#ifndef DEVICE_CONV2D_WRW_XDL_C_SHUFFLE_NHWC_KYXC_NHWK_HPP
#define DEVICE_CONV2D_WRW_XDL_C_SHUFFLE_NHWC_KYXC_NHWK_HPP

#include <iostream>
#include <sstream>
#include "batched_gemm_util.hpp"
#include "device.hpp"
#include "device_base.hpp"
#include "device_conv_backward_weight.hpp"
#include "convolution_forward_specialization.hpp"
#include "common_header.hpp"
#include "tensor_layout.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_xdlops_v2r4r2.hpp"
#include "batched_gemm_util.hpp"

namespace ck {
namespace tensor_operation {
namespace device {

// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
template <typename InDataType,
          typename WeiDataType,
          typename OutDataType,
          typename AccDataType,
          typename InElementwiseOperation,
          typename WeiElementwiseOperation,
          typename OutElementwiseOperation,
          index_t NumGemmKPrefetchStage,
          index_t BlockSize,
          index_t MPerBlock,
          index_t NPerBlock,
          index_t K0PerBlock,
          index_t AK1,
          ck::index_t MPerXdl,
          ck::index_t NPerXdl,
          ck::index_t MXdlPerWave,
          ck::index_t NXdlPerWave,
          typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
          typename ABlockTransferThreadClusterArrangeOrder,
          typename ABlockTransferSrcAccessOrder,
          index_t ABlockTransferSrcVectorDim,
          index_t ABlockTransferSrcScalarPerVector,
          index_t ABlockTransferDstScalarPerVector_AK1,
          bool ABlockLdsExtraM,
          typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
          typename BBlockTransferThreadClusterArrangeOrder,
          typename BBlockTransferSrcAccessOrder,
          index_t BBlockTransferSrcVectorDim,
          index_t BBlockTransferSrcScalarPerVector,
          index_t BBlockTransferDstScalarPerVector_BK1,
          bool BBlockLdsExtraN,
          index_t CShuffleMXdlPerWavePerShuffle,
          index_t CShuffleNXdlPerWavePerShuffle,
          typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
          index_t CShuffleBlockTransferScalarPerVector_NPerBlock>
struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
    : public DeviceConvBwdWeight<InElementwiseOperation,
                                 WeiElementwiseOperation,
                                 OutElementwiseOperation>
{
    using DeviceOp =
        DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K;

    using ADataType = OutDataType;
    using BDataType = InDataType;
    using CDataType = WeiDataType;

    using AElementwiseOperation = OutElementwiseOperation;
    using BElementwiseOperation = InElementwiseOperation;
    using CElementwiseOperation = WeiElementwiseOperation;

    // TODO make A/B datatype different
    using ABDataType = InDataType;

    static constexpr auto I0 = Number<0>{};
    static constexpr auto I1 = Number<1>{};
    static constexpr auto I2 = Number<2>{};
    static constexpr auto I3 = Number<3>{};
    static constexpr auto I4 = Number<4>{};
    static constexpr auto I5 = Number<5>{};

    static constexpr auto K1Number     = Number<K1>{};
    static constexpr auto GemmK1Number = K1Number;

    static auto
    MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(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,
                                                    ck::index_t k_batch)
    {
        using namespace ck;

        const index_t Hi = input_spatial_lengths[0];
        const index_t Wi = input_spatial_lengths[1];

        const index_t Ho = output_spatial_lengths[0];
        const index_t Wo = output_spatial_lengths[1];

        const index_t Y = filter_spatial_lengths[0];
        const index_t X = filter_spatial_lengths[1];

        const index_t ConvStrideH = conv_filter_strides[0];
        const index_t ConvStrideW = conv_filter_strides[1];

        const index_t ConvDilationH = conv_filter_dilations[0];
        const index_t ConvDilationW = conv_filter_dilations[1];

        const index_t InLeftPadH = input_left_pads[0];
        const index_t InLeftPadW = input_left_pads[1];

        const index_t InRightPadH = input_right_pads[0];
        const index_t InRightPadW = input_right_pads[1];

        const index_t GemmKTotal     = N * Ho * Wo;
        const index_t GemmM          = K;
        const index_t GemmN          = C * X * Y;
        const index_t GemmAKPerBatch = GemmAK0 * GemmAK1Number;
        const index_t GemmBKPerBatch = GemmBK0 * GemmBK1Number;

        const index_t GemmAK0 =
            math::integer_divide_ceil(GemmKTotal, GemmAK1Number * K0PerBlock * k_batch) *
            K0PerBlock;
        const index_t GemmBK0 =
            math::integer_divide_ceil(GemmKTotal, GemmBK1Number * K0PerBlock * k_batch) *
            K0PerBlock;
        const index_t GemmAKPad = GemmKBatch * GemmAK0 * GemmAK1Number;
        const index_t GemmBKPad = GemmKBatch * GemmBK0 * GemmBK1Number;

        const auto out_gemmk_gemmm_grid_desc =
            make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
        const auto in_n_hi_wi_c_grid_desc =
            make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));

        // A: output tensor
        const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
            out_gemmk_gemmm_grid_desc,
            make_tuple(make_right_pad_transform(GemmKTotal, GemmAKPad - GemmKTotal),
                       make_pass_through_transform(GemmM)),
            make_tuple(Sequence<0>{}, Sequence<1>{}),
            make_tuple(Sequence<0>{}, Sequence<1>{}));

        const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
            out_gemmkpad_gemmm_grid_desc,
            make_tuple(make_unmerge_transform(make_tuple(GemmAK0, GemmAK1Number)),
                       make_pass_through_transform(GemmM)),
            make_tuple(Sequence<0>{}, Sequence<1>{}),
            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

        // B: input tensor
        const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
            in_n_hi_wi_c_grid_desc,
            make_tuple(make_pass_through_transform(N),
                       make_pad_transform(Hi, InLeftPadH, InRightPadH),
                       make_pad_transform(Wi, InLeftPadW, InRightPadW),
                       make_pass_through_transform(C)),
            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));

        const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
            in_n_hip_wip_c_grid_desc,
            make_tuple(
                make_pass_through_transform(N),
                make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
                make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
                make_pass_through_transform(C)),
            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
            make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));

        const auto in_gemmktotal_gemmn_grid_desc =
            transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
                                        make_tuple(make_merge_transform(make_tuple(Y, X, C)),
                                                   make_merge_transform(make_tuple(N, Ho, Wo))),
                                        make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
                                        make_tuple(Sequence<1>{}, Sequence<0>{}));

        const auto in_gemmkpad_gemmn_grid_desc = transform_tensor_descriptor(
            in_gemmktotal_gemmn_grid_desc,
            make_tuple(make_right_pad_transform(GemmKTotal, GemmBKPad - GemmKTotal),
                       make_pass_through_transform(GemmN)),
            make_tuple(Sequence<0>{}, Sequence<1>{}),
            make_tuple(Sequence<0>{}, Sequence<1>{}));

        const auto in_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
            in_gemmkpad_gemmn_grid_desc,
            make_tuple(make_unmerge_transform(make_tuple(GemmBK0, GemmBK1Number)),
                       make_pass_through_transform(GemmN)),
            make_tuple(Sequence<0>{}, Sequence<1>{}),
            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

        // C: weight tensor
        const auto wei_gemmm_gemmn_grid_desc =
            make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C));

        return make_tuple(out_gemmk0_gemmm_gemmk1_grid_desc,
                          in_gemmk0_gemmn_gemmk1_grid_desc,
                          wei_gemmm_gemmn_grid_desc);
    }

    using ABCGridDescs = decltype(MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
        1, 1, 1, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, 1));

    using AGridDesc_K0_M_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I0])>;
    using BGridDesc_K0_N_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I1])>;
    using CGridDesc_M_N     = remove_cvref_t<decltype(ABCGridDescs{}[I2])>;

    // GridwiseGemm
    using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1<
        ADataType, // TODO: distinguish A/B datatype
        GemmAccDataType,
        CShuffleDataType,
        CDataType,
        AElementwiseOperation,
        BElementwiseOperation,
        CElementwiseOperation,
        InMemoryDataOperationEnum::Set,
        AGridDesc_AK0_M_AK1,
        BGridDesc_BK0_N_BK1,
        CGridDesc_M_N,
        NumGemmKPrefetchStage,
        BlockSize,
        MPerBlock,
        NPerBlock,
        KPerBlock,
        AK1,
        BK1,
        MPerXDL,
        NPerXDL,
        MXdlPerWave,
        NXdlPerWave,
        ABlockTransferThreadClusterLengths_AK0_M_AK1,
        ABlockTransferThreadClusterArrangeOrder,
        ABlockTransferSrcAccessOrder,
        ABlockTransferSrcVectorDim,
        ABlockTransferSrcScalarPerVector,
        ABlockTransferDstScalarPerVector_AK1,
        false,
        ABlockLdsExtraM,
        BBlockTransferThreadClusterLengths_BK0_N_BK1,
        BBlockTransferThreadClusterArrangeOrder,
        BBlockTransferSrcAccessOrder,
        BBlockTransferSrcVectorDim,
        BBlockTransferSrcScalarPerVector,
        BBlockTransferDstScalarPerVector_BK1,
        false,
        BBlockLdsExtraN,
        CShuffleMXdlPerWavePerShuffle,
        CShuffleNXdlPerWavePerShuffle,
        CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock>;
    CShuffleBlockTransferScalarPerVector_NPerBlock > ;

    using GridwiseGemmAtomicAdd = GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1<
        ADataType, // TODO: distinguish A/B datatype
        GemmAccDataType,
        CShuffleDataType,
        CDataType,
        AElementwiseOperation,
        BElementwiseOperation,
        CElementwiseOperation,
        InMemoryDataOperationEnum::AtomicAdd,
        AGridDesc_AK0_M_AK1,
        BGridDesc_BK0_N_BK1,
        CGridDesc_M_N,
        NumGemmKPrefetchStage,
        BlockSize,
        MPerBlock,
        NPerBlock,
        KPerBlock,
        AK1,
        BK1,
        MPerXDL,
        NPerXDL,
        MXdlPerWave,
        NXdlPerWave,
        ABlockTransferThreadClusterLengths_AK0_M_AK1,
        ABlockTransferThreadClusterArrangeOrder,
        ABlockTransferSrcAccessOrder,
        ABlockTransferSrcVectorDim,
        ABlockTransferSrcScalarPerVector,
        ABlockTransferDstScalarPerVector_AK1,
        false,
        ABlockLdsExtraM,
        BBlockTransferThreadClusterLengths_BK0_N_BK1,
        BBlockTransferThreadClusterArrangeOrder,
        BBlockTransferSrcAccessOrder,
        BBlockTransferSrcVectorDim,
        BBlockTransferSrcScalarPerVector,
        BBlockTransferDstScalarPerVector_BK1,
        false,
        BBlockLdsExtraN,
        CShuffleMXdlPerWavePerShuffle,
        CShuffleNXdlPerWavePerShuffle,
        CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock>;
    CShuffleBlockTransferScalarPerVector_NPerBlock > ;

    using Block2CTileMap =
        decltype(BatchedGemmUtil::MakeBlock2CTileMap<MPerBlock, NPerBlock>(1, 1, 1));

    struct Argument : public BaseArgument
    {
        Argument(const InDataType* p_in_grid,
                 WeiDataType* p_wei_grid,
                 const OutDataType* p_out_grid,
                 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,
                 ck::index_t M01,
                 ck::index_t N01,
                 InElementwiseOperation in_element_op,
                 WeiElementwiseOperation wei_element_op,
                 OutElementwiseOperation out_element_op,
                 ck::index_t split_k)
            : p_a_grid_{p_out_grid},
              p_b_grid_{p_in_grid},
              p_c_grid_{p_wei_grid},
              a_grid_desc_k0_m_k1_{},
              b_grid_desc_k0_n_k1_{},
              c_grid_desc_m_n_{},
              c_grid_desc_mblock_mperblock_nblock_nperblock_{},
              block_2_ctile_map_{},
              M01_{M01},
              N01_{N01},
              a_element_op_{out_element_op},
              b_element_op_{in_element_op},
              c_element_op_{wei_element_op},
              Conv_N_{N},
              Conv_K_{K},
              Conv_C_{C},
              output_spatial_lengths_{output_spatial_lengths},
              filter_spatial_lengths_{filter_spatial_lengths},
              conv_filter_strides_{conv_filter_strides},
              input_left_pads_{input_left_pads},
              input_right_pads_{input_right_pads},
              k_batch_{split_k}
        {
            const auto descs =
                DeviceOp::MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(N,
                                                                          K,
                                                                          C,
                                                                          input_spatial_lengths,
                                                                          filter_spatial_lengths,
                                                                          output_spatial_lengths,
                                                                          conv_filter_strides,
                                                                          conv_filter_dilations,
                                                                          input_left_pads,
                                                                          input_right_pads,
                                                                          k_batch_);

            a_grid_desc_k0_m_k1_ = descs[I0];
            b_grid_desc_k0_n_k1_ = descs[I1];
            c_grid_desc_m_n_     = descs[I2];

            if(GridwiseGemm::CheckValidity(
                   a_grid_desc_k0_m_k1_, b_grid_desc_k0_n_k1_, c_grid_desc_m_n_, M01_, N01_))
            {
                c_grid_desc_mblock_mperblock_nblock_nperblock_ =
                    GridwiseGemm::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
                        c_grid_desc_m_n_);

                block_2_ctile_map_ = BatchedGemmUtil::MakeBlock2CTileMap<MPerBlock, NPerBlock>(
                    k_batch_,
                    c_grid_desc_m_n_.GetLength(I0),
                    c_grid_desc_m_n_.GetLength(I1),
                    M01_,
                    N01_);
            }
        }

        const ADataType* p_a_grid_;
        const BDataType* p_b_grid_;
        CDataType* p_c_grid_;
        index_t k_batch_;
        AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1_;
        BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1_;
        CGridDesc_M_N c_grid_desc_m_n_;
        typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
            c_grid_desc_mblock_mperblock_nblock_nperblock_;
        BatchedGemmUtil::ComputePtrOffsetOfStridedBatch compute_ptr_offset_of_batch_;
        Block2CTileMap block_2_ctile_map_;
        index_t M01_;
        index_t N01_;
        InElementwiseOperation a_element_op_;
        OutElementwiseOperation b_element_op_;
        WeiElementwiseOperation c_element_op_;
        // for checking IsSupportedArgument()
        index_t Conv_N_;
        index_t Conv_K_;
        index_t Conv_C_;
        std::vector<index_t> output_spatial_lengths_;
        std::vector<index_t> filter_spatial_lengths_;
        std::vector<index_t> conv_filter_strides_;
        std::vector<index_t> input_left_pads_;
        std::vector<index_t> input_right_pads_;
        index_t k_batch_;
    };

    // Invoker
    struct Invoker : public BaseInvoker
    {
        using Argument = DeviceOp::Argument;

        void ShowInfo(const Argument& arg)
        {
            std::cout << "k_batch = " << arg.BatchCount_ << "\n";
            std::cout << "arg.a_grid_desc_k0_m_k1_{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
                      << ", " << arg.a_grid_desc_k0_m_k1_.GetLength(I1) << ", "
                      << arg.a_grid_desc_k0_m_k1_.GetLength(I2) << "}" << std::endl;

            std::cout << "arg.b_grid_desc_k0_n_k1_{" << arg.b_grid_desc_k0_n_k1_.GetLength(I0)
                      << ", " << arg.b_grid_desc_k0_n_k1_.GetLength(I1) << ", "
                      << arg.b_grid_desc_k0_n_k1_.GetLength(I2) << "}" << std::endl;

            std::cout << "arg.c_grid_desc_m_n_{ " << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
                      << arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
        }

        float Run(const Argument& arg, int nrepeat = 1)
        {
            ShowInfo(arg);
            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
                                            arg.b_grid_desc_k0_n_k1_,
                                            arg.c_grid_desc_m_n_,
                                            arg.M01_,
                                            arg.N01_))
            {
                throw std::runtime_error(
                    "wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v3r1 has invalid setting");
            }
            const auto k_batch = arg.k_batch_;
            const index_t grid_size =
                GridwiseGemm::CalculateGridSize(arg.c_grid_desc_m_n_) * k_batch;

            const auto K0 = arg.a_grid_desc_ak0_m_ak1_.GetLength(I0);

            const bool has_main_k0_block_loop = GridwiseGemm::CalculateHasMainK0BlockLoop(K0);

            float ave_time = 0;

            const auto Run = [&](const auto& kernel) {
                if(nrepeat > 0)
                {
                    ave_time =
                        launch_and_time_kernel(kernel,
                                               nrepeat,
                                               dim3(grid_size),
                                               dim3(BlockSize),
                                               0,
                                               arg.p_a_grid_,
                                               arg.p_b_grid_,
                                               arg.p_c_grid_,
                                               arg.a_grid_desc_k0_m_k1_,
                                               arg.b_grid_desc_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_);
                }

                if(k_batch > 1 || nrepeat <= 0)
                {
                    hipGetErrorString(hipMemset(
                        arg.p_c_grid_,
                        0,
                        arg.c_grid_desc_mblock_mperblock_nblock_nperblock_.GetElementSpaceSize() *
                            sizeof(CDataType)));

                    launch_kernel(kernel,
                                  dim3(grid_size),
                                  dim3(BlockSize),
                                  0,
                                  arg.p_a_grid_,
                                  arg.p_b_grid_,
                                  arg.p_c_grid_,
                                  arg.a_grid_desc_k0_m_k1_,
                                  arg.b_grid_desc_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_);
                }
            };

            if(has_main_k0_block_loop)
            {
                if(k_batch == 1)
                {
                    const auto kernel = kernel_batched_gemm_xdl_cshuffle_v1<
                        GridwiseGemm,
                        ADataType, // TODO: distiguish A/B datatype
                        CDataType,
                        AElementwiseOperation,
                        BElementwiseOperation,
                        CElementwiseOperation,
                        DeviceOp::AGridDesc_AK0_M_AK1,
                        DeviceOp::BGridDesc_BK0_N_BK1,
                        typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
                        ComputePtrOffsetOfStridedBatch,
                        Block2CTileMap,
                        true>;

                    Run(kernel);
                }
                else
                {
                    const auto kernel = kernel_batched_gemm_xdl_cshuffle_v1<
                        GridwiseGemmAtomicAdd,
                        ADataType, // TODO: distiguish A/B datatype
                        CDataType,
                        AElementwiseOperation,
                        BElementwiseOperation,
                        CElementwiseOperation,
                        DeviceOp::AGridDesc_AK0_M_AK1,
                        DeviceOp::BGridDesc_BK0_N_BK1,
                        typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
                        ComputePtrOffsetOfStridedBatch,
                        Block2CTileMap,
                        true>;

                    Run(kernel);
                }
            }
            else
            {
                if(k_batch == 1)
                {
                    const auto kernel = kernel_batched_gemm_xdl_cshuffle_v1<
                        GridwiseGemm,
                        ADataType, // TODO: distiguish A/B datatype
                        CDataType,
                        AElementwiseOperation,
                        BElementwiseOperation,
                        CElementwiseOperation,
                        DeviceOp::AGridDesc_AK0_M_AK1,
                        DeviceOp::BGridDesc_BK0_N_BK1,
                        typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
                        ComputePtrOffsetOfStridedBatch,
                        Block2CTileMap,
                        false>;

                    Run(kernel);
                }
                else
                {
                    const auto kernel = kernel_batched_gemm_xdl_cshuffle_v1<
                        GridwiseGemmAtomicAdd,
                        ADataType, // TODO: distiguish A/B datatype
                        CDataType,
                        AElementwiseOperation,
                        BElementwiseOperation,
                        CElementwiseOperation,
                        DeviceOp::AGridDesc_AK0_M_AK1,
                        DeviceOp::BGridDesc_BK0_N_BK1,
                        typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
                        ComputePtrOffsetOfStridedBatch,
                        Block2CTileMap,
                        false>;

                    Run(kernel);
                }
            }

            return ave_time;
        }

        float Run(const BaseArgument* p_arg, int nrepeat = 1) override
        {
            return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
        }
    };

    static constexpr bool IsValidCompilationParameter()
    {
        // TODO: properly implement this check
        return true;
    }

    static bool IsSupportedArgument(const Argument& arg)
    {
        // vector load A/B matrix from global memory
        if(!(ABlockTransferSrcVectorDim == 2 && BBlockTransferSrcVectorDim == 2 &&
             arg.Conv_K_ % ABlockTransferSrcScalarPerVector == 0 &&
             arg.Conv_C_ % BBlockTransferSrcScalarPerVector == 0))
        {
            return false;
        }

        // vector store C matrix into global memory
        if(!(arg.Conv_C_ % CShuffleBlockTransferScalarPerVector_NPerBlock == 0))
        {
            return false;
        }

        // Gridwise GEMM size
        return GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
                                           arg.b_grid_desc_k0_n_k1_,
                                           arg.c_grid_desc_m_n_,
                                           arg.M01_,
                                           arg.N01_);
    }

    bool IsSupportedArgument(const BaseArgument* p_arg) override
    {
        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
    }

    static auto MakeArgument(const InDataType* p_in_grid,
                             WeiDataType* p_wei_grid,
                             const OutDataType* p_out_grid,
                             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)
    {
        return Argument{p_in_grid,
                        p_wei_grid,
                        p_out_grid,
                        N,
                        K,
                        C,
                        input_spatial_lengths,
                        filter_spatial_lengths,
                        output_spatial_lengths,
                        conv_filter_strides,
                        conv_filter_dilations,
                        input_left_pads,
                        input_right_pads,
                        1,
                        1,
                        in_element_op,
                        wei_element_op,
                        out_element_op,
                        split_k};
    }

    static auto MakeInvoker() { return Invoker{}; }

    std::unique_ptr<BaseArgument>
    MakeArgumentPointer(const void* p_in_grid,
                        void* p_wei_grid,
                        const void* p_out_grid,
                        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) override
    {
        return std::make_unique<Argument>(static_cast<const InDataType*>(p_in_grid),
                                          static_cast<WeiDataType*>(p_wei_grid),
                                          static_cast<const OutDataType*>(p_out_grid),
                                          N,
                                          K,
                                          C,
                                          input_spatial_lengths,
                                          filter_spatial_lengths,
                                          output_spatial_lengths,
                                          conv_filter_strides,
                                          conv_filter_dilations,
                                          input_left_pads,
                                          input_right_pads,
                                          1,
                                          1,
                                          in_element_op,
                                          wei_element_op,
                                          out_element_op,
                                          split_k);
    }

    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
    {
        return std::make_unique<Invoker>(Invoker{});
    }

    std::string GetTypeString() const override
    {
        auto str = std::stringstream();

        // clang-format off
        str << "DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K"
            << "<"
            << BlockSize << ", "
            << MPerBlock << ", "
            << NPerBlock << ", "
            << K0PerBlock
            << ">";
        // clang-format on

        return str.str();
    }
};

} // namespace device
} // namespace tensor_operation
} // namespace ck
#endif
