = invoker.Run(argument, nrepeat);
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
 
     std::size_t flop = std::size_t(2) * M * N * K;
     std::size_t num_btype =
@@ -198,7 +196,7 @@ int main(int argc, char* argv[])
 
         ref_invoker.Run(ref_argument);
 
-        ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
+        return ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData) ? 0 : 1;
     }
 
     return 0;

example/01_gemm/gemm_xdl_fp64.cpp

+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include <half.hpp>
+
+#include "check_err.hpp"
+#include "config.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "device_tensor.hpp"
+#include "device_gemm_xdl.hpp"
+#include "device_gemm_xdl_cshuffle.hpp"
+#include "element_wise_operation.hpp"
+#include "reference_gemm.hpp"
+#include "gemm_specialization.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F64 = double;
+
+using ADataType   = double;
+using BDataType   = double;
+using CDataType   = double;
+using AccDataType = double;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using ALayout = ck::tensor_layout::gemm::RowMajor;
+using BLayout = ck::tensor_layout::gemm::ColumnMajor;
+using CLayout = ck::tensor_layout::gemm::RowMajor;
+
+using AElementOp = ck::tensor_operation::element_wise::PassThrough;
+using BElementOp = ck::tensor_operation::element_wise::PassThrough;
+using CElementOp = ck::tensor_operation::element_wise::PassThrough;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+
+// clang-format off
+using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl
+//##########| AData| BData| CData| AccData| ALayout| BLayout| CLayout|           A|           B|           C|          GEMM| Block|  MPer|  NPer| K0Per| K1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
+//##########|  Type|  Type|  Type|    Type|        |        |        | Elementwise| Elementwise| Elementwise|Spacialization|  Size| Block| Block| Block|   |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| SrcDstVectorDim|       DstScalar|
+//##########|      |      |      |        |        |        |        |   Operation|   Operation|   Operation|              |      |      |      |      |   |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |                |       PerVector|
+//##########|      |      |      |        |        |        |        |            |            |            |              |      |      |      |      |   |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |                |                |
+#if 0
+             <  F64,   F64,   F64,     F64,     Row,     Col,     Row, PassThrough, PassThrough, PassThrough,   GemmDefault,   64,    32,    32,     4,  1,   16,   16,    2,    2,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              1,              1,      true,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              1,              1,      true,               7,               1>;
+#else
+             <  F64,   F64,   F64,     F64,     Row,     Col,     Row, PassThrough, PassThrough, PassThrough,   GemmDefault,  256,   128,   128,     4,  2,   16,   16,    4,    4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              2,              2,      true,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              2,              2,      true,               7,               1>;
+#endif
+    // clang-format on
+
+    using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                            BDataType,
+                                                                            CDataType,
+                                                                            AccDataType,
+                                                                            AElementOp,
+                                                                            BElementOp,
+                                                                            CElementOp>;
+
+template <typename DataType>
+std::ostream& show_2d_matrix(std::ostream& os, Tensor<DataType>& matrix)
+{
+    os << "[" << std::endl;
+    for(int x = 0; x < matrix.mDesc.GetLengths()[0]; x++)
+    {
+        os << "[";
+        for(int y = 0; y < matrix.mDesc.GetLengths()[1]; y++)
+        {
+            os << std::setw(4) << static_cast<float>(matrix(x, y));
+        }
+        os << "]" << std::endl;
+    }
+    os << "]";
+    return os;
+}
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = 0;
+    int init_method      = 0;
+    bool time_kernel     = false;
+
+    // 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 == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+    }
+    else if(argc == 10)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+
+        M = std::stoi(argv[4]);
+        N = std::stoi(argv[5]);
+        K = std::stoi(argv[6]);
+
+        StrideA = std::stoi(argv[7]);
+        StrideB = std::stoi(argv[8]);
+        StrideC = std::stoi(argv[9]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: run kernel # of times (>1)\n");
+        printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
+        exit(0);
+    }
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({stride, 1}));
+            }
+            else
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({1, stride}));
+            }
+        };
+
+    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<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 << "data type: " << typeid(ADataType{}).name() << std::endl;
+    std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
+    std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
+    std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+        break;
+    case 2:
+        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+        break;
+    default:
+        a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
+        b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
+    }
+
+    DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
+    DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
+    DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());
+
+    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
+    b_k_n_device_buf.ToDevice(b_k_n.mData.data());
+
+    auto a_element_op = AElementOp{};
+    auto b_element_op = BElementOp{};
+    auto c_element_op = CElementOp{};
+
+    // do GEMM
+    auto gemm     = DeviceGemmInstance{};
+    auto invoker  = gemm.MakeInvoker();
+    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,
+                                      a_element_op,
+                                      b_element_op,
+                                      c_element_op);
+
+    if(!gemm.IsSupportedArgument(argument))
+    {
+        std::cout << gemm.GetTypeString() << " does not support this problem" << std::endl;
+
+        return 0;
+    }
+
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop = std::size_t(2) * M * N * K;
+    std::size_t num_btype =
+        sizeof(ADataType) * M * K + sizeof(BDataType) * K * 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: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
+              << gemm.GetTypeString() << std::endl;
+
+    c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
+
+    if(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, a_element_op, b_element_op, c_element_op);
+
+        ref_invoker.Run(ref_argument);
+
+#if 0
+        {
+            show_2d_matrix(std::cout << "a : ", a_m_k) << std::endl;
+            show_2d_matrix(std::cout << "b: ", b_k_n) << std::endl;
+            show_2d_matrix(std::cout << "c_device: ", c_m_n_device_result) << std::endl;
+            show_2d_matrix(std::cout << "c_host  :", c_m_n_host_result) << std::endl;
+        }
+#endif
+        return ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData) ? 0 : 1;
+    }
+
+    return 0;
+}

example/01_gemm/gemm_xdl_int8.cpp

@@ -11,8 +11,7 @@
 #include "host_tensor.hpp"
 #include "host_tensor_generator.hpp"
 #include "device_tensor.hpp"
-#include "device_gemm_xdl.hpp"
-#include "device_gemm_xdl_c_shuffle.hpp"
+#include "device_gemm_xdl_cshuffle.hpp"
 #include "element_wise_operation.hpp"
 #include "reference_gemm.hpp"
 #include "gemm_specialization.hpp"
@@ -20,74 +19,78 @@
 template <ck::index_t... Is>
 using S = ck::Sequence<Is...>;
 
-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 ADataType        = int8_t;
 using BDataType        = int8_t;
-using CDataType        = int32_t;
+using CDataType        = int8_t;
 using AccDataType      = int32_t;
-using CShuffleDataType = int32_t;
+using CShuffleDataType = int8_t;
 
 using ALayout = ck::tensor_layout::gemm::RowMajor;
 using BLayout = ck::tensor_layout::gemm::ColumnMajor;
 using CLayout = ck::tensor_layout::gemm::RowMajor;
 
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+
 // clang-format off
-using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl_C_Shuffle<
-    ADataType,              // ADataType
-    BDataType,              // BDataType
-    CDataType,              // CDataType
-    AccDataType,            // AccDataType
-    CShuffleDataType,        // CShuffleDataType
-    ALayout,                // ALayout
-    BLayout,                // BLayout
-    CLayout,                // CLayout
-    PassThrough,            // AElementwiseOperation
-    PassThrough,            // BElementwiseOperation
-    PassThrough,            // CElementwiseOperation
-    256,                    // BlockSize
-    256,                    // MPerBlock
-    128,                    // NPerBlock
-    64,                     // KPerBlock
-    16,                     // AK1
-    16,                     // BK1
-    32,                     // MPerXDL
-    32,                     // NPerXDL
-    4,                      // MXdlPerWave
-    2,                      // NXdlPerWave
-    S<4, 64, 1>,            // ABlockTransferThreadClusterLengths_K0_M_K1
-    S<1, 0, 2>,             // ABlockTransferThreadClusterArrangeOrder
-    S<1, 0, 2>,             // ABlockTransferSrcAccessOrder
-    2,                      // ABlockTransferSrcVectorDim
-    16,                     // ABlockTransferSrcScalarPerVector
-    16,                     // ABlockTransferDstScalarPerVector_K1
-    true,                   // ABlockLdsAddExtraM
-    S<4, 64, 1>,            // BBlockTransferThreadClusterLengths_K0_N_K1
-    S<1, 0, 2>,             // BBlockTransferThreadClusterArrangeOrder
-    S<1, 0, 2>,             // BBlockTransferSrcAccessOrder
-    2,                      // BBlockTransferSrcVectorDim
-    16,                     // BBlockTransferSrcScalarPerVector
-    16,                     // BBlockTransferDstScalarPerVector_K1
-    true,                   // BBlockLdsAddExtraN
-    1,                      // CShuffleMXdlPerWavePerShuffle
-    1,                      // CShuffleNXdlPerWavePerShuffle
-    S<1, 1, 32, 1, 1, 8>,   // CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
-    4>;                     // CBlockTransferScalarPerVector_NWaveNPerXdl
+using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle<
+     ALayout,                    // typename ALayout
+     BLayout,                    // typename BLayout
+     CLayout,                    // typename CLayout
+     ADataType,                  // typename ADataType
+     BDataType,                  // typename BDataType
+     CDataType,                  // typename CDataType
+     AccDataType,                // typename GemmAccDataType
+     CShuffleDataType,           // typename CShuffleDataType
+     PassThrough,                // typename AElementwiseOperation
+     PassThrough,                // typename BElementwiseOperation
+     PassThrough,                // typename CElementwiseOperation
+     GemmDefault,                // GemmSpecialization GemmSpec
+     1,                          // index_t NumGemmKPrefetchStage
+     256,                        // index_t BlockSize
+     256,                        // index_t MPerBlock
+     128,                        // index_t NPerBlock
+     64,                         // index_t KPerBlock
+     16,                         // index_t AK1
+     16,                         // index_t BK1
+     32,                         // index_t MPerXDL
+     32,                         // index_t NPerXDL
+     4,                          // index_t MXdlPerWave
+     2,                          // index_t NXdlPerWave
+     S<4, 64, 1>,                // typename ABlockTransferThreadClusterLengths_AK0_M_AK1
+     S<1, 0, 2>,                 // typename ABlockTransferThreadClusterArrangeOrder
+     S<1, 0, 2>,                 // typename ABlockTransferSrcAccessOrder
+     2,                          // index_t ABlockTransferSrcVectorDim
+     16,                         // index_t ABlockTransferSrcScalarPerVector
+     16,                         // index_t ABlockTransferDstScalarPerVector_AK1
+     1,                          // index_t ABlockLdsExtraM
+     S<4, 64, 1>,                // typename BBlockTransferThreadClusterLengths_BK0_N_BK1
+     S<1, 0, 2>,                 // typename BBlockTransferThreadClusterArrangeOrder
+     S<1, 0, 2>,                 // typename BBlockTransferSrcAccessOrder
+     2,                          // index_t BBlockTransferSrcVectorDim
+     8,                          // index_t BBlockTransferSrcScalarPerVector
+     8,                          // index_t BBlockTransferDstScalarPerVector_BK1
+     1,                          // index_t BBlockLdsExtraN
+     1,                          // index_t CShuffleMXdlPerWavePerShuffle
+     1,                          // index_t CShuffleNXdlPerWavePerShuffle
+     S<1, 64, 1, 4>,             // typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
+     16>;                        // index_t CShuffleBlockTransferScalarPerVector_NPerBlock
 // clang-format on
 
-using ReferenceGemmInstance = ck::tensor_operation::host::
-    ReferenceGemm<ADataType, BDataType, CDataType, PassThrough, PassThrough, PassThrough>;
+using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                        BDataType,
+                                                                        CDataType,
+                                                                        AccDataType,
+                                                                        PassThrough,
+                                                                        PassThrough,
+                                                                        PassThrough>;
 
 int main(int argc, char* argv[])
 {
-    bool do_verification = 0;
-    int init_method      = 0;
-    int nrepeat          = 5;
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
 
     // GEMM shape
     ck::index_t M = 3840;
@@ -102,13 +105,13 @@ int main(int argc, char* argv[])
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
-        nrepeat         = std::stoi(argv[3]);
+        time_kernel     = std::stoi(argv[3]);
     }
     else if(argc == 10)
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
-        nrepeat         = std::stoi(argv[3]);
+        time_kernel     = std::stoi(argv[3]);
 
         M = std::stoi(argv[4]);
         N = std::stoi(argv[5]);
@@ -122,7 +125,7 @@ int main(int argc, char* argv[])
     {
         printf("arg1: verification (0=no, 1=yes)\n");
         printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
-        printf("arg3: run kernel # of times (>1)\n");
+        printf("arg3: time kernel (0=n0, 1=yes)\n");
         printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
         exit(0);
     }
@@ -191,12 +194,12 @@ int main(int argc, char* argv[])
 
     if(!gemm.IsSupportedArgument(argument))
     {
-        throw std::runtime_error(
-            "wrong! device_gemm with the specified compilation parameters does "
-            "not support this GEMM problem");
+        std::cout << gemm.GetTypeString() << " does not support this problem" << std::endl;
+
+        return 0;
     }
 
-    float ave_time = invoker.Run(argument, nrepeat);
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
 
     std::size_t flop = std::size_t(2) * M * N * K;
     std::size_t num_btype =
@@ -221,7 +224,7 @@ int main(int argc, char* argv[])
 
         ref_invoker.Run(ref_argument);
 
-        ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
+        return ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData) ? 0 : 1;
     }
 
     return 0;

example/02_gemm_alpha_beta/gemm_xdl_alpha_beta.cpp

@@ -86,9 +86,9 @@ using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemmBias2D<AD
 
 int main(int argc, char* argv[])
 {
-    bool do_verification = 0;
-    int init_method      = 0;
-    int nrepeat          = 5;
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
 
     // GEMM shape
     ck::index_t M = 3840;
@@ -106,13 +106,13 @@ int main(int argc, char* argv[])
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
-        nrepeat         = std::stoi(argv[3]);
+        time_kernel     = std::stoi(argv[3]);
     }
     else if(argc == 6)
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
-        nrepeat         = std::stoi(argv[3]);
+        time_kernel     = std::stoi(argv[3]);
 
         alpha = std::stof(argv[4]);
         beta  = std::stof(argv[5]);
@@ -121,7 +121,7 @@ int main(int argc, char* argv[])
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
-        nrepeat         = std::stoi(argv[3]);
+        time_kernel     = std::stoi(argv[3]);
 
         M = std::stoi(argv[4]);
         N = std::stoi(argv[5]);
@@ -138,7 +138,7 @@ int main(int argc, char* argv[])
     {
         printf("arg1: verification (0=no, 1=yes)\n");
         printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
-        printf("arg3: run kernel # of times (>1)\n");
+        printf("arg3: time kernel (0=n0, 1=yes)\n");
         printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC, alpha, beta\n");
         exit(0);
     }
@@ -216,7 +216,7 @@ int main(int argc, char* argv[])
             "not support this GEMM problem");
     }
 
-    float ave_time = invoker.Run(argument, nrepeat);
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
 
     std::size_t flop = std::size_t(2) * M * N * K;
     std::size_t num_btype =
@@ -246,6 +246,8 @@ int main(int argc, char* argv[])
 
         ref_invoker.Run(ref_argument);
 
-        ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
+        return ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData) ? 0 : 1;
     }
+
+    return 0;
 }

example/03_gemm_bias_relu/gemm_xdl_bias_relu.cpp

@@ -83,9 +83,9 @@ using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemmBiasActiv
 
 int main(int argc, char* argv[])
 {
-    bool do_verification = 0;
-    int init_method      = 0;
-    int nrepeat          = 5;
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
 
     // GEMM shape
     ck::index_t M = 3840;
@@ -100,13 +100,13 @@ int main(int argc, char* argv[])
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
-        nrepeat         = std::stoi(argv[3]);
+        time_kernel     = std::stoi(argv[3]);
     }
     else if(argc == 10)
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
-        nrepeat         = std::stoi(argv[3]);
+        time_kernel     = std::stoi(argv[3]);
 
         M = std::stoi(argv[4]);
         N = std::stoi(argv[5]);
@@ -120,7 +120,7 @@ int main(int argc, char* argv[])
     {
         printf("arg1: verification (0=no, 1=yes)\n");
         printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
-        printf("arg3: run kernel # of times (>1)\n");
+        printf("arg3: time kernel (0=n0, 1=yes)\n");
         printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
         exit(0);
     }
@@ -206,7 +206,7 @@ int main(int argc, char* argv[])
             "not support this GEMM problem");
     }
 
-    float ave_time = invoker.Run(argument, nrepeat);
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
 
     std::size_t flop = std::size_t(2) * M * N * K;
 
@@ -232,6 +232,8 @@ int main(int argc, char* argv[])
 
         ref_invoker.Run(ref_argument);
 
-        ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
+        return ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData) ? 0 : 1;
     }
+
+    return 0;
 }

example/04_gemm_bias_relu_add/gemm_xdl_bias_relu_add.cpp

@@ -83,9 +83,9 @@ using ReferenceGemmInstance =
                                                                CElementOp>;
 int main(int argc, char* argv[])
 {
-    bool do_verification = 0;
-    int init_method      = 0;
-    int nrepeat          = 5;
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
 
     // GEMM shape
     ck::index_t M = 3840;
@@ -101,13 +101,13 @@ int main(int argc, char* argv[])
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
-        nrepeat         = std::stoi(argv[3]);
+        time_kernel     = std::stoi(argv[3]);
     }
     else if(argc == 11)
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
-        nrepeat         = std::stoi(argv[3]);
+        time_kernel     = std::stoi(argv[3]);
 
         M = std::stoi(argv[4]);
         N = std::stoi(argv[5]);
@@ -122,7 +122,7 @@ int main(int argc, char* argv[])
     {
         printf("arg1: verification (0=no, 1=yes)\n");
         printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
-        printf("arg3: run kernel # of times (>1)\n");
+        printf("arg3: time kernel (0=n0, 1=yes)\n");
         printf("arg4 to 10: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC, StrideC1\n");
         exit(0);
     }
@@ -218,7 +218,7 @@ int main(int argc, char* argv[])
             "not support this GEMM problem");
     }
 
-    float ave_time = invoker.Run(argument, nrepeat);
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
 
     std::size_t flop      = std::size_t(2) * M * N * K;
     std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * M +
@@ -250,6 +250,8 @@ int main(int argc, char* argv[])
 
         ref_invoker.Run(ref_argument);
 
-        ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
+        return ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData) ? 0 : 1;
     }
+
+    return 0;
 }

example/06_conv2d_fwd_bias_relu/CMakeLists.txt

 add_example_executable(example_conv2d_fwd_xdl_bias_relu conv2d_fwd_xdl_bias_relu.cpp)
-target_link_libraries(example_conv2d_fwd_xdl_bias_relu PRIVATE conv_fwd_util)
+target_link_libraries(example_conv2d_fwd_xdl_bias_relu PRIVATE conv_util)

example/06_conv2d_fwd_bias_relu/conv2d_fwd_xdl_bias_relu.cpp

@@ -7,7 +7,7 @@
 
 #include "check_err.hpp"
 #include "config.hpp"
-#include "conv_fwd_util.hpp"
+#include "conv_util.hpp"
 #include "device.hpp"
 #include "device_conv2d_fwd_xdl_c_shuffle_bias_activation_nhwc_kyxc_nhwk.hpp"
 #include "device_tensor.hpp"
@@ -93,7 +93,7 @@ void PrintUseMsg()
 {
     std::cout << "arg1: verification (0=no, 1=yes)\n"
               << "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
-              << "arg3: run kernel # of times (>1)\n"
+              << "arg3: time kernel (0=n0, 1=yes)\n"
               << "Following arguments:\n"
               << " N, K, C, \n"
               << " <filter spatial dimensions>, (ie Y, X for 2D)\n"
@@ -120,40 +120,40 @@ ck::utils::conv::ConvParams ParseConvParams(int argc, char* argv[])
     ck::utils::conv::ConvParams params;
     int arg_idx = 4;
 
-    params.num_dim_spatial = num_dim_spatial;
-    params.N               = std::stoi(argv[arg_idx++]);
-    params.K               = std::stoi(argv[arg_idx++]);
-    params.C               = std::stoi(argv[arg_idx++]);
+    params.num_dim_spatial_ = num_dim_spatial;
+    params.N_               = std::stoi(argv[arg_idx++]);
+    params.K_               = std::stoi(argv[arg_idx++]);
+    params.C_               = std::stoi(argv[arg_idx++]);
 
-    params.filter_spatial_lengths.resize(num_dim_spatial);
+    params.filter_spatial_lengths_.resize(num_dim_spatial);
     for(int i = 0; i < num_dim_spatial; ++i)
     {
-        params.filter_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
+        params.filter_spatial_lengths_[i] = std::stoi(argv[arg_idx++]);
     }
-    params.input_spatial_lengths.resize(num_dim_spatial);
+    params.input_spatial_lengths_.resize(num_dim_spatial);
     for(int i = 0; i < num_dim_spatial; ++i)
     {
-        params.input_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
+        params.input_spatial_lengths_[i] = std::stoi(argv[arg_idx++]);
     }
-    params.conv_filter_strides.resize(num_dim_spatial);
+    params.conv_filter_strides_.resize(num_dim_spatial);
     for(int i = 0; i < num_dim_spatial; ++i)
     {
-        params.conv_filter_strides[i] = std::stoi(argv[arg_idx++]);
+        params.conv_filter_strides_[i] = std::stoi(argv[arg_idx++]);
     }
-    params.conv_filter_dilations.resize(num_dim_spatial);
+    params.conv_filter_dilations_.resize(num_dim_spatial);
     for(int i = 0; i < num_dim_spatial; ++i)
     {
-        params.conv_filter_dilations[i] = std::stoi(argv[arg_idx++]);
+        params.conv_filter_dilations_[i] = std::stoi(argv[arg_idx++]);
     }
-    params.input_left_pads.resize(num_dim_spatial);
+    params.input_left_pads_.resize(num_dim_spatial);
     for(int i = 0; i < num_dim_spatial; ++i)
     {
-        params.input_left_pads[i] = std::stoi(argv[arg_idx++]);
+        params.input_left_pads_[i] = std::stoi(argv[arg_idx++]);
     }
-    params.input_right_pads.resize(num_dim_spatial);
+    params.input_right_pads_.resize(num_dim_spatial);
     for(int i = 0; i < num_dim_spatial; ++i)
     {
-        params.input_right_pads[i] = std::stoi(argv[arg_idx++]);
+        params.input_right_pads_[i] = std::stoi(argv[arg_idx++]);
     }
 
     return params;
@@ -165,9 +165,9 @@ int main(int argc, char* argv[])
 {
     using namespace ck::utils::conv;
 
-    bool do_verification      = 0;
-    int init_method           = 0;
-    int nrepeat               = 5;
+    bool do_verification      = true;
+    int init_method           = 1;
+    bool time_kernel          = false;
     const int num_dim_spatial = 2;
 
     ck::utils::conv::ConvParams params;
@@ -176,7 +176,7 @@ int main(int argc, char* argv[])
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
-        nrepeat         = std::stoi(argv[3]);
+        time_kernel     = std::stoi(argv[3]);
     }
 
     if(argc >= 5)
@@ -184,21 +184,21 @@ int main(int argc, char* argv[])
         params = ParseConvParams(argc, argv);
     }
 
-    std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N),
-                                        static_cast<std::size_t>(params.C)};
+    std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N_),
+                                        static_cast<std::size_t>(params.C_)};
     input_dims.insert(std::end(input_dims),
-                      std::begin(params.input_spatial_lengths),
-                      std::end(params.input_spatial_lengths));
+                      std::begin(params.input_spatial_lengths_),
+                      std::end(params.input_spatial_lengths_));
 
-    std::vector<std::size_t> filter_dims{static_cast<std::size_t>(params.K),
-                                         static_cast<std::size_t>(params.C)};
+    std::vector<std::size_t> filter_dims{static_cast<std::size_t>(params.K_),
+                                         static_cast<std::size_t>(params.C_)};
     filter_dims.insert(std::end(filter_dims),
-                       std::begin(params.filter_spatial_lengths),
-                       std::end(params.filter_spatial_lengths));
+                       std::begin(params.filter_spatial_lengths_),
+                       std::end(params.filter_spatial_lengths_));
 
     const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
-    std::vector<std::size_t> output_dims{static_cast<std::size_t>(params.N),
-                                         static_cast<std::size_t>(params.K)};
+    std::vector<std::size_t> output_dims{static_cast<std::size_t>(params.N_),
+                                         static_cast<std::size_t>(params.K_)};
     output_dims.insert(std::end(output_dims),
                        std::begin(output_spatial_lengths),
                        std::end(output_spatial_lengths));
@@ -211,7 +211,7 @@ int main(int argc, char* argv[])
         get_output_host_tensor_descriptor(output_dims, num_dim_spatial));
     // bias: assume contiguous 1d vector
     Tensor<OutDataType> bias(
-        HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(params.K)})));
+        HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(params.K_)})));
 
     std::cout << "input: " << input.mDesc << std::endl;
     std::cout << "weights: " << weights.mDesc << std::endl;
@@ -248,16 +248,16 @@ int main(int argc, char* argv[])
                           static_cast<const WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
                           static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
                           static_cast<const OutDataType*>(bias_device_buf.GetDeviceBuffer()),
-                          params.N,
-                          params.K,
-                          params.C,
-                          params.input_spatial_lengths,
-                          params.filter_spatial_lengths,
+                          params.N_,
+                          params.K_,
+                          params.C_,
+                          params.input_spatial_lengths_,
+                          params.filter_spatial_lengths_,
                           output_spatial_lengths,
-                          params.conv_filter_strides,
-                          params.conv_filter_dilations,
-                          params.input_left_pads,
-                          params.input_right_pads,
+                          params.conv_filter_strides_,
+                          params.conv_filter_dilations_,
+                          params.input_left_pads_,
+                          params.input_right_pads_,
                           InElementOp{},
                           WeiElementOp{},
                           OutElementOp{});
@@ -269,18 +269,18 @@ int main(int argc, char* argv[])
             "not support this problem");
     }
 
-    float ave_time = invoker.Run(argument, nrepeat);
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
 
     std::size_t flop = get_flops(
-        params.N, params.C, params.K, params.filter_spatial_lengths, output_spatial_lengths);
+        params.N_, params.C_, params.K_, params.filter_spatial_lengths_, output_spatial_lengths);
     std::size_t num_btype =
-        get_btype<InDataType, WeiDataType, OutDataType>(params.N,
-                                                        params.C,
-                                                        params.K,
-                                                        params.input_spatial_lengths,
-                                                        params.filter_spatial_lengths,
+        get_btype<InDataType, WeiDataType, OutDataType>(params.N_,
+                                                        params.C_,
+                                                        params.K_,
+                                                        params.input_spatial_lengths_,
+                                                        params.filter_spatial_lengths_,
                                                         output_spatial_lengths) +
-        sizeof(OutDataType) * (params.K);
+        sizeof(OutDataType) * (params.K_);
 
     float tflops     = static_cast<float>(flop) / 1.E9 / ave_time;
     float gb_per_sec = num_btype / 1.E6 / ave_time;
@@ -296,16 +296,17 @@ int main(int argc, char* argv[])
                                                   weights,
                                                   host_output,
                                                   bias,
-                                                  params.conv_filter_strides,
-                                                  params.conv_filter_dilations,
-                                                  params.input_left_pads,
-                                                  params.input_right_pads,
+                                                  params.conv_filter_strides_,
+                                                  params.conv_filter_dilations_,
+                                                  params.input_left_pads_,
+                                                  params.input_right_pads_,
                                                   InElementOp{},
                                                   WeiElementOp{},
                                                   OutElementOp{});
         ref_invoker.Run(ref_argument);
         out_device_buf.FromDevice(device_output.mData.data());
-        ck::utils::check_err(
-            host_output.mData, device_output.mData, "Error: incorrect results!", 1e-5f, 1e-4f);
+        return ck::utils::check_err(device_output.mData, host_output.mData) ? 0 : 1;
     }
+
+    return 0;
 }