Merge branch 'amd-develop' into amd-master

CHANGELOG.md

@@ -2,6 +2,18 @@
 
 Full documentation for Composable Kernel is not yet available.
 
+## (Unreleased) CK for ROCm 6.0.0
+
+### Fixed
+
+### Optimizations
+
+### Added
+- Added image to column (#867) and column to image kernels (#930).
+
+### Changed
+
+
 ## CK 0.2.0 for ROCm 5.5.0
 
 ### Fixed
@@ -29,4 +41,3 @@ Full documentation for Composable Kernel is not yet available.
 - Added MaxPool backward (#750).
 
 ### Changed
-- Changed ...

CMakeLists.txt

@@ -446,14 +446,14 @@ if(NOT DEFINED INSTANCES_ONLY)
 
    rocm_package_setup_component(profiler
         LIBRARY_NAME composablekernel
-        PACKAGE_NAME ckProfiler
+        PACKAGE_NAME ckprofiler
    )
    add_subdirectory(profiler)
   else()
     #When building PROFILER_ONLY, label the package with GPU_ARCH
     rocm_package_setup_component(profiler
        LIBRARY_NAME composablekernel
-       PACKAGE_NAME ckProfiler_${GPU_ARCH}
+       PACKAGE_NAME ckprofiler_${GPU_ARCH}
     )
     add_subdirectory(profiler)
   endif()

client_example/20_image_to_column/CMakeLists.txt → client_example/22_im2col_col2im/CMakeLists.txt

 add_executable(client_image_to_column image_to_column.cpp)
 target_link_libraries(client_image_to_column PRIVATE composable_kernel::device_operations)
+
+add_executable(client_column_to_image column_to_image.cpp)
+target_link_libraries(client_column_to_image PRIVATE composable_kernel::device_operations)

client_example/22_im2col_col2im/column_to_image.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <cstdlib>
+#include <iomanip>
+#include <iostream>
+#include <iterator>
+#include <numeric>
+#include <vector>
+
+#include "ck/ck.hpp"
+#include "ck/library/tensor_operation_instance/gpu/conv_tensor_rearrange.hpp"
+#include "ck/tensor_operation/gpu/device/conv_tensor_rearrange_op.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+
+using InDataType  = ck::half_t;
+using OutDataType = ck::half_t;
+
+using ImageLayout = ck::tensor_layout::convolution::GNHWC;
+
+static constexpr ck::index_t NumDimSpatial = 2;
+static constexpr ck::index_t G             = 1;
+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
+static constexpr ck::index_t X             = 3;  // filter W
+static constexpr ck::index_t Hi            = 28; // input H
+static constexpr ck::index_t Wi            = 28; // input W
+static constexpr ck::index_t Ho            = 28; // output H
+static constexpr ck::index_t Wo            = 28; // output W
+
+struct SimpleDeviceMem
+{
+    SimpleDeviceMem() = delete;
+
+    SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
+    {
+        (void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
+    }
+
+    void* GetDeviceBuffer() { return p_mem_; }
+
+    ~SimpleDeviceMem() { (void)hipFree(p_mem_); }
+
+    void* p_mem_;
+};
+
+int main()
+{
+
+    std::array<ck::index_t, 2> in_spatial_lengths{Hi, Wi};
+    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
+    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, 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 out(sizeof(OutDataType) * N * Hi * Wi * G * C);
+
+    using namespace ck::conv_tensor_rearrange_op;
+
+    using DeviceOp = ck::tensor_operation::device::DeviceConvTensorRearrange<NumDimSpatial,
+                                                                             ImageLayout,
+                                                                             InDataType,
+                                                                             OutDataType,
+                                                                             ColumnToImage>;
+
+    // get device op instances
+    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
+        DeviceOp>::GetInstances();
+
+    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
+
+    std::string best_op_name;
+    int best_op_id        = -1;
+    float best_avg_time   = std::numeric_limits<float>::max();
+    float best_gb_per_sec = 0;
+
+    // profile device operation instances
+    std::cout << "Run all instances and do timing" << std::endl;
+
+    for(int i = 0; i < op_ptrs.size(); ++i)
+    {
+        auto& op_ptr        = op_ptrs[i];
+        auto argument_ptr   = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
+                                                        out.GetDeviceBuffer(),
+                                                        N,
+                                                        C,
+                                                        in_spatial_lengths,
+                                                        out_spatial_lengths,
+                                                        wei_spatial_lengths,
+                                                        image_strides,
+                                                        gemm_strides,
+                                                        filter_strides,
+                                                        filter_dilations,
+                                                        input_left_pads,
+                                                        input_right_pads);
+        auto invoker_ptr    = op_ptr->MakeInvokerPointer();
+        std::string op_name = op_ptr->GetTypeString();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            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;
+
+            float gb_per_sec = num_bytes / 1.E6 / avg_time;
+
+            std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << gb_per_sec << " GB/s, "
+                      << op_name << std::endl;
+
+            if(avg_time < best_avg_time)
+            {
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_avg_time   = avg_time;
+                best_gb_per_sec = gb_per_sec;
+            }
+        }
+        else
+        {
+            std::cerr << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    if(best_op_id < 0)
+    {
+        std::cerr << "no suitable instance" << std::endl;
+        return EXIT_FAILURE;
+    }
+
+    std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_gb_per_sec
+              << " GB/s, " << best_op_name << std::endl;
+
+    // run the best intance
+    {
+        auto& op_ptr = op_ptrs[best_op_id];
+        std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
+                  << std::endl;
+        auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
+                                                        out.GetDeviceBuffer(),
+                                                        N,
+                                                        C,
+                                                        in_spatial_lengths,
+                                                        out_spatial_lengths,
+                                                        wei_spatial_lengths,
+                                                        image_strides,
+                                                        gemm_strides,
+                                                        filter_strides,
+                                                        filter_dilations,
+                                                        input_left_pads,
+                                                        input_right_pads);
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
+        }
+
+        std::cout << "Done" << std::endl;
+    }
+}

client_example/20_image_to_column/image_to_column.cpp → client_example/22_im2col_col2im/image_to_column.cpp

@@ -9,13 +9,14 @@
 #include <vector>
 
 #include "ck/ck.hpp"
-#include "ck/library/tensor_operation_instance/gpu/image_to_column.hpp"
+#include "ck/library/tensor_operation_instance/gpu/conv_tensor_rearrange.hpp"
+#include "ck/tensor_operation/gpu/device/conv_tensor_rearrange_op.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 
 using InDataType  = ck::half_t;
 using OutDataType = ck::half_t;
 
-using InLayout = ck::tensor_layout::convolution::GNHWC;
+using ImageLayout = ck::tensor_layout::convolution::GNHWC;
 
 static constexpr ck::index_t NumDimSpatial = 2;
 static constexpr ck::index_t G             = 1;
@@ -54,8 +55,8 @@ int main()
     // 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
-    std::array<ck::index_t, 5> in_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
-    std::array<ck::index_t, 2> out_strides{Y * X * C, 1};
+    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, NumDimSpatial> filter_strides{1, 1};
     std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1};
@@ -65,8 +66,13 @@ int main()
     SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * G * C);
     SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * Y * X * C);
 
-    using DeviceOp = ck::tensor_operation::device::
-        DeviceImageToColumn<NumDimSpatial, InLayout, InDataType, OutDataType>;
+    using namespace ck::conv_tensor_rearrange_op;
+
+    using DeviceOp = ck::tensor_operation::device::DeviceConvTensorRearrange<NumDimSpatial,
+                                                                             ImageLayout,
+                                                                             InDataType,
+                                                                             OutDataType,
+                                                                             ImageToColumn>;
 
     // get device op instances
     const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
@@ -92,8 +98,8 @@ int main()
                                                         in_spatial_lengths,
                                                         out_spatial_lengths,
                                                         wei_spatial_lengths,
-                                                        in_strides,
-                                                        out_strides,
+                                                        image_strides,
+                                                        gemm_strides,
                                                         filter_strides,
                                                         filter_dilations,
                                                         input_left_pads,
@@ -148,8 +154,8 @@ int main()
                                                         in_spatial_lengths,
                                                         out_spatial_lengths,
                                                         wei_spatial_lengths,
-                                                        in_strides,
-                                                        out_strides,
+                                                        image_strides,
+                                                        gemm_strides,
                                                         filter_strides,
                                                         filter_dilations,
                                                         input_left_pads,

example/52_image_to_column/CMakeLists.txt → example/52_im2col_col2im/CMakeLists.txt

@@ -2,9 +2,11 @@ list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
 set(target 0)
 foreach(gpu IN LISTS GPU_TARGETS)
  if(gpu IN_LIST gpu_list AND target EQUAL 0)
-   add_custom_target(example_image_to_column)
+   add_custom_target(example_im2col_col2im)
    add_example_executable(example_image_to_column_f32 image_to_column_f32.cpp)
-   add_dependencies(example_image_to_column example_image_to_column_f32)
+   add_dependencies(example_im2col_col2im example_image_to_column_f32)
+   add_example_executable(example_column_to_image_f32 column_to_image_f32.cpp)
+   add_dependencies(example_im2col_col2im example_column_to_image_f32)
    set(target 1)
  endif()
 endforeach()

example/52_im2col_col2im/column_to_image_f32.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "common.hpp"
+
+using InDataType  = FP32; // ck::bhalf_t;//FP32;
+using OutDataType = FP32; // ck::bhalf_t;//FP32;
+
+using ImLayout        = ck::tensor_layout::convolution::GNHWC;
+using ColumnToImageOp = ck::conv_tensor_rearrange_op::ColumnToImage;
+
+// clang-format off
+using DeviceColToImgInstance = ck::tensor_operation::device::DeviceColumnToImageImpl
+        //#####################|        Num| ImLayout| InDataType| OutDataType| Block|  MPer|  KPer|    Thread| Scalar|
+        //#####################|        Dim|         |           |            |  Size| Block| Block|   Cluster|    Per|
+        //#####################|    Spatial|         |           |            |      |      |      |   Lengths| Vector|
+        //#####################|           |         |           |            |      |      |      |          |       |
+                              < NDimSpatial, ImLayout, InDataType, OutDataType,   256,   128,   128, S<16, 16>,     1>;
+// clang-format on
+
+bool RunColumnToImage(const ExecutionConfig& config, const ck::utils::conv::ConvParam& conv_params)
+{
+
+    const auto N = conv_params.N_;
+    const auto C = conv_params.C_;
+
+    const ck::index_t NDoHoWo =
+        N * ck::accumulate_n<ck::index_t>(
+                conv_params.output_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>());
+    const ck::index_t CZYX =
+        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 out_desc =
+        ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<ImLayout>(conv_params);
+
+    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, NDimSpatial> conv_filter_strides{};
+    std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
+    std::array<ck::index_t, NDimSpatial> input_left_pads{};
+    std::array<ck::index_t, NDimSpatial> input_right_pads{};
+
+    auto copy = [](const auto& x, auto& y) { std::copy(x.begin(), x.end(), y.begin()); };
+
+    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(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);
+    copy(conv_params.input_left_pads_, input_left_pads);
+    copy(conv_params.input_right_pads_, input_right_pads);
+
+    Tensor<InDataType> in(in_desc);
+    Tensor<OutDataType> out_device(out_desc);
+    Tensor<OutDataType> out_host(out_desc);
+
+    std::cout << "in: " << in.mDesc << std::endl;
+    std::cout << "out: " << out_device.mDesc << std::endl;
+
+    switch(config.init_method)
+    {
+    case 0: break;
+    case 1: in.GenerateTensorValue(GeneratorTensor_2<InDataType>{1, 2}); break;
+    default: in.GenerateTensorValue(GeneratorTensor_3<InDataType>{-0.5, 0.5});
+    }
+
+    DeviceMem in_device_buf(sizeof(InDataType) * in.mDesc.GetElementSpaceSize());
+    DeviceMem out_device_buf(sizeof(OutDataType) * out_device.mDesc.GetElementSpaceSize());
+
+    in_device_buf.ToDevice(in.mData.data());
+
+    // reset input to zero
+    out_device_buf.SetZero();
+
+    static_assert(std::is_default_constructible_v<DeviceColToImgInstance>);
+
+    // do conv
+    auto col2img  = DeviceColToImgInstance{};
+    auto invoker  = col2img.MakeInvoker();
+    auto argument = col2img.MakeArgument(in_device_buf.GetDeviceBuffer(),
+                                         out_device_buf.GetDeviceBuffer(),
+                                         N,
+                                         C,
+                                         input_spatial_lengths,
+                                         filter_spatial_lengths,
+                                         output_spatial_lengths,
+                                         image_g_n_c_wis_strides,
+                                         gemm_m_k_strides,
+                                         conv_filter_strides,
+                                         conv_filter_dilations,
+                                         input_left_pads,
+                                         input_right_pads);
+
+    if(!col2img.IsSupportedArgument(argument))
+    {
+        std::cerr << "wrong! device_col2img with the specified compilation parameters does "
+                     "not support this col2img problem"
+                  << std::endl;
+
+        return false;
+    }
+
+    float ave_time        = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
+    std::size_t num_btype = 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;
+
+    if(config.do_verification)
+    {
+        auto ref_column_to_image = ck::tensor_operation::host::
+            ReferenceColumnToImage<NDimSpatial, ImLayout, InDataType, OutDataType>();
+
+        auto ref_invoker = ref_column_to_image.MakeInvoker();
+
+        auto ref_argument = ref_column_to_image.MakeArgument(in,
+                                                             out_host,
+                                                             conv_params.filter_spatial_lengths_,
+                                                             conv_params.conv_filter_strides_,
+                                                             conv_params.conv_filter_dilations_,
+                                                             conv_params.input_left_pads_,
+                                                             conv_params.input_right_pads_);
+
+        if(!ref_column_to_image.IsSupportedArgument(&ref_argument))
+        {
+            std::cerr << "wrong! ref_col2img with the specified compilation parameters does "
+                         "not support this col2img problem"
+                      << std::endl;
+            return false;
+        }
+
+        ref_invoker.Run(ref_argument);
+        out_device_buf.FromDevice(out_device.mData.data());
+        return ck::utils::check_err(out_device.mData, out_host.mData);
+    }
+
+    return true;
+}
+
+int RunColumnToImageExample(int argc, char* argv[])
+{
+    ExecutionConfig config;
+    ck::utils::conv::ConvParam conv_params = DefaultConvParams;
+
+    if(!parse_cmd_args(argc, argv, config, conv_params))
+    {
+        return EXIT_FAILURE;
+    }
+
+    if(conv_params.num_dim_spatial_ != NDimSpatial)
+    {
+        std::cerr << "unsupported # of spatial dimensions" << std::endl;
+        return EXIT_FAILURE;
+    }
+
+    return !RunColumnToImage(config, conv_params);
+}
+
+int main(int argc, char* argv[]) { return RunColumnToImageExample(argc, argv); }

example/52_image_to_column/common.hpp → example/52_im2col_col2im/common.hpp

@@ -10,6 +10,7 @@
 
 #include "ck/ck.hpp"
 #include "ck/tensor_operation/gpu/device/impl/device_image_to_column_impl.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_column_to_image_impl.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 
 #include "ck/library/utility/algorithm.hpp"
@@ -20,6 +21,7 @@
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_image_to_column.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_column_to_image.hpp"
 
 template <ck::index_t... Is>
 using S = ck::Sequence<Is...>;
@@ -32,7 +34,7 @@ struct ExecutionConfig final
 {
     bool do_verification = true;
     int init_method      = 1;
-    bool time_kernel     = true;
+    bool time_kernel     = false;
 };
 
 #define DefaultConvParams                                                            \

example/52_image_to_column/image_to_column_f32.cpp → example/52_im2col_col2im/image_to_column_f32.cpp

@@ -6,15 +6,16 @@
 using InDataType  = FP32;
 using OutDataType = FP32;
 
-using InLayout = ck::tensor_layout::convolution::GNHWC;
+using ImLayout        = ck::tensor_layout::convolution::GNHWC;
+using ImageToColumnOp = ck::conv_tensor_rearrange_op::ImageToColumn;
 
 // clang-format off
 using DeviceImgToColInstance = ck::tensor_operation::device::DeviceImageToColumnImpl
-        //#####################|        Num| InLayout| InDataType| OutDataType| Block|  MPer|  KPer|    Thread| Scalar|
+        //#####################|        Num| ImLayout| InDataType| OutDataType| Block|  MPer|  KPer|    Thread| Scalar|
         //#####################|        Dim|         |           |            |  Size| Block| Block|   Cluster|    Per|
         //#####################|    Spatial|         |           |            |      |      |      |   Lengths| Vector|
         //#####################|           |         |           |            |      |      |      |          |       |
-                              < NDimSpatial, InLayout, InDataType, OutDataType,   256,   128,   128, S<16, 16>,     1>;
+                              < NDimSpatial, ImLayout, InDataType, OutDataType,   256,   128,   128, S<16, 16>,      1>;
 // clang-format on
 
 bool RunImageToColumn(const ExecutionConfig& config, const ck::utils::conv::ConvParam& conv_params)
@@ -31,14 +32,14 @@ bool RunImageToColumn(const ExecutionConfig& config, const ck::utils::conv::Conv
                 conv_params.filter_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>());
 
     const auto in_desc =
-        ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_params);
+        ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<ImLayout>(conv_params);
     const auto out_desc = HostTensorDescriptor({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> input_g_n_c_wis_strides{};
-    std::array<ck::index_t, 2> output_m_k_strides{};
+    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, NDimSpatial> conv_filter_strides{};
     std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
     std::array<ck::index_t, NDimSpatial> input_left_pads{};
@@ -49,8 +50,8 @@ bool RunImageToColumn(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(), input_g_n_c_wis_strides);
-    copy(out_desc.GetStrides(), output_m_k_strides);
+    copy(in_desc.GetStrides(), image_g_n_c_wis_strides);
+    copy(out_desc.GetStrides(), gemm_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);
@@ -90,8 +91,8 @@ bool RunImageToColumn(const ExecutionConfig& config, const ck::utils::conv::Conv
                                          input_spatial_lengths,
                                          filter_spatial_lengths,
                                          output_spatial_lengths,
-                                         input_g_n_c_wis_strides,
-                                         output_m_k_strides,
+                                         image_g_n_c_wis_strides,
+                                         gemm_m_k_strides,
                                          conv_filter_strides,
                                          conv_filter_dilations,
                                          input_left_pads,
@@ -114,7 +115,7 @@ bool RunImageToColumn(const ExecutionConfig& config, const ck::utils::conv::Conv
     if(config.do_verification)
     {
         auto ref_image_to_column = ck::tensor_operation::host::
-            ReferenceImageToColumn<NDimSpatial, InLayout, InDataType, OutDataType>();
+            ReferenceImageToColumn<NDimSpatial, ImLayout, InDataType, OutDataType>();
 
         auto ref_invoker = ref_image_to_column.MakeInvoker();
 

example/60_gemm_multiABD/CMakeLists.txt

+if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
+list(APPEND gpu_list2 gfx908 gfx90a gfx940 gfx941 gfx942)
+set(target 0)
+foreach(gpu IN LISTS GPU_TARGETS)
+ if(gpu IN_LIST gpu_list2 AND target EQUAL 0)
+     add_example_executable(example_gemm_multiABD_xdl_fp16 gemm_multiABD_xdl_fp16.cpp)
+   set(target 1)
+ endif()
+endforeach()
+endif()

example/60_gemm_multiABD/gemm_multiABD_xdl_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_abd_xdl_cshuffle.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+#include "ck/library/utility/check_err.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using ADataType        = F16;
+using BDataType        = F16;
+using AccDataType      = F32;
+using CShuffleDataType = F32;
+using DDataType        = F16;
+using EDataType        = F16;
+
+using ALayout = Row;
+using BLayout = Col;
+using DLayout = Row;
+using ELayout = Row;
+
+struct AddScale
+{
+    static constexpr auto I0 = ck::Number<0>{};
+    static constexpr auto I1 = ck::Number<1>{};
+    static constexpr auto I2 = ck::Number<2>{};
+    static constexpr auto I3 = ck::Number<3>{};
+
+    __host__ __device__ constexpr void
+    operator()(ck::half4_t& a, const ck::half4_t& a0, const ck::half4_t& a1) const
+    {
+        const auto a0_v_t = ck::vector_type<ck::half_t, 4>{a0};
+        const auto a1_v_t = ck::vector_type<ck::half_t, 4>{a1};
+
+        auto r_v_t = ck::vector_type<ck::half_t, 4>{};
+
+        r_v_t.AsType<ck::half_t>()(I0) =
+            scale * (a0_v_t.AsType<ck::half_t>()[I0] + a1_v_t.AsType<ck::half_t>()[I0]);
+        r_v_t.AsType<ck::half_t>()(I1) =
+            scale * (a0_v_t.AsType<ck::half_t>()[I1] + a1_v_t.AsType<ck::half_t>()[I1]);
+        r_v_t.AsType<ck::half_t>()(I2) =
+            scale * (a0_v_t.AsType<ck::half_t>()[I2] + a1_v_t.AsType<ck::half_t>()[I2]);
+        r_v_t.AsType<ck::half_t>()(I3) =
+            scale * (a0_v_t.AsType<ck::half_t>()[I3] + a1_v_t.AsType<ck::half_t>()[I3]);
+
+        a = r_v_t.AsType<ck::half4_t>()[I0];
+    }
+
+    __host__ __device__ constexpr void
+    operator()(ck::half_t& a, const ck::half_t& a0, const ck::half_t& a1) const
+    {
+        a = scale * (a0 + a1);
+    }
+
+    static constexpr ck::index_t vec_len = 4;
+
+    float scale = 1.0;
+};
+
+struct AlphaBetaAdd
+{
+    AlphaBetaAdd(float alpha, float beta) : alpha_(alpha), beta_(beta){};
+
+    template <typename E, typename C, typename D>
+    __host__ __device__ constexpr void operator()(E& e, const C& c, const D& d) const;
+
+    template <>
+    __host__ __device__ constexpr void operator()<ck::half_t, float, ck::half_t>(
+        ck::half_t& e, const float& c, const ck::half_t& d) const
+    {
+        e = ck::type_convert<ck::half_t>(alpha_ * c + beta_ * ck::type_convert<float>(d));
+    };
+
+    float alpha_;
+    float beta_;
+};
+
+using AElementOp   = AddScale;
+using BElementOp   = PassThrough;
+using CDEElementOp = AlphaBetaAdd;
+
+static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
+
+using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleABD_Xdl_CShuffle<
+    ck::Tuple<ALayout, ALayout>,
+    ck::Tuple<BLayout>,
+    ck::Tuple<DLayout>,
+    ELayout,
+    ck::Tuple<ADataType, ADataType>,
+    ck::Tuple<BDataType>,
+    AccDataType,
+    CShuffleDataType,
+    ck::Tuple<DDataType>,
+    EDataType,
+    AElementOp,
+    BElementOp,
+    CDEElementOp,
+    GemmSpec,
+    1,
+    256,
+    256,
+    128,
+    32,
+    8,
+    8,
+    32,
+    32,
+    4,
+    2,
+    S<4, 64, 1>,
+    S<1, 0, 2>,
+    S<1, 0, 2>,
+    2,
+    8,
+    8,
+    1,
+    S<4, 64, 1>,
+    S<1, 0, 2>,
+    S<1, 0, 2>,
+    2,
+    8,
+    8,
+    1,
+    1,
+    1,
+    S<1, 32, 1, 8>,
+    8>;
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
+
+    // GEMM shape
+    ck::index_t M = 3840;
+    ck::index_t N = 4096;
+    ck::index_t K = 4096;
+
+    ck::index_t StrideA = 4096;
+    ck::index_t StrideB = 4096;
+    ck::index_t StrideD = 4096;
+    ck::index_t StrideE = 4096;
+
+    float alpha = 1.0f;
+    float beta  = 1.0f;
+
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+    }
+    else if(argc == 6)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+
+        alpha = std::stof(argv[4]);
+        beta  = std::stof(argv[5]);
+    }
+    else if(argc == 13)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+
+        M = std::stoi(argv[4]);
+        N = std::stoi(argv[5]);
+        K = std::stoi(argv[6]);
+
+        StrideA = std::stoi(argv[7]);
+        StrideB = std::stoi(argv[8]);
+        StrideD = std::stoi(argv[9]);
+        StrideE = std::stoi(argv[10]);
+
+        alpha = std::stof(argv[11]);
+        beta  = std::stof(argv[12]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: time kernel (0=no, 1=yes)\n");
+        printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE, alpha, "
+               "beta\n");
+        exit(0);
+    }
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            using namespace ck::literals;
+
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
+            }
+            else
+            {
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
+            }
+        };
+
+    Tensor<ADataType> a0_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
+    Tensor<ADataType> a1_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
+    Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+    Tensor<DDataType> d_m_n(f_host_tensor_descriptor(M, N, StrideD, DLayout{}));
+    Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
+    Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
+
+    std::cout << "a0_m_k: " << a0_m_k.mDesc << std::endl;
+    std::cout << "a1_m_k: " << a1_m_k.mDesc << std::endl;
+    std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
+    std::cout << "d_m_n: " << d_m_n.mDesc << std::endl;
+    std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a0_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+        a1_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+        d_m_n.GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5});
+        break;
+    default:
+        a0_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        a1_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+        d_m_n.GenerateTensorValue(GeneratorTensor_3<DDataType>{-0.5, 0.5});
+    }
+
+    DeviceMem a0_device_buf(sizeof(ADataType) * a0_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem a1_device_buf(sizeof(ADataType) * a1_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem d_device_buf(sizeof(DDataType) * d_m_n.mDesc.GetElementSpaceSize());
+    DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
+
+    a0_device_buf.ToDevice(a0_m_k.mData.data());
+    a1_device_buf.ToDevice(a1_m_k.mData.data());
+    b_device_buf.ToDevice(b_k_n.mData.data());
+    d_device_buf.ToDevice(d_m_n.mData.data());
+    e_device_buf.ToDevice(e_m_n_device_result.mData.data());
+
+    auto a_element_op   = AElementOp{0.2};
+    auto b_element_op   = BElementOp{};
+    auto cde_element_op = CDEElementOp{alpha, beta};
+
+    // do GEMM
+    auto device_op = DeviceOpInstance{};
+    auto invoker   = device_op.MakeInvoker();
+    auto argument =
+        device_op.MakeArgument(std::array<const void*, 2>{a0_device_buf.GetDeviceBuffer(),
+                                                          a1_device_buf.GetDeviceBuffer()},
+                               std::array<const void*, 1>{b_device_buf.GetDeviceBuffer()},
+                               std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
+                               e_device_buf.GetDeviceBuffer(),
+                               M,
+                               N,
+                               K,
+                               std::array<ck::index_t, 2>{StrideA, StrideA},
+                               std::array<ck::index_t, 1>{StrideB},
+                               std::array<ck::index_t, 1>{StrideD},
+                               StrideE,
+                               a_element_op,
+                               b_element_op,
+                               cde_element_op);
+
+    if(!device_op.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_gemm with the specified compilation parameters does "
+            "not support this GEMM problem");
+    }
+
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop = std::size_t(2) * M * N * K;
+    std::size_t num_btype =
+        sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
+
+    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+    float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
+              << std::endl;
+
+    e_device_buf.FromDevice(e_m_n_device_result.mData.data());
+
+    if(do_verification)
+    {
+        Tensor<CShuffleDataType> c_m_n({M, N});
+
+        Tensor<ADataType> a_m_k({M, K});
+
+        for(int m = 0; m < M; ++m)
+        {
+            for(int k = 0; k < K; ++k)
+            {
+                a_element_op(a_m_k(m, k), a0_m_k(m, k), a1_m_k(m, k));
+            }
+        }
+
+        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                                BDataType,
+                                                                                CShuffleDataType,
+                                                                                AccDataType,
+                                                                                PassThrough,
+                                                                                BElementOp,
+                                                                                PassThrough>;
+        auto ref_gemm               = ReferenceGemmInstance{};
+        auto ref_invoker            = ref_gemm.MakeInvoker();
+
+        auto ref_argument =
+            ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, PassThrough{}, b_element_op, PassThrough{});
+
+        ref_invoker.Run(ref_argument);
+
+        for(int m = 0; m < M; ++m)
+        {
+            for(int n = 0; n < N; ++n)
+            {
+                cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d_m_n(m, n));
+            }
+        }
+
+        e_device_buf.FromDevice(e_m_n_device_result.mData.data());
+
+        return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result) ? 0 : 1;
+    }
+
+    return 0;
+}

include/ck/host_utility/kernel_launch.hpp

@@ -34,6 +34,7 @@ float launch_and_time_kernel(const StreamConfig& stream_config,
 #endif
         // warm up
         kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args...);
+        hip_check_error(hipGetLastError());
 
         const int nrepeat = 10;
 #if DEBUG_LOG
@@ -50,6 +51,7 @@ float launch_and_time_kernel(const StreamConfig& stream_config,
         for(int i = 0; i < nrepeat; ++i)
         {
             kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args...);
+            hip_check_error(hipGetLastError());
         }
 
         hip_check_error(hipEventRecord(stop, stream_config.stream_id_));
@@ -64,11 +66,13 @@ float launch_and_time_kernel(const StreamConfig& stream_config,
     else
     {
         kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args...);
+        hip_check_error(hipGetLastError());
 
         return 0;
     }
 #else
     kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args...);
+    hip_check_error(hipGetLastError());
 
     return 0;
 #endif
@@ -101,6 +105,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
         // warm up
         preprocess();
         kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args...);
+        hip_check_error(hipGetLastError());
 
         const int nrepeat = 10;
 #if DEBUG_LOG
@@ -118,6 +123,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
         {
             preprocess();
             kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args...);
+            hip_check_error(hipGetLastError());
         }
 
         hip_check_error(hipEventRecord(stop, stream_config.stream_id_));
@@ -133,11 +139,13 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
     {
         preprocess();
         kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args...);
+        hip_check_error(hipGetLastError());
 
         return 0;
     }
 #else
     kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args...);
+    hip_check_error(hipGetLastError());
 
     return 0;
 #endif

include/ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v7r2.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_description/cluster_descriptor.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_v7r2.hpp"
+#include "ck/utility/is_detected.hpp"
+
+namespace ck {
+
+// Thread-group level multi-source, multi-destination tensor slice data movement
+// Assume:
+//   1. All sources and destinations are DynamicBuffer
+//   2. Same VectorDim and ScalerPerVector for all sources and destinations
+//   3. DstInMemOps are per destination tensor
+//   4. ThreadTransferSrcResetCoordinateAfterRunFlags are per source tensor
+//   5. ThreadTransferDstResetCoordinateAfterRunFlags are per destination tensor
+//
+// Does following things to avoid scratch memory issue
+//   1. Pass tensor descritpors by reference (or tuple of references)
+//   2. Does not keep reference to tensor descriptor
+//   3. Does not construct new tensor coordinate when call Run()
+template <typename ThreadGroup,
+          typename SrcDatas,
+          typename DstDatas,
+          typename SrcDescs,
+          typename DstDescs,
+          typename ElementwiseOperation,
+          typename DstInMemOps, // Sequence<InMemoryDataOperationEnum ...>
+          typename SliceLengths,
+          typename ThreadClusterLengths,
+          typename ThreadClusterArrangeOrder,
+          typename SrcDimAccessOrder,
+          typename DstDimAccessOrder,
+          index_t SrcVectorDim,
+          index_t DstVectorDim,
+          index_t SrcScalarPerVector,
+          index_t DstScalarPerVector,
+          typename ThreadTransferSrcResetCoordinateAfterRunFlags,
+          typename ThreadTransferDstResetCoordinateAfterRunFlags>
+struct ThreadGroupTensorSliceTransfer_v7r2
+{
+    static constexpr index_t nDim =
+        remove_cvref_t<tuple_element_t<0, SrcDescs>>::GetNumOfDimension();
+
+    static constexpr index_t nSrc = remove_cvref_t<SrcDescs>::Size();
+    static constexpr index_t nDst = remove_cvref_t<DstDescs>::Size();
+
+    using Index = MultiIndex<nDim>;
+
+    static constexpr auto thread_slice_lengths = SliceLengths{} / ThreadClusterLengths{};
+
+    __device__ constexpr ThreadGroupTensorSliceTransfer_v7r2(
+        const SrcDescs& src_descs,
+        const StaticallyIndexedArray<Index, nSrc>& src_block_slice_origins,
+        const DstDescs& dst_descs,
+        const StaticallyIndexedArray<Index, nDst>& dst_block_slice_origins,
+        const ElementwiseOperation& element_op)
+        : threadwise_transfer_(src_descs,
+                               StaticallyIndexedArray<Index, nSrc>{},
+                               dst_descs,
+                               StaticallyIndexedArray<Index, nDst>{},
+                               element_op)
+    {
+        static_assert(nSrc == SrcDatas::Size() && nSrc == SrcDescs::Size() &&
+                          nSrc == ThreadTransferSrcResetCoordinateAfterRunFlags::Size() &&
+                          nDst == DstDatas::Size() && nDst == DstDescs::Size() &&
+                          nDst == ThreadTransferDstResetCoordinateAfterRunFlags::Size(),
+                      "wrong!");
+
+        static_for<0, nSrc, 1>{}([&](auto i) {
+            static_assert(
+                nDim == remove_cvref_t<tuple_element_t<i.value, SrcDescs>>::GetNumOfDimension(),
+                "wrong!");
+        });
+
+        static_for<0, nDst, 1>{}([&](auto i) {
+            static_assert(
+                nDim == remove_cvref_t<tuple_element_t<i.value, DstDescs>>::GetNumOfDimension(),
+                "wrong!");
+        });
+
+        static_assert(nDim == ThreadClusterLengths::Size() &&
+                          nDim == ThreadClusterArrangeOrder::Size() &&
+                          nDim == SrcDimAccessOrder::Size() && nDim == DstDimAccessOrder::Size(),
+                      "wrong! nDim not consistent");
+
+        static_assert(
+            is_same<SliceLengths, decltype(thread_slice_lengths * ThreadClusterLengths{})>{},
+            "wrong! threads should be mapped to cover entire slicing window");
+
+        static_assert(ThreadGroup::GetNumOfThread() >= thread_cluster_desc_.GetElementSize(),
+                      "wrong! ThreadGroup::GetNumOfThread() too small");
+
+        if(ThreadGroup::GetNumOfThread() == thread_cluster_desc_.GetElementSize() or
+           ThreadGroup::GetThreadId() < thread_cluster_desc_.GetElementSize())
+        {
+            const auto thread_cluster_idx = thread_cluster_desc_.CalculateBottomIndex(
+                make_multi_index(get_thread_local_1d_id()));
+
+            const auto thread_data_idx_begin = thread_cluster_idx * thread_slice_lengths;
+
+            const auto src_thread_slice_origins = generate_tuple(
+                [&](auto i) { return src_block_slice_origins[i] + thread_data_idx_begin; },
+                Number<nSrc>{});
+
+            const auto dst_thread_slice_origins = generate_tuple(
+                [&](auto i) { return dst_block_slice_origins[i] + thread_data_idx_begin; },
+                Number<nDst>{});
+
+            threadwise_transfer_.SetSrcSliceOrigins(src_descs, src_thread_slice_origins);
+            threadwise_transfer_.SetDstSliceOrigins(dst_descs, dst_thread_slice_origins);
+        }
+    }
+
+    template <typename SrcBuffers>
+    __device__ void RunRead(const SrcDescs& src_descs, const SrcBuffers& src_bufs)
+    {
+        if(ThreadGroup::GetNumOfThread() == thread_cluster_desc_.GetElementSize() or
+           ThreadGroup::GetThreadId() < thread_cluster_desc_.GetElementSize())
+        {
+            threadwise_transfer_.RunRead(src_descs, src_bufs);
+        }
+    }
+
+    template <typename T>
+    using is_tuple = decltype(std::declval<T&>().IsTuple());
+
+    template <typename DstBuffers>
+    __device__ void RunWrite(const DstDescs& dst_descs, DstBuffers dst_bufs)
+    {
+        if(ThreadGroup::GetNumOfThread() == thread_cluster_desc_.GetElementSize() or
+           ThreadGroup::GetThreadId() < thread_cluster_desc_.GetElementSize())
+        {
+            if constexpr(is_detected<is_tuple, decltype(dst_bufs)>::value)
+                threadwise_transfer_.RunWrite(dst_descs, dst_bufs);
+            else
+                threadwise_transfer_.RunWrite(dst_descs, tie(dst_bufs));
+        }
+    }
+
+    template <typename SrcBuffers, typename DstBuffers>
+    __device__ void Run(const SrcDescs& src_descs,
+                        const SrcBuffers& src_bufs,
+                        const DstDescs& dst_descs,
+                        DstBuffers dst_bufs)
+    {
+        RunRead(src_descs, src_bufs);
+        RunWrite(dst_descs, dst_bufs);
+    }
+
+    template <index_t ISrc>
+    __device__ void
+    MoveSrcSliceWindow(const SrcDescs& src_descs, Number<ISrc> iSrc, const Index& step)
+    {
+        if(ThreadGroup::GetNumOfThread() == thread_cluster_desc_.GetElementSize() or
+           ThreadGroup::GetThreadId() < thread_cluster_desc_.GetElementSize())
+        {
+            threadwise_transfer_.MoveSrcSliceWindow(src_descs, iSrc, step);
+        }
+    }
+
+    __device__ void MoveSrcSliceWindow(const SrcDescs& src_descs, const Index& step)
+    {
+        static_for<0, SrcDescs::Size(), 1>{}(
+            [&](auto i) { MoveSrcSliceWindow(src_descs, i, step); });
+    }
+
+    template <index_t IDst>
+    __device__ void
+    MoveDstSliceWindow(const DstDescs& dst_descs, Number<IDst> iDst, const Index& step)
+    {
+        if(ThreadGroup::GetNumOfThread() == thread_cluster_desc_.GetElementSize() or
+           ThreadGroup::GetThreadId() < thread_cluster_desc_.GetElementSize())
+        {
+            threadwise_transfer_.MoveDstSliceWindow(dst_descs, iDst, step);
+        }
+    }
+
+    __device__ void MoveDstSliceWindow(const DstDescs& dst_descs, const Index& step)
+    {
+        static_for<0, DstDescs::Size(), 1>{}(
+            [&](auto i) { MoveDstSliceWindow(dst_descs, i, step); });
+    }
+
+    private:
+    static constexpr auto thread_cluster_desc_ =
+        make_cluster_descriptor(ThreadClusterLengths{}, ThreadClusterArrangeOrder{});
+
+    using ThreadwiseTransfer =
+        ThreadwiseTensorSliceTransfer_v7r2<SrcDatas,
+                                           DstDatas,
+                                           SrcDescs,
+                                           DstDescs,
+                                           ElementwiseOperation,
+                                           DstInMemOps,
+                                           decltype(thread_slice_lengths),
+                                           SrcDimAccessOrder,
+                                           DstDimAccessOrder,
+                                           SrcVectorDim,
+                                           DstVectorDim,
+                                           SrcScalarPerVector,
+                                           DstScalarPerVector,
+                                           ThreadTransferSrcResetCoordinateAfterRunFlags,
+                                           ThreadTransferDstResetCoordinateAfterRunFlags>;
+
+    ThreadwiseTransfer threadwise_transfer_;
+};
+
+} // namespace ck

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+namespace ck {
+namespace conv_tensor_rearrange_op {
+
+struct BaseConvTensorRearrangeOp
+{
+};
+
+struct ImageToColumn : public BaseConvTensorRearrangeOp
+{
+    static constexpr const char* name = "Image to Column";
+};
+
+struct ColumnToImage : public BaseConvTensorRearrangeOp
+{
+    static constexpr const char* name = "Column to Image";
+};
+
+template <typename Op,
+          typename std::enable_if<std::is_base_of<BaseConvTensorRearrangeOp, Op>::value,
+                                  bool>::type = false>
+std::ostream& operator<<(std::ostream& os, const BaseConvTensorRearrangeOp&)
+{
+    os << Op::name;
+    return os;
+}
+
+} // namespace conv_tensor_rearrange_op
+} // namespace ck

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

@@ -12,21 +12,26 @@ namespace tensor_operation {
 namespace device {
 
 /**
- * \brief Image to column.
+ * \brief Convolution Tensor Rearrange.
  *
- * This Device operator converts image ([G, N, Di, Hi, Wi, C]) to the gemm
- * problem([N * Do * Ho * Wo, Z *  Y * X * C]). G must be equal to 1.
+ * 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.
  *
  * \tparam NDimSpatial Number of spatial dimensions.
- * \tparam InputLayout Input Layout.
+ * \tparam ImageLayout Input Layout.
  * \tparam InputDataType Input Data Type.
  * \tparam OutputDataType Output Data Type.
+ * \tparam ConvTensorRearrangeOp Operation type: ImageToColumn, ColumnToImage.
  */
 template <index_t NDimSpatial,
-          typename InputLayout,
+          typename ImageLayout,
           typename InputDataType,
-          typename OutputDataType>
-struct DeviceImageToColumn : public BaseOperator
+          typename OutputDataType,
+          typename ConvTensorRearrangeOp>
+struct DeviceConvTensorRearrange : public BaseOperator
 {
 
     /**
@@ -39,8 +44,8 @@ struct DeviceImageToColumn : public BaseOperator
      * \param input_spatial_lengths Input spatial lengths.
      * \param filter_spatial_lengths Filter spatial lengths.
      * \param output_spatial_lengths Output spatial lengths.
-     * \param input_g_n_c_wis_strides Input strides in order [G, N, C, D, H, W].
-     * \param output_m_k_strides Output strides.
+     * \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 conv_filter_strides Convolution filter strides.
      * \param conv_filter_dilations Convolution filter dilations.
      * \param input_left_pads Convolution left pads.
@@ -55,8 +60,8 @@ struct DeviceImageToColumn : public BaseOperator
                         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>& input_g_n_c_wis_strides,
-                        const std::array<index_t, 2>& output_m_k_strides,
+                        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, 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/device_gemm_multiple_abd.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <array>
+
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// GEMM:
+//   input : A0[M, K], B0[K, N],
+//   input : D0[M, N], D1[M, N], ...
+//   output : E[M, N]
+//   C = a_op(A) * b_op(B)
+//   E = cde_op(C, D0, D1, ...)
+// Assume:
+//   D0, D1, ... and E have the same layout
+template <typename AsLayout,
+          typename BsLayout,
+          typename DsLayout,
+          typename ELayout,
+          typename AsDataType,
+          typename BsDataType,
+          typename DsDataType,
+          typename EDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation>
+struct DeviceGemmMultipleABD : public BaseOperator
+{
+    static constexpr index_t NumATensor = AsDataType::Size();
+    static constexpr index_t NumBTensor = BsDataType::Size();
+    static constexpr index_t NumDTensor = DsDataType::Size();
+
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(std::array<const void*, NumATensor> p_as,
+                        std::array<const void*, NumBTensor> p_bs,
+                        std::array<const void*, NumDTensor> p_ds,
+                        void* p_e,
+                        ck::index_t M,
+                        ck::index_t N,
+                        ck::index_t K,
+                        std::array<ck::index_t, NumATensor> StrideAs,
+                        std::array<ck::index_t, NumBTensor> StrideBs,
+                        std::array<ck::index_t, NumDTensor> StrideDs,
+                        ck::index_t StrideE,
+                        AElementwiseOperation a_element_op,
+                        BElementwiseOperation b_element_op,
+                        CDEElementwiseOperation cde_element_op) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

@@ -5,64 +5,41 @@
 
 #include "ck/tensor_description/tensor_descriptor.hpp"
 #include "ck/tensor_description/tensor_descriptor_helper.hpp"
-#include "ck/tensor_operation/gpu/device/device_image_to_column.hpp"
-#include "ck/tensor_operation/gpu/grid/gridwise_image_to_column.hpp"
+#include "ck/tensor_operation/gpu/device/device_conv_tensor_rearrange.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_tensor_rearrange.hpp"
 #include "ck/host_utility/kernel_launch.hpp"
 #include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
 #include "ck/tensor_operation/operator_transform/transform_conv_fwd_to_gemm.hpp"
 #include "ck/tensor_operation/gpu/device/convolution_forward_specialization.hpp"
 #include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
 #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/host_utility/io.hpp"
 
 namespace ck {
 namespace tensor_operation {
 namespace device {
 
-template <typename InputGridDesc,
-          typename InputDataType,
-          typename OutputGridDesc,
-          typename OutputDataType,
-          typename Block2ETileMap,
-          typename GridwiseImageToColumnKernel>
-__global__ void
-#if CK_USE_LAUNCH_BOUNDS
-    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
-#endif
-        kernel_image_to_column(const InputGridDesc in_grid_desc,
-                               const InputDataType* __restrict__ p_in_global,
-                               const OutputGridDesc out_grid_desc,
-                               OutputDataType* __restrict__ p_out_global,
-                               const Block2ETileMap block_2_tile_map)
-{
-#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__))
-    GridwiseImageToColumnKernel::Run(
-        in_grid_desc, p_in_global, out_grid_desc, p_out_global, block_2_tile_map);
-#else
-    ignore = in_grid_desc;
-    ignore = p_in_global;
-    ignore = out_grid_desc;
-    ignore = p_out_global;
-    ignore = block_2_tile_map;
-#endif
-}
-
 // Image to column for input layout NDHWC:
-//   input : input image [N, Di, Hi, Wi, C],
-//   output : output image [N * Do * Ho * Wo, Z * Y * X * C]
+//   input : input image [N, Di, Hi, Wi, C]
+//   output : gemm form [N * Do * Ho * Wo, Z * Y * X * C]
 template <index_t NDimSpatial,
-          typename InputLayout,
+          typename ImageLayout,
           typename InputDataType,
           typename OutputDataType,
           index_t BlockSize,
           index_t MPerBlock,
           index_t KPerBlock,
           typename ThreadClusterLengths,
-          index_t ScalarPerVector>
+          index_t ScalarPerVector,
+          typename std::enable_if<NDimSpatial >= 1 && NDimSpatial <= 3, bool>::type = false>
 struct DeviceImageToColumnImpl
-    : public DeviceImageToColumn<NDimSpatial, InputLayout, InputDataType, OutputDataType>
+    : public DeviceConvTensorRearrange<NDimSpatial,
+                                       ImageLayout,
+                                       InputDataType,
+                                       OutputDataType,
+                                       conv_tensor_rearrange_op::ImageToColumn>
 {
 
     static constexpr auto I0 = Number<0>{};
@@ -83,7 +60,7 @@ struct DeviceImageToColumnImpl
                             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>& input_g_n_c_wis_strides,
+                            const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_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,
@@ -110,9 +87,9 @@ struct DeviceImageToColumnImpl
         c_g_n_k_wos_lengths[I1] = N;
 
         const auto in_gemmmraw_gemmkraw_desc =
-            conv_to_gemm_transformer.template MakeADescriptor_M_K<InputLayout>(
+            conv_to_gemm_transformer.template MakeADescriptor_M_K<ImageLayout>(
                 a_g_n_c_wis_lengths,
-                input_g_n_c_wis_strides,
+                image_g_n_c_wis_strides,
                 b_g_k_c_xs_lengths,
                 {}, // not needed for A Descriptor
                 c_g_n_k_wos_lengths,
@@ -132,7 +109,7 @@ 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>& output_m_k_strides)
+                          const std::array<index_t, 2>& gemm_m_k_strides)
     {
         const index_t NDoHoWo =
             N * ck::accumulate_n<index_t>(
@@ -141,7 +118,7 @@ struct DeviceImageToColumnImpl
             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(output_m_k_strides[I0], output_m_k_strides[I1]));
+            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;
@@ -155,7 +132,7 @@ struct DeviceImageToColumnImpl
         decltype(BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, KPerBlock, OutputGridDesc>(
             OutputGridDesc{}))>;
 
-    using GridwiseImageToColumnKernel = GridwiseImageToColumn<InputGridDesc,
+    using GridwiseTensorRearrangeKernel = GridwiseTensorRearrange<InputGridDesc,
                                                                   InputDataType,
                                                                   OutputGridDesc,
                                                                   OutputDataType,
@@ -164,19 +141,20 @@ struct DeviceImageToColumnImpl
                                                                   KPerBlock,
                                                                   ThreadClusterLengths,
                                                                   ScalarPerVector,
+                                                                  InMemoryDataOperationEnum::Set,
                                                                   Block2ETileMap>;
 
     struct Argument : public BaseArgument
     {
         Argument(const void* p_in, // input image
-                 void* p_out,      // output image
+                 void* p_out,      // gemm form
                  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>& input_g_n_c_wis_strides,
-                 const std::array<index_t, 2>& output_m_k_strides,
+                 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, NDimSpatial>& conv_filter_strides,
                  const std::array<index_t, NDimSpatial>& conv_filter_dilations,
                  const std::array<index_t, NDimSpatial>& input_left_pads,
@@ -185,7 +163,7 @@ struct DeviceImageToColumnImpl
               X_(filter_spatial_lengths[NDimSpatial - I1]),
               p_in_{static_cast<const InputDataType*>(p_in)},
               p_out_{static_cast<OutputDataType*>(p_out)},
-              input_g_n_c_wis_strides_{input_g_n_c_wis_strides},
+              image_g_n_c_wis_strides_{image_g_n_c_wis_strides},
               conv_filter_strides_{conv_filter_strides},
               conv_filter_dilations_{conv_filter_dilations},
               input_left_pads_{input_left_pads},
@@ -197,7 +175,7 @@ struct DeviceImageToColumnImpl
                                                         input_spatial_lengths,
                                                         filter_spatial_lengths,
                                                         output_spatial_lengths,
-                                                        input_g_n_c_wis_strides,
+                                                        image_g_n_c_wis_strides,
 
                                                         conv_filter_strides,
                                                         conv_filter_dilations,
@@ -205,7 +183,7 @@ struct DeviceImageToColumnImpl
                                                         input_right_pads);
 
             out_grid_desc_m_k_ = MakeOutDescriptor_M_K(
-                N, C, filter_spatial_lengths, output_spatial_lengths, output_m_k_strides);
+                N, C, filter_spatial_lengths, output_spatial_lengths, gemm_m_k_strides);
         }
 
         void Print() const
@@ -220,7 +198,7 @@ struct DeviceImageToColumnImpl
         const InputDataType* p_in_;
         OutputDataType* p_out_;
 
-        const std::array<index_t, NDimSpatial + 3>& input_g_n_c_wis_strides_;
+        const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_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_;
@@ -243,12 +221,12 @@ struct DeviceImageToColumnImpl
                 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 auto kernel       = kernel_image_to_column<InputGridDesc,
+            const auto kernel       = kernel_tensor_rearrange<InputGridDesc,
                                                         InputDataType,
                                                         OutputGridDesc,
                                                         OutputDataType,
                                                         Block2ETileMap,
-                                                       GridwiseImageToColumnKernel>;
+                                                        GridwiseTensorRearrangeKernel>;
 
             float elapsed_time = launch_and_time_kernel(stream_config,
                                                         kernel,
@@ -273,12 +251,8 @@ struct DeviceImageToColumnImpl
     bool IsSupportedArgument(const Argument& arg)
     {
         using namespace tensor_layout::convolution;
-        if(!(std::is_same_v<InputLayout, GNWC> || std::is_same_v<InputLayout, GNHWC> ||
-             std::is_same_v<InputLayout, GNDHWC>))
-        {
-            return false;
-        }
-        if(!(NDimSpatial >= 1 && NDimSpatial <= 3))
+        if constexpr(!(std::is_same_v<ImageLayout, GNWC> || std::is_same_v<ImageLayout, GNHWC> ||
+                       std::is_same_v<ImageLayout, GNDHWC>))
         {
             return false;
         }
@@ -287,8 +261,8 @@ struct DeviceImageToColumnImpl
         const auto w_pad_right = arg.input_right_pads_[NDimSpatial - I1];
         const auto dilation_x  = arg.conv_filter_dilations_[NDimSpatial - I1];
         const auto stride_x    = arg.conv_filter_strides_[NDimSpatial - I1];
-        bool is_w_packed       = arg.input_g_n_c_wis_strides_[NDimSpatial + I2] == arg.C_;
-        bool is_c_packed       = arg.input_g_n_c_wis_strides_[I2] == 1;
+        bool is_w_packed       = arg.image_g_n_c_wis_strides_[NDimSpatial + I2] == arg.C_;
+        bool is_c_packed       = arg.image_g_n_c_wis_strides_[I2] == 1;
 
         // check vector acces with c not packed
         if(!is_c_packed && ScalarPerVector != 1)
@@ -310,7 +284,7 @@ struct DeviceImageToColumnImpl
         if(dilation_x > 1 && arg.C_ % ScalarPerVector != 0)
             return false;
 
-        return GridwiseImageToColumnKernel::CheckValidity(arg.in_grid_desc_m_k_,
+        return GridwiseTensorRearrangeKernel::CheckValidity(arg.in_grid_desc_m_k_,
                                                             arg.out_grid_desc_m_k_);
     }
 
@@ -320,14 +294,14 @@ struct DeviceImageToColumnImpl
     }
 
     static auto MakeArgument(const void* p_in, // input image
-                             void* p_out,      // output image
+                             void* p_out,      // gemm form
                              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>& input_g_n_c_wis_strides,
-                             const std::array<index_t, 2>& output_m_k_strides,
+                             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, NDimSpatial>& conv_filter_strides,
                              const std::array<index_t, NDimSpatial>& conv_filter_dilations,
                              const std::array<index_t, NDimSpatial>& input_left_pads,
@@ -340,8 +314,8 @@ struct DeviceImageToColumnImpl
                         input_spatial_lengths,
                         filter_spatial_lengths,
                         output_spatial_lengths,
-                        input_g_n_c_wis_strides,
-                        output_m_k_strides,
+                        image_g_n_c_wis_strides,
+                        gemm_m_k_strides,
                         conv_filter_strides,
                         conv_filter_dilations,
                         input_left_pads,
@@ -352,14 +326,14 @@ struct DeviceImageToColumnImpl
 
     std::unique_ptr<BaseArgument>
     MakeArgumentPointer(const void* p_in, // input image
-                        void* p_out,      // output image
+                        void* p_out,      // gemm form
                         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>& input_g_n_c_wis_strides,
-                        const std::array<index_t, 2>& output_m_k_strides,
+                        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, NDimSpatial>& conv_filter_strides,
                         const std::array<index_t, NDimSpatial>& conv_filter_dilations,
                         const std::array<index_t, NDimSpatial>& input_left_pads,
@@ -372,8 +346,8 @@ struct DeviceImageToColumnImpl
                                           input_spatial_lengths,
                                           filter_spatial_lengths,
                                           output_spatial_lengths,
-                                          input_g_n_c_wis_strides,
-                                          output_m_k_strides,
+                                          image_g_n_c_wis_strides,
+                                          gemm_m_k_strides,
                                           conv_filter_strides,
                                           conv_filter_dilations,
                                           input_left_pads,