// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.

#pragma once

#include <functional>
#include <iostream>
#include <iterator>
#include <numeric>
#include <sstream>

#include "ck/utility/common_header.hpp"
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_conv_fwd.hpp"
#include "ck/tensor_operation/gpu/device/convolution_forward_specialization.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_cshuffle.hpp"
#include "ck/device_utility/device_prop.hpp"
#include "ck/device_utility/kernel_launch.hpp"

namespace ck {
namespace tensor_operation {
namespace device {

namespace {

template <typename GridwiseGemm,
          typename FloatAB,
          typename FloatDsPointer,
          typename FloatE,
          typename AElementwiseOperation,
          typename BElementwiseOperation,
          typename CDEElementwiseOperation,
          typename AGridDesc_AK0_M_AK1,
          typename BGridDesc_BK0_N_BK1,
          typename DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
          typename EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
          typename Block2ETileMap,
          bool HasMainKBlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
        kernel_gemm_multiple_d_xdl_cshuffle(const FloatAB* __restrict__ p_a_grid,
                                            const FloatAB* __restrict__ p_b_grid,
                                            FloatDsPointer p_ds_grid,
                                            FloatE* __restrict__ p_e_grid,
                                            const AElementwiseOperation a_element_op,
                                            const BElementwiseOperation b_element_op,
                                            const CDEElementwiseOperation cde_element_op,
                                            const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
                                            const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
                                            const DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
                                                ds_grid_desc_mblock_mperblock_nblock_nperblock,
                                            const EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
                                                e_grid_desc_mblock_mperblock_nblock_nperblock,
                                            const Block2ETileMap block_2_etile_map)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];

    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
                                                  p_b_grid,
                                                  p_ds_grid,
                                                  p_e_grid,
                                                  p_shared,
                                                  a_element_op,
                                                  b_element_op,
                                                  cde_element_op,
                                                  a_grid_desc_ak0_m_ak1,
                                                  b_grid_desc_bk0_n_bk1,
                                                  ds_grid_desc_mblock_mperblock_nblock_nperblock,
                                                  e_grid_desc_mblock_mperblock_nblock_nperblock,
                                                  block_2_etile_map);
#else
    ignore               = p_a_grid;
    ignore               = p_b_grid;
    ignore               = p_ds_grid;
    ignore               = p_e_grid;
    ignore               = a_element_op;
    ignore               = b_element_op;
    ignore               = cde_element_op;
    ignore               = a_grid_desc_ak0_m_ak1;
    ignore               = b_grid_desc_bk0_n_bk1;
    ignore               = ds_grid_desc_mblock_mperblock_nblock_nperblock;
    ignore               = e_grid_desc_mblock_mperblock_nblock_nperblock;
    ignore               = block_2_etile_map;
#endif
}
} // namespace

//
// @brief      Device Convolution operation.
//
// Supports:
//  @li         Forward convolution with up to 3 spatial dimentions
//  @li         Input tensor in NWC data format
//  @li         Weight tensor in KXC data format
//  @li         Output tensor in NWK data format
//
// 1D:
// out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C]
// 2D:
// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
// 3D:
// out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C]
//
template <index_t NDimSpatial,
          typename ADataType,
          typename BDataType,
          typename AccDataType,
          typename CShuffleDataType,
          typename DsDataType,
          typename EDataType,
          typename AElementwiseOperation,
          typename BElementwiseOperation,
          typename CDEElementwiseOperation,
          ConvolutionForwardSpecialization ConvForwardSpecialization,
          GemmSpecialization GemmSpec,
          index_t NumGemmKPrefetchStage,
          index_t BlockSize,
          index_t MPerBlock,
          index_t NPerBlock,
          index_t KPerBlock,
          index_t K1,
          index_t MPerXDL,
          index_t NPerXDL,
          index_t MXdlPerWave,
          index_t NXdlPerWave,
          typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
          typename ABlockTransferThreadClusterArrangeOrder,
          typename ABlockTransferSrcAccessOrder,
          index_t ABlockTransferSrcVectorDim,
          index_t ABlockTransferSrcScalarPerVector,
          index_t ABlockTransferDstScalarPerVector_AK1,
          index_t ABlockLdsExtraM,
          typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
          typename BBlockTransferThreadClusterArrangeOrder,
          typename BBlockTransferSrcAccessOrder,
          index_t BBlockTransferSrcVectorDim,
          index_t BBlockTransferSrcScalarPerVector,
          index_t BBlockTransferDstScalarPerVector_BK1,
          index_t BBlockLdsExtraN,
          index_t CShuffleMXdlPerWavePerShuffle,
          index_t CShuffleNXdlPerWavePerShuffle,
          typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
          index_t CDEBlockTransferScalarPerVector_NPerBlock,
          LoopScheduler LoopSched = make_default_loop_scheduler()>
struct DeviceConvNdFwdMultipleD_NwcKxcNwk_Xdl_CShuffle
    : public DeviceConvFwd<NDimSpatial,
                           ck::tuple_element_t<NDimSpatial - 1,
                                               ck::Tuple<ck::tensor_layout::convolution::NWC,
                                                         ck::tensor_layout::convolution::NHWC,
                                                         ck::tensor_layout::convolution::NDHWC>>,
                           ck::tuple_element_t<NDimSpatial - 1,
                                               ck::Tuple<ck::tensor_layout::convolution::KXC,
                                                         ck::tensor_layout::convolution::KYXC,
                                                         ck::tensor_layout::convolution::KZYXC>>,
                           ck::tuple_element_t<NDimSpatial - 1,
                                               ck::Tuple<ck::tensor_layout::convolution::NWK,
                                                         ck::tensor_layout::convolution::NHWK,
                                                         ck::tensor_layout::convolution::NDHWK>>,
                           ADataType,
                           BDataType,
                           EDataType,
                           AElementwiseOperation,
                           BElementwiseOperation,
                           CDEElementwiseOperation>
{

    using DeviceOp = DeviceConvNdFwdMultipleD_NwcKxcNwk_Xdl_CShuffle;

    static constexpr index_t NumDTensor = DsDataType::Size();

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

    static constexpr auto matrix_padder =
        MatrixPadder<GemmSpec, index_t, index_t, index_t>{MPerBlock, NPerBlock, KPerBlock};

    static auto GetWeightTensorDescriptor(index_t GemmNRaw, index_t GemmKRaw)
    {
        const auto wei_k_yxc_grid_desc =
            make_naive_tensor_descriptor_packed(make_tuple(GemmNRaw, GemmKRaw));

        const auto wei_gemmn_gemmk_grid_desc =
            matrix_padder.PadBDescriptor_N_K(wei_k_yxc_grid_desc);

        const auto GemmN = wei_gemmn_gemmk_grid_desc.GetLength(I0);
        const auto GemmK = wei_gemmn_gemmk_grid_desc.GetLength(I1);

        const index_t GemmK0 = GemmK / GemmK1Number;

        // wei_gemmk0_gemmn_gemmk1_grid_desc
        return transform_tensor_descriptor(
            wei_gemmn_gemmk_grid_desc,
            make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
                       make_pass_through_transform(GemmN)),
            make_tuple(Sequence<1>{}, Sequence<0>{}),
            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
    }

    static auto GetOutputTensorDescriptor(index_t GemmMRaw, index_t GemmN)
    {
        const index_t GemmM = math::integer_least_multiple(GemmMRaw, MPerBlock);

        const auto out_gemmmraw_gemmn_grid_desc =
            make_naive_tensor_descriptor_packed(make_tuple(GemmM, GemmN));

        const auto out_gemmm_gemmn_grid_desc =
            matrix_padder.PadCDescriptor_M_N(out_gemmmraw_gemmn_grid_desc);

        return out_gemmm_gemmn_grid_desc;
    }

    template <index_t NDim, typename std::enable_if<NDim == 1, bool>::type = false>
    static auto GetInputTensorDescriptor(index_t N,
                                         index_t C,
                                         index_t GemmMRaw,
                                         index_t GemmKRaw,
                                         const std::vector<index_t>& input_spatial_lengths,
                                         const std::vector<index_t>& filter_spatial_lengths,
                                         const std::vector<index_t>& output_spatial_lengths,
                                         const std::vector<index_t>& conv_filter_strides,
                                         const std::vector<index_t>& conv_filter_dilations,
                                         const std::vector<index_t>& input_left_pads,
                                         const std::vector<index_t>& input_right_pads)
    {
        const index_t Wi          = input_spatial_lengths[0];
        const index_t Wo          = output_spatial_lengths[0];
        const index_t ConvStrideW = conv_filter_strides[0];

        if constexpr(ConvForwardSpecialization ==
                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
        {
            const auto in_gemmmraw_gemmk_grid_desc =
                make_naive_tensor_descriptor_packed(make_tuple(GemmMRaw, GemmKRaw));

            const auto in_gemmm_gemmk_grid_desc =
                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmk_grid_desc);

            const auto GemmM = in_gemmm_gemmk_grid_desc.GetLength(I0);
            const auto GemmK = in_gemmm_gemmk_grid_desc.GetLength(I1);

            const auto GemmK0 = GemmK / GemmK1Number;

            // in_gemmk0_gemmm_gemmk1_grid_desc
            return transform_tensor_descriptor(
                in_gemmm_gemmk_grid_desc,
                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
                           make_pass_through_transform(GemmM)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
        }
        else if constexpr(ConvForwardSpecialization ==
                          ConvolutionForwardSpecialization::Filter1x1Pad0)
        {
            const auto in_n_wi_c_grid_desc =
                make_naive_tensor_descriptor_packed(make_tuple(N, Wi, C));

            const auto in_n_wo_c_grid_desc = transform_tensor_descriptor(
                in_n_wi_c_grid_desc,
                make_tuple(make_pass_through_transform(N),
                           make_embed_transform(make_tuple(Wo), make_tuple(ConvStrideW)),
                           make_pass_through_transform(C)),
                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));

            const auto in_gemmmraw_gemmkraw_grid_desc = transform_tensor_descriptor(
                in_n_wo_c_grid_desc,
                make_tuple(make_merge_transform(make_tuple(N, Wo)), make_pass_through_transform(C)),
                make_tuple(Sequence<0, 1>{}, Sequence<2>{}),
                make_tuple(Sequence<0>{}, Sequence<1>{}));

            const auto in_gemmm_gemmk_grid_desc =
                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmkraw_grid_desc);

            const auto GemmM = in_gemmm_gemmk_grid_desc.GetLength(I0);
            const auto GemmK = in_gemmm_gemmk_grid_desc.GetLength(I1);

            const auto GemmK0 = GemmK / GemmK1Number;

            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
                in_gemmm_gemmk_grid_desc,
                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
                           make_pass_through_transform(GemmM)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return in_gemmk0_gemmm_gemmk1_grid_desc;
        }
        else
        {
            const index_t X             = filter_spatial_lengths[0];
            const index_t ConvDilationW = conv_filter_dilations[0];
            const index_t InLeftPadW    = input_left_pads[0];
            const index_t InRightPadW   = input_right_pads[0];

            const auto in_n_wi_c_grid_desc =
                make_naive_tensor_descriptor_packed(make_tuple(N, Wi, C));

            const auto in_n_wip_c_grid_desc = transform_tensor_descriptor(
                in_n_wi_c_grid_desc,
                make_tuple(make_pass_through_transform(N),
                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
                           make_pass_through_transform(C)),
                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));

            const auto in_n_x_wo_c_grid_desc = transform_tensor_descriptor(
                in_n_wip_c_grid_desc,
                make_tuple(
                    make_pass_through_transform(N),
                    make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
                    make_pass_through_transform(C)),
                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));

            const auto in_gemmmraw_gemmk_grid_desc =
                transform_tensor_descriptor(in_n_x_wo_c_grid_desc,
                                            make_tuple(make_merge_transform(make_tuple(N, Wo)),
                                                       make_merge_transform(make_tuple(X, C))),
                                            make_tuple(Sequence<0, 2>{}, Sequence<1, 3>{}),
                                            make_tuple(Sequence<0>{}, Sequence<1>{}));

            const auto in_gemmm_gemmk_grid_desc =
                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmk_grid_desc);

            const auto GemmM = in_gemmm_gemmk_grid_desc.GetLength(I0);
            const auto GemmK = in_gemmm_gemmk_grid_desc.GetLength(I1);

            const auto GemmK0 = GemmK / GemmK1Number;

            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
                in_gemmm_gemmk_grid_desc,
                make_tuple(make_pass_through_transform(GemmM),
                           make_unmerge_transform(make_tuple(GemmK0, GemmK1Number))),
                make_tuple(Sequence<0>{}, Sequence<1>{}),
                make_tuple(Sequence<1>{}, Sequence<0, 2>{}));

            return in_gemmk0_gemmm_gemmk1_grid_desc;
        }
    }

    template <index_t NDim, typename std::enable_if<NDim == 2, bool>::type = false>
    static auto GetInputTensorDescriptor(index_t N,
                                         index_t C,
                                         index_t GemmMRaw,
                                         index_t GemmKRaw,
                                         const std::vector<index_t>& input_spatial_lengths,
                                         const std::vector<index_t>& filter_spatial_lengths,
                                         const std::vector<index_t>& output_spatial_lengths,
                                         const std::vector<index_t>& conv_filter_strides,
                                         const std::vector<index_t>& conv_filter_dilations,
                                         const std::vector<index_t>& input_left_pads,
                                         const std::vector<index_t>& input_right_pads)
    {
        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 ConvStrideH = conv_filter_strides[0];
        const index_t ConvStrideW = conv_filter_strides[1];

        if constexpr(ConvForwardSpecialization ==
                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
        {
            const auto in_gemmmraw_gemmkraw_grid_desc =
                make_naive_tensor_descriptor_packed(make_tuple(GemmMRaw, GemmKRaw));

            const auto in_gemmm_gemmk_grid_desc =
                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmkraw_grid_desc);

            const auto GemmM = in_gemmm_gemmk_grid_desc.GetLength(I0);
            const auto GemmK = in_gemmm_gemmk_grid_desc.GetLength(I1);

            const auto GemmK0 = GemmK / GemmK1Number;

            // in_gemmk0_gemmm_gemmk1_grid_desc
            return transform_tensor_descriptor(
                in_gemmm_gemmk_grid_desc,
                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
                           make_pass_through_transform(GemmM)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
        }
        else if constexpr(ConvForwardSpecialization ==
                          ConvolutionForwardSpecialization::Filter1x1Pad0)
        {
            const auto in_n_hi_wi_c_grid_desc =
                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));

            const auto in_n_ho_wo_c_grid_desc = transform_tensor_descriptor(
                in_n_hi_wi_c_grid_desc,
                make_tuple(make_pass_through_transform(N),
                           make_embed_transform(make_tuple(Ho), make_tuple(ConvStrideH)),
                           make_embed_transform(make_tuple(Wo), make_tuple(ConvStrideW)),
                           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_gemmmraw_gemmk_grid_desc =
                transform_tensor_descriptor(in_n_ho_wo_c_grid_desc,
                                            make_tuple(make_merge_transform(make_tuple(N, Ho, Wo)),
                                                       make_pass_through_transform(C)),
                                            make_tuple(Sequence<0, 1, 2>{}, Sequence<3>{}),
                                            make_tuple(Sequence<0>{}, Sequence<1>{}));

            const auto in_gemmm_gemmk_grid_desc =
                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmk_grid_desc);

            const auto GemmM = in_gemmm_gemmk_grid_desc.GetLength(I0);
            const auto GemmK = in_gemmm_gemmk_grid_desc.GetLength(I1);

            const auto GemmK0 = GemmK / GemmK1Number;

            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
                in_gemmm_gemmk_grid_desc,
                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
                           make_pass_through_transform(GemmM)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return in_gemmk0_gemmm_gemmk1_grid_desc;
        }
        else
        {
            const index_t Y = filter_spatial_lengths[0];
            const index_t X = filter_spatial_lengths[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 auto in_n_hi_wi_c_grid_desc =
                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));

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

            const auto in_gemmm_gemmk_grid_desc =
                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmk_grid_desc);

            const auto GemmM = in_gemmm_gemmk_grid_desc.GetLength(I0);
            const auto GemmK = in_gemmm_gemmk_grid_desc.GetLength(I1);

            const auto GemmK0 = GemmK / GemmK1Number;

            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
                in_gemmm_gemmk_grid_desc,
                make_tuple(make_pass_through_transform(GemmM),
                           make_unmerge_transform(make_tuple(GemmK0, GemmK1Number))),
                make_tuple(Sequence<0>{}, Sequence<1>{}),
                make_tuple(Sequence<1>{}, Sequence<0, 2>{}));

            return in_gemmk0_gemmm_gemmk1_grid_desc;
        }
    }

    template <index_t NDim, typename std::enable_if<NDim == 3, bool>::type = false>
    static auto GetInputTensorDescriptor(index_t N,
                                         index_t C,
                                         index_t GemmMRaw,
                                         index_t GemmKRaw,
                                         const std::vector<index_t>& input_spatial_lengths,
                                         const std::vector<index_t>& filter_spatial_lengths,
                                         const std::vector<index_t>& output_spatial_lengths,
                                         const std::vector<index_t>& conv_filter_strides,
                                         const std::vector<index_t>& conv_filter_dilations,
                                         const std::vector<index_t>& input_left_pads,
                                         const std::vector<index_t>& input_right_pads)
    {
        const index_t Di = input_spatial_lengths[0];
        const index_t Hi = input_spatial_lengths[1];
        const index_t Wi = input_spatial_lengths[2];

        const index_t Do = output_spatial_lengths[0];
        const index_t Ho = output_spatial_lengths[1];
        const index_t Wo = output_spatial_lengths[2];

        const index_t ConvStrideD = conv_filter_strides[0];
        const index_t ConvStrideH = conv_filter_strides[1];
        const index_t ConvStrideW = conv_filter_strides[2];

        if constexpr(ConvForwardSpecialization ==
                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
        {
            const auto in_gemmmraw_gemmkraw_grid_desc =
                make_naive_tensor_descriptor_packed(make_tuple(GemmMRaw, GemmKRaw));

            const auto in_gemmm_gemmk_grid_desc =
                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmkraw_grid_desc);

            const auto GemmM = in_gemmm_gemmk_grid_desc.GetLength(I0);
            const auto GemmK = in_gemmm_gemmk_grid_desc.GetLength(I1);

            const auto GemmK0 = GemmK / GemmK1Number;

            // in_gemmk0_gemmm_gemmk1_grid_desc
            return transform_tensor_descriptor(
                in_gemmm_gemmk_grid_desc,
                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
                           make_pass_through_transform(GemmM)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
        }
        else if constexpr(ConvForwardSpecialization ==
                          ConvolutionForwardSpecialization::Filter1x1Pad0)
        {
            const auto in_n_di_hi_wi_c_grid_desc =
                make_naive_tensor_descriptor_packed(make_tuple(N, Di, Hi, Wi, C));

            const auto in_n_do_ho_wo_c_grid_desc = transform_tensor_descriptor(
                in_n_di_hi_wi_c_grid_desc,
                make_tuple(make_pass_through_transform(N),
                           make_embed_transform(make_tuple(Do), make_tuple(ConvStrideD)),
                           make_embed_transform(make_tuple(Ho), make_tuple(ConvStrideH)),
                           make_embed_transform(make_tuple(Wo), make_tuple(ConvStrideW)),
                           make_pass_through_transform(C)),
                make_tuple(
                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
                make_tuple(
                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));

            const auto in_gemmmraw_gemmkraw_grid_desc = transform_tensor_descriptor(
                in_n_do_ho_wo_c_grid_desc,
                make_tuple(make_merge_transform(make_tuple(N, Do, Ho, Wo)),
                           make_pass_through_transform(C)),
                make_tuple(Sequence<0, 1, 2, 3>{}, Sequence<4>{}),
                make_tuple(Sequence<0>{}, Sequence<1>{}));

            const auto in_gemmm_gemmk_grid_desc =
                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmkraw_grid_desc);

            const auto GemmM = in_gemmm_gemmk_grid_desc.GetLength(I0);
            const auto GemmK = in_gemmm_gemmk_grid_desc.GetLength(I1);

            const auto GemmK0 = GemmK / GemmK1Number;

            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
                in_gemmm_gemmk_grid_desc,
                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
                           make_pass_through_transform(GemmM)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return in_gemmk0_gemmm_gemmk1_grid_desc;
        }
        else
        {
            const index_t Z = filter_spatial_lengths[0];
            const index_t Y = filter_spatial_lengths[1];
            const index_t X = filter_spatial_lengths[2];

            const index_t ConvDilationD = conv_filter_dilations[0];
            const index_t ConvDilationH = conv_filter_dilations[1];
            const index_t ConvDilationW = conv_filter_dilations[2];

            const index_t InLeftPadD = input_left_pads[0];
            const index_t InLeftPadH = input_left_pads[1];
            const index_t InLeftPadW = input_left_pads[2];

            const index_t InRightPadD = input_right_pads[0];
            const index_t InRightPadH = input_right_pads[1];
            const index_t InRightPadW = input_right_pads[2];

            const auto in_n_di_hi_wi_c_grid_desc =
                make_naive_tensor_descriptor_packed(make_tuple(N, Di, Hi, Wi, C));

            const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
                in_n_di_hi_wi_c_grid_desc,
                make_tuple(make_pass_through_transform(N),
                           make_pad_transform(Di, InLeftPadD, InRightPadD),
                           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>{}, Sequence<4>{}),
                make_tuple(
                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));

            const auto in_n_z_do_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(Z, Do), make_tuple(ConvDilationD, ConvStrideD)),
                    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>{}, Sequence<4>{}),
                make_tuple(Sequence<0>{},
                           Sequence<1, 2>{},
                           Sequence<3, 4>{},
                           Sequence<5, 6>{},
                           Sequence<7>{}));

            const auto in_gemmmraw_gemmkraw_grid_desc = transform_tensor_descriptor(
                in_n_z_do_y_ho_x_wo_c_grid_desc,
                make_tuple(make_merge_transform(make_tuple(N, Do, Ho, Wo)),
                           make_merge_transform(make_tuple(Z, Y, X, C))),
                make_tuple(Sequence<0, 2, 4, 6>{}, Sequence<1, 3, 5, 7>{}),
                make_tuple(Sequence<0>{}, Sequence<1>{}));

            const auto in_gemmm_gemmk_grid_desc =
                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmkraw_grid_desc);

            const auto GemmM = in_gemmm_gemmk_grid_desc.GetLength(I0);
            const auto GemmK = in_gemmm_gemmk_grid_desc.GetLength(I1);

            const auto GemmK0 = GemmK / GemmK1Number;

            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
                in_gemmm_gemmk_grid_desc,
                make_tuple(make_pass_through_transform(GemmM),
                           make_unmerge_transform(make_tuple(GemmK0, GemmK1Number))),
                make_tuple(Sequence<0>{}, Sequence<1>{}),
                make_tuple(Sequence<1>{}, Sequence<0, 2>{}));

            return in_gemmk0_gemmm_gemmk1_grid_desc;
        }
    }

    static index_t GetGemmMRaw(index_t N, const std::vector<index_t>& output_spatial_lengths)
    {
        return N * std::accumulate(std::begin(output_spatial_lengths),
                                   std::end(output_spatial_lengths),
                                   1,
                                   std::multiplies<index_t>());
    }

    static index_t GetGemmKRaw(index_t C, const std::vector<index_t>& filter_spatial_lengths)
    {
        return C * std::accumulate(std::begin(filter_spatial_lengths),
                                   std::end(filter_spatial_lengths),
                                   1,
                                   std::multiplies<index_t>());
    }

    static auto
    MakeABEGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(index_t N,
                                                    index_t K,
                                                    index_t C,
                                                    std::vector<index_t> input_spatial_lengths,
                                                    std::vector<index_t> filter_spatial_lengths,
                                                    std::vector<index_t> output_spatial_lengths,
                                                    std::vector<index_t> conv_filter_strides,
                                                    std::vector<index_t> conv_filter_dilations,
                                                    std::vector<index_t> input_left_pads,
                                                    std::vector<index_t> input_right_pads)
    {
        using namespace ck;

        const index_t GemmMRaw = GetGemmMRaw(N, output_spatial_lengths);
        const index_t GemmNRaw = K;
        const index_t GemmKRaw = GetGemmKRaw(C, filter_spatial_lengths);

        // TODO: remove
        assert(GemmKRaw % GemmK1Number == 0);

        // A:
        const auto in_gemmk0_gemmm_gemmk1_grid_desc =
            GetInputTensorDescriptor<NDimSpatial>(N,
                                                  C,
                                                  GemmMRaw,
                                                  GemmKRaw,
                                                  input_spatial_lengths,
                                                  filter_spatial_lengths,
                                                  output_spatial_lengths,
                                                  conv_filter_strides,
                                                  conv_filter_dilations,
                                                  input_left_pads,
                                                  input_right_pads);

        // B:
        const auto wei_gemmk0_gemmn_gemmk1_grid_desc =
            GetWeightTensorDescriptor(GemmNRaw, GemmKRaw);

        // E:
        const auto out_gemmm_gemmn_grid_desc = GetOutputTensorDescriptor(GemmMRaw, GemmNRaw);

        return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
                          wei_gemmk0_gemmn_gemmk1_grid_desc,
                          out_gemmm_gemmn_grid_desc);
    }

    template <index_t NDim, typename std::enable_if<NDim == 1, bool>::type = false>
    static auto GetABEGridDesc()
    {
        return MakeABEGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
            1, 1, 1, {1}, {1}, {1}, {1}, {1}, {1}, {1});
    }

    template <index_t NDim, typename std::enable_if<NDim == 2, bool>::type = false>
    static auto GetABEGridDesc()
    {
        return MakeABEGridDescriptor_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});
    }

    template <index_t NDim, typename std::enable_if<NDim == 3, bool>::type = false>
    static auto GetABEGridDesc()
    {
        return MakeABEGridDescriptor_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}, {1, 1, 1}, {1, 1, 1});
    }

    using ABEGridDescs = decltype(GetABEGridDesc<NDimSpatial>());

    using AGridDesc_AK0_M_AK1 = remove_cvref_t<decltype(ABEGridDescs{}[I0])>;
    using BGridDesc_BK0_N_BK1 = remove_cvref_t<decltype(ABEGridDescs{}[I1])>;
    using EGridDesc_M_N       = remove_cvref_t<decltype(ABEGridDescs{}[I2])>;

    // GridwiseGemm
    using GridwiseGemm = GridwiseGemmMultipleD_k0mk1_k0nk1_mn_xdl_cshuffle<
        ADataType, // TODO: distinguish A/B datatype
        AccDataType,
        CShuffleDataType,
        DsDataType,
        EDataType,
        AElementwiseOperation,
        BElementwiseOperation,
        CDEElementwiseOperation,
        InMemoryDataOperationEnum::Set,
        AGridDesc_AK0_M_AK1,
        BGridDesc_BK0_N_BK1,
        EGridDesc_M_N,
        NumGemmKPrefetchStage,
        BlockSize,
        MPerBlock,
        NPerBlock,
        KPerBlock,
        K1,
        K1,
        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,
        CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
        CDEBlockTransferScalarPerVector_NPerBlock,
        LoopSched>;

#if 0
    using Block2ETileMap = BlockToCTileMap_M00_N0_M01<MPerBlock, NPerBlock, EGridDesc_M_N>;
#else
    using Block2ETileMap = typename GridwiseGemm::DefaultBlock2ETileMap;
#endif

    // Argument
    struct Argument : public BaseArgument
    {
        Argument(const ADataType* p_in_grid,
                 const BDataType* p_wei_grid,
                 EDataType* p_out_grid,
                 index_t N,
                 index_t K,
                 index_t C,
                 std::vector<index_t> input_spatial_lengths,
                 std::vector<index_t> filter_spatial_lengths,
                 std::vector<index_t> output_spatial_lengths,
                 std::vector<index_t> conv_filter_strides,
                 std::vector<index_t> conv_filter_dilations,
                 std::vector<index_t> input_left_pads,
                 std::vector<index_t> input_right_pads,
                 AElementwiseOperation in_element_op,
                 BElementwiseOperation wei_element_op,
                 CDEElementwiseOperation out_element_op)
            : p_a_grid_{static_cast<const ADataType*>(p_in_grid)},
              p_b_grid_{static_cast<const BDataType*>(p_wei_grid)},
              p_ds_grid_{}, // FIXME
              p_e_grid_{static_cast<EDataType*>(p_out_grid)},
              a_grid_desc_ak0_m_ak1_{},
              b_grid_desc_bk0_n_bk1_{},
              e_grid_desc_m_n_{},
              e_grid_desc_mblock_mperblock_nblock_nperblock_{},
              block_2_etile_map_{},
              a_element_op_{in_element_op},
              b_element_op_{wei_element_op},
              cde_element_op_{out_element_op},
              Conv_N_{N},
              Conv_K_{K},
              Conv_C_{C},
              filter_spatial_lengths_{filter_spatial_lengths},
              conv_filter_strides_{conv_filter_strides},
              input_left_pads_{input_left_pads},
              input_right_pads_{input_right_pads}
        {
            const auto descs =
                DeviceOp::MakeABEGridDescriptor_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);

            a_grid_desc_ak0_m_ak1_ = descs[I0];
            b_grid_desc_bk0_n_bk1_ = descs[I1];
            e_grid_desc_m_n_       = descs[I2];

            block_2_etile_map_ = Block2ETileMap{e_grid_desc_m_n_};

            if(GridwiseGemm::CheckValidity(a_grid_desc_ak0_m_ak1_,
                                           b_grid_desc_bk0_n_bk1_,
                                           e_grid_desc_m_n_,
                                           block_2_etile_map_))
            {
                e_grid_desc_mblock_mperblock_nblock_nperblock_ =
                    GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
                        e_grid_desc_m_n_);
            }
        }

        //  private:
        // pointers
        const ADataType* p_a_grid_;
        const BDataType* p_b_grid_;
        typename GridwiseGemm::DsGridPointer p_ds_grid_;
        EDataType* p_e_grid_;

        // tensor descriptors
        AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
        BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
        StaticallyIndexedArray<
            typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
            NumDTensor>
            ds_grid_desc_mblock_mperblock_nblock_nperblock_; // FIXME: Ds desc may be of different
                                                             // type from E
        EGridDesc_M_N e_grid_desc_m_n_;
        typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
            e_grid_desc_mblock_mperblock_nblock_nperblock_;

        // block-to-e-tile map
        Block2ETileMap block_2_etile_map_;

        // element-wise op
        AElementwiseOperation a_element_op_;
        BElementwiseOperation b_element_op_;
        CDEElementwiseOperation cde_element_op_;

        // for checking IsSupportedArgument()
        index_t Conv_N_;
        index_t Conv_K_;
        index_t Conv_C_;
        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_;
    };

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

        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
        {
#if 0
            {
                std::cout << "arg.a_grid_desc_ak0_m_ak1_{"
                          << arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) << ", "
                          << arg.a_grid_desc_ak0_m_ak1_.GetLength(I1) << ", "
                          << arg.a_grid_desc_ak0_m_ak1_.GetLength(I2) << "}" << std::endl;

                std::cout << "arg.b_grid_desc_bk0_n_bk1_{"
                          << arg.b_grid_desc_bk0_n_bk1_.GetLength(I0) << ", "
                          << arg.b_grid_desc_bk0_n_bk1_.GetLength(I1) << ", "
                          << arg.b_grid_desc_bk0_n_bk1_.GetLength(I2) << "}" << std::endl;

                std::cout << "arg.e_grid_desc_m_n_{ " << arg.e_grid_desc_m_n_.GetLength(I0) << ", "
                          << arg.e_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
            }
#endif
            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
                                            arg.b_grid_desc_bk0_n_bk1_,
                                            arg.e_grid_desc_m_n_,
                                            arg.block_2_etile_map_))
            {
                throw std::runtime_error(
                    "wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v2r3 has invalid setting");
            }

            const index_t grid_size =
                arg.block_2_etile_map_.CalculateGridSize(arg.e_grid_desc_m_n_);

            const auto K =
                arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);

            auto launch_kernel = [&](auto has_main_k_block_loop) {
                constexpr bool has_main_loop = has_main_k_block_loop.value;

                const auto kernel = kernel_gemm_multiple_d_xdl_cshuffle<
                    GridwiseGemm,
                    ADataType, // TODO: distiguish A/B datatype
                    typename GridwiseGemm::DsGridPointer,
                    EDataType,
                    AElementwiseOperation,
                    BElementwiseOperation,
                    CDEElementwiseOperation,
                    DeviceOp::AGridDesc_AK0_M_AK1,
                    DeviceOp::BGridDesc_BK0_N_BK1,
                    ck::StaticallyIndexedArray<
                        typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
                        NumDTensor>,
                    typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
                    Block2ETileMap,
                    has_main_loop>;

                return launch_and_time_kernel(stream_config,
                                              kernel,
                                              dim3(grid_size),
                                              dim3(BlockSize),
                                              0,
                                              arg.p_a_grid_,
                                              arg.p_b_grid_,
                                              arg.p_ds_grid_,
                                              arg.p_e_grid_,
                                              arg.a_element_op_,
                                              arg.b_element_op_,
                                              arg.cde_element_op_,
                                              arg.a_grid_desc_ak0_m_ak1_,
                                              arg.b_grid_desc_bk0_n_bk1_,
                                              arg.ds_grid_desc_mblock_mperblock_nblock_nperblock_,
                                              arg.e_grid_desc_mblock_mperblock_nblock_nperblock_,
                                              arg.block_2_etile_map_);
            };

            float avg_time = 0;

            if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
            {
                avg_time = launch_kernel(integral_constant<bool, true>{});
            }
            else
            {
                avg_time = launch_kernel(integral_constant<bool, false>{});
            }

            return avg_time;
        }

        float Run(const BaseArgument* p_arg,
                  const StreamConfig& stream_config = StreamConfig{}) override
        {
            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
        }
    };

    static bool IsSupportedArgument(const Argument& arg)
    {
        if(ck::get_device_name() == "gfx908")
        {
            if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, float> ||
                           is_same_v<AccDataType, int32_t>))
            {
                return false;
            }
        }
        else if(ck::get_device_name() == "gfx90a")
        {
            if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, float> ||
                           is_same_v<AccDataType, int32_t> || is_same_v<AccDataType, double>))
            {
                return false;
            }
        }
        else
        {
            return false;
        }

        // tensors can't be bigger than 2GB each.
        constexpr ck::long_index_t GB2 = (ck::long_index_t{1} << 31);

        if(arg.a_grid_desc_ak0_m_ak1_.GetElementSpaceSize() * sizeof(ADataType) > GB2 ||
           arg.b_grid_desc_bk0_n_bk1_.GetElementSpaceSize() * sizeof(BDataType) > GB2 ||
           arg.e_grid_desc_m_n_.GetElementSpaceSize() * sizeof(EDataType) > GB2)
        {
            return false;
        }

        if constexpr(ConvForwardSpecialization ==
                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
        {
            // check if it's 1x1, stride=1 conv
            for(index_t i = 0; i < NDimSpatial; ++i)
            {
                if(!(arg.filter_spatial_lengths_[i] == 1 && arg.conv_filter_strides_[i] == 1 &&
                     arg.input_left_pads_[i] == 0 && arg.input_right_pads_[i] == 0))
                {
                    return false;
                }
            }
        }
        else if constexpr(ConvForwardSpecialization ==
                          ConvolutionForwardSpecialization::Filter1x1Pad0)
        {
            // check if it's 1x1 conv
            for(index_t i = 0; i < NDimSpatial; ++i)
            {
                if(!(arg.filter_spatial_lengths_[i] == 1 && arg.input_left_pads_[i] == 0 &&
                     arg.input_right_pads_[i] == 0))
                {
                    return false;
                }
            }
        }

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

        // vector store D/E matrix into global memory
        if(!(arg.Conv_K_ % CDEBlockTransferScalarPerVector_NPerBlock == 0))
        {
            return false;
        }

        // Gridwise GEMM size
        return GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
                                           arg.b_grid_desc_bk0_n_bk1_,
                                           arg.e_grid_desc_m_n_,
                                           arg.block_2_etile_map_);
    }

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

    static auto MakeArgument(const ADataType* p_in_grid,
                             const BDataType* p_wei_grid,
                             EDataType* p_out_grid,
                             index_t N,
                             index_t K,
                             index_t C,
                             std::vector<index_t> input_spatial_lengths,
                             std::vector<index_t> filter_spatial_lengths,
                             std::vector<index_t> output_spatial_lengths,
                             std::vector<index_t> conv_filter_strides,
                             std::vector<index_t> conv_filter_dilations,
                             std::vector<index_t> input_left_pads,
                             std::vector<index_t> input_right_pads,
                             AElementwiseOperation in_element_op,
                             BElementwiseOperation wei_element_op,
                             CDEElementwiseOperation out_element_op)
    {
        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,
                        in_element_op,
                        wei_element_op,
                        out_element_op};
    }

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

    std::unique_ptr<BaseArgument>
    MakeArgumentPointer(const void* p_in_grid,
                        const void* p_wei_grid,
                        void* p_out_grid,
                        index_t N,
                        index_t K,
                        index_t C,
                        std::vector<index_t> input_spatial_lengths,
                        std::vector<index_t> filter_spatial_lengths,
                        std::vector<index_t> output_spatial_lengths,
                        std::vector<index_t> conv_filter_strides,
                        std::vector<index_t> conv_filter_dilations,
                        std::vector<index_t> input_left_pads,
                        std::vector<index_t> input_right_pads,
                        AElementwiseOperation in_element_op,
                        BElementwiseOperation wei_element_op,
                        CDEElementwiseOperation out_element_op) override
    {
        return std::make_unique<Argument>(static_cast<const ADataType*>(p_in_grid),
                                          static_cast<const BDataType*>(p_wei_grid),
                                          static_cast<EDataType*>(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,
                                          in_element_op,
                                          wei_element_op,
                                          out_element_op);
    }

    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 << "DeviceConvNdFwdMultipleD_NwcKxcNwk_Xdl_CShuffle"
            << "<"
            << BlockSize << ", "
            << MPerBlock << ", "
            << NPerBlock << ", "
            << KPerBlock << ", "
            << getConvForwardSpecializationString(ConvForwardSpecialization)
            << ">";
        // clang-format on

        return str.str();
    }
};

} // namespace device
} // namespace tensor_operation
} // namespace ck
