Merge branch 'develop' into barkocot/activ-scaleaddx2relu-conv-fwd

CMakeLists.txt

@@ -373,9 +373,10 @@ include_directories(BEFORE
 
 SET(BUILD_DEV ON CACHE BOOL "BUILD_DEV")
 if(BUILD_DEV)
-    add_compile_options(-Werror)
-    add_compile_options(-Weverything)
+    add_compile_options(-Werror -Weverything)
 endif()
+#add flags to reduce the size of binaries
+add_compile_options(-Oz -flto=thin)
 message("CMAKE_CXX_FLAGS: ${CMAKE_CXX_FLAGS}")
 
 add_custom_target(check COMMAND ${CMAKE_CTEST_COMMAND} --output-on-failure -C ${CMAKE_CFG_INTDIR})
@@ -390,35 +391,27 @@ IF(IS_DIRECTORY "${PROJECT_SOURCE_DIR}/library/src/tensor_operation_instance/gpu
     file(READ "${PROJECT_SOURCE_DIR}/library/src/tensor_operation_instance/gpu/${subdir_path}/CMakeLists.txt" cmake_instance)
     set(add_inst 0)
     if(("${cmake_instance}" MATCHES "fp8" OR "${cmake_instance}" MATCHES "_f8") AND DTYPES MATCHES "fp8")
-        #message("fp8 instance found!")
         set(add_inst 1)
     endif()
     if(("${cmake_instance}" MATCHES "bf8" OR "${cmake_instance}" MATCHES "_b8") AND DTYPES MATCHES "bf8")
-        #message("bf8 instance found!")
         set(add_inst 1)
     endif()
     if(("${cmake_instance}" MATCHES "fp16" OR "${cmake_instance}" MATCHES "_f16") AND DTYPES MATCHES "fp16")
-        #message("fp16 instance found!")
         set(add_inst 1)
     endif()
     if(("${cmake_instance}" MATCHES "fp32" OR "${cmake_instance}" MATCHES "_f32") AND DTYPES MATCHES "fp32")
-        #message("fp32 instance found!")
         set(add_inst 1)
     endif()
     if(("${cmake_instance}" MATCHES "fp64" OR "${cmake_instance}" MATCHES "_f64") AND DTYPES MATCHES "fp64")
-        #message("fp64 instance found!")
         set(add_inst 1)
     endif()
     if(("${cmake_instance}" MATCHES "bf16" OR "${cmake_instance}" MATCHES "_b16") AND DTYPES MATCHES "bf16")
-        #message("bf16 instance found!")
         set(add_inst 1)
     endif()
     if(("${cmake_instance}" MATCHES "int8" OR "${cmake_instance}" MATCHES "_i8") AND DTYPES MATCHES "int8")
-        #message("int8 instance found!")
         set(add_inst 1)
     endif()
     if(NOT "${cmake_instance}" MATCHES "DTYPES")
-        #message("instance should be built for all types!")
         set(add_inst 1)
     endif()
     if(add_inst EQUAL 1 OR NOT DEFINED DTYPES)

Dockerfile

@@ -26,25 +26,37 @@ RUN wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add - && \
 RUN sh -c "echo deb http://mirrors.kernel.org/ubuntu focal main universe | tee -a /etc/apt/sources.list"
 RUN amdgpu-install -y --usecase=rocm --no-dkms
 
+## Sccache binary built from source for ROCm
+ARG SCCACHE_REPO_URL=http://compute-artifactory.amd.com/artifactory/rocm-generic-experimental/rocm-sccache
+ENV SCCACHE_INSTALL_LOCATION=/usr/local/.cargo/bin
+RUN mkdir -p ${SCCACHE_INSTALL_LOCATION} && \
+curl ${SCCACHE_REPO_URL}/portable/0.2.16/sccache-0.2.16-alpha.1-rocm --output ${SCCACHE_INSTALL_LOCATION}/sccache && \
+chmod +x ${SCCACHE_INSTALL_LOCATION}/sccache
+ENV PATH=$PATH:${SCCACHE_INSTALL_LOCATION}
+
 # Install dependencies
 RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \
     build-essential \
-    ccache \
     cmake \
+    ccache \
     git \
     hip-rocclr \
+    iputils-ping \
     jq \
     libelf-dev \
     libncurses5-dev \
     libnuma-dev \
     libpthread-stubs0-dev \
     llvm-amdgpu \
+    net-tools \
     pkg-config \
     python \
     python3 \
     python3-dev \
     python3-pip \
+    redis \
     sshpass \
+    stunnel \
     software-properties-common \
     vim \
     nano \
@@ -62,7 +74,7 @@ RUN gunzip /usr/local/bin/ninja.gz
 RUN chmod a+x /usr/local/bin/ninja
 RUN git clone https://github.com/nico/ninjatracing.git
 # Update the cmake to the latest version
-RUN pip install --upgrade cmake
+RUN pip install --upgrade cmake==3.27.5
 
 # Setup ubsan environment to printstacktrace
 RUN ln -s /usr/bin/llvm-symbolizer-3.8 /usr/local/bin/llvm-symbolizer
@@ -77,9 +89,9 @@ ARG PREFIX=/opt/rocm
 RUN pip3 install --upgrade pip
 RUN pip3 install sqlalchemy==1.4.46
 RUN pip3 install pymysql
-RUN pip3 install pandas
+RUN pip3 install pandas==2.0.3
 RUN pip3 install setuptools-rust
-RUN pip3 install sshtunnel
+RUN pip3 install sshtunnel==0.4.0
 # Setup ubsan environment to printstacktrace
 ENV UBSAN_OPTIONS=print_stacktrace=1
 
@@ -115,6 +127,8 @@ RUN if [ "$compiler_version" = "amd-stg-open" ] && [ "$compiler_commit" != "" ];
     else echo "using the release compiler"; \
     fi
 
+#clean-up the deb package
+RUN sh -c "rm -rf amdgpu-install*"
 
 #ENV HIP_CLANG_PATH='/llvm-project/build/bin'
 #RUN sh -c "echo HIP_CLANG_PATH = '$HIP_CLANG_PATH'"

Jenkinsfile

@@ -65,10 +65,10 @@ def getDockerImageName(){
 }
 
 def check_host() {
-    if ("${env.CK_CCACHE}" != "null"){
-        def CCACHE_SERVER="${env.CK_CCACHE.split(':')[0]}"
-        echo "ccache server: ${CCACHE_SERVER}"
-        sh '''ping -c 1 -p 6379 "${CCACHE_SERVER}" | echo $? > tmp.txt'''
+    if ("${env.CK_SCCACHE}" != "null"){
+        def SCCACHE_SERVER="${env.CK_SCCACHE.split(':')[0]}"
+        echo "sccache server: ${SCCACHE_SERVER}"
+        sh '''ping -c 1 -p 6379 "${SCCACHE_SERVER}" | echo $? > tmp.txt'''
         def output = readFile(file: "tmp.txt")
         echo "tmp.txt contents: \$output"
         return (output != "0")
@@ -96,24 +96,9 @@ def build_compiler(){
 
 def getDockerImage(Map conf=[:]){
     env.DOCKER_BUILDKIT=1
-    def prefixpath = conf.get("prefixpath", "/opt/rocm") // prefix:/opt/rocm
+    def prefixpath = conf.get("prefixpath", "/opt/rocm")
     def no_cache = conf.get("no_cache", false)
     def dockerArgs = "--build-arg BUILDKIT_INLINE_CACHE=1 --build-arg PREFIX=${prefixpath} --build-arg compiler_version='${params.COMPILER_VERSION}' --build-arg compiler_commit='${params.COMPILER_COMMIT}' --build-arg ROCMVERSION='${params.ROCMVERSION}' "
-    echo "ccache server: ${env.CK_CCACHE}"
-    if(env.CK_CCACHE)
-    {
-        if(check_host())
-        {
-            echo "FOUND CCACHE SERVER: ${env.CK_CCACHE}"
-        }
-        else 
-        {
-            echo "CCACHE SERVER: ${env.CK_CCACHE} NOT FOUND, got ${check_host} response"
-        }
-        dockerArgs = dockerArgs + " --build-arg CCACHE_SECONDARY_STORAGE='redis://${env.CK_CCACHE}' --build-arg COMPILER_LAUNCHER='ccache' "
-        env.CCACHE_DIR = """/tmp/ccache_store"""
-        env.CCACHE_SECONDARY_STORAGE="""redis://${env.CK_CCACHE}"""
-    }
     if(no_cache)
     {
         dockerArgs = dockerArgs + " --no-cache "
@@ -142,21 +127,6 @@ def buildDocker(install_prefix){
     def image_name = getDockerImageName()
     echo "Building Docker for ${image_name}"
     def dockerArgs = "--build-arg BUILDKIT_INLINE_CACHE=1 --build-arg PREFIX=${install_prefix} --build-arg compiler_version='${params.COMPILER_VERSION}' --build-arg compiler_commit='${params.COMPILER_COMMIT}' --build-arg ROCMVERSION='${params.ROCMVERSION}' "
-    echo "ccache server: ${env.CK_CCACHE}"
-    if(env.CK_CCACHE)
-    {
-        if(check_host())
-        {
-            echo "FOUND CCACHE SERVER: ${env.CK_CCACHE}"
-        }
-        else 
-        {
-            echo "CCACHE SERVER: ${env.CK_CCACHE} NOT FOUND, got ${check_host} response"
-        }
-        dockerArgs = dockerArgs + " --build-arg CCACHE_SECONDARY_STORAGE='redis://${env.CK_CCACHE}' --build-arg COMPILER_LAUNCHER='ccache' "
-        env.CCACHE_DIR = """/tmp/ccache_store"""
-        env.CCACHE_SECONDARY_STORAGE="""redis://${env.CK_CCACHE}"""
-    }
 
     echo "Build Args: ${dockerArgs}"
     try{
@@ -219,13 +189,9 @@ def cmake_build(Map conf=[:]){
     }else{
         setup_args = " -DCMAKE_BUILD_TYPE=release" + setup_args
     }
-    if(env.CK_CCACHE)
-    {
-        setup_args = " -DCMAKE_CXX_COMPILER_LAUNCHER='ccache' -DCMAKE_C_COMPILER_LAUNCHER='ccache' " + setup_args
-    }
-    echo "ccache server: ${env.CK_CCACHE}"
 
     def pre_setup_cmd = """
+            #!/bin/bash
             echo \$HSA_ENABLE_SDMA
             ulimit -c unlimited
             rm -rf build
@@ -234,6 +200,46 @@ def cmake_build(Map conf=[:]){
             mkdir install
             cd build
         """
+    def invocation_tag=""
+    if (setup_args.contains("gfx11")){
+        invocation_tag="gfx11"
+    }
+    if (setup_args.contains("gfx10")){
+        invocation_tag="gfx10"
+    }
+    if (setup_args.contains("gfx90")){
+        invocation_tag="gfx90"
+    }
+    if (setup_args.contains("gfx94")){
+        invocation_tag="gfx94"
+    }
+    if(check_host() && params.USE_SCCACHE && "${env.CK_SCCACHE}" != "null" && "${invocation_tag}" != "") {
+        pre_setup_cmd = pre_setup_cmd + """
+            #!/bin/bash
+            export ROCM_PATH=/opt/rocm
+            export SCCACHE_ENABLED=true
+            export SCCACHE_LOG_LEVEL=debug
+            export SCCACHE_IDLE_TIMEOUT=14400
+            export COMPILERS_HASH_DIR=/tmp/.sccache
+            export SCCACHE_BIN=/usr/local/.cargo/bin/sccache
+            export SCCACHE_EXTRAFILES=/tmp/.sccache/rocm_compilers_hash_file
+            export SCCACHE_REDIS="redis://${env.CK_SCCACHE}"
+            echo "connect = ${env.CK_SCCACHE}" >> ../script/redis-cli.conf
+            export SCCACHE_C_CUSTOM_CACHE_BUSTER="${invocation_tag}"
+            echo \$SCCACHE_C_CUSTOM_CACHE_BUSTER
+            stunnel ../script/redis-cli.conf
+            (
+                set -e
+                ../script/sccache_wrapper.sh --enforce_redis
+            )
+            error_code=\$?
+            if [ \$error_code -ne 0 ]; then
+                echo "could not connect to the redis server. using sccache locally."
+                ../script/sccache_wrapper.sh
+            fi
+        """
+        setup_args = " -DCMAKE_CXX_COMPILER_LAUNCHER=sccache -DCMAKE_C_COMPILER_LAUNCHER=sccache " + setup_args
+    }
     def setup_cmd = conf.get("setup_cmd", "${cmake_envs} cmake ${setup_args}   .. ")
     // reduce parallelism when compiling, clang uses too much memory
     def nt = nthreads()
@@ -251,7 +257,7 @@ def cmake_build(Map conf=[:]){
     sh cmd
 
     // Only archive from master or develop
-    if (package_build == true && (env.BRANCH_NAME == "develop" || env.BRANCH_NAME == "master")) {
+    if (package_build == true && (env.BRANCH_NAME == "develop" || env.BRANCH_NAME == "amd-master")) {
         archiveArtifacts artifacts: "build/*.deb", allowEmptyArchive: true, fingerprint: true
     }
 }
@@ -635,7 +641,7 @@ def process_results(Map conf=[:]){
 //launch develop branch daily at 23:00 UT in FULL_QA mode and at 19:00 UT with latest staging compiler version
 CRON_SETTINGS = BRANCH_NAME == "develop" ? '''0 23 * * * % RUN_FULL_QA=true;ROCMVERSION=5.7;COMPILER_VERSION=
                                               0 21 * * * % ROCMVERSION=5.7;COMPILER_VERSION=;COMPILER_COMMIT=
-                                              0 19 * * * % BUILD_DOCKER=true;DL_KERNELS=true;COMPILER_VERSION=amd-stg-open;COMPILER_COMMIT=''' : ""
+                                              0 19 * * * % BUILD_DOCKER=true;DL_KERNELS=true;COMPILER_VERSION=amd-stg-open;COMPILER_COMMIT=;USE_SCCACHE=false''' : ""
 
 pipeline {
     agent none
@@ -682,7 +688,10 @@ pipeline {
             name: 'hipTensor_branch',
             defaultValue: 'mainline',
             description: 'Specify which branch of hipTensor to use (default: mainline)')
-
+        booleanParam(
+            name: "USE_SCCACHE",
+            defaultValue: true,
+            description: "Use the sccache for building CK (default: ON)")
     }
     environment{
         dbuser = "${dbuser}"

README.md

 # Composable Kernel
 
-## Methodology
+The Composable Kernel (CK) library provides a programming model for writing performance-critical
+kernels for machine learning workloads across multiple architectures (GPUs, CPUs, etc.). The CK library
+uses general purpose kernel languages, such as HIP C++.
 
-Composable Kernel (CK) library aims to provide a programming model for writing performance critical kernels for machine learning workloads across multiple architectures including GPUs, CPUs, etc, through general purpose kernel languages, like HIP C++.
+CK uses two concepts to achieve performance portability and code maintainability:
 
-CK utilizes two concepts to achieve performance portability and code maintainability:
 * A tile-based programming model
-* Algorithm complexity reduction for complex ML operators, using innovative technique we call "Tensor Coordinate Transformation".
+* Algorithm complexity reduction for complex machine learning (ML) operators. This uses an innovative
+   technique called *Tensor Coordinate Transformation*.
 
 ![ALT](/docs/data/ck_component.png "CK Components")
 
-## Code Structure
+The current CK library is structured into four layers:
 
-Current CK library are structured into 4 layers:
-* "Templated Tile Operators" layer
-* "Templated Kernel and Invoker" layer
-* "Instantiated Kernel and Invoker" layer
-* "Client API" layer
+* Templated Tile Operators
+* Templated Kernel and Invoker
+* Instantiated Kernel and Invoker
+* Client API
 
 ![ALT](/docs/data/ck_layer.png "CK Layers")
 
-## Documentation
+## General information
 
-Run the steps below to build documentation locally.
+To build our documentation locally, use the following code:
 
-```
+``` bash
 cd docs
 pip3 install -r sphinx/requirements.txt
 python3 -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html
 ```
 
-## Contributors
-
-The list of developers and contributors is here: [Contributors](/CONTRIBUTORS.md)
+You can find a list of our developers and contributors on our [Contributors](/CONTRIBUTORS.md) page.
+page.
 
-## Citation
+```note
+If you use CK, cite us as follows:
 
-If you use CK, please use following citations:
-* CK paper will be freely available on arXiv soon: [Realizing Tensor Operators Using Coordinate Transformations and Tile Based Programming](???)
+* [Realizing Tensor Operators Using Coordinate Transformations and Tile Based Programming](???):
+  This paper will be available on arXiv soon.
 * [CITATION.cff](/CITATION.cff)
+```
 
-## License
+CK is released under the **[MIT license](/LICENSE)**.
 
-CK is released under the MIT license. [License File](/LICENSE)
+## Building CK
 
+We recommend building CK inside Docker containers, which include all necessary packages. Pre-built
+Docker images are available on [DockerHub](https://hub.docker.com/r/rocm/composable_kernel/tags).
 
-# Build CK
+1. To build a new Docker image, use the Dockerfile provided with the source code:
 
-## Build docker image
+    ```bash
+    DOCKER_BUILDKIT=1 docker build -t ck:latest -f Dockerfile .
+    ```
 
-```bash
-DOCKER_BUILDKIT=1 docker build -t ck:latest -f Dockerfile .
-```
-Pre-built dockers are available from this public repo: 
-https://hub.docker.com/r/rocm/composable_kernel/tags
+2. Launch the Docker container:
 
-## Launch docker
+    ```bash
+    docker run                                     \
+    -it                                            \
+    --privileged                                   \
+    --group-add sudo                               \
+    -w /root/workspace                             \
+    -v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace  \
+    ck:latest                                      \
+    /bin/bash
+    ```
 
-```bash
-docker run                                     \
--it                                            \
---privileged                                   \
---group-add sudo                               \
--w /root/workspace                             \
--v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace  \
-ck:latest                                      \
-/bin/bash
-```
+3. Clone CK source code from the GitHub repository and start the build:
 
-## Build CK
+    ```bash
+    git clone https://github.com/ROCmSoftwarePlatform/composable_kernel.git && \
+    cd composable_kernel && \
+    mkdir build && \
+    cd build
+    ```
 
-```bash
-mkdir build && cd build
+    You must set the `GPU_TARGETS` macro to specify the GPU target architecture(s) you want
+    to run CK on. You can specify single or multiple architectures. If you specify multiple architectures,
+    use a semicolon between each; for example, `gfx908;gfx90a;gfx940`.
 
-# Need to specify target ID, example below is for gfx908 and gfx90a
+    ```bash
+    cmake                                                                                             \
+    -D CMAKE_PREFIX_PATH=/opt/rocm                                                                    \
+    -D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc                                                         \
+    -D CMAKE_BUILD_TYPE=Release                                                                       \
+    -D GPU_TARGETS="gfx908;gfx90a"                                                                    \
+    ..
+    ```
 
-cmake                                                                                             \
--D CMAKE_PREFIX_PATH=/opt/rocm                                                                    \
--D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc                                                         \
--D CMAKE_BUILD_TYPE=Release                                                                       \
--D GPU_TARGETS="gfx908;gfx90a"                                                                    \
-..
-```
-
-If GPU_TARGETS is not set on the cmake command line, CK will be built for all targets supported by the 
-current compiler.
+    If you don't set `GPU_TARGETS` on the cmake command line, CK is built for all GPU targets
+    supported by the current compiler (this may take a long time).
 
+4. Build the entire CK library:
 
-Additional cmake flags can be used to significantly speed-up the build:
+    ```bash
+    make -j
+    ```
 
-INSTANCES_ONLY (by default is OFF) must be set to ON in order to build only the instances and library
-while skipping all tests, examples, and profiler. This is useful for libraries that use CK as a dependency.
+5. Install CK:
 
-DTYPES (by default not set) can be set to any subset of "fp64;fp32;fp16;fp8;bf16;int8" to build instances 
-of select data types only. Currently, building of int8 instances is taking a lot of time (the compiler fix is in the works).
+    ```bash
+    make -j install
+    ```
 
-DL_KERNELS (by default is OFF) must be set to ON in order to build the gemm_dl and batched_gemm_multi_d_dl 
-instances. Those instances are only needed for the NAVI2x platforms.
+## Optional post-install steps
 
-### Build examples and tests
+* Build examples and tests:
 
-```bash
+    ```bash
     make -j examples tests
- make test
-```
+    ```
 
-Instructions for running each individual examples are under [example](/example)
+* Build and run all examples and tests:
 
+    ```bash
+    make -j check
+    ```
 
-## Build ckProfiler
+    You can find instructions for running each individual example in [example](/example).
 
-```bash
+* Build ckProfiler:
+
+    ```bash
     make -j ckProfiler
+    ```
+
+    You can find instructions for running ckProfiler in [profiler](/profiler).
+
+Note the `-j` option for building with multiple threads in parallel. This speeds up the build significantly.
+Depending on the number of CPU cores and the amount of RAM on your system, you may want to
+limit the number of threads. For example, if you have a 128-core CPU and 64 Gb of RAM.
+
+By default, `-j` launches one thread per CPU core, which can cause the build to run out of memory and
+crash. In such cases, you can reduce the number of threads to 32 by using `-j32`.
+
+Additional cmake flags can be used to significantly speed-up the build:
+
+* `INSTANCES_ONLY` (default is OFF) must be set to ON in order to build only the instances and library
+  while skipping all tests, examples, and profiler. This is useful in cases when you plan to use CK as a
+  dependency and don't plan to run any examples or tests.
+
+* `DTYPES` (default is not set) can be set to any subset of "fp64;fp32;fp16;fp8;bf16;int8" to build
+  instances of select data types only. The main default data types are fp32 and fp16; you can safely skip
+  other data types.
+
+* `DL_KERNELS` (default is OFF) must be set to ON in order to build instances, such as `gemm_dl` or
+  `batched_gemm_multi_d_dl`. These instances are useful on architectures like the NAVI2x, as most
+  other platforms have faster instances, such as `xdl` or `wmma`, available.
+
+## Using sccache for building
+
+The default CK Docker images come with a pre-installed version of sccache, which supports clang
+being used as hip-compiler (" -x hip"). Using sccache can help reduce the time to re-build code from
+hours to 1-2 minutes. In order to invoke sccache, you need to run:
+
+```bash
+ sccache --start-server
 ```
-Instructions for running ckProfiler are under [profiler](/profiler)
 
-## Install CK
+then add the following flags to the cmake command line:
 
 ```bash
-make install
+ -DCMAKE_CXX_COMPILER_LAUNCHER=sccache -DCMAKE_C_COMPILER_LAUNCHER=sccache
 ```
 
+You may need to clean up the build folder and repeat the cmake and make steps in order to take
+advantage of the sccache during subsequent builds.
+
 ## Using CK as pre-built kernel library
 
-Instructions for using CK as a pre-built kernel library are under [client_example](/client_example)
+You can find instructions for using CK as a pre-built kernel library in [client_example](/client_example).
 
-## Contributing
+## Contributing to CK
 
-When you contribute to Composable Kernel, make sure to run `clang-format` on all the changed files. We highly recommend using git hooks that are managed by the `pre-commit` framework. To install hooks, run:
+When you contribute to CK, make sure you run `clang-format` on all changed files. We highly
+recommend using git hooks that are managed by the `pre-commit` framework. To install hooks, run:
 
 ```bash
 sudo script/install_precommit.sh
 ```
 
-This way, `pre-commit` will add the appropriate hooks to your local repository and automatically run `clang-format` (and possibly additional checks) before any commit is created.
+With this approach, `pre-commit` adds the appropriate hooks to your local repository and
+automatically runs `clang-format` (and possibly additional checks) before any commit is created.
 
 If you need to uninstall hooks from the repository, you can do so by running the following command:
 
@@ -141,14 +191,5 @@ If you need to uninstall hooks from the repository, you can do so by running the
 script/uninstall_precommit.sh
 ```
 
-If for any reason, you need to temporarily disable precommit hooks, you can add the `--no-verify` option to the `git commit` command.
-
-## 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 verification 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.
+If you need to temporarily disable pre-commit hooks, you can add the `--no-verify` option to the
+`git commit` command.

client_example/22_im2col_col2im/column_to_image.cpp

@@ -16,10 +16,10 @@
 using InDataType  = ck::half_t;
 using OutDataType = ck::half_t;
 
-using ImageLayout = ck::tensor_layout::convolution::GNHWC;
+using ImageLayout = ck::tensor_layout::convolution::NHWGC;
 
 static constexpr ck::index_t NumDimSpatial = 2;
-static constexpr ck::index_t G             = 1;
+static constexpr ck::index_t G             = 2;
 static constexpr ck::index_t N             = 32; // batch size
 static constexpr ck::index_t C             = 32; // input channel (per group)
 static constexpr ck::index_t Y             = 3;  // filter H
@@ -52,18 +52,18 @@ int main()
     std::array<ck::index_t, 2> wei_spatial_lengths{Y, X};
     std::array<ck::index_t, 2> out_spatial_lengths{Ho, Wo};
 
-    // We have NHWGC in memory space (G is dummy)
-    // However, CK's API only accept length and stride with order of GNCHW
-    // Hence, we need to adjust the order of stride
+    // We have NHWGC in memory space
+    // However, CK's API only accepts lengths and strides with order of GNCHW.
+    // Hence, we need to adjust the order of strides.
     std::array<ck::index_t, 5> image_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
-    std::array<ck::index_t, 2> gemm_strides{Y * X * C, 1};
+    std::array<ck::index_t, 3> gemm_strides{Y * X * C, G * Y * X * C, 1};
 
     std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1};
     std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1};
     std::array<ck::index_t, NumDimSpatial> input_left_pads{1, 1};
     std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1};
 
-    SimpleDeviceMem in(sizeof(InDataType) * N * Ho * Wo * Y * X * C);
+    SimpleDeviceMem in(sizeof(InDataType) * G * N * Ho * Wo * Y * X * C);
     SimpleDeviceMem out(sizeof(OutDataType) * N * Hi * Wi * G * C);
 
     using namespace ck::conv_tensor_rearrange_op;
@@ -93,6 +93,7 @@ int main()
         auto& op_ptr        = op_ptrs[i];
         auto argument_ptr   = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
                                                         out.GetDeviceBuffer(),
+                                                        G,
                                                         N,
                                                         C,
                                                         in_spatial_lengths,
@@ -112,7 +113,7 @@ int main()
             float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
 
             std::size_t num_bytes = sizeof(InDataType) * N * Hi * Wi * G * C +
-                                    sizeof(OutDataType) * N * Ho * Wo * Y * X * C;
+                                    sizeof(OutDataType) * G * N * Ho * Wo * Y * X * C;
 
             float gb_per_sec = num_bytes / 1.E6 / avg_time;
 
@@ -149,6 +150,7 @@ int main()
                   << std::endl;
         auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
                                                         out.GetDeviceBuffer(),
+                                                        G,
                                                         N,
                                                         C,
                                                         in_spatial_lengths,

client_example/22_im2col_col2im/image_to_column.cpp

@@ -16,10 +16,10 @@
 using InDataType  = ck::half_t;
 using OutDataType = ck::half_t;
 
-using ImageLayout = ck::tensor_layout::convolution::GNHWC;
+using ImageLayout = ck::tensor_layout::convolution::NHWGC;
 
 static constexpr ck::index_t NumDimSpatial = 2;
-static constexpr ck::index_t G             = 1;
+static constexpr ck::index_t G             = 2;
 static constexpr ck::index_t N             = 32; // batch size
 static constexpr ck::index_t C             = 32; // input channel (per group)
 static constexpr ck::index_t Y             = 3;  // filter H
@@ -52,11 +52,11 @@ int main()
     std::array<ck::index_t, 2> wei_spatial_lengths{Y, X};
     std::array<ck::index_t, 2> out_spatial_lengths{Ho, Wo};
 
-    // We have NHWGC in memory space (G is dummy)
-    // However, CK's API only accept length and stride with order of GNCHW
-    // Hence, we need to adjust the order of stride
+    // We have NHWGC in memory space
+    // However, CK's API only accepts lengths and strides with order of GNCHW.
+    // Hence, we need to adjust the order of strides.
     std::array<ck::index_t, 5> image_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
-    std::array<ck::index_t, 2> gemm_strides{Y * X * C, 1};
+    std::array<ck::index_t, 3> gemm_strides{Y * X * C, G * Y * X * C, 1};
 
     std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1};
     std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1};
@@ -64,7 +64,7 @@ int main()
     std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1};
 
     SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * G * C);
-    SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * Y * X * C);
+    SimpleDeviceMem out(sizeof(OutDataType) * G * N * Ho * Wo * Y * X * C);
 
     using namespace ck::conv_tensor_rearrange_op;
 
@@ -93,6 +93,7 @@ int main()
         auto& op_ptr        = op_ptrs[i];
         auto argument_ptr   = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
                                                         out.GetDeviceBuffer(),
+                                                        G,
                                                         N,
                                                         C,
                                                         in_spatial_lengths,
@@ -112,7 +113,7 @@ int main()
             float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
 
             std::size_t num_bytes = sizeof(InDataType) * N * Hi * Wi * G * C +
-                                    sizeof(OutDataType) * N * Ho * Wo * Y * X * C;
+                                    sizeof(OutDataType) * G * N * Ho * Wo * Y * X * C;
 
             float gb_per_sec = num_bytes / 1.E6 / avg_time;
 
@@ -149,6 +150,7 @@ int main()
                   << std::endl;
         auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
                                                         out.GetDeviceBuffer(),
+                                                        G,
                                                         N,
                                                         C,
                                                         in_spatial_lengths,

example/52_im2col_col2im/column_to_image_f32.cpp

@@ -20,7 +20,7 @@ using DeviceColToImgInstance = ck::tensor_operation::device::DeviceColumnToImage
 
 bool RunColumnToImage(const ExecutionConfig& config, const ck::utils::conv::ConvParam& conv_params)
 {
-
+    const auto G = conv_params.G_;
     const auto N = conv_params.N_;
     const auto C = conv_params.C_;
 
@@ -31,7 +31,7 @@ bool RunColumnToImage(const ExecutionConfig& config, const ck::utils::conv::Conv
         C * ck::accumulate_n<ck::index_t>(
                 conv_params.filter_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>());
 
-    const auto in_desc = HostTensorDescriptor({NDoHoWo, CZYX});
+    const auto in_desc = HostTensorDescriptor({G, NDoHoWo, CZYX});
     const auto out_desc =
         ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<ImLayout>(conv_params);
 
@@ -39,7 +39,7 @@ bool RunColumnToImage(const ExecutionConfig& config, const ck::utils::conv::Conv
     std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{};
     std::array<ck::index_t, NDimSpatial> output_spatial_lengths{};
     std::array<ck::index_t, NDimSpatial + 3> image_g_n_c_wis_strides{};
-    std::array<ck::index_t, 2> gemm_m_k_strides{};
+    std::array<ck::index_t, 3> gemm_g_m_k_strides{};
     std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
     std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
     std::array<ck::index_t, NDimSpatial> input_left_pads{};
@@ -50,7 +50,7 @@ bool RunColumnToImage(const ExecutionConfig& config, const ck::utils::conv::Conv
     copy(conv_params.input_spatial_lengths_, input_spatial_lengths);
     copy(conv_params.filter_spatial_lengths_, filter_spatial_lengths);
     copy(conv_params.output_spatial_lengths_, output_spatial_lengths);
-    copy(in_desc.GetStrides(), gemm_m_k_strides);
+    copy(in_desc.GetStrides(), gemm_g_m_k_strides);
     copy(out_desc.GetStrides(), image_g_n_c_wis_strides);
     copy(conv_params.conv_filter_strides_, conv_filter_strides);
     copy(conv_params.conv_filter_dilations_, conv_filter_dilations);
@@ -86,13 +86,14 @@ bool RunColumnToImage(const ExecutionConfig& config, const ck::utils::conv::Conv
     auto invoker  = col2img.MakeInvoker();
     auto argument = col2img.MakeArgument(in_device_buf.GetDeviceBuffer(),
                                          out_device_buf.GetDeviceBuffer(),
+                                         G,
                                          N,
                                          C,
                                          input_spatial_lengths,
                                          filter_spatial_lengths,
                                          output_spatial_lengths,
                                          image_g_n_c_wis_strides,
-                                         gemm_m_k_strides,
+                                         gemm_g_m_k_strides,
                                          conv_filter_strides,
                                          conv_filter_dilations,
                                          input_left_pads,
@@ -108,7 +109,7 @@ bool RunColumnToImage(const ExecutionConfig& config, const ck::utils::conv::Conv
     }
 
     float ave_time        = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
-    std::size_t num_btype = NDoHoWo * CZYX * (sizeof(OutDataType) + sizeof(InDataType));
+    std::size_t num_btype = G * NDoHoWo * CZYX * (sizeof(OutDataType) + sizeof(InDataType));
     float gb_per_sec      = num_btype / 1.E6 / ave_time;
     std::cout << "Perf: " << ave_time << " ms, " << gb_per_sec << " GB/s" << std::endl;
 

example/52_im2col_col2im/image_to_column_f32.cpp

@@ -20,7 +20,7 @@ using DeviceImgToColInstance = ck::tensor_operation::device::DeviceImageToColumn
 
 bool RunImageToColumn(const ExecutionConfig& config, const ck::utils::conv::ConvParam& conv_params)
 {
-
+    const auto G = conv_params.G_;
     const auto N = conv_params.N_;
     const auto C = conv_params.C_;
 
@@ -33,13 +33,13 @@ bool RunImageToColumn(const ExecutionConfig& config, const ck::utils::conv::Conv
 
     const auto in_desc =
         ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<ImLayout>(conv_params);
-    const auto out_desc = HostTensorDescriptor({NDoHoWo, CZYX});
+    const auto out_desc = HostTensorDescriptor({G, NDoHoWo, CZYX});
 
     std::array<ck::index_t, NDimSpatial> input_spatial_lengths{};
     std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{};
     std::array<ck::index_t, NDimSpatial> output_spatial_lengths{};
     std::array<ck::index_t, NDimSpatial + 3> image_g_n_c_wis_strides{};
-    std::array<ck::index_t, 2> gemm_m_k_strides{};
+    std::array<ck::index_t, 3> gemm_g_m_k_strides{};
     std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
     std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
     std::array<ck::index_t, NDimSpatial> input_left_pads{};
@@ -51,7 +51,7 @@ bool RunImageToColumn(const ExecutionConfig& config, const ck::utils::conv::Conv
     copy(conv_params.filter_spatial_lengths_, filter_spatial_lengths);
     copy(conv_params.output_spatial_lengths_, output_spatial_lengths);
     copy(in_desc.GetStrides(), image_g_n_c_wis_strides);
-    copy(out_desc.GetStrides(), gemm_m_k_strides);
+    copy(out_desc.GetStrides(), gemm_g_m_k_strides);
     copy(conv_params.conv_filter_strides_, conv_filter_strides);
     copy(conv_params.conv_filter_dilations_, conv_filter_dilations);
     copy(conv_params.input_left_pads_, input_left_pads);
@@ -86,13 +86,14 @@ bool RunImageToColumn(const ExecutionConfig& config, const ck::utils::conv::Conv
     auto invoker  = img2col.MakeInvoker();
     auto argument = img2col.MakeArgument(in_device_buf.GetDeviceBuffer(),
                                          out_device_buf.GetDeviceBuffer(),
+                                         G,
                                          N,
                                          C,
                                          input_spatial_lengths,
                                          filter_spatial_lengths,
                                          output_spatial_lengths,
                                          image_g_n_c_wis_strides,
-                                         gemm_m_k_strides,
+                                         gemm_g_m_k_strides,
                                          conv_filter_strides,
                                          conv_filter_dilations,
                                          input_left_pads,
@@ -108,7 +109,7 @@ bool RunImageToColumn(const ExecutionConfig& config, const ck::utils::conv::Conv
     }
 
     float ave_time        = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
-    std::size_t num_btype = NDoHoWo * CZYX * (sizeof(OutDataType) + sizeof(InDataType));
+    std::size_t num_btype = G * NDoHoWo * CZYX * (sizeof(OutDataType) + sizeof(InDataType));
     float gb_per_sec      = num_btype / 1.E6 / ave_time;
     std::cout << "Perf: " << ave_time << " ms, " << gb_per_sec << " GB/s" << std::endl;
 

include/ck/tensor_operation/gpu/device/device_conv_tensor_rearrange.hpp

@@ -14,11 +14,12 @@ namespace device {
 /**
  * \brief Convolution Tensor Rearrange.
  *
- * This Device operator supports conversion image ([G, N, Di, Hi, Wi, C]) to
- * the gemm problem([N * Do * Ho * Wo, Z *  Y * X * C]) (Image to Column) and
- * conversion gemm form to the image (Column to Image).
- *
- * Note that G must be equal to 1.
+ * This Device operator supports converting an image to
+ * the GEMM representation (Image to Column) and
+ * converting a GEMM form to the image (Column to Image).
+ * Supported layouts:
+ * [G, N, Di, Hi, Wi, C] <-> [G, N * Do * Ho * Wo, Z *  Y * X * C]
+ * [N, Di, Hi, Wi, G, C] <-> [N * Do * Ho * Wo, G, Z *  Y * X * C]
  *
  * \tparam NDimSpatial Number of spatial dimensions.
  * \tparam ImageLayout Input Layout.
@@ -39,13 +40,14 @@ struct DeviceConvTensorRearrange : public BaseOperator
      *
      * \param p_in A pointer to the device memory of the input image.
      * \param p_out A pointer to the device memory of the output.
+     * \param G Convolution number of groups.
      * \param N Convolution batch size.
      * \param C Convolution number of channels.
      * \param input_spatial_lengths Input spatial lengths.
      * \param filter_spatial_lengths Filter spatial lengths.
      * \param output_spatial_lengths Output spatial lengths.
      * \param image_g_n_c_wis_strides Image strides in order [G, N, C, D, H, W].
-     * \param gemm_m_k_strides Gemm form strides.
+     * \param gemm_g_m_k_strides Gemm form strides.
      * \param conv_filter_strides Convolution filter strides.
      * \param conv_filter_dilations Convolution filter dilations.
      * \param input_left_pads Convolution left pads.
@@ -55,13 +57,14 @@ struct DeviceConvTensorRearrange : public BaseOperator
     virtual std::unique_ptr<BaseArgument>
     MakeArgumentPointer(const void* p_in,
                         void* p_out,
+                        const ck::index_t G,
                         const ck::index_t N,
                         const ck::index_t C,
                         const std::array<index_t, NDimSpatial>& input_spatial_lengths,
                         const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
                         const std::array<index_t, NDimSpatial>& output_spatial_lengths,
                         const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides,
-                        const std::array<index_t, 2>& gemm_m_k_strides,
+                        const std::array<index_t, 3>& gemm_g_m_k_strides,
                         const std::array<index_t, NDimSpatial>& conv_filter_strides,
                         const std::array<index_t, NDimSpatial>& conv_filter_dilations,
                         const std::array<index_t, NDimSpatial>& input_left_pads,

include/ck/tensor_operation/gpu/device/impl/device_column_to_image_impl.hpp

@@ -17,15 +17,18 @@
 #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 #include "ck/tensor_operation/gpu/device/conv_tensor_rearrange_op.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.hpp"
 #include "ck/host_utility/io.hpp"
 
 namespace ck {
 namespace tensor_operation {
 namespace device {
 
-// Image to column for input layout NDHWC:
-//   input : image converted to the gemm problem [N * Do * Ho * Wo, Z * Y * X * C]
-//   output : image [N, Di, Hi, Wi, C]
+// Column to Image:
+//   input : gemm form [G, N * Do * Ho * Wo, Z * Y * X * C]
+//   output : input image [G, N, Di, Hi, Wi, C]
+//   input : gemm form [N * Do * Ho * Wo, G, Z * Y * X * C]
+//   output : input image [N, Di, Hi, Wi, G, C]
 template <index_t NDimSpatial,
           typename ImageLayout,
           typename InputDataType,
@@ -43,6 +46,14 @@ struct DeviceColumnToImageImpl
                                        OutputDataType,
                                        conv_tensor_rearrange_op::ColumnToImage>
 {
+    static constexpr bool is_NSpatialGC =
+        std::is_same_v<ImageLayout, tensor_layout::convolution::NWGC> ||
+        std::is_same_v<ImageLayout, tensor_layout::convolution::NHWGC> ||
+        std::is_same_v<ImageLayout, tensor_layout::convolution::NDHWGC>;
+    static constexpr bool is_GNSpatialC =
+        std::is_same_v<ImageLayout, tensor_layout::convolution::GNWC> ||
+        std::is_same_v<ImageLayout, tensor_layout::convolution::GNHWC> ||
+        std::is_same_v<ImageLayout, tensor_layout::convolution::GNDHWC>;
 
     static constexpr auto I0 = Number<0>{};
     static constexpr auto I1 = Number<1>{};
@@ -90,7 +101,7 @@ struct DeviceColumnToImageImpl
                             const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
                             const std::array<index_t, NDimSpatial>& output_spatial_lengths,
                             const std::array<index_t, NDimSpatial>& conv_filter_strides,
-                            const std::array<index_t, 2>& gemm_m_k_strides,
+                            const std::array<index_t, 3>& gemm_g_m_k_strides,
                             const std::array<index_t, NDimSpatial>& independent_filters,
                             const std::array<index_t, NDimSpatial>& effs)
     {
@@ -100,23 +111,23 @@ struct DeviceColumnToImageImpl
             C * ck::accumulate_n<index_t>(
                     filter_spatial_lengths.begin(), NDimSpatial, 1, std::multiplies<>());
 
-        const index_t NStride = DoHoWo * gemm_m_k_strides[I0] * gemm_m_k_strides[I1];
+        const index_t NStride = DoHoWo * gemm_g_m_k_strides[I1] * gemm_g_m_k_strides[I2];
         // Calculate the appropriate stride for each set of independent filters
         // in each dimension
-        const index_t WStride =
-            math::integer_divide_ceil(effs[XIdx], conv_filter_strides[XIdx]) * gemm_m_k_strides[I0];
+        const index_t WStride = math::integer_divide_ceil(effs[XIdx], conv_filter_strides[XIdx]) *
+                                gemm_g_m_k_strides[I1];
         const index_t HStride = math::integer_divide_ceil(effs[YIdx], conv_filter_strides[YIdx]) *
-                                output_spatial_lengths[XIdx] * gemm_m_k_strides[I0];
+                                output_spatial_lengths[XIdx] * gemm_g_m_k_strides[I1];
         const index_t DStride = math::integer_divide_ceil(effs[ZIdx], conv_filter_strides[ZIdx]) *
                                 output_spatial_lengths[YIdx] * output_spatial_lengths[XIdx] *
-                                gemm_m_k_strides[I0];
+                                gemm_g_m_k_strides[I1];
         // Create descriptor for independent filters in each dimension and
         // then merge them into column form
         if constexpr(NDimSpatial == 1)
         {
             const auto desc_gemm_form =
                 make_naive_tensor_descriptor(make_tuple(N, independent_filters[XIdx], CZYX),
-                                             make_tuple(NStride, WStride, gemm_m_k_strides[I1]));
+                                             make_tuple(NStride, WStride, gemm_g_m_k_strides[I2]));
             const auto desc_gemm_form_merged_filters = transform_tensor_descriptor(
                 desc_gemm_form,
                 make_tuple(make_merge_transform(make_tuple(N, independent_filters[XIdx])),
@@ -130,7 +141,7 @@ struct DeviceColumnToImageImpl
         {
             const auto desc_gemm_form = make_naive_tensor_descriptor(
                 make_tuple(N, independent_filters[YIdx], independent_filters[XIdx], CZYX),
-                make_tuple(NStride, HStride, WStride, gemm_m_k_strides[I1]));
+                make_tuple(NStride, HStride, WStride, gemm_g_m_k_strides[I2]));
             const auto desc_gemm_form_merged_filters = transform_tensor_descriptor(
                 desc_gemm_form,
                 make_tuple(make_merge_transform(
@@ -149,7 +160,7 @@ struct DeviceColumnToImageImpl
                            independent_filters[YIdx],
                            independent_filters[XIdx],
                            CZYX),
-                make_tuple(NStride, DStride, HStride, WStride, gemm_m_k_strides[I1]));
+                make_tuple(NStride, DStride, HStride, WStride, gemm_g_m_k_strides[I2]));
             const auto desc_gemm_form_merged_filters = transform_tensor_descriptor(
                 desc_gemm_form,
                 make_tuple(make_merge_transform(make_tuple(N,
@@ -252,7 +263,8 @@ struct DeviceColumnToImageImpl
         decltype(BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, KPerBlock, InputGridDesc>(
             InputGridDesc{}))>;
 
-    using GridwiseTensorRearrangeKernel = GridwiseTensorRearrange<InputGridDesc,
+    using GridwiseTensorRearrangeKernel =
+        GridwiseTensorRearrange<InputGridDesc,
                                 InputDataType,
                                 OutputGridDesc,
                                 OutputDataType,
@@ -262,24 +274,27 @@ struct DeviceColumnToImageImpl
                                 ThreadClusterLengths,
                                 ScalarPerVector,
                                 InMemoryDataOperationEnum::Add,
-                                                                  Block2ETileMap>;
+                                Block2ETileMap,
+                                ComputePtrOffsetOfStridedBatch<I0>>;
 
     struct Argument : public BaseArgument
     {
         Argument(const void* p_in, // input image
                  void* p_out,      // output image
+                 const ck::index_t G,
                  const ck::index_t N,
                  const ck::index_t C,
                  const std::array<index_t, NDimSpatial>& input_spatial_lengths,
                  const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
                  const std::array<index_t, NDimSpatial>& output_spatial_lengths,
                  const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides,
-                 const std::array<index_t, 2>& gemm_m_k_strides,
+                 const std::array<index_t, 3>& gemm_g_m_k_strides,
                  const std::array<index_t, NDimSpatial>& conv_filter_strides,
                  const std::array<index_t, NDimSpatial>& conv_filter_dilations,
                  const std::array<index_t, NDimSpatial>& input_left_pads,
                  const std::array<index_t, NDimSpatial>& input_right_pads)
-            : C_(C),
+            : G_(G),
+              C_(C),
               X_(filter_spatial_lengths[NDimSpatial - I1]),
               p_in_{static_cast<const InputDataType*>(p_in)},
               p_out_{static_cast<OutputDataType*>(p_out)},
@@ -289,6 +304,9 @@ struct DeviceColumnToImageImpl
               input_left_pads_{input_left_pads},
               input_right_pads_{input_right_pads}
         {
+            compute_ptr_offset_of_batch_.BatchStrideA_ = gemm_g_m_k_strides[I0];
+            compute_ptr_offset_of_batch_.BatchStrideC_ = image_g_n_c_wis_strides[I0];
+
             const index_t x_eff =
                 (filter_spatial_lengths[XIdx] - 1) * conv_filter_dilations[XIdx] + 1;
             const index_t y_eff =
@@ -354,7 +372,7 @@ struct DeviceColumnToImageImpl
                                                     filter_spatial_lengths,
                                                     output_spatial_lengths,
                                                     conv_filter_strides,
-                                                    gemm_m_k_strides,
+                                                    gemm_g_m_k_strides,
                                                     independent_filters,
                                                     effs);
                         const auto out_grid_desc_m_k =
@@ -387,10 +405,9 @@ struct DeviceColumnToImageImpl
                         // Memory offsets to next set of independent filters,
                         // move to independent filters in each dimension
                         const index_t in_offset =
-                            x_idx * gemm_m_k_strides[0] +
-                            y_idx * gemm_m_k_strides[0] * output_spatial_lengths[XIdx] +
-                            z_idx * gemm_m_k_strides[0] * output_spatial_lengths[YIdx] *
-                                output_spatial_lengths[XIdx];
+                            (x_idx + y_idx * output_spatial_lengths[XIdx] +
+                             z_idx * output_spatial_lengths[YIdx] * output_spatial_lengths[XIdx]) *
+                            gemm_g_m_k_strides[I1];
                         // Move to independent filters in appropriate dimensions
                         const index_t out_offset =
                             x_offset_with_pad * image_g_n_c_wis_strides[spatial_offset + XIdx] +
@@ -417,6 +434,7 @@ struct DeviceColumnToImageImpl
             }
         }
 
+        const ck::index_t G_;
         const ck::index_t C_;
         const ck::index_t X_;
 
@@ -434,6 +452,8 @@ struct DeviceColumnToImageImpl
 
         std::vector<const InputDataType*> p_in_container_;
         std::vector<OutputDataType*> p_out_container_;
+
+        ComputePtrOffsetOfStridedBatch<I0> compute_ptr_offset_of_batch_;
     };
 
     struct Invoker : public BaseInvoker
@@ -451,6 +471,7 @@ struct DeviceColumnToImageImpl
                                                         OutputGridDesc,
                                                         OutputDataType,
                                                         Block2ETileMap,
+                                                        ComputePtrOffsetOfStridedBatch<I0>,
                                                         GridwiseTensorRearrangeKernel>;
 
             // Execute each set of independent filters
@@ -460,7 +481,7 @@ struct DeviceColumnToImageImpl
                     BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, KPerBlock, InputGridDesc>(
                         arg.out_grid_desc_m_k_container_[i]);
                 const index_t grid_size =
-                    block_2_tile_map.CalculateGridSize(arg.in_grid_desc_m_k_container_[i]);
+                    block_2_tile_map.CalculateGridSize(arg.in_grid_desc_m_k_container_[i]) * arg.G_;
                 elapsed_time += launch_and_time_kernel(stream_config,
                                                        kernel,
                                                        dim3(grid_size),
@@ -470,7 +491,9 @@ struct DeviceColumnToImageImpl
                                                        arg.p_in_container_[i],
                                                        arg.out_grid_desc_m_k_container_[i],
                                                        arg.p_out_container_[i],
-                                                       block_2_tile_map);
+                                                       arg.G_,
+                                                       block_2_tile_map,
+                                                       arg.compute_ptr_offset_of_batch_);
             }
             return elapsed_time;
         }
@@ -485,8 +508,7 @@ struct DeviceColumnToImageImpl
     bool IsSupportedArgument(const Argument& arg)
     {
         using namespace tensor_layout::convolution;
-        if constexpr(!(std::is_same_v<ImageLayout, GNWC> || std::is_same_v<ImageLayout, GNHWC> ||
-                       std::is_same_v<ImageLayout, GNDHWC>))
+        if constexpr(!(is_NSpatialGC || is_GNSpatialC))
         {
             return false;
         }
@@ -534,13 +556,14 @@ struct DeviceColumnToImageImpl
 
     static auto MakeArgument(const void* p_in, // input image
                              void* p_out,      // output image
+                             const ck::index_t G,
                              const ck::index_t N,
                              const ck::index_t C,
                              const std::array<index_t, NDimSpatial>& input_spatial_lengths,
                              const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
                              const std::array<index_t, NDimSpatial>& output_spatial_lengths,
                              const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides,
-                             const std::array<index_t, 2>& gemm_m_k_strides,
+                             const std::array<index_t, 3>& gemm_g_m_k_strides,
                              const std::array<index_t, NDimSpatial>& conv_filter_strides,
                              const std::array<index_t, NDimSpatial>& conv_filter_dilations,
                              const std::array<index_t, NDimSpatial>& input_left_pads,
@@ -548,13 +571,14 @@ struct DeviceColumnToImageImpl
     {
         return Argument{static_cast<const InputDataType*>(p_in),
                         static_cast<OutputDataType*>(p_out),
+                        G,
                         N,
                         C,
                         input_spatial_lengths,
                         filter_spatial_lengths,
                         output_spatial_lengths,
                         image_g_n_c_wis_strides,
-                        gemm_m_k_strides,
+                        gemm_g_m_k_strides,
                         conv_filter_strides,
                         conv_filter_dilations,
                         input_left_pads,
@@ -566,13 +590,14 @@ struct DeviceColumnToImageImpl
     std::unique_ptr<BaseArgument>
     MakeArgumentPointer(const void* p_in, // input image
                         void* p_out,      // output image
+                        const ck::index_t G,
                         const ck::index_t N,
                         const ck::index_t C,
                         const std::array<index_t, NDimSpatial>& input_spatial_lengths,
                         const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
                         const std::array<index_t, NDimSpatial>& output_spatial_lengths,
                         const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides,
-                        const std::array<index_t, 2>& gemm_m_k_strides,
+                        const std::array<index_t, 3>& gemm_g_m_k_strides,
                         const std::array<index_t, NDimSpatial>& conv_filter_strides,
                         const std::array<index_t, NDimSpatial>& conv_filter_dilations,
                         const std::array<index_t, NDimSpatial>& input_left_pads,
@@ -580,13 +605,14 @@ struct DeviceColumnToImageImpl
     {
         return std::make_unique<Argument>(static_cast<const InputDataType*>(p_in),
                                           static_cast<OutputDataType*>(p_out),
+                                          G,
                                           N,
                                           C,
                                           input_spatial_lengths,
                                           filter_spatial_lengths,
                                           output_spatial_lengths,
                                           image_g_n_c_wis_strides,
-                                          gemm_m_k_strides,
+                                          gemm_g_m_k_strides,
                                           conv_filter_strides,
                                           conv_filter_dilations,
                                           input_left_pads,

include/ck/tensor_operation/gpu/device/impl/device_image_to_column_impl.hpp

@@ -15,15 +15,18 @@
 #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 #include "ck/tensor_operation/gpu/device/conv_tensor_rearrange_op.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.hpp"
 #include "ck/host_utility/io.hpp"
 
 namespace ck {
 namespace tensor_operation {
 namespace device {
 
-// Image to column for input layout NDHWC:
-//   input : input image [N, Di, Hi, Wi, C]
-//   output : gemm form [N * Do * Ho * Wo, Z * Y * X * C]
+// Image to column:
+//   input : input image [G, N, Di, Hi, Wi, C]
+//   output : gemm form [G * N * Do * Ho * Wo, Z * Y * X * C]
+//   input : input image [N, Di, Hi, Wi, G, C]
+//   output : gemm form [N * Do * Ho * Wo * G, Z * Y * X * C]
 template <index_t NDimSpatial,
           typename ImageLayout,
           typename InputDataType,
@@ -41,6 +44,14 @@ struct DeviceImageToColumnImpl
                                        OutputDataType,
                                        conv_tensor_rearrange_op::ImageToColumn>
 {
+    static constexpr bool is_NSpatialGC =
+        std::is_same_v<ImageLayout, tensor_layout::convolution::NWGC> ||
+        std::is_same_v<ImageLayout, tensor_layout::convolution::NHWGC> ||
+        std::is_same_v<ImageLayout, tensor_layout::convolution::NDHWGC>;
+    static constexpr bool is_GNSpatialC =
+        std::is_same_v<ImageLayout, tensor_layout::convolution::GNWC> ||
+        std::is_same_v<ImageLayout, tensor_layout::convolution::GNHWC> ||
+        std::is_same_v<ImageLayout, tensor_layout::convolution::GNDHWC>;
 
     static constexpr auto I0 = Number<0>{};
     static constexpr auto I1 = Number<1>{};
@@ -109,7 +120,7 @@ struct DeviceImageToColumnImpl
                           const ck::index_t C,
                           const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
                           const std::array<index_t, NDimSpatial>& output_spatial_lengths,
-                          const std::array<index_t, 2>& gemm_m_k_strides)
+                          const std::array<index_t, 3>& gemm_g_m_k_strides)
     {
         const index_t NDoHoWo =
             N * ck::accumulate_n<index_t>(
@@ -117,11 +128,10 @@ struct DeviceImageToColumnImpl
         const index_t CZYX =
             C * ck::accumulate_n<index_t>(
                     filter_spatial_lengths.begin(), NDimSpatial, 1, std::multiplies<>());
-        const auto desc_mraw_kraw = make_naive_tensor_descriptor(
-            make_tuple(NDoHoWo, CZYX), make_tuple(gemm_m_k_strides[I0], gemm_m_k_strides[I1]));
 
-        const auto desc_m_k = matrix_padder.PadADescriptor_M_K(desc_mraw_kraw);
-        return desc_m_k;
+        const auto desc_mraw_kraw = make_naive_tensor_descriptor(
+            make_tuple(NDoHoWo, CZYX), make_tuple(gemm_g_m_k_strides[I1], gemm_g_m_k_strides[I2]));
+        return matrix_padder.PadADescriptor_M_K(desc_mraw_kraw);
     }
 
     using InputGridDesc =
@@ -132,7 +142,8 @@ struct DeviceImageToColumnImpl
         decltype(BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, KPerBlock, OutputGridDesc>(
             OutputGridDesc{}))>;
 
-    using GridwiseTensorRearrangeKernel = GridwiseTensorRearrange<InputGridDesc,
+    using GridwiseTensorRearrangeKernel =
+        GridwiseTensorRearrange<InputGridDesc,
                                 InputDataType,
                                 OutputGridDesc,
                                 OutputDataType,
@@ -142,24 +153,27 @@ struct DeviceImageToColumnImpl
                                 ThreadClusterLengths,
                                 ScalarPerVector,
                                 InMemoryDataOperationEnum::Set,
-                                                                  Block2ETileMap>;
+                                Block2ETileMap,
+                                ComputePtrOffsetOfStridedBatch<I0>>;
 
     struct Argument : public BaseArgument
     {
         Argument(const void* p_in, // input image
                  void* p_out,      // gemm form
+                 const ck::index_t G,
                  const ck::index_t N,
                  const ck::index_t C,
                  const std::array<index_t, NDimSpatial>& input_spatial_lengths,
                  const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
                  const std::array<index_t, NDimSpatial>& output_spatial_lengths,
                  const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides,
-                 const std::array<index_t, 2>& gemm_m_k_strides,
+                 const std::array<index_t, 3>& gemm_g_m_k_strides,
                  const std::array<index_t, NDimSpatial>& conv_filter_strides,
                  const std::array<index_t, NDimSpatial>& conv_filter_dilations,
                  const std::array<index_t, NDimSpatial>& input_left_pads,
                  const std::array<index_t, NDimSpatial>& input_right_pads)
-            : C_(C),
+            : G_(G),
+              C_(C),
               X_(filter_spatial_lengths[NDimSpatial - I1]),
               p_in_{static_cast<const InputDataType*>(p_in)},
               p_out_{static_cast<OutputDataType*>(p_out)},
@@ -176,14 +190,16 @@ struct DeviceImageToColumnImpl
                                                         filter_spatial_lengths,
                                                         output_spatial_lengths,
                                                         image_g_n_c_wis_strides,
-
                                                         conv_filter_strides,
                                                         conv_filter_dilations,
                                                         input_left_pads,
                                                         input_right_pads);
 
             out_grid_desc_m_k_ = MakeOutDescriptor_M_K(
-                N, C, filter_spatial_lengths, output_spatial_lengths, gemm_m_k_strides);
+                N, C, filter_spatial_lengths, output_spatial_lengths, gemm_g_m_k_strides);
+
+            compute_ptr_offset_of_batch_.BatchStrideA_ = image_g_n_c_wis_strides[I0];
+            compute_ptr_offset_of_batch_.BatchStrideC_ = gemm_g_m_k_strides[I0];
         }
 
         void Print() const
@@ -192,6 +208,7 @@ struct DeviceImageToColumnImpl
             std::cout << out_grid_desc_m_k_ << std::endl;
         }
 
+        const ck::index_t G_;
         const ck::index_t C_;
         const ck::index_t X_;
 
@@ -206,6 +223,8 @@ struct DeviceImageToColumnImpl
 
         InputGridDesc in_grid_desc_m_k_;
         OutputGridDesc out_grid_desc_m_k_;
+
+        ComputePtrOffsetOfStridedBatch<I0> compute_ptr_offset_of_batch_;
     };
 
     struct Invoker : public BaseInvoker
@@ -220,12 +239,14 @@ struct DeviceImageToColumnImpl
             const auto block_2_tile_map =
                 BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, KPerBlock, OutputGridDesc>(
                     arg.out_grid_desc_m_k_);
-            const index_t grid_size = block_2_tile_map.CalculateGridSize(arg.out_grid_desc_m_k_);
+            const index_t grid_size =
+                block_2_tile_map.CalculateGridSize(arg.out_grid_desc_m_k_) * arg.G_;
             const auto kernel = kernel_tensor_rearrange<InputGridDesc,
                                                         InputDataType,
                                                         OutputGridDesc,
                                                         OutputDataType,
                                                         Block2ETileMap,
+                                                        ComputePtrOffsetOfStridedBatch<I0>,
                                                         GridwiseTensorRearrangeKernel>;
 
             float elapsed_time = launch_and_time_kernel(stream_config,
@@ -237,7 +258,9 @@ struct DeviceImageToColumnImpl
                                                         arg.p_in_,
                                                         arg.out_grid_desc_m_k_,
                                                         arg.p_out_,
-                                                        block_2_tile_map);
+                                                        arg.G_,
+                                                        block_2_tile_map,
+                                                        arg.compute_ptr_offset_of_batch_);
             return elapsed_time;
         }
 
@@ -250,9 +273,7 @@ struct DeviceImageToColumnImpl
 
     bool IsSupportedArgument(const Argument& arg)
     {
-        using namespace tensor_layout::convolution;
-        if constexpr(!(std::is_same_v<ImageLayout, GNWC> || std::is_same_v<ImageLayout, GNHWC> ||
-                       std::is_same_v<ImageLayout, GNDHWC>))
+        if constexpr(!(is_NSpatialGC || is_GNSpatialC))
         {
             return false;
         }
@@ -295,13 +316,14 @@ struct DeviceImageToColumnImpl
 
     static auto MakeArgument(const void* p_in, // input image
                              void* p_out,      // gemm form
+                             const ck::index_t G,
                              const ck::index_t N,
                              const ck::index_t C,
                              const std::array<index_t, NDimSpatial>& input_spatial_lengths,
                              const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
                              const std::array<index_t, NDimSpatial>& output_spatial_lengths,
                              const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides,
-                             const std::array<index_t, 2>& gemm_m_k_strides,
+                             const std::array<index_t, 3>& gemm_g_m_k_strides,
                              const std::array<index_t, NDimSpatial>& conv_filter_strides,
                              const std::array<index_t, NDimSpatial>& conv_filter_dilations,
                              const std::array<index_t, NDimSpatial>& input_left_pads,
@@ -309,13 +331,14 @@ struct DeviceImageToColumnImpl
     {
         return Argument{static_cast<const InputDataType*>(p_in),
                         static_cast<OutputDataType*>(p_out),
+                        G,
                         N,
                         C,
                         input_spatial_lengths,
                         filter_spatial_lengths,
                         output_spatial_lengths,
                         image_g_n_c_wis_strides,
-                        gemm_m_k_strides,
+                        gemm_g_m_k_strides,
                         conv_filter_strides,
                         conv_filter_dilations,
                         input_left_pads,
@@ -327,13 +350,14 @@ struct DeviceImageToColumnImpl
     std::unique_ptr<BaseArgument>
     MakeArgumentPointer(const void* p_in, // input image
                         void* p_out,      // gemm form
+                        const ck::index_t G,
                         const ck::index_t N,
                         const ck::index_t C,
                         const std::array<index_t, NDimSpatial>& input_spatial_lengths,
                         const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
                         const std::array<index_t, NDimSpatial>& output_spatial_lengths,
                         const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides,
-                        const std::array<index_t, 2>& gemm_m_k_strides,
+                        const std::array<index_t, 3>& gemm_g_m_k_strides,
                         const std::array<index_t, NDimSpatial>& conv_filter_strides,
                         const std::array<index_t, NDimSpatial>& conv_filter_dilations,
                         const std::array<index_t, NDimSpatial>& input_left_pads,
@@ -341,13 +365,14 @@ struct DeviceImageToColumnImpl
     {
         return std::make_unique<Argument>(static_cast<const InputDataType*>(p_in),
                                           static_cast<OutputDataType*>(p_out),
+                                          G,
                                           N,
                                           C,
                                           input_spatial_lengths,
                                           filter_spatial_lengths,
                                           output_spatial_lengths,
                                           image_g_n_c_wis_strides,
-                                          gemm_m_k_strides,
+                                          gemm_g_m_k_strides,
                                           conv_filter_strides,
                                           conv_filter_dilations,
                                           input_left_pads,

include/ck/tensor_operation/gpu/grid/gridwise_tensor_rearrange.hpp

@@ -21,6 +21,7 @@ template <typename InputGridDesc,
           typename OutputGridDesc,
           typename OutputDataType,
           typename Block2ETileMap,
+          typename ComputePtrOffsetOfStridedBatch,
           typename GridwiseTensorRearrangeKernel>
 __global__ void
 #if CK_USE_LAUNCH_BOUNDS
@@ -30,13 +31,20 @@ __global__ void
                                 const InputDataType* __restrict__ p_in_global,
                                 const OutputGridDesc out_grid_desc,
                                 OutputDataType* __restrict__ p_out_global,
-                                const Block2ETileMap block_2_tile_map)
+                                const index_t batch_count,
+                                const Block2ETileMap block_2_tile_map,
+                                const ComputePtrOffsetOfStridedBatch compute_ptr_offset_of_batch)
 {
 #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx906__) || defined(__gfx908__) ||             \
     defined(__gfx90a__) || defined(__gfx940__) || defined(__gfx1030__) || defined(__gfx1100__) || \
     defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx941__) || defined(__gfx942__))
-    GridwiseTensorRearrangeKernel::Run(
-        in_grid_desc, p_in_global, out_grid_desc, p_out_global, block_2_tile_map);
+    GridwiseTensorRearrangeKernel::Run(in_grid_desc,
+                                       p_in_global,
+                                       out_grid_desc,
+                                       p_out_global,
+                                       batch_count,
+                                       block_2_tile_map,
+                                       compute_ptr_offset_of_batch);
 #else
     ignore = in_grid_desc;
     ignore = p_in_global;
@@ -56,7 +64,8 @@ template <typename InputGridDesc,
           typename ThreadClusterLengths,
           index_t ScalarPerVector,
           InMemoryDataOperationEnum DstInMemOp,
-          typename Block2ETileMap>
+          typename Block2ETileMap,
+          typename ComputePtrOffsetOfStridedBatch>
 struct GridwiseTensorRearrange
 {
 
@@ -69,7 +78,9 @@ struct GridwiseTensorRearrange
                                const InputDataType* __restrict__ p_in_global,
                                const OutputGridDesc& out_grid_desc,
                                OutputDataType* __restrict__ p_out_global,
-                               const Block2ETileMap& block_2_tile_map)
+                               const index_t batch_count,
+                               const Block2ETileMap& block_2_tile_map,
+                               const ComputePtrOffsetOfStridedBatch& compute_ptr_offset_of_batch)
     {
         const auto block_work_idx =
             block_2_tile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
@@ -80,12 +91,6 @@ struct GridwiseTensorRearrange
         const index_t k_block_data_idx_on_grid =
             __builtin_amdgcn_readfirstlane(block_work_idx[I1] * KPerBlock);
 
-        // Global Memory
-        const auto in_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
-            p_in_global, in_grid_desc.GetElementSpaceSize());
-        auto out_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
-            p_out_global, out_grid_desc.GetElementSpaceSize());
-
         auto copy_global_to_global =
             ThreadGroupTensorSliceTransfer_v7<ThisThreadBlock,
                                               Tuple<InputDataType>,
@@ -108,6 +113,22 @@ struct GridwiseTensorRearrange
                 make_tuple(make_multi_index(m_block_data_idx_on_grid, k_block_data_idx_on_grid)),
                 tensor_operation::element_wise::PassThrough{}};
 
+        const index_t num_blocks_per_batch =
+            __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
+        const index_t g_idx =
+            __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
+
+        // Global Memory
+        const index_t a_batch_offset =
+            __builtin_amdgcn_readfirstlane(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx));
+        const index_t c_batch_offset =
+            __builtin_amdgcn_readfirstlane(compute_ptr_offset_of_batch.GetCPtrOffset(g_idx));
+
+        const auto in_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_in_global + a_batch_offset, in_grid_desc.GetElementSpaceSize());
+        auto out_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_out_global + c_batch_offset, out_grid_desc.GetElementSpaceSize());
+
         copy_global_to_global.Run(
             tie(in_grid_desc), tie(in_global_buf), tie(out_grid_desc), tie(out_global_buf));
     }

library/include/ck/library/reference_tensor_operation/cpu/reference_column_to_image.hpp

@@ -19,9 +19,7 @@ namespace host {
  * \brief Reference implementation for column to image.
  *
  * Input tensor descriptor has [N * Do * Ho * Wo, Z * Y * X * C] data layout.
- * Memory layout is the same.
  * Output tensor descriptor has [G, N, C, Di, Hi, Wi] data layout.
- * G must be equal to 1. Memory layout is [G, N, Di, Hi, Wi, C].
  *
  * \tparam NDimSpatial Number of spatial dimensions.
  * \tparam ImageLayout Image Layout.
@@ -95,18 +93,19 @@ struct ReferenceColumnToImage : public device::BaseOperator
         float Run(const Argument& arg)
         {
             if(!(arg.output_.GetNumOfDimension() == NDimSpatial + 3 &&
-                 arg.input_.GetNumOfDimension() == 2))
+                 arg.input_.GetNumOfDimension() == 3))
             {
                 throw std::runtime_error("wrong! inconsistent dimension");
             }
 
+            const index_t G = arg.output_.GetLengths()[0];
             const index_t N = arg.output_.GetLengths()[1];
             const index_t C = arg.output_.GetLengths()[2];
 
             if constexpr(NDimSpatial == 1)
             {
                 const index_t Wo = arg.output_spatial_lengths_[0];
-                auto func        = [&](auto n) {
+                auto func        = [&](auto g, auto n) {
                     for(index_t wo = 0; wo < Wo; ++wo)
                     {
                         index_t row    = n * Wo + wo;
@@ -123,9 +122,10 @@ struct ReferenceColumnToImage : public device::BaseOperator
                                 if(wi >= 0 &&
                                    ck::type_convert<std::size_t>(wi) < arg.output_.GetLengths()[3])
                                 {
-                                    float v_in  = ck::type_convert<float>(arg.input_(row, column));
-                                    float v_out = ck::type_convert<float>(arg.output_(0, n, c, wi));
-                                    arg.output_(0, n, c, wi) =
+                                    float v_in =
+                                        ck::type_convert<float>(arg.input_(g, row, column));
+                                    float v_out = ck::type_convert<float>(arg.output_(g, n, c, wi));
+                                    arg.output_(g, n, c, wi) =
                                         ck::type_convert<OutDataType>(v_in + v_out);
                                 }
                                 column++;
@@ -134,7 +134,7 @@ struct ReferenceColumnToImage : public device::BaseOperator
                     }
                 };
 
-                make_ParallelTensorFunctor(func, N)(std::thread::hardware_concurrency());
+                make_ParallelTensorFunctor(func, G, N)(std::thread::hardware_concurrency());
 
                 return 0;
             }
@@ -143,7 +143,7 @@ struct ReferenceColumnToImage : public device::BaseOperator
                 const index_t Ho = arg.output_spatial_lengths_[0];
                 const index_t Wo = arg.output_spatial_lengths_[1];
 
-                auto func = [&](auto n) {
+                auto func = [&](auto g, auto n) {
                     for(index_t ho = 0; ho < Ho; ++ho)
                     {
                         for(index_t wo = 0; wo < Wo; ++wo)
@@ -176,10 +176,10 @@ struct ReferenceColumnToImage : public device::BaseOperator
                                                arg.output_.GetLengths()[4])
                                         {
                                             float v_in =
-                                                ck::type_convert<float>(arg.input_(row, column));
+                                                ck::type_convert<float>(arg.input_(g, row, column));
                                             float v_out = ck::type_convert<float>(
-                                                arg.output_(0, n, c, hi, wi));
-                                            arg.output_(0, n, c, hi, wi) =
+                                                arg.output_(g, n, c, hi, wi));
+                                            arg.output_(g, n, c, hi, wi) =
                                                 ck::type_convert<OutDataType>(v_in + v_out);
                                         }
                                         column++;
@@ -190,7 +190,7 @@ struct ReferenceColumnToImage : public device::BaseOperator
                     }
                 };
 
-                make_ParallelTensorFunctor(func, N)(std::thread::hardware_concurrency());
+                make_ParallelTensorFunctor(func, G, N)(std::thread::hardware_concurrency());
 
                 return 0;
             }
@@ -200,7 +200,7 @@ struct ReferenceColumnToImage : public device::BaseOperator
                 const index_t Ho = arg.output_spatial_lengths_[1];
                 const index_t Wo = arg.output_spatial_lengths_[2];
 
-                auto func = [&](auto n) {
+                auto func = [&](auto g, auto n) {
                     for(index_t d_o = 0; d_o < Do; ++d_o)
                     {
                         for(index_t ho = 0; ho < Ho; ++ho)
@@ -245,10 +245,10 @@ struct ReferenceColumnToImage : public device::BaseOperator
                                                        arg.output_.GetLengths()[5])
                                                 {
                                                     float v_in = ck::type_convert<float>(
-                                                        arg.input_(row, column));
+                                                        arg.input_(g, row, column));
                                                     float v_out = ck::type_convert<float>(
-                                                        arg.output_(0, n, c, di, hi, wi));
-                                                    arg.output_(0, n, c, di, hi, wi) =
+                                                        arg.output_(g, n, c, di, hi, wi));
+                                                    arg.output_(g, n, c, di, hi, wi) =
                                                         ck::type_convert<OutDataType>(v_in + v_out);
                                                 }
                                                 column++;
@@ -261,7 +261,7 @@ struct ReferenceColumnToImage : public device::BaseOperator
                     }
                 };
 
-                make_ParallelTensorFunctor(func, N)(std::thread::hardware_concurrency());
+                make_ParallelTensorFunctor(func, G, N)(std::thread::hardware_concurrency());
 
                 return 0;
             }
@@ -303,8 +303,9 @@ struct ReferenceColumnToImage : public device::BaseOperator
             C * ck::accumulate_n<index_t>(
                     arg.filter_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>());
 
-        if(!(arg.input_.GetLengths()[0] == static_cast<std::size_t>(NDoHoWo) &&
-             arg.input_.GetLengths()[1] == static_cast<std::size_t>(CZYX)))
+        if(!(arg.input_.GetLengths()[0] == static_cast<std::size_t>(G) &&
+             arg.input_.GetLengths()[1] == static_cast<std::size_t>(NDoHoWo) &&
+             arg.input_.GetLengths()[2] == static_cast<std::size_t>(CZYX)))
         {
             return false;
         }

library/include/ck/library/reference_tensor_operation/cpu/reference_image_to_column.hpp

@@ -19,9 +19,7 @@ namespace host {
  * \brief Reference implementation for image to column.
  *
  * Input tensor descriptor has [G, N, C, Di, Hi, Wi] data layout.
- * G must be equal to 1. Memory layout is [G, N, Di, Hi, Wi, C].
- * Output tensor descriptor has [N * Do * Ho * Wo, Z * Y * X * C] data layout.
- * Memory layout is the same.
+ * Output tensor descriptor has [G * N * Do * Ho * Wo, Z * Y * X * C] data layout.
  *
  * \tparam NDimSpatial Number of spatial dimensions.
  * \tparam ImageLayout Image Layout.
@@ -95,18 +93,19 @@ struct ReferenceImageToColumn : public device::BaseOperator
         float Run(const Argument& arg)
         {
             if(!(arg.input_.GetNumOfDimension() == NDimSpatial + 3 &&
-                 arg.output_.GetNumOfDimension() == 2))
+                 arg.output_.GetNumOfDimension() == 3))
             {
                 throw std::runtime_error("wrong! inconsistent dimension");
             }
 
+            const index_t G = arg.input_.GetLengths()[0];
             const index_t N = arg.input_.GetLengths()[1];
             const index_t C = arg.input_.GetLengths()[2];
 
             if constexpr(NDimSpatial == 1)
             {
                 const index_t Wo = arg.output_spatial_lengths_[0];
-                auto func        = [&](auto n, auto wo) {
+                auto func        = [&](auto g, auto n, auto wo) {
                     index_t row    = n * Wo + wo;
                     index_t column = 0;
 
@@ -121,15 +120,15 @@ struct ReferenceImageToColumn : public device::BaseOperator
                             if(wi >= 0 &&
                                ck::type_convert<std::size_t>(wi) < arg.input_.GetLengths()[3])
                             {
-                                InDataType v_in          = arg.input_(0, n, c, wi);
-                                arg.output_(row, column) = ck::type_convert<OutDataType>(v_in);
+                                InDataType v_in             = arg.input_(g, n, c, wi);
+                                arg.output_(g, row, column) = ck::type_convert<OutDataType>(v_in);
                             }
                             column++;
                         }
                     }
                 };
 
-                make_ParallelTensorFunctor(func, N, Wo)(std::thread::hardware_concurrency());
+                make_ParallelTensorFunctor(func, G, N, Wo)(std::thread::hardware_concurrency());
 
                 return 0;
             }
@@ -138,7 +137,7 @@ struct ReferenceImageToColumn : public device::BaseOperator
                 const index_t Ho = arg.output_spatial_lengths_[0];
                 const index_t Wo = arg.output_spatial_lengths_[1];
 
-                auto func = [&](auto n, auto ho, auto wo) {
+                auto func = [&](auto g, auto n, auto ho, auto wo) {
                     index_t row    = n * Ho * Wo + ho * Wo + wo;
                     index_t column = 0;
 
@@ -162,8 +161,9 @@ struct ReferenceImageToColumn : public device::BaseOperator
                                    wi >= 0 &&
                                    ck::type_convert<std::size_t>(wi) < arg.input_.GetLengths()[4])
                                 {
-                                    InDataType v_in          = arg.input_(0, n, c, hi, wi);
-                                    arg.output_(row, column) = ck::type_convert<OutDataType>(v_in);
+                                    InDataType v_in = arg.input_(g, n, c, hi, wi);
+                                    arg.output_(g, row, column) =
+                                        ck::type_convert<OutDataType>(v_in);
                                 }
                                 column++;
                             }
@@ -171,7 +171,7 @@ struct ReferenceImageToColumn : public device::BaseOperator
                     }
                 };
 
-                make_ParallelTensorFunctor(func, N, Ho, Wo)(std::thread::hardware_concurrency());
+                make_ParallelTensorFunctor(func, G, N, Ho, Wo)(std::thread::hardware_concurrency());
 
                 return 0;
             }
@@ -181,7 +181,7 @@ struct ReferenceImageToColumn : public device::BaseOperator
                 const index_t Ho = arg.output_spatial_lengths_[1];
                 const index_t Wo = arg.output_spatial_lengths_[2];
 
-                auto func = [&](auto n, auto d_o, auto ho, auto wo) {
+                auto func = [&](auto g, auto n, auto d_o, auto ho, auto wo) {
                     index_t row    = n * Do * Ho * Wo + d_o * Ho * Wo + ho * Wo + wo;
                     index_t column = 0;
 
@@ -213,8 +213,8 @@ struct ReferenceImageToColumn : public device::BaseOperator
                                        ck::type_convert<std::size_t>(wi) <
                                            arg.input_.GetLengths()[5])
                                     {
-                                        InDataType v_in = arg.input_(0, n, c, di, hi, wi);
-                                        arg.output_(row, column) =
+                                        InDataType v_in = arg.input_(g, n, c, di, hi, wi);
+                                        arg.output_(g, row, column) =
                                             ck::type_convert<OutDataType>(v_in);
                                     }
                                     column++;
@@ -224,7 +224,7 @@ struct ReferenceImageToColumn : public device::BaseOperator
                     }
                 };
 
-                make_ParallelTensorFunctor(func, N, Do, Ho, Wo)(
+                make_ParallelTensorFunctor(func, G, N, Do, Ho, Wo)(
                     std::thread::hardware_concurrency());
 
                 return 0;
@@ -267,8 +267,9 @@ struct ReferenceImageToColumn : public device::BaseOperator
             C * ck::accumulate_n<index_t>(
                     arg.filter_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>());
 
-        if(!(arg.output_.GetLengths()[0] == static_cast<std::size_t>(NDoHoWo) &&
-             arg.output_.GetLengths()[1] == static_cast<std::size_t>(CZYX)))
+        if(!(arg.output_.GetLengths()[0] == static_cast<std::size_t>(G) &&
+             arg.output_.GetLengths()[1] == static_cast<std::size_t>(NDoHoWo) &&
+             arg.output_.GetLengths()[2] == static_cast<std::size_t>(CZYX)))
         {
             return false;
         }

library/src/tensor_operation_instance/gpu/column_to_image/CMakeLists.txt

 add_instance_library(device_column_to_image_instance
-   device_column_to_image_nhwc_1d_instance.cpp
-   device_column_to_image_nhwc_2d_instance.cpp
-   device_column_to_image_nhwc_3d_instance.cpp
+   device_column_to_image_gnwc_1d_instance.cpp
+   device_column_to_image_gnhwc_2d_instance.cpp
+   device_column_to_image_gndhwc_3d_instance.cpp
+   device_column_to_image_nwgc_1d_instance.cpp
+   device_column_to_image_nhwgc_2d_instance.cpp
+   device_column_to_image_ndhwgc_3d_instance.cpp
 )

library/src/tensor_operation_instance/gpu/column_to_image/device_column_to_image_nhwc_3d_instance.cpp → library/src/tensor_operation_instance/gpu/column_to_image/device_column_to_image_gndhwc_3d_instance.cpp

@@ -11,7 +11,7 @@ namespace instance {
 
 using namespace ck::conv_tensor_rearrange_op;
 
-void add_device_column_to_image_ndhwc_3d_bf16_instances(
+void add_device_column_to_image_gndhwc_3d_bf16_instances(
     std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, BF16, BF16, ColumnToImage>>>&
         instances)
 {
@@ -22,7 +22,7 @@ void add_device_column_to_image_ndhwc_3d_bf16_instances(
 #endif
 }
 
-void add_device_column_to_image_ndhwc_3d_f16_instances(
+void add_device_column_to_image_gndhwc_3d_f16_instances(
     std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, F16, F16, ColumnToImage>>>&
         instances)
 {
@@ -33,7 +33,7 @@ void add_device_column_to_image_ndhwc_3d_f16_instances(
 #endif
 }
 
-void add_device_column_to_image_ndhwc_3d_f32_instances(
+void add_device_column_to_image_gndhwc_3d_f32_instances(
     std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, F32, F32, ColumnToImage>>>&
         instances)
 {
@@ -44,7 +44,7 @@ void add_device_column_to_image_ndhwc_3d_f32_instances(
 #endif
 }
 
-void add_device_column_to_image_ndhwc_3d_i8_instances(
+void add_device_column_to_image_gndhwc_3d_i8_instances(
     std::vector<
         std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, int8_t, int8_t, ColumnToImage>>>&
         instances)

library/src/tensor_operation_instance/gpu/column_to_image/device_column_to_image_nhwc_2d_instance.cpp → library/src/tensor_operation_instance/gpu/column_to_image/device_column_to_image_gnhwc_2d_instance.cpp

@@ -11,7 +11,7 @@ namespace instance {
 
 using namespace ck::conv_tensor_rearrange_op;
 
-void add_device_column_to_image_nhwc_2d_bf16_instances(
+void add_device_column_to_image_gnhwc_2d_bf16_instances(
     std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, BF16, BF16, ColumnToImage>>>&
         instances)
 {
@@ -22,7 +22,7 @@ void add_device_column_to_image_nhwc_2d_bf16_instances(
 #endif
 }
 
-void add_device_column_to_image_nhwc_2d_f16_instances(
+void add_device_column_to_image_gnhwc_2d_f16_instances(
     std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, F16, F16, ColumnToImage>>>&
         instances)
 {
@@ -33,7 +33,7 @@ void add_device_column_to_image_nhwc_2d_f16_instances(
 #endif
 }
 
-void add_device_column_to_image_nhwc_2d_f32_instances(
+void add_device_column_to_image_gnhwc_2d_f32_instances(
     std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, F32, F32, ColumnToImage>>>&
         instances)
 {
@@ -44,7 +44,7 @@ void add_device_column_to_image_nhwc_2d_f32_instances(
 #endif
 }
 
-void add_device_column_to_image_nhwc_2d_i8_instances(
+void add_device_column_to_image_gnhwc_2d_i8_instances(
     std::vector<
         std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, int8_t, int8_t, ColumnToImage>>>&
         instances)

library/src/tensor_operation_instance/gpu/column_to_image/device_column_to_image_nhwc_1d_instance.cpp → library/src/tensor_operation_instance/gpu/column_to_image/device_column_to_image_gnwc_1d_instance.cpp

@@ -11,7 +11,7 @@ namespace instance {
 
 using namespace ck::conv_tensor_rearrange_op;
 
-void add_device_column_to_image_nwc_1d_bf16_instances(
+void add_device_column_to_image_gnwc_1d_bf16_instances(
     std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, BF16, BF16, ColumnToImage>>>&
         instances)
 {
@@ -22,7 +22,7 @@ void add_device_column_to_image_nwc_1d_bf16_instances(
 #endif
 }
 
-void add_device_column_to_image_nwc_1d_f16_instances(
+void add_device_column_to_image_gnwc_1d_f16_instances(
     std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, F16, F16, ColumnToImage>>>&
         instances)
 {
@@ -33,7 +33,7 @@ void add_device_column_to_image_nwc_1d_f16_instances(
 #endif
 }
 
-void add_device_column_to_image_nwc_1d_f32_instances(
+void add_device_column_to_image_gnwc_1d_f32_instances(
     std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, F32, F32, ColumnToImage>>>&
         instances)
 {
@@ -44,7 +44,7 @@ void add_device_column_to_image_nwc_1d_f32_instances(
 #endif
 }
 
-void add_device_column_to_image_nwc_1d_i8_instances(
+void add_device_column_to_image_gnwc_1d_i8_instances(
     std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, int8_t, int8_t, ColumnToImage>>>&
         instances)
 {

library/src/tensor_operation_instance/gpu/column_to_image/device_column_to_image_ndhwgc_3d_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/library/tensor_operation_instance/gpu/conv_tensor_rearrange/device_column_to_image_instance.hpp"
+#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+using namespace ck::conv_tensor_rearrange_op;
+
+void add_device_column_to_image_ndhwgc_3d_bf16_instances(
+    std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, BF16, BF16, ColumnToImage>>>&
+        instances)
+{
+#ifdef CK_ENABLE_BF16
+    add_device_operation_instances(instances, device_column_to_image_bf16_instances<3, NDHWGC>{});
+#else
+    ignore = instances;
+#endif
+}
+
+void add_device_column_to_image_ndhwgc_3d_f16_instances(
+    std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, F16, F16, ColumnToImage>>>&
+        instances)
+{
+#ifdef CK_ENABLE_FP16
+    add_device_operation_instances(instances, device_column_to_image_f16_instances<3, NDHWGC>{});
+#else
+    ignore = instances;
+#endif
+}
+
+void add_device_column_to_image_ndhwgc_3d_f32_instances(
+    std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, F32, F32, ColumnToImage>>>&
+        instances)
+{
+#ifdef CK_ENABLE_FP32
+    add_device_operation_instances(instances, device_column_to_image_f32_instances<3, NDHWGC>{});
+#else
+    ignore = instances;
+#endif
+}
+
+void add_device_column_to_image_ndhwgc_3d_i8_instances(
+    std::vector<
+        std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, int8_t, int8_t, ColumnToImage>>>&
+        instances)
+{
+#ifdef CK_ENABLE_INT8
+    add_device_operation_instances(instances, device_column_to_image_i8_instances<3, NDHWGC>{});
+#else
+    ignore = instances;
+#endif
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck