example for convnd bwd weight bf16 splitk (#265)

* add GetWorkSpaceSize to base arg and make an example on convnd_bwd_weight

* add bwd weight for bf16: init

* remove redundant compute

* use datatype and split k to check whether a workspace is used

* remove unused computation for work space size

* add some code for bfp16

* add device/grid unary op

* add unary type convert to bwd-weight example

* support bf16 splitk kernel for convnd bwd weight

* 1. remove comments. 2. add checkvalidity. 3. add gridsize computation

* add workspace size check

* fix format

* change function name

example/20_convnd_bwd_weight_xdl/CMakeLists.txt

 add_example_executable(example_convnd_bwd_weight_xdl convnd_bwd_weight_xdl.cpp)
+add_example_executable(example_convnd_bwd_weight_xdl_bf16_splitk convnd_bwd_weight_xdl_bf16_splitk.cpp)
 target_link_libraries(example_convnd_bwd_weight_xdl PRIVATE conv_util)
+target_link_libraries(example_convnd_bwd_weight_xdl_bf16_splitk PRIVATE conv_util)
\ No newline at end of file

example/20_convnd_bwd_weight_xdl/convnd_bwd_weight_xdl.cpp

@@ -297,43 +297,7 @@ int main(int argc, char* argv[])
                                   split_k);
 
     // alloc work space
-    size_t bwd_weight_workspace_size = conv->GetWorkSpaceSize(argument.get());
     float ave_time = 0.f;
-    if(std::is_same<InDataType, ck::bhalf_t>::value && split_k > 1)
-    {
-        DeviceMem wei_work_space_device_buf(bwd_weight_workspace_size);
-        wei_work_space_device_buf.SetZero();
-        argument = conv->MakeArgumentPointer(
-            static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
-            static_cast<AccDataType*>(wei_work_space_device_buf.GetDeviceBuffer()),
-            static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
-            params.N_,
-            params.K_,
-            params.C_,
-            params.input_spatial_lengths_,
-            params.filter_spatial_lengths_,
-            output_spatial_lengths,
-            params.conv_filter_strides_,
-            params.conv_filter_dilations_,
-            params.input_left_pads_,
-            params.input_right_pads_,
-            InElementOp{},
-            WeiElementOp{},
-            OutElementOp{},
-            split_k);
-
-        if(!conv->IsSupportedArgument(argument.get()))
-        {
-            std::cout << "wrong! device_conv with the specified compilation parameters does "
-                         "not support this Conv problem"
-                      << std::endl;
-            return 1;
-        }
-
-        ave_time = invoker->Run(argument.get(), StreamConfig{nullptr, time_kernel});
-    }
-    else
-    {
     if(!conv->IsSupportedArgument(argument.get()))
     {
         std::cout << "wrong! device_conv with the specified compilation parameters does "
@@ -342,7 +306,6 @@ int main(int argc, char* argv[])
         return 1;
     }
     ave_time = invoker->Run(argument.get(), StreamConfig{nullptr, time_kernel});
-    }
 
     std::size_t flop = ck::utils::conv::get_flops(
         params.N_, params.C_, params.K_, params.filter_spatial_lengths_, output_spatial_lengths);

example/20_convnd_bwd_weight_xdl/convnd_bwd_weight_xdl_bf16_splitk.cpp

+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include <half.hpp>
+
+#include "check_err.hpp"
+#include "conv_util.hpp"
+#include "config.hpp"
+#include "print.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "device_tensor.hpp"
+#include "tensor_layout.hpp"
+#include "element_wise_operation.hpp"
+#include "device_unary_elementwise.hpp"
+#include "device_convnd_backward_weight_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp"
+#include "reference_conv_backward_weight.hpp"
+
+using InDataType  = ck::bhalf_t;
+using WeiDataType = ck::bhalf_t;
+using OutDataType = ck::bhalf_t;
+using AccDataType = float;
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using InElementOp  = ck::tensor_operation::element_wise::PassThrough;
+using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
+using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
+
+using UnaryTypeConvert = ck::tensor_operation::element_wise::UnaryTypeConvert<ck::bhalf_t, float>;
+
+using DeviceUnaryElementwiseTypeConvertInstance = ck::tensor_operation::device::
+    DeviceUnaryElementwise<AccDataType, WeiDataType, UnaryTypeConvert, 1, 4>;
+
+static constexpr auto ConvBwdWeightDefault =
+    ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
+
+using DeviceConvBwdWeightBasePtr =
+    ck::tensor_operation::device::DeviceConvBwdWeightPtr<InElementOp, WeiElementOp, OutElementOp>;
+
+// clang-format off
+template <ck::index_t NumDimSpatial>
+using DeviceConvndBwdWeightInstance_bf16_splitk = ck::tensor_operation::device::
+    DeviceConvndBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<
+        InDataType,                       // InDataType
+        AccDataType,                      // WeiDataType
+        OutDataType,                      // OutDataType
+        AccDataType,                      // AccDataType
+        InElementOp,                      // InElementwiseOperation
+        WeiElementOp,                     // WeiElementwiseOperation
+        OutElementOp,                     // OutElementwiseOperation
+        ConvBwdWeightDefault,             // ConvolutionBackwardWeightSpecialization
+        NumDimSpatial,                    // NumDimSpatial
+        256,                              // BlockSize
+        128,                              // MPerBlock
+        128,                              // NPerBlock
+        4,                                // K0PerBlock
+        8,                                // K1
+        32,                               // MPerXdl
+        32,                               // NPerXdl
+        2,                                // MXdlPerWave
+        2,                                // NXdlPerWave
+        S<1, 4, 16, 4>,                   // ABlockTransferThreadClusterLengths_K0_M_K1
+        S<0, 3, 1, 2>,                    // ABlockTransferThreadClusterArrangeOrder
+        S<0, 2, 1, 3>,                    // ABlockTransferSrcAccessOrder
+        2,                                // ABlockTransferSrcVectorDim
+        8,                                // ABlockTransferSrcScalarPerVector
+        2,                                // ABlockTransferDstScalarPerVector_K1
+        true,                             // ABlockLdsAddExtraM
+        S<1, 4, 16, 4>,                   // BBlockTransferThreadClusterLengths_K0_N_K1
+        S<0, 3, 1, 2>,                    // BBlockTransferThreadClusterArrangeOrder
+        S<0, 2, 1, 3>,                    // BBlockTransferSrcAccessOrder
+        2,                                // BBlockTransferSrcVectorDim
+        8,                                // BBlockTransferSrcScalarPerVector
+        2,                                // BBlockTransferDstScalarPerVector_K1
+        true,                             // BBlockLdsAddExtraN
+        1,                                // CShuffleMXdlPerWavePerShuffle
+        1,                                // CShuffleNXdlPerWavePerShuffle
+        S<1, 32, 1, 4>,                   // CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
+        4>;                               // CBlockTransferScalarPerVector_NWaveNPerXdl
+// clang-format on
+
+template <ck::index_t NumDimSpatial>
+using ReferenceConvBwdWeightInstance =
+    ck::tensor_operation::host::ReferenceConvBwdWeight<InDataType,
+                                                       WeiDataType,
+                                                       OutDataType,
+                                                       InElementOp,
+                                                       WeiElementOp,
+                                                       OutElementOp,
+                                                       NumDimSpatial>;
+
+template <typename HostTensorB, typename HostTensorA, typename Functor>
+void host_elementwise(HostTensorB& B,
+                      const HostTensorA& A,
+                      const std::vector<std::size_t>& shape,
+                      Functor functor)
+{
+    size_t tensor_size = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>{});
+    std::cout << __LINE__ << ":" << tensor_size << ", " << A.mData[0] << std::endl;
+    for(std::size_t n = 0; n < tensor_size; ++n)
+    {
+        B.mData[n] = functor(A.mData[n]);
+    }
+}
+
+void print_use_msg()
+{
+    std::cout << "arg1: verification (0=no, 1=yes)\n"
+              << "arg2: initialization (0=no init, 1=random value, 2= init to 1 )\n"
+              << "arg3: time kernel (0=n0, 1=yes)\n"
+              << "arg4: is show log (0=no, 1=yes)\n"
+              << "arg5: split-k : in this example split-k must be larger than 1\n"
+              << "arg6: N spatial dimensions (default 2)\n"
+              << "Following arguments (depending on number of spatial dims):\n"
+              << " N, K, C, \n"
+              << " <filter spatial dimensions>, (ie Y, X for 2D)\n"
+              << " <input image spatial dimensions>, (ie Hi, Wi for 2D)\n"
+              << " <strides>, (ie Sy, Sx for 2D)\n"
+              << " <dilations>, (ie Dy, Dx for 2D)\n"
+              << " <left padding>, (ie LeftPy, LeftPx for 2D)\n"
+              << " <right padding>, (ie RightPy, RightPx for 2D)\n"
+              << std::endl;
+}
+
+ck::utils::conv::ConvParams parse_conv_params(int num_dim_spatial, char* argv[])
+{
+    // (N, K, C) + num_dim_spatial * 6 (filter, input, strides, dilations, pad left, pad right)
+    ck::utils::conv::ConvParams params;
+    int arg_idx = 7;
+
+    params.num_dim_spatial_ = num_dim_spatial;
+    params.N_               = std::stoi(argv[arg_idx++]);
+    params.K_               = std::stoi(argv[arg_idx++]);
+    params.C_               = std::stoi(argv[arg_idx++]);
+
+    params.filter_spatial_lengths_.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.filter_spatial_lengths_[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.input_spatial_lengths_.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.input_spatial_lengths_[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.conv_filter_strides_.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.conv_filter_strides_[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.conv_filter_dilations_.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.conv_filter_dilations_[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.input_left_pads_.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.input_left_pads_[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.input_right_pads_.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.input_right_pads_[i] = std::stoi(argv[arg_idx++]);
+    }
+
+    return params;
+}
+
+DeviceConvBwdWeightBasePtr get_conv_instance(int num_dim_spatial)
+{
+    switch(num_dim_spatial)
+    {
+    case 3: {
+        return std::make_unique<DeviceConvndBwdWeightInstance_bf16_splitk<3>>();
+    }
+    case 2: {
+        return std::make_unique<DeviceConvndBwdWeightInstance_bf16_splitk<2>>();
+    }
+    case 1: {
+        return std::make_unique<DeviceConvndBwdWeightInstance_bf16_splitk<1>>();
+    }
+    default: {
+        throw std::runtime_error("Unsupported number of spatial dimensions provided!");
+    }
+    }
+}
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
+    int num_dim_spatial  = 2;
+    int do_log           = 0;
+    int split_k          = 2;
+
+    ck::utils::conv::ConvParams params;
+    params.C_ = 128;
+
+    if(argc == 6)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+        do_log          = std::stoi(argv[4]);
+        split_k         = std::stoi(argv[5]);
+    }
+    else if(argc > 6)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+        do_log          = std::stoi(argv[4]);
+        split_k         = std::stoi(argv[5]);
+        num_dim_spatial = std::stoi(argv[6]);
+        // check args number
+        int conv_args     = 3 + num_dim_spatial * 6;
+        int cmdline_nargs = conv_args + 7;
+        if(cmdline_nargs != argc)
+        {
+            print_use_msg();
+            exit(1);
+        }
+
+        params = parse_conv_params(num_dim_spatial, argv);
+    }
+    else if(argc != 1)
+    {
+        print_use_msg();
+        exit(1);
+    }
+
+    if(split_k <= 1)
+    {
+        print_use_msg();
+        exit(1);
+    }
+
+    std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N_),
+                                        static_cast<std::size_t>(params.C_)};
+    input_dims.insert(std::end(input_dims),
+                      std::begin(params.input_spatial_lengths_),
+                      std::end(params.input_spatial_lengths_));
+
+    std::vector<std::size_t> filter_dims{static_cast<std::size_t>(params.K_),
+                                         static_cast<std::size_t>(params.C_)};
+    filter_dims.insert(std::end(filter_dims),
+                       std::begin(params.filter_spatial_lengths_),
+                       std::end(params.filter_spatial_lengths_));
+
+    const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
+    std::vector<std::size_t> output_dims{static_cast<std::size_t>(params.N_),
+                                         static_cast<std::size_t>(params.K_)};
+    output_dims.insert(std::end(output_dims),
+                       std::begin(output_spatial_lengths),
+                       std::end(output_spatial_lengths));
+
+    Tensor<InDataType> in_n_c_hi_wi(
+        ck::utils::conv::get_input_host_tensor_descriptor(input_dims, num_dim_spatial));
+    Tensor<WeiDataType> wei_k_c_y_x_host_result(
+        ck::utils::conv::get_filters_host_tensor_descriptor(filter_dims, num_dim_spatial));
+    Tensor<WeiDataType> wei_k_c_y_x_device_result(
+        ck::utils::conv::get_filters_host_tensor_descriptor(filter_dims, num_dim_spatial));
+    Tensor<OutDataType> out_n_k_ho_wo(
+        ck::utils::conv::get_output_host_tensor_descriptor(output_dims, num_dim_spatial));
+
+    std::cout << "in_n_c_hi_wi: " << in_n_c_hi_wi.mDesc << std::endl;
+    std::cout << "wei_k_c_y_x: " << wei_k_c_y_x_device_result.mDesc << std::endl;
+    std::cout << "out_n_k_ho_wo: " << out_n_k_ho_wo.mDesc << std::endl;
+
+    std::cout << "in_n_c_hi_wi: " << in_n_c_hi_wi.mDesc << std::endl;
+    std::cout << "wei_k_c_y_x: " << wei_k_c_y_x_host_result.mDesc << std::endl;
+    std::cout << "out_n_k_ho_wo: " << out_n_k_ho_wo.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-2, 2});
+        in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_2<InDataType>{-2, 2});
+        break;
+    default:
+        out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_1<OutDataType>{1});
+        in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_1<InDataType>{1});
+    }
+
+    DeviceMem in_device_buf(sizeof(InDataType) * in_n_c_hi_wi.mDesc.GetElementSpace());
+    DeviceMem wei_device_buf(sizeof(WeiDataType) *
+                             wei_k_c_y_x_device_result.mDesc.GetElementSpace());
+    DeviceMem out_device_buf(sizeof(OutDataType) * out_n_k_ho_wo.mDesc.GetElementSpace());
+
+    in_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
+    out_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
+    // reset input to zero
+    wei_device_buf.SetZero();
+
+    // do GEMM
+    auto conv    = get_conv_instance(num_dim_spatial);
+    auto invoker = conv->MakeInvokerPointer();
+    auto argument =
+        conv->MakeArgumentPointer(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
+                                  static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
+                                  static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
+                                  params.N_,
+                                  params.K_,
+                                  params.C_,
+                                  params.input_spatial_lengths_,
+                                  params.filter_spatial_lengths_,
+                                  output_spatial_lengths,
+                                  params.conv_filter_strides_,
+                                  params.conv_filter_dilations_,
+                                  params.input_left_pads_,
+                                  params.input_right_pads_,
+                                  InElementOp{},
+                                  WeiElementOp{},
+                                  OutElementOp{},
+                                  split_k);
+
+    // alloc work space
+    size_t bwd_weight_workspace_size = conv->GetWorkSpaceSize(argument.get());
+    if(bwd_weight_workspace_size <= 0)
+    {
+        print_use_msg();
+        exit(1);
+    }
+
+    float conv_ave_time = 0.f;
+
+    DeviceMem wei_work_space_device_buf(bwd_weight_workspace_size);
+    wei_work_space_device_buf.SetZero();
+    conv->SetWorkSpacePointer(argument.get(), wei_work_space_device_buf.GetDeviceBuffer());
+
+    if(!conv->IsSupportedArgument(argument.get()))
+    {
+        std::cout << "wrong! device_conv with the specified compilation parameters does "
+                     "not support this Conv problem"
+                  << std::endl;
+        return 1;
+    }
+
+    conv_ave_time = invoker->Run(argument.get(), StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop = ck::utils::conv::get_flops(
+        params.N_, params.C_, params.K_, params.filter_spatial_lengths_, output_spatial_lengths);
+    std::size_t num_btype = ck::utils::conv::get_btype<InDataType, WeiDataType, OutDataType>(
+        params.N_,
+        params.C_,
+        params.K_,
+        params.input_spatial_lengths_,
+        params.filter_spatial_lengths_,
+        output_spatial_lengths);
+
+    float tflops = static_cast<float>(flop) / 1.E9 / conv_ave_time;
+
+    float gb_per_sec = num_btype / 1.E6 / conv_ave_time;
+
+    std::cout << "Perf: conv: " << conv_ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
+              << " GB/s" << std::endl;
+
+    if(do_verification)
+    {
+        auto verify_f = [&](const auto& ref_conv) {
+            auto ref_invoker = ref_conv.MakeInvoker();
+
+            auto ref_argument = ref_conv.MakeArgument(in_n_c_hi_wi,
+                                                      wei_k_c_y_x_host_result,
+                                                      out_n_k_ho_wo,
+                                                      params.conv_filter_strides_,
+                                                      params.conv_filter_dilations_,
+                                                      params.input_left_pads_,
+                                                      params.input_right_pads_,
+                                                      InElementOp{},
+                                                      WeiElementOp{},
+                                                      OutElementOp{});
+
+            ref_invoker.Run(ref_argument);
+
+            wei_device_buf.FromDevice(wei_k_c_y_x_device_result.mData.data());
+
+            if(do_log)
+            {
+                LogRangeAsType<float>(std::cout << "out: ", out_n_k_ho_wo.mData, ",") << std::endl;
+                LogRangeAsType<float>(std::cout << "in : ", in_n_c_hi_wi.mData, ",") << std::endl;
+                LogRangeAsType<float>(
+                    std::cout << "wei_device(after): ", wei_k_c_y_x_device_result.mData, ",")
+                    << std::endl;
+                LogRangeAsType<float>(
+                    std::cout << "wei_host  : ", wei_k_c_y_x_host_result.mData, ",")
+                    << std::endl;
+            }
+
+            return ck::utils::check_err(wei_k_c_y_x_device_result.mData,
+                                        wei_k_c_y_x_host_result.mData)
+                       ? 0
+                       : 1;
+        };
+
+        switch(num_dim_spatial)
+        {
+        case 3: {
+            auto ref_conv = ReferenceConvBwdWeightInstance<3>();
+            verify_f(ref_conv);
+            break;
+        }
+        case 2: {
+            auto ref_conv = ReferenceConvBwdWeightInstance<2>();
+            verify_f(ref_conv);
+            break;
+        }
+        case 1: {
+            auto ref_conv = ReferenceConvBwdWeightInstance<1>();
+            verify_f(ref_conv);
+            break;
+        }
+        default: {
+            throw std::runtime_error("Unsupported number of spatial dimensions provided!");
+        }
+        }
+    }
+    return 0;
+}

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

@@ -11,6 +11,7 @@
 #include "tensor_descriptor.hpp"
 #include "tensor_descriptor_helper.hpp"
 #include "gridwise_gemm_xdlops_bwd_weight.hpp"
+#include "gridwise_unary_elementwise_1d.hpp"
 
 namespace ck {
 namespace tensor_operation {
@@ -628,6 +629,54 @@ struct DeviceConvndBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
                                                                   1);
     }
 
+    // type convert descs
+    template <typename Desc_M0>
+    static auto PadDescriptor_M0_1d(Desc_M0 desc_m0, index_t gridSize, index_t blockSize)
+    {
+        const auto m0           = desc_m0.GetLength(I0);
+        const index_t loop_step = gridSize * blockSize * 4;
+        const auto pad          = math::integer_least_multiple(m0, loop_step) - m0;
+        const auto desc_m0_pad =
+            transform_tensor_descriptor(desc_m0,
+                                        make_tuple(make_right_pad_transform(m0, pad)),
+                                        make_tuple(Sequence<0>{}),
+                                        make_tuple(Sequence<0>{}));
+        return desc_m0_pad;
+    }
+
+    template <index_t Dim>
+    static auto MakeDescriptor_M0(const std::vector<index_t>& shape,
+                                  const std::vector<index_t>& stride,
+                                  index_t gridSize,
+                                  index_t blockSize)
+    {
+        auto tupleOfShape  = generate_tuple([&](auto I) { return shape[I]; }, Number<Dim>{});
+        auto tupleOfStride = generate_tuple([&](auto I) { return stride[I]; }, Number<Dim>{});
+
+        // nd desc - [s0, s1, s2, ...]
+        const auto desc = make_naive_tensor_descriptor(tupleOfShape, tupleOfStride);
+
+        // merge nd to 1d desc - [s0 * s1 * ...]
+        if constexpr(Dim > 1)
+        {
+            const auto desc_m0 = transform_tensor_descriptor(
+                desc,
+                make_tuple(make_merge_transform(tupleOfShape)),
+                make_tuple(generate_sequence_v2([&](auto I) { return I; }, Number<Dim>{})),
+                make_tuple(Sequence<0>{}));
+
+            return PadDescriptor_M0_1d(desc_m0, gridSize, blockSize);
+        }
+        else
+            return PadDescriptor_M0_1d(desc, gridSize, blockSize);
+    }
+
+    using TypeConvertFunctor =
+        ck::tensor_operation::element_wise::UnaryTypeConvert<ck::bhalf_t, float>;
+    using GridDesc_M0 = decltype(MakeDescriptor_M0<1>({1}, {1}, 1, 1));
+    using GridwiseUEltwise =
+        GridwiseUnaryElementwise_1D<AccDataType, InDataType, GridDesc_M0, TypeConvertFunctor, 4>;
+
     using ABCGridDescs = decltype(GetABCGridDesc<NumDimSpatial>());
 
     using AGridDesc_K0_M_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I0])>;
@@ -733,6 +782,55 @@ struct DeviceConvndBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
         true,
         true>;
 
+    using GridwiseGemmAtomicAddFloatBf16Splitk = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight<
+        BlockSize,
+        ADataType, // TODO: distinguish A/B datatype
+        AccDataType,
+        AccDataType,
+        InMemoryDataOperationEnum::AtomicAdd,
+        AGridDesc_K0_M_K1,
+        BGridDesc_K0_N_K1,
+        CGridDesc_M_N,
+        AElementwiseOperation,
+        BElementwiseOperation,
+        CElementwiseOperation,
+        MPerBlock,
+        NPerBlock,
+        K0PerBlock,
+        MPerXdl,
+        NPerXdl,
+        K1,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_K0_M_K1,
+        ABlockTransferThreadClusterArrangeOrder,
+        ABlockTransferSrcAccessOrder,
+        ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_K1,
+        false, // AThreadTransferSrcResetCoordinateAfterRun,
+        ABlockLdsAddExtraM,
+        ABlockLdsM1PerBlock,
+        ABlockLdsM0PerBlock,
+        ABlockLdsM1Padding,
+        BBlockTransferThreadClusterLengths_K0_N_K1,
+        BBlockTransferThreadClusterArrangeOrder,
+        BBlockTransferSrcAccessOrder,
+        BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_K1,
+        false, // BThreadTransferSrcResetCoordinateAfterRun,
+        BBlockLdsAddExtraN,
+        BBlockLdsN1PerBlock,
+        BBlockLdsN0PerBlock,
+        BBlockLdsN1Padding,
+        CShuffleMXdlPerWavePerShuffle,
+        CShuffleNXdlPerWavePerShuffle,
+        CBlockTransferScalarPerVector_NWaveNPerXdl,
+        CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+        true,
+        true>;
+
     // Argument
     using CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
         decltype(GridwiseGemm::MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(CGridDesc_M_N{}));
@@ -802,6 +900,9 @@ struct DeviceConvndBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
             b_grid_desc_kbatch_k0_n_k1_ = descs[I1];
             c_grid_desc_m_n_            = descs[I2];
 
+            // init work space
+            p_c_workspace_grid_ = nullptr;
+
             block_2_ctile_map_ =
                 GridwiseGemm::MakeCBlockClusterAdaptor(c_grid_desc_m_n_, M01, N01, k_batch_);
 
@@ -838,6 +939,9 @@ struct DeviceConvndBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
         std::vector<index_t> input_left_pads_;
         std::vector<index_t> input_right_pads_;
         index_t k_batch_;
+
+        // external work space
+        void* p_c_workspace_grid_;
     };
 
     // Invoker
@@ -910,9 +1014,77 @@ struct DeviceConvndBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
                                            arg.block_2_ctile_map_);
             };
 
+            // run kernel for bf16 with splitk
+            const auto run_bf16_splitk = [&](const auto& kernel) {
+                hipGetErrorString(hipMemset(
+                    arg.p_c_workspace_grid_,
+                    0,
+                    arg.c_grid_desc_mblock_mperblock_nblock_nperblock_.GetElementSpaceSize() *
+                        sizeof(AccDataType)));
+
+                ave_time =
+                    launch_and_time_kernel(stream_config,
+                                           kernel,
+                                           dim3(grid_size),
+                                           dim3(BlockSize),
+                                           0,
+                                           arg.p_a_grid_,
+                                           arg.p_b_grid_,
+                                           static_cast<AccDataType*>(arg.p_c_workspace_grid_),
+                                           arg.a_grid_desc_kbatch_k0_m_k1_,
+                                           arg.b_grid_desc_kbatch_k0_n_k1_,
+                                           arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                           arg.a_element_op_,
+                                           arg.b_element_op_,
+                                           arg.c_element_op_,
+                                           arg.block_2_ctile_map_);
+            };
+
+            // kernel for type conversion
+            std::vector<std::size_t> filter_dims{static_cast<std::size_t>(arg.Conv_K_),
+                                                 static_cast<std::size_t>(arg.Conv_C_)};
+
+            filter_dims.insert(std::end(filter_dims),
+                               std::begin(arg.filter_spatial_lengths_),
+                               std::end(arg.filter_spatial_lengths_));
+
+            int tensor_size =
+                std::accumulate(filter_dims.begin(), filter_dims.end(), 1, std::multiplies<int>{});
+
+            const index_t type_convert_grid_size = GridwiseUEltwise::CalculateGridSize(tensor_size);
+            GridDesc_M0 a_grid_desc_m0_ =
+                MakeDescriptor_M0<1>({tensor_size}, {1}, type_convert_grid_size, 256);
+            GridDesc_M0 b_grid_desc_m0_ =
+                MakeDescriptor_M0<1>({tensor_size}, {1}, type_convert_grid_size, 256);
+
+            if(!GridwiseUEltwise::CheckValidity(a_grid_desc_m0_, b_grid_desc_m0_))
+            {
+                throw std::runtime_error("wrong! GridwiseUnaryElementwise_1D has invalid setting");
+            }
+
+            // run kernel for type conversion
+            void* p_c_grid_tmp_            = static_cast<void*>(arg.p_c_grid_);
+            InDataType* p_c_grid_tmp_bf16_ = static_cast<InDataType*>(p_c_grid_tmp_);
+            const auto Run_type_convert    = [&](const auto& kernel) {
+                float elapsed_time =
+                    launch_and_time_kernel(stream_config,
+                                           kernel,
+                                           dim3(type_convert_grid_size),
+                                           dim3(256),
+                                           0,
+                                           static_cast<AccDataType*>(arg.p_c_workspace_grid_),
+                                           p_c_grid_tmp_bf16_,
+                                           a_grid_desc_m0_,
+                                           b_grid_desc_m0_,
+                                           TypeConvertFunctor{});
+                return elapsed_time;
+            };
+
             if constexpr(std::is_same<InDataType, ck::bhalf_t>::value)
             {
                 if(has_main_k0_block_loop)
+                {
+                    if(kbatch == 1)
                     {
                         const auto kernel = kernel_gemm_xdlops_bwd_weight<
                             GridwiseGemm,
@@ -920,7 +1092,8 @@ struct DeviceConvndBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
                             CDataType,
                             remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
                             remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
-                        remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                            remove_reference_t<
+                                DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
                             OutElementwiseOperation,
                             InElementwiseOperation,
                             WeiElementwiseOperation,
@@ -930,6 +1103,35 @@ struct DeviceConvndBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
                         Run(kernel);
                     }
                     else
+                    {
+                        const auto kernel_type_convert =
+                            kernel_unary_elementwise_1d<GridwiseUEltwise,
+                                                        AccDataType,
+                                                        InDataType,
+                                                        GridDesc_M0,
+                                                        TypeConvertFunctor>;
+
+                        const auto kernel_conv = kernel_gemm_xdlops_bwd_weight<
+                            GridwiseGemmAtomicAddFloatBf16Splitk,
+                            ADataType, // TODO: distiguish A/B datatype
+                            AccDataType,
+                            remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                            remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                            remove_reference_t<
+                                DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                            OutElementwiseOperation,
+                            InElementwiseOperation,
+                            WeiElementwiseOperation,
+                            remove_reference_t<DeviceOp::Block2CTileMap>,
+                            true>;
+
+                        run_bf16_splitk(kernel_conv);
+                        ave_time += Run_type_convert(kernel_type_convert);
+                    }
+                }
+                else
+                {
+                    if(kbatch == 1)
                     {
                         const auto kernel = kernel_gemm_xdlops_bwd_weight<
                             GridwiseGemm,
@@ -937,7 +1139,8 @@ struct DeviceConvndBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
                             CDataType,
                             remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
                             remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
-                        remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                            remove_reference_t<
+                                DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
                             OutElementwiseOperation,
                             InElementwiseOperation,
                             WeiElementwiseOperation,
@@ -946,6 +1149,25 @@ struct DeviceConvndBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
 
                         Run(kernel);
                     }
+                    else
+                    {
+                        const auto kernel = kernel_gemm_xdlops_bwd_weight<
+                            GridwiseGemmAtomicAddFloatBf16Splitk,
+                            ADataType, // TODO: distiguish A/B datatype
+                            AccDataType,
+                            remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                            remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                            remove_reference_t<
+                                DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                            OutElementwiseOperation,
+                            InElementwiseOperation,
+                            WeiElementwiseOperation,
+                            remove_reference_t<DeviceOp::Block2CTileMap>,
+                            false>;
+
+                        run_bf16_splitk(kernel);
+                    }
+                }
             }
             else
             {
@@ -1226,6 +1448,11 @@ struct DeviceConvndBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
     {
         return GetWorkSpaceSize<NumDimSpatial>(*dynamic_cast<const Argument*>(p_arg));
     }
+
+    void SetWorkSpacePointer(BaseArgument* p_arg, void* workspace_ptr) const override
+    {
+        dynamic_cast<Argument*>(p_arg)->p_c_workspace_grid_ = workspace_ptr;
+    }
 };
 
 } // namespace device

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

+#pragma once
+#include <iostream>
+#include <vector>
+
+#include "device.hpp"
+#include "device_base.hpp"
+#include "gridwise_unary_elementwise_1d.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <typename ADataType,
+          typename BDataType,
+          typename ElementwiseFunctor,
+          index_t Dim,
+          index_t ScalarPerVector>
+struct DeviceUnaryElementwise : public BaseOperator
+{
+    static constexpr auto I0 = Number<0>{};
+
+    template <typename Desc_M0>
+    static auto PadDescriptor_M0_1d(Desc_M0 desc_m0, index_t gridSize, index_t blockSize)
+    {
+        const auto m0           = desc_m0.GetLength(I0);
+        const index_t loop_step = gridSize * blockSize * ScalarPerVector;
+        const auto pad          = math::integer_least_multiple(m0, loop_step) - m0;
+        const auto desc_m0_pad =
+            transform_tensor_descriptor(desc_m0,
+                                        make_tuple(make_right_pad_transform(m0, pad)),
+                                        make_tuple(Sequence<0>{}),
+                                        make_tuple(Sequence<0>{}));
+        return desc_m0_pad;
+    }
+
+    static auto MakeDescriptor_M0(const std::vector<index_t>& shape,
+                                  const std::vector<index_t>& stride,
+                                  index_t gridSize,
+                                  index_t blockSize)
+    {
+        auto tupleOfShape  = generate_tuple([&](auto I) { return shape[I]; }, Number<Dim>{});
+        auto tupleOfStride = generate_tuple([&](auto I) { return stride[I]; }, Number<Dim>{});
+
+        // nd desc - [s0, s1, s2, ...]
+        const auto desc = make_naive_tensor_descriptor(tupleOfShape, tupleOfStride);
+
+        // merge nd to 1d desc - [s0 * s1 * ...]
+        if constexpr(Dim > 1)
+        {
+            const auto desc_m0 = transform_tensor_descriptor(
+                desc,
+                make_tuple(make_merge_transform(tupleOfShape)),
+                make_tuple(generate_sequence_v2([&](auto I) { return I; }, Number<Dim>{})),
+                make_tuple(Sequence<0>{}));
+
+            return PadDescriptor_M0_1d(desc_m0, gridSize, blockSize);
+        }
+        else
+            return PadDescriptor_M0_1d(desc, gridSize, blockSize);
+    }
+
+    using GridDesc_M0      = decltype(MakeDescriptor_M0({1, 1}, {1, 1}, 1, 1));
+    using GridwiseUEltwise = GridwiseUnaryElementwise_1D<ADataType,
+                                                         BDataType,
+                                                         GridDesc_M0,
+                                                         ElementwiseFunctor,
+                                                         ScalarPerVector>;
+
+    struct Argument : public BaseArgument
+    {
+        Argument(const ADataType* p_a,
+                 BDataType* p_b,
+                 const std::vector<index_t>& shape,
+                 const std::vector<index_t>& stride_a,
+                 const std::vector<index_t>& stride_b,
+                 ElementwiseFunctor functor)
+            : p_a_(p_a),
+              p_b_(p_b),
+              shape_(shape),
+              functor_(functor),
+              blockSize_(256) // FIXME - Calculate the grid size by number of CU in the future
+        {
+            index_t tensor_size =
+                std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>{});
+            gridSize_       = GridwiseUEltwise::CalculateGridSize(tensor_size);
+            a_grid_desc_m0_ = MakeDescriptor_M0(shape, stride_a, gridSize_, blockSize_);
+            b_grid_desc_m0_ = MakeDescriptor_M0(shape, stride_b, gridSize_, blockSize_);
+        }
+
+        const ADataType* p_a_;
+        BDataType* p_b_;
+        std::vector<int> shape_;
+        GridDesc_M0 a_grid_desc_m0_;
+        GridDesc_M0 b_grid_desc_m0_;
+        ElementwiseFunctor functor_;
+        index_t blockSize_;
+        index_t gridSize_;
+    };
+
+    struct Invoker : public BaseInvoker
+    {
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            const auto kernel = kernel_unary_elementwise_1d<GridwiseUEltwise,
+                                                            ADataType,
+                                                            BDataType,
+                                                            GridDesc_M0,
+                                                            ElementwiseFunctor>;
+
+            float elapsed_time = launch_and_time_kernel(stream_config,
+                                                        kernel,
+                                                        dim3(arg.gridSize_),
+                                                        dim3(arg.blockSize_),
+                                                        0,
+                                                        arg.p_a_,
+                                                        arg.p_b_,
+                                                        arg.a_grid_desc_m0_,
+                                                        arg.b_grid_desc_m0_,
+                                                        arg.functor_);
+            return elapsed_time;
+        }
+
+        // polymorphic
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        const Argument* pArg = dynamic_cast<const Argument*>(p_arg);
+
+        if(pArg == nullptr)
+            return false;
+
+        if(pArg->shape_.back() % ScalarPerVector != 0)
+            return false;
+
+        return true;
+    };
+
+    std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
+                                                      void* p_b,
+                                                      std::vector<index_t> shape,
+                                                      std::vector<index_t> stride_a,
+                                                      std::vector<index_t> stride_b,
+                                                      ElementwiseFunctor functor)
+    {
+        return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
+                                          static_cast<BDataType*>(p_b),
+                                          shape,
+                                          stride_a,
+                                          stride_b,
+                                          functor);
+    }
+
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() { return std::make_unique<Invoker>(); }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceBinaryElementwise"
+            << "<"
+            << "ScalarPerVector = " << ScalarPerVector
+            << ">";
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

include/ck/tensor_operation/gpu/element/element_wise_operation.hpp

@@ -346,6 +346,27 @@ struct UnarySqrt<double, double>
     };
 };
 
+template <typename Y, typename X>
+struct UnaryTypeConvert;
+
+template <>
+struct UnaryTypeConvert<float, ck::bhalf_t>
+{
+    __host__ __device__ void operator()(float& y, ck::bhalf_t& x) const
+    {
+        y = ck::type_convert<float, ck::bhalf_t>(x);
+    };
+};
+
+template <>
+struct UnaryTypeConvert<ck::bhalf_t, float>
+{
+    __host__ __device__ void operator()(ck::bhalf_t& y, float& x) const
+    {
+        y = ck::type_convert<ck::bhalf_t, float>(x);
+    };
+};
+
 } // namespace element_wise
 } // namespace tensor_operation
 } // namespace ck

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

@@ -791,8 +791,10 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight
             constexpr auto c_block_desc_mblock_mperblock_nblock_nperblock =
                 GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
 
+            void* p_shared = static_cast<void*>(p_shared_block);
+
             auto c_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-                static_cast<FloatC*>(p_shared_block),
+                static_cast<FloatC*>(p_shared),
                 c_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
 
             static_assert(M1 == MWave, "");

include/ck/tensor_operation/gpu/grid/gridwise_unary_elementwise_1d.hpp

+#pragma once
+
+#include "cluster_descriptor.hpp"
+#include "data_type.hpp"
+#include "element_wise_operation.hpp"
+#include "threadwise_tensor_slice_transfer.hpp"
+
+namespace ck {
+
+template <typename GridwiseUEltwise,
+          typename ADataType,
+          typename BDataType,
+          typename GridDesc_M0,
+          typename ElementwiseFunctor>
+__global__ void kernel_unary_elementwise_1d(const ADataType* __restrict__ p_a_global,
+                                            BDataType* __restrict__ p_b_global,
+                                            const GridDesc_M0 a_grid_desc_m0,
+                                            const GridDesc_M0 b_grid_desc_m0,
+                                            const ElementwiseFunctor functor)
+{
+    GridwiseUEltwise::Run(p_a_global, p_b_global, a_grid_desc_m0, b_grid_desc_m0, functor);
+}
+
+template <typename ADataType,
+          typename BDataType,
+          typename GridDesc_M0,
+          typename ElementwiseFunctor,
+          index_t ScalarPerVector>
+struct GridwiseUnaryElementwise_1D
+{
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto thread_desc_m0 =
+        make_naive_tensor_descriptor_packed(make_tuple(Number<ScalarPerVector>{}));
+
+    using PassThrough = tensor_operation::element_wise::PassThrough;
+
+    static __device__ auto CalculateElementwiseIndex()
+    {
+        const index_t global_thread_id = get_thread_global_1d_id();
+        return make_multi_index(global_thread_id * ScalarPerVector);
+    }
+
+    __host__ __device__ static constexpr bool CheckValidity(const GridDesc_M0 a_grid_desc_m0,
+                                                            const GridDesc_M0 b_grid_desc_m0)
+    {
+        return a_grid_desc_m0.GetLength(I0) == b_grid_desc_m0.GetLength(I0);
+    }
+
+    __host__ __device__ static constexpr index_t CalculateGridSize(const index_t tensor_size)
+    {
+        const index_t grid_size = math::integer_divide_ceil(tensor_size, 256 * ScalarPerVector);
+
+        return grid_size;
+    }
+
+    __device__ static void Run(const ADataType* __restrict__ p_a_global,
+                               BDataType* __restrict__ p_b_global,
+                               const GridDesc_M0 a_grid_desc_m0,
+                               const GridDesc_M0 b_grid_desc_m0,
+                               const ElementwiseFunctor functor)
+    {
+        const auto a_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_a_global, a_grid_desc_m0.GetElementSpaceSize());
+        auto b_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_b_global, b_grid_desc_m0.GetElementSpaceSize());
+
+        StaticBuffer<AddressSpaceEnum::Vgpr, ADataType, ScalarPerVector, true> a_thread_buf;
+        StaticBuffer<AddressSpaceEnum::Vgpr, BDataType, ScalarPerVector, true> b_thread_buf;
+
+        const auto thread_store_global_offset = CalculateElementwiseIndex();
+
+        auto a_global_load =
+            ThreadwiseTensorSliceTransfer_v2<ADataType,
+                                             ADataType,
+                                             GridDesc_M0,
+                                             decltype(thread_desc_m0),
+                                             Sequence<ScalarPerVector>, // SliceLengths
+                                             Sequence<0>,               // DimAccessOrder
+                                             0,                         // SrcVectorDim
+                                             ScalarPerVector,
+                                             1, // SrcScalarStrideInVector
+                                             false>{a_grid_desc_m0, thread_store_global_offset};
+
+        auto b_global_write =
+            ThreadwiseTensorSliceTransfer_v1r3<BDataType,
+                                               BDataType,
+                                               decltype(thread_desc_m0),
+                                               GridDesc_M0,
+                                               PassThrough,
+                                               Sequence<ScalarPerVector>, // SliceLengths
+                                               Sequence<0>,               // DimAccessOrder
+                                               0,                         // DstVectorDim
+                                               ScalarPerVector,
+                                               InMemoryDataOperationEnum::Set,
+                                               1, // DstScalarStrideInVector
+                                               false>{
+                b_grid_desc_m0, thread_store_global_offset, PassThrough{}};
+
+        const index_t blockSize    = get_block_size();
+        const index_t blockPerGrid = get_grid_size();
+        const auto m0              = b_grid_desc_m0.GetLength(I0);
+        const index_t loop_step    = blockPerGrid * blockSize * ScalarPerVector;
+        const auto loop_step_index = make_multi_index(loop_step);
+
+        index_t num_iter = m0 / (loop_step);
+        do
+        {
+            // read and process ScalarPerVector elements
+            a_global_load.Run(
+                a_grid_desc_m0, a_global_buf, thread_desc_m0, make_tuple(I0), a_thread_buf);
+
+            static_for<0, ScalarPerVector, 1>{}([&](auto m) {
+                constexpr auto offset = thread_desc_m0.CalculateOffset(make_tuple(m));
+                functor(b_thread_buf(Number<offset>{}), a_thread_buf(Number<offset>{}));
+            });
+
+            b_global_write.Run(thread_desc_m0,
+                               make_tuple(I0), // SrcSliceOriginIdx
+                               b_thread_buf,
+                               b_grid_desc_m0,
+                               b_global_buf);
+
+            a_global_load.MoveSrcSliceWindow(a_grid_desc_m0, loop_step_index);
+            b_global_write.MoveDstSliceWindow(b_grid_desc_m0, loop_step_index);
+        } while(--num_iter);
+    }
+};
+
+} // namespace ck