Example for conv2d backward weight fp16 (#106)

* add wrw reference

* start device

* raw not split version

* run simple example

* start to use atomic add

* simple transform result correct

* first version that can run

* fix atomic and set operator choice

* add check split-k

* format

* change input parameter

* add pad for t total

* rename example index
Co-authored-by: ltqin <letaoqin@amd.com>

device_operation/include/device_conv_backward_weight.hpp

+#ifndef DEVICE_CONV_WRW_HPP
+#define DEVICE_CONV_WRW_HPP
+
+#include <iostream>
+#include "device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation>
+struct DeviceConvWrw : public BaseOperator
+{
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_in,
+                        void* p_wei,
+                        const void* p_out,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t C,
+                        std::vector<ck::index_t> input_spatial_lengths,
+                        std::vector<ck::index_t> filter_spatial_lengths,
+                        std::vector<ck::index_t> output_spatial_lengths,
+                        std::vector<ck::index_t> conv_filter_strides,
+                        std::vector<ck::index_t> conv_filter_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op,
+                        ck::index_t split_k) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+template <typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation>
+using DeviceConvWrwPtr = std::unique_ptr<
+    DeviceConvWrw<InElementwiseOperation, WeiElementwiseOperation, OutElementwiseOperation>>;
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck
+#endif

example/13_conv2d_backward_weight_xdl/README.md

+# Instructions for ```conv2d_wrw_xdl``` Example
+
+## Docker script
+```bash
+docker run                                                                   \
+-it                                                                          \
+--rm                                                                         \
+--privileged                                                                 \
+--group-add sudo                                                             \
+-w /root/workspace                                                           \
+-v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace                                \
+rocm/tensorflow:rocm4.3.1-tf2.6-dev                                          \
+/bin/bash
+```
+
+## Build ```conv2d_wrw_xdl```
+```bash
+mkdir build && cd build
+```
+
+```bash
+# Need to specify target ID, example below is gfx908
+cmake                                                                  \
+-D BUILD_DEV=OFF                                                       \
+-D CMAKE_BUILD_TYPE=Release                                            \
+-D CMAKE_CXX_FLAGS="-DCK_AMD_GPU_GFX908 --amdgpu-target=gfx908 -O3 "   \
+-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc                              \
+-D CMAKE_PREFIX_PATH=/opt/rocm                                         \
+..
+```
+
+```bash
+ make -j conv2d_wrw_xdl
+```
+
+## Run ```conv2d_wrw_xdl```
+```bash
+#arg1: verification (0=no, 1=yes)
+#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
+#arg3: run kernel # of times (>1)
+#arg4: is show log (0=no, 1=yes)
+#arg5 to 19: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx, split-k
+./example/conv2d_fwd_xdl 0 1 5 0 4
+```
+
+Result 
+```
+in_n_c_hi_wi: dim 4, lengths {128, 1024, 14, 14}, strides {200704, 1, 14336, 1024}
+wei_k_c_y_x: dim 4, lengths {256, 1024, 3, 3}, strides {9216, 1, 3072, 1024}
+out_n_k_ho_wo: dim 4, lengths {128, 256, 6, 6}, strides {9216, 1, 1536, 256}
+arg.a_grid_desc_kbatch_k0_m_k1_{4, 144, 256, 8}
+arg.b_grid_desc_kbatch_k0_n_k1_{4, 144, 9216, 8}
+arg.c_grid_desc_m_n_{ 256, 9216}
+launch_and_time_kernel: grid_dim {576, 1, 1}, block_dim {256, 1, 1} 
+Warm up
+Start running 5 times...
+Perf: 0.401084 ms, 54.2112 TFlops, 145.75 GB/s
+```

example/13_conv2d_backward_weight_xdl/main.cpp

+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include <half.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_conv2d_backward_weight_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp"
+#include "reference_conv_backward_weight.hpp"
+
+using InDataType  = ck::half_t;
+using WeiDataType = ck::half_t;
+using OutDataType = ck::half_t;
+using AccDataType = float;
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using InLayout  = ck::tensor_layout::convolution::NHWC;
+using WeiLayout = ck::tensor_layout::convolution::KYXC;
+using OutLayout = ck::tensor_layout::convolution::NHWK;
+
+using InElementOp  = ck::tensor_operation::element_wise::PassThrough;
+using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
+using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
+
+// clang-format off
+using DeviceConvWrWInstance = ck::tensor_operation::device::
+    DeviceConv2dWrWXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<
+        InDataType,                       // InDataType
+        WeiDataType,                      // WeiDataType
+        OutDataType,                      // OutDataType
+        AccDataType,                      // AccDataType
+        InElementOp,                      // InElementwiseOperation
+        WeiElementOp,                     // WeiElementwiseOperation
+        OutElementOp,                     // OutElementwiseOperation
+        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
+        8>;                               // CBlockTransferScalarPerVector_NWaveNPerXdl
+// clang-format on
+
+using ReferenceConvWrwInstance = ck::tensor_operation::host::
+    ReferenceConvWrw<InDataType, WeiDataType, OutDataType, InElementOp, WeiElementOp, OutElementOp>;
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = 0;
+    int init_method      = 0;
+    int nrepeat          = 5;
+    int do_log           = 0;
+    int split_k          = 4;
+
+    // Conv shape
+    ck::index_t N               = 128;
+    ck::index_t K               = 256;
+    ck::index_t C               = 1024;
+    ck::index_t Y               = 3;
+    ck::index_t X               = 3;
+    ck::index_t Hi              = 14;
+    ck::index_t Wi              = 14;
+    ck::index_t conv_stride_h   = 2;
+    ck::index_t conv_stride_w   = 2;
+    ck::index_t conv_dilation_h = 1;
+    ck::index_t conv_dilation_w = 1;
+    ck::index_t in_left_pad_h   = 0;
+    ck::index_t in_left_pad_w   = 0;
+    ck::index_t in_right_pad_h  = 0;
+    ck::index_t in_right_pad_w  = 0;
+
+    if(argc == 6)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        nrepeat         = std::stoi(argv[3]);
+        do_log          = std::stoi(argv[4]);
+        split_k         = std::stoi(argv[5]);
+    }
+    else if(argc == 21)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        nrepeat         = std::stoi(argv[3]);
+        do_log          = std::stoi(argv[4]);
+        split_k         = std::stoi(argv[5]);
+
+        N               = std::stoi(argv[6]);
+        K               = std::stoi(argv[7]);
+        C               = std::stoi(argv[8]);
+        Y               = std::stoi(argv[9]);
+        X               = std::stoi(argv[10]);
+        Hi              = std::stoi(argv[11]);
+        Wi              = std::stoi(argv[12]);
+        conv_stride_h   = std::stoi(argv[13]);
+        conv_stride_w   = std::stoi(argv[14]);
+        conv_dilation_h = std::stoi(argv[15]);
+        conv_dilation_w = std::stoi(argv[16]);
+        in_left_pad_h   = std::stoi(argv[17]);
+        in_left_pad_w   = std::stoi(argv[18]);
+        in_right_pad_h  = std::stoi(argv[19]);
+        in_right_pad_w  = std::stoi(argv[20]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: run kernel # of times (>1)\n");
+        printf("arg4: is show log (0=no, 1=yes)\n");
+        printf("arg5: split-k \n");
+        printf("arg6 to 19: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
+               "RightPx\n");
+        exit(0);
+    }
+
+    const ck::index_t YEff = (Y - 1) * conv_dilation_h + 1;
+    const ck::index_t XEff = (X - 1) * conv_dilation_w + 1;
+
+    const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
+    const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
+
+    const std::vector<ck::index_t> conv_filter_strides{{conv_stride_h, conv_stride_w}};
+    const std::vector<ck::index_t> conv_filter_dilations{{conv_dilation_h, conv_dilation_w}};
+    const std::vector<ck::index_t> input_left_pads{{in_left_pad_h, in_left_pad_w}};
+    const std::vector<ck::index_t> input_right_pads{{in_right_pad_h, in_right_pad_w}};
+
+    // tensor layout
+    auto f_host_tensor_descriptor = [](std::size_t N_,
+                                       std::size_t C_,
+                                       std::size_t H,
+                                       std::size_t W,
+                                       auto layout) {
+        if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value ||
+                     ck::is_same<decltype(layout), ck::tensor_layout::convolution::KCYX>::value ||
+                     ck::is_same<decltype(layout), ck::tensor_layout::convolution::NKHW>::value)
+        {
+            return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
+                                        std::vector<std::size_t>({C_ * H * W, H * W, W, 1}));
+        }
+        else if constexpr(ck::is_same<decltype(layout),
+                                      ck::tensor_layout::convolution::NHWC>::value ||
+                          ck::is_same<decltype(layout),
+                                      ck::tensor_layout::convolution::KYXC>::value ||
+                          ck::is_same<decltype(layout),
+                                      ck::tensor_layout::convolution::NHWK>::value)
+        {
+            return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
+                                        std::vector<std::size_t>({C_ * H * W, 1, W * C_, C_}));
+        }
+    };
+
+    Tensor<InDataType> in_n_c_hi_wi(f_host_tensor_descriptor(N, C, Hi, Wi, InLayout{}));
+    Tensor<WeiDataType> wei_k_c_y_x_host_result(f_host_tensor_descriptor(K, C, Y, X, WeiLayout{}));
+    Tensor<WeiDataType> wei_k_c_y_x_device_result(
+        f_host_tensor_descriptor(K, C, Y, X, WeiLayout{}));
+    Tensor<OutDataType> out_n_k_ho_wo(f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
+
+    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:
+        in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
+        out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
+        break;
+    default:
+        in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_1<InDataType>{1});
+        out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_1<OutDataType>{1});
+    }
+    wei_k_c_y_x_device_result.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{0});
+
+    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());
+    wei_device_buf.ToDevice(wei_k_c_y_x_device_result.mData.data());
+
+    // do GEMM
+    auto conv     = DeviceConvWrWInstance{};
+    auto invoker  = conv.MakeInvoker();
+    auto argument = conv.MakeArgument(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
+                                      static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
+                                      static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
+                                      N,
+                                      K,
+                                      C,
+                                      std::vector<ck::index_t>{{Hi, Wi}},
+                                      std::vector<ck::index_t>{{Y, X}},
+                                      std::vector<ck::index_t>{{Ho, Wo}},
+                                      conv_filter_strides,
+                                      conv_filter_dilations,
+                                      input_left_pads,
+                                      input_right_pads,
+                                      InElementOp{},
+                                      WeiElementOp{},
+                                      OutElementOp{},
+                                      split_k);
+
+    if(!conv.IsSupportedArgument(argument))
+    {
+        std::cout << "wrong! device_conv with the specified compilation parameters does "
+                     "not support this Conv problem"
+                  << std::endl;
+        return 1;
+    }
+
+    float ave_time = invoker.Run(argument, nrepeat);
+
+    std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;
+
+    std::size_t num_btype = sizeof(InDataType) * (N * C * Hi * Wi) +
+                            sizeof(WeiDataType) * (K * C * Y * X) +
+                            sizeof(OutDataType) * (N * K * Ho * Wo);
+
+    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;
+
+    if(do_verification)
+    {
+        auto ref_conv    = ReferenceConvWrwInstance{};
+        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,
+                                                  conv_filter_strides,
+                                                  conv_filter_dilations,
+                                                  input_left_pads,
+                                                  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;
+        }
+        check_error(wei_k_c_y_x_host_result, wei_k_c_y_x_device_result);
+    }
+}

example/CMakeLists.txt

@@ -24,6 +24,7 @@ set(CONV2D_FWD_XDL_BIAS_RELU_ADD_SOURCE 6_conv2d_fwd_xdl_bias_relu_add/conv2d_fw
 set(CONV2D_FWD_XDL_BIAS_RELU_ATOMIC_ADD_SOURCE 7_conv2d_fwd_xdl_bias_relu_atomic_add/conv2d_fwd_xdl_bias_relu_atomic_add.cpp)
 set(GEMM_XDL_ALPHA_BETA_SOURCE 8_gemm_xdl_alpha_beta/gemm_xdl_alpha_beta.cpp)
 set(CONV2D_FWD_XDL_INT8_SOURCE 9_conv2d_fwd_xdl_int8/conv2d_fwd_xdl_int8.cpp)
+set(CONV2D_WRW_XDL_SOURCE 13_conv2d_backward_weight_xdl/main.cpp)
 set(CONV3D_FWD_XDL_SOURCE 10_conv3d_fwd_xdl/conv3d_fwd_xdl.cpp)
 set(CONVND_FWD_XDL_SOURCE 11_convnd_fwd_xdl/convnd_fwd_xdl.cpp)
 set(CONV2D_BWD_DATA_XDL_SOURCE 12_conv2d_bwd_data_xdl/conv2d_bwd_data_xdl.cpp)
@@ -39,6 +40,7 @@ add_executable(conv2d_fwd_xdl_bias_relu_add ${CONV2D_FWD_XDL_BIAS_RELU_ADD_SOURC
 add_executable(conv2d_fwd_xdl_bias_relu_atomic_add ${CONV2D_FWD_XDL_BIAS_RELU_ATOMIC_ADD_SOURCE})
 add_executable(gemm_xdl_alpha_beta ${GEMM_XDL_ALPHA_BETA_SOURCE})
 add_executable(conv2d_fwd_xdl_int8 ${CONV2D_FWD_XDL_INT8_SOURCE})
+add_executable(conv2d_wrw_xdl ${CONV2D_WRW_XDL_SOURCE})
 add_executable(conv3d_fwd_xdl ${CONV3D_FWD_XDL_SOURCE})
 add_executable(convnd_fwd_xdl ${CONVND_FWD_XDL_SOURCE})
 add_executable(conv2d_bwd_data_xdl ${CONV2D_BWD_DATA_XDL_SOURCE})
@@ -54,6 +56,7 @@ target_link_libraries(conv2d_fwd_xdl_bias_relu_add PRIVATE host_tensor)
 target_link_libraries(conv2d_fwd_xdl_bias_relu_atomic_add PRIVATE host_tensor)
 target_link_libraries(gemm_xdl_alpha_beta PRIVATE host_tensor)
 target_link_libraries(conv2d_fwd_xdl_int8 PRIVATE host_tensor)
+target_link_libraries(conv2d_wrw_xdl PRIVATE host_tensor)
 target_link_libraries(conv3d_fwd_xdl PRIVATE host_tensor)
 target_link_libraries(convnd_fwd_xdl PRIVATE host_tensor)
 target_link_libraries(conv2d_bwd_data_xdl PRIVATE host_tensor)

reference_operation/include/reference_conv_backward_weight.hpp

+#ifndef REFERENCE_CONV_WRW_HPP
+#define REFERENCE_CONV_WRW_HPP
+
+#include <iostream>
+#include <sstream>
+#include "device_base.hpp"
+#include "host_tensor.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace host {
+
+// out[N, K, Ho, Wo] = in[N, C, Hi, Wi] * wei[K, C, Y, X]
+template <typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation>
+struct ReferenceConvWrw : public device::BaseOperator
+{
+    // Argument
+    struct Argument : public device::BaseArgument
+    {
+        Argument(const Tensor<InDataType>& in_n_c_hi_wi,
+                 Tensor<WeiDataType>& wei_k_c_y_x,
+                 const Tensor<OutDataType>& out_n_k_ho_wo,
+                 std::vector<ck::index_t> conv_filter_strides,
+                 std::vector<ck::index_t> conv_filter_dilations,
+                 std::vector<ck::index_t> input_left_pads,
+                 std::vector<ck::index_t> input_right_pads,
+                 InElementwiseOperation in_element_op,
+                 WeiElementwiseOperation wei_element_op,
+                 OutElementwiseOperation out_element_op)
+            : in_n_c_hi_wi_{in_n_c_hi_wi},
+              wei_k_c_y_x_{wei_k_c_y_x},
+              out_n_k_ho_wo_{out_n_k_ho_wo},
+              conv_strides_{conv_filter_strides},
+              conv_dilations_{conv_filter_dilations},
+              in_left_pads_{input_left_pads},
+              in_right_pads_{input_right_pads},
+              in_element_op_{in_element_op},
+              wei_element_op_{wei_element_op},
+              out_element_op_{out_element_op}
+        {
+        }
+
+        const Tensor<InDataType>& in_n_c_hi_wi_;
+        Tensor<WeiDataType>& wei_k_c_y_x_;
+        const Tensor<OutDataType>& out_n_k_ho_wo_;
+
+        std::vector<index_t> conv_strides_;
+        std::vector<index_t> conv_dilations_;
+        std::vector<index_t> in_left_pads_;
+        std::vector<index_t> in_right_pads_;
+
+        InElementwiseOperation in_element_op_;
+        WeiElementwiseOperation wei_element_op_;
+        OutElementwiseOperation out_element_op_;
+    };
+
+    // Invoker
+    struct Invoker : public device::BaseInvoker
+    {
+        using Argument = ReferenceConvWrw::Argument;
+
+        float Run(const Argument& arg)
+        {
+            constexpr auto I0 = Number<0>{};
+            constexpr auto I1 = Number<1>{};
+            auto f_kcyx       = [&](auto k, auto c, auto y, auto x) {
+                float v_acc = 0;
+                for(int n = 0; n < arg.out_n_k_ho_wo_.mDesc.GetLengths()[0]; ++n)
+                {
+                    for(int ho = 0; ho < arg.out_n_k_ho_wo_.mDesc.GetLengths()[2]; ++ho)
+                    {
+                        int hi = ho * arg.conv_strides_[I0] + y * arg.conv_dilations_[I0] -
+                                 arg.in_left_pads_[I0];
+                        for(int wo = 0; wo < arg.out_n_k_ho_wo_.mDesc.GetLengths()[3]; ++wo)
+                        {
+                            int wi = wo * arg.conv_strides_[I1] + x * arg.conv_dilations_[I1] -
+                                     arg.in_left_pads_[I1];
+                            if(hi >= 0 && hi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[2] && wi >= 0 &&
+                               wi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])
+                            {
+                                float v_out;
+                                float v_in;
+
+                                arg.out_element_op_(
+                                    v_out,
+                                    ck::type_convert<float>(arg.out_n_k_ho_wo_(n, k, ho, wo)));
+                                arg.in_element_op_(
+                                    v_in, ck::type_convert<float>(arg.in_n_c_hi_wi_(n, c, hi, wi)));
+
+                                v_acc += v_out * v_in;
+                            }
+                        }
+                    }
+                }
+                float v_wei;
+
+                arg.wei_element_op_(v_wei, v_acc);
+
+                arg.wei_k_c_y_x_(k, c, y, x) = ck::type_convert<OutDataType>(v_wei);
+            };
+
+            make_ParallelTensorFunctor(f_kcyx,
+                                       arg.wei_k_c_y_x_.mDesc.GetLengths()[0],
+                                       arg.wei_k_c_y_x_.mDesc.GetLengths()[1],
+                                       arg.wei_k_c_y_x_.mDesc.GetLengths()[2],
+                                       arg.wei_k_c_y_x_.mDesc.GetLengths()[3])(
+                std::thread::hardware_concurrency());
+
+            return 0;
+        }
+
+        float Run(const device::BaseArgument* p_arg, int) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg));
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
+
+    static auto MakeArgument(const Tensor<InDataType>& in_n_c_hi_wi,
+                             Tensor<WeiDataType>& wei_k_c_y_x,
+                             const Tensor<OutDataType>& out_n_k_ho_wo,
+                             std::vector<ck::index_t> conv_filter_strides,
+                             std::vector<ck::index_t> conv_filter_dilations,
+                             std::vector<ck::index_t> input_left_pads,
+                             std::vector<ck::index_t> input_right_pads,
+                             InElementwiseOperation in_element_op,
+                             WeiElementwiseOperation wei_element_op,
+                             OutElementwiseOperation out_element_op)
+    {
+        return Argument{in_n_c_hi_wi,
+                        wei_k_c_y_x,
+                        out_n_k_ho_wo,
+                        conv_filter_strides,
+                        conv_filter_dilations,
+                        input_left_pads,
+                        input_right_pads,
+                        in_element_op,
+                        wei_element_op,
+                        out_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "ReferenceConvFwd"
+            << std::endl;
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace host
+} // namespace tensor_operation
+} // namespace ck
+#endif