grouped_convolution_backward_data_kernel.hpp Source File

grouped_convolution_backward_data_kernel.hpp Source File#

Composable Kernel: grouped_convolution_backward_data_kernel.hpp Source File
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
 
#pragma once
 
#include <iostream>
#include <string>
 
#include "ck_tile/core.hpp"
#include "ck_tile/ops/common.hpp"
#include "ck_tile/host/concat.hpp"
#include "ck_tile/core/utility/env.hpp"
#include "ck_tile/host/convolution_parameter.hpp"
#include "ck_tile/ops/grouped_convolution/utils/transform_conv_bwd_data_to_gemm.hpp"
#include "ck_tile/ops/grouped_convolution/utils/grouped_convolution_utils.hpp"
 
namespace ck_tile {
template <typename GroupedConvTraitsType_, typename TilePartitioner_>
struct GroupedConvBwdDataKernelArgs
{
    using TilePartitioner = remove_cvref_t<TilePartitioner_>;
 
    using ConvToGemmTransformer =
        TransformConvBwdDataToGemm<GroupedConvTraitsType_::NDimSpatial,
                                   GroupedConvTraitsType_::ConvSpecialization,
                                   GroupedConvTraitsType_::VectorSizeA,
                                   GroupedConvTraitsType_::VectorSizeB,
                                   GroupedConvTraitsType_::VectorSizeC,
                                   true>; // Split N enabled
    static constexpr index_t NumDTensor = GroupedConvTraitsType_::NumDTensor;
 
    static constexpr auto I0 = number<0>();
    static constexpr auto I1 = number<1>();
 
    template <
        typename InLay                      = typename GroupedConvTraitsType_::InLayout,
        typename WeiLay                     = typename GroupedConvTraitsType_::WeiLayout,
        typename OutLay                     = typename GroupedConvTraitsType_::OutLayout,
        typename std::enable_if<std::is_same_v<InLay, tensor_layout::convolution::NWGC> &&
                                    std::is_same_v<WeiLay, tensor_layout::convolution::GKXC> &&
                                    std::is_same_v<OutLay, tensor_layout::convolution::NWGK>,
                                bool>::type = false>
    CK_TILE_HOST GroupedConvBwdDataKernelArgs(const GroupedConvBwdDataHostArgs& args)
    {
        in_g_n_c_wis_lengths  = {static_cast<index_t>(args.G_),
                                 static_cast<index_t>(args.N_),
                                 static_cast<index_t>(args.C_),
                                 static_cast<index_t>(args.input_spatial_lengths_[0])};
        wei_g_k_c_xs_lengths  = {static_cast<index_t>(args.G_),
                                 static_cast<index_t>(args.K_),
                                 static_cast<index_t>(args.C_),
                                 static_cast<index_t>(args.filter_spatial_lengths_[0])};
        out_g_n_k_wos_lengths = {static_cast<index_t>(args.G_),
                                 static_cast<index_t>(args.N_),
                                 static_cast<index_t>(args.K_),
                                 static_cast<index_t>(args.output_spatial_lengths_[0])};
 
        conv_filter_strides   = {static_cast<index_t>(args.conv_filter_strides_[0])};
        conv_filter_dilations = {static_cast<index_t>(args.conv_filter_dilations_[0])};
        input_left_pads       = {static_cast<index_t>(args.input_left_pads_[0])};
        input_right_pads      = {static_cast<index_t>(args.input_right_pads_[0])};
 
        k_batch = args.k_batch;
 
        in_ptr  = args.in_ptr;
        wei_ptr = args.wei_ptr;
        for(index_t d = 0; d < NumDTensor; d++)
        {
            ds_ptr[d] = args.ds_ptr[d];
        }
        out_ptr = args.out_ptr;
 
        const index_t X               = wei_g_k_c_xs_lengths[3];
        const index_t ConvStrideW     = conv_filter_strides[0];
        const index_t ConvDilationW   = conv_filter_dilations[0];
        const auto GcdStrideDilationW = gcd(ConvStrideW, ConvDilationW);
        const auto XTilde             = ConvStrideW / GcdStrideDilationW;
 
        for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
        {
            const auto XDotSlice = integer_divide_ceil(X - i_xtilde, XTilde);
 
            if(XDotSlice <= 0)
            {
                continue;
            }
 
            if(gemm_count >= MaxGroupedGemmGroupsNum)
            {
                gemm_count++;
                // Avoid array segfault
                continue;
            }
 
            tildes = {i_xtilde};
 
            ConvToGemmTransformer conv_to_gemm_transformer{in_g_n_c_wis_lengths,
                                                           wei_g_k_c_xs_lengths,
                                                           out_g_n_k_wos_lengths,
                                                           conv_filter_strides,
                                                           conv_filter_dilations,
                                                           input_left_pads,
                                                           input_right_pads,
                                                           tildes};
 
            auto grid_descs =
                conv_to_gemm_transformer.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<
                    GroupedConvTraitsType_::NDimSpatial>(1);
 
            a_grid_descs_m_k[gemm_count] = grid_descs.at(number<0>{});
            b_grid_descs_n_k[gemm_count] = grid_descs.at(number<1>{});
            c_grid_descs_m_n[gemm_count] = grid_descs.at(number<2>{});
 
            const index_t grid_size_grp =
                TilePartitioner::GridSize(c_grid_descs_m_n[gemm_count].get_length(I0),
                                          c_grid_descs_m_n[gemm_count].get_length(I1));
 
            block_starts[gemm_count] = grid_size_;
            block_ends[gemm_count]   = grid_size_ + grid_size_grp;
 
            grid_size_ += grid_size_grp;
 
            // Get the actual split N from transformer
            n_per_split = conv_to_gemm_transformer.GetN();
            original_n  = conv_to_gemm_transformer.GetOriginalN();
            n_splits    = ck_tile::integer_divide_ceil(original_n, n_per_split);
 
            ++gemm_count;
        }
        group_stride_a = args.K_; // A: Out NWGK
        group_stride_b = args.K_ * args.C_ *
                         std::accumulate(args.filter_spatial_lengths_.begin(),
                                         args.filter_spatial_lengths_.end(),
                                         1,
                                         std::multiplies<index_t>()); // B: Wei GKXC
        group_stride_c = args.C_;                                     // C: In  NWGC
 
        input_batch_stride  = args.C_ * args.G_ * args.input_spatial_lengths_[0];
        output_batch_stride = args.K_ * args.G_ * args.output_spatial_lengths_[0];
 
        GemmBatch = args.G_;
    }
 
    template <
        typename InLay                      = typename GroupedConvTraitsType_::InLayout,
        typename WeiLay                     = typename GroupedConvTraitsType_::WeiLayout,
        typename OutLay                     = typename GroupedConvTraitsType_::OutLayout,
        typename std::enable_if<std::is_same_v<InLay, tensor_layout::convolution::NHWGC> &&
                                    std::is_same_v<WeiLay, tensor_layout::convolution::GKYXC> &&
                                    std::is_same_v<OutLay, tensor_layout::convolution::NHWGK>,
                                bool>::type = false>
    CK_TILE_HOST GroupedConvBwdDataKernelArgs(const GroupedConvBwdDataHostArgs& args)
    {
        in_g_n_c_wis_lengths  = {static_cast<index_t>(args.G_),
                                 static_cast<index_t>(args.N_),
                                 static_cast<index_t>(args.C_),
                                 static_cast<index_t>(args.input_spatial_lengths_[0]),
                                 static_cast<index_t>(args.input_spatial_lengths_[1])};
        wei_g_k_c_xs_lengths  = {static_cast<index_t>(args.G_),
                                 static_cast<index_t>(args.K_),
                                 static_cast<index_t>(args.C_),
                                 static_cast<index_t>(args.filter_spatial_lengths_[0]),
                                 static_cast<index_t>(args.filter_spatial_lengths_[1])};
        out_g_n_k_wos_lengths = {static_cast<index_t>(args.G_),
                                 static_cast<index_t>(args.N_),
                                 static_cast<index_t>(args.K_),
                                 static_cast<index_t>(args.output_spatial_lengths_[0]),
                                 static_cast<index_t>(args.output_spatial_lengths_[1])};
 
        conv_filter_strides   = {static_cast<index_t>(args.conv_filter_strides_[0]),
                                 static_cast<index_t>(args.conv_filter_strides_[1])};
        conv_filter_dilations = {static_cast<index_t>(args.conv_filter_dilations_[0]),
                                 static_cast<index_t>(args.conv_filter_dilations_[1])};
        input_left_pads       = {static_cast<index_t>(args.input_left_pads_[0]),
                                 static_cast<index_t>(args.input_left_pads_[1])};
        input_right_pads      = {static_cast<index_t>(args.input_right_pads_[0]),
                                 static_cast<index_t>(args.input_right_pads_[1])};
 
        k_batch = args.k_batch;
 
        in_ptr  = args.in_ptr;
        wei_ptr = args.wei_ptr;
        for(index_t d = 0; d < NumDTensor; d++)
        {
            ds_ptr[d] = args.ds_ptr[d];
        }
        out_ptr = args.out_ptr;
 
        const index_t Y               = wei_g_k_c_xs_lengths[3];
        const index_t X               = wei_g_k_c_xs_lengths[4];
        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 auto GcdStrideDilationH = gcd(ConvStrideH, ConvDilationH);
        const auto GcdStrideDilationW = gcd(ConvStrideW, ConvDilationW);
        const auto YTilde             = ConvStrideH / GcdStrideDilationH;
        const auto XTilde             = ConvStrideW / GcdStrideDilationW;
 
        for(index_t i_ytilde = 0; i_ytilde < YTilde; ++i_ytilde)
        {
            for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
            {
                const auto YDotSlice = integer_divide_ceil(Y - i_ytilde, YTilde);
                const auto XDotSlice = integer_divide_ceil(X - i_xtilde, XTilde);
 
                if(XDotSlice * YDotSlice <= 0)
                {
                    continue;
                }
 
                if(gemm_count >= MaxGroupedGemmGroupsNum)
                {
                    gemm_count++;
                    // Avoid array segfault
                    continue;
                }
 
                tildes = {i_ytilde, i_xtilde};
 
                ConvToGemmTransformer conv_to_gemm_transformer{in_g_n_c_wis_lengths,
                                                               wei_g_k_c_xs_lengths,
                                                               out_g_n_k_wos_lengths,
                                                               conv_filter_strides,
                                                               conv_filter_dilations,
                                                               input_left_pads,
                                                               input_right_pads,
                                                               tildes};
 
                auto grid_descs = conv_to_gemm_transformer
                                      .template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<
                                          GroupedConvTraitsType_::NDimSpatial>(1);
 
                a_grid_descs_m_k[gemm_count] = grid_descs.at(number<0>{});
                b_grid_descs_n_k[gemm_count] = grid_descs.at(number<1>{});
                c_grid_descs_m_n[gemm_count] = grid_descs.at(number<2>{});
 
                const index_t grid_size_grp =
                    TilePartitioner::GridSize(c_grid_descs_m_n[gemm_count].get_length(I0),
                                              c_grid_descs_m_n[gemm_count].get_length(I1));
 
                block_starts[gemm_count] = grid_size_;
                block_ends[gemm_count]   = grid_size_ + grid_size_grp;
 
                grid_size_ += grid_size_grp;
 
                // Get the actual split N from transformer
                n_per_split = conv_to_gemm_transformer.GetN();
                original_n  = conv_to_gemm_transformer.GetOriginalN();
                n_splits    = ck_tile::integer_divide_ceil(original_n, n_per_split);
 
                ++gemm_count;
            }
        }
        group_stride_a = args.K_; // A: Out NWGK
        group_stride_b = args.K_ * args.C_ *
                         std::accumulate(args.filter_spatial_lengths_.begin(),
                                         args.filter_spatial_lengths_.end(),
                                         1,
                                         std::multiplies<index_t>()); // B: Wei GKXC
        group_stride_c = args.C_;                                     // C: In  NWGC
 
        input_batch_stride =
            args.C_ * args.G_ * args.input_spatial_lengths_[0] * args.input_spatial_lengths_[1];
        output_batch_stride =
            args.K_ * args.G_ * args.output_spatial_lengths_[0] * args.output_spatial_lengths_[1];
 
        GemmBatch = args.G_;
    }
 
    template <
        typename InLay                      = typename GroupedConvTraitsType_::InLayout,
        typename WeiLay                     = typename GroupedConvTraitsType_::WeiLayout,
        typename OutLay                     = typename GroupedConvTraitsType_::OutLayout,
        typename std::enable_if<std::is_same_v<InLay, tensor_layout::convolution::NDHWGC> &&
                                    std::is_same_v<WeiLay, tensor_layout::convolution::GKZYXC> &&
                                    std::is_same_v<OutLay, tensor_layout::convolution::NDHWGK>,
                                bool>::type = false>
    CK_TILE_HOST GroupedConvBwdDataKernelArgs(const GroupedConvBwdDataHostArgs& args)
    {
        in_g_n_c_wis_lengths  = {static_cast<index_t>(args.G_),
                                 static_cast<index_t>(args.N_),
                                 static_cast<index_t>(args.C_),
                                 static_cast<index_t>(args.input_spatial_lengths_[0]),
                                 static_cast<index_t>(args.input_spatial_lengths_[1]),
                                 static_cast<index_t>(args.input_spatial_lengths_[2])};
        wei_g_k_c_xs_lengths  = {static_cast<index_t>(args.G_),
                                 static_cast<index_t>(args.K_),
                                 static_cast<index_t>(args.C_),
                                 static_cast<index_t>(args.filter_spatial_lengths_[0]),
                                 static_cast<index_t>(args.filter_spatial_lengths_[1]),
                                 static_cast<index_t>(args.filter_spatial_lengths_[2])};
        out_g_n_k_wos_lengths = {static_cast<index_t>(args.G_),
                                 static_cast<index_t>(args.N_),
                                 static_cast<index_t>(args.K_),
                                 static_cast<index_t>(args.output_spatial_lengths_[0]),
                                 static_cast<index_t>(args.output_spatial_lengths_[1]),
                                 static_cast<index_t>(args.output_spatial_lengths_[2])};
 
        conv_filter_strides   = {static_cast<index_t>(args.conv_filter_strides_[0]),
                                 static_cast<index_t>(args.conv_filter_strides_[1]),
                                 static_cast<index_t>(args.conv_filter_strides_[2])};
        conv_filter_dilations = {static_cast<index_t>(args.conv_filter_dilations_[0]),
                                 static_cast<index_t>(args.conv_filter_dilations_[1]),
                                 static_cast<index_t>(args.conv_filter_dilations_[2])};
        input_left_pads       = {static_cast<index_t>(args.input_left_pads_[0]),
                                 static_cast<index_t>(args.input_left_pads_[1]),
                                 static_cast<index_t>(args.input_left_pads_[2])};
        input_right_pads      = {static_cast<index_t>(args.input_right_pads_[0]),
                                 static_cast<index_t>(args.input_right_pads_[1]),
                                 static_cast<index_t>(args.input_right_pads_[2])};
 
        k_batch = args.k_batch;
 
        in_ptr  = args.in_ptr;
        wei_ptr = args.wei_ptr;
        for(index_t d = 0; d < NumDTensor; d++)
        {
            ds_ptr[d] = args.ds_ptr[d];
        }
        out_ptr = args.out_ptr;
 
        const index_t Z               = wei_g_k_c_xs_lengths[3];
        const index_t Y               = wei_g_k_c_xs_lengths[4];
        const index_t X               = wei_g_k_c_xs_lengths[5];
        const index_t ConvStrideD     = conv_filter_strides[0];
        const index_t ConvStrideH     = conv_filter_strides[1];
        const index_t ConvStrideW     = conv_filter_strides[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 auto GcdStrideDilationD = gcd(ConvStrideD, ConvDilationD);
        const auto GcdStrideDilationH = gcd(ConvStrideH, ConvDilationH);
        const auto GcdStrideDilationW = gcd(ConvStrideW, ConvDilationW);
        const auto ZTilde             = ConvStrideD / GcdStrideDilationD;
        const auto YTilde             = ConvStrideH / GcdStrideDilationH;
        const auto XTilde             = ConvStrideW / GcdStrideDilationW;
 
        for(index_t i_ztilde = 0; i_ztilde < ZTilde; ++i_ztilde)
        {
            for(index_t i_ytilde = 0; i_ytilde < YTilde; ++i_ytilde)
            {
                for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
                {
                    const auto ZDotSlice = integer_divide_ceil(Z - i_ztilde, ZTilde);
                    const auto YDotSlice = integer_divide_ceil(Y - i_ytilde, YTilde);
                    const auto XDotSlice = integer_divide_ceil(X - i_xtilde, XTilde);
 
                    if(ZDotSlice * XDotSlice * YDotSlice <= 0)
                    {
                        continue;
                    }
 
                    if(gemm_count >= MaxGroupedGemmGroupsNum)
                    {
                        gemm_count++;
                        // Avoid array segfault
                        continue;
                    }
 
                    tildes = {i_ztilde, i_ytilde, i_xtilde};
 
                    ConvToGemmTransformer conv_to_gemm_transformer{in_g_n_c_wis_lengths,
                                                                   wei_g_k_c_xs_lengths,
                                                                   out_g_n_k_wos_lengths,
                                                                   conv_filter_strides,
                                                                   conv_filter_dilations,
                                                                   input_left_pads,
                                                                   input_right_pads,
                                                                   tildes};
 
                    auto grid_descs = conv_to_gemm_transformer
                                          .template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<
                                              GroupedConvTraitsType_::NDimSpatial>(1);
 
                    a_grid_descs_m_k[gemm_count] = grid_descs.at(number<0>{});
                    b_grid_descs_n_k[gemm_count] = grid_descs.at(number<1>{});
                    c_grid_descs_m_n[gemm_count] = grid_descs.at(number<2>{});
 
                    const index_t grid_size_grp =
                        TilePartitioner::GridSize(c_grid_descs_m_n[gemm_count].get_length(I0),
                                                  c_grid_descs_m_n[gemm_count].get_length(I1));
 
                    block_starts[gemm_count] = grid_size_;
                    block_ends[gemm_count]   = grid_size_ + grid_size_grp;
 
                    grid_size_ += grid_size_grp;
 
                    // Get the actual split N from transformer
                    n_per_split = conv_to_gemm_transformer.GetN();
                    original_n  = conv_to_gemm_transformer.GetOriginalN();
                    n_splits    = ck_tile::integer_divide_ceil(original_n, n_per_split);
 
                    ++gemm_count;
                }
            }
        }
 
        group_stride_a = args.K_; // A: Out NWGK
        group_stride_b = args.K_ * args.C_ *
                         std::accumulate(args.filter_spatial_lengths_.begin(),
                                         args.filter_spatial_lengths_.end(),
                                         1,
                                         std::multiplies<index_t>()); // B: Wei GKXC
        group_stride_c = args.C_;                                     // C: In  NWGC
 
        input_batch_stride = args.C_ * args.G_ * args.input_spatial_lengths_[0] *
                             args.input_spatial_lengths_[1] * args.input_spatial_lengths_[2];
        output_batch_stride = args.K_ * args.G_ * args.output_spatial_lengths_[0] *
                              args.output_spatial_lengths_[1] * args.output_spatial_lengths_[2];
 
        GemmBatch = args.G_; // C: In  NWGC
    }
 
    static constexpr index_t MaxGroupedGemmGroupsNum = 128;
 
    using ABCGridDescs = remove_cvref_t<
        decltype(ConvToGemmTransformer{}.MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(1))>;
 
    using AGridDescMK = remove_cvref_t<decltype(ABCGridDescs{}[number<0>{}])>;
    using BGridDescNK = remove_cvref_t<decltype(ABCGridDescs{}[number<1>{}])>;
    using CGridDescMN = remove_cvref_t<decltype(ABCGridDescs{}[number<2>{}])>;
 
    static constexpr index_t NonSpatialDims = 3;
    array<index_t, NonSpatialDims + GroupedConvTraitsType_::NDimSpatial> in_g_n_c_wis_lengths;
    array<index_t, NonSpatialDims + GroupedConvTraitsType_::NDimSpatial> wei_g_k_c_xs_lengths;
    array<index_t, NonSpatialDims + GroupedConvTraitsType_::NDimSpatial> out_g_n_k_wos_lengths;
 
    array<index_t, GroupedConvTraitsType_::NDimSpatial> conv_filter_strides;
    array<index_t, GroupedConvTraitsType_::NDimSpatial> conv_filter_dilations;
    array<index_t, GroupedConvTraitsType_::NDimSpatial> input_left_pads;
    array<index_t, GroupedConvTraitsType_::NDimSpatial> input_right_pads;
    array<index_t, GroupedConvTraitsType_::NDimSpatial> tildes;
 
    index_t k_batch;
    index_t GemmBatch;
    index_t grid_size_ = 0;
    index_t gemm_count = 0;
 
    const void* out_ptr;
    void* in_ptr;
    std::array<const void*, NumDTensor> ds_ptr;
    const void* wei_ptr;
 
    array<AGridDescMK, MaxGroupedGemmGroupsNum> a_grid_descs_m_k;
    array<BGridDescNK, MaxGroupedGemmGroupsNum> b_grid_descs_n_k;
    array<CGridDescMN, MaxGroupedGemmGroupsNum> c_grid_descs_m_n;
 
    array<index_t, MaxGroupedGemmGroupsNum> block_starts;
    array<index_t, MaxGroupedGemmGroupsNum> block_ends;
 
    long_index_t group_stride_a;
    long_index_t group_stride_b;
    long_index_t group_stride_c;
 
    // Split-N support fields - initialize to safe defaults
    index_t n_splits            = 1; // Number of batch splits (e.g., 2 for 128→64×2)
    index_t n_per_split         = 1; // Batches per split (N_ from transformer)
    index_t original_n          = 1; // Original batch size before splitting
    index_t input_batch_stride  = 0; // Stride to next batch in input tensor
    index_t output_batch_stride = 0; // Stride to next batch in output tensor
};
template <typename GroupedConvTraitsType_,
          typename TilePartitioner_,
          typename GemmPipeline_,
          typename EpiloguePipeline_>
struct GroupedConvolutionBackwardDataKernel
{
    static constexpr index_t NDimSpatial = GroupedConvTraitsType_::NDimSpatial_;
    static constexpr ConvolutionSpecialization ConvSpecialization =
        GroupedConvTraitsType_::ConvSpecialization;
    using TilePartitioner  = remove_cvref_t<TilePartitioner_>;
    using GemmPipeline     = remove_cvref_t<GemmPipeline_>;
    using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
    using GemmALayout      = remove_cvref_t<typename GemmPipeline::ALayout>;
    using GemmBLayout      = remove_cvref_t<typename GemmPipeline::BLayout>;
    using GemmCLayout      = remove_cvref_t<typename GemmPipeline::CLayout>;
 
    using InLayout  = remove_cvref_t<typename GroupedConvTraitsType_::InLayout>;
    using WeiLayout = remove_cvref_t<typename GroupedConvTraitsType_::WeiLayout>;
    using OutLayout = remove_cvref_t<typename GroupedConvTraitsType_::OutLayout>;
    using DsLayout  = remove_cvref_t<typename GroupedConvTraitsType_::DsLayout>;
 
    using GemmDsLayout                  = remove_cvref_t<typename EpiloguePipeline::DsLayout>;
    static constexpr index_t NumDTensor = GroupedConvTraitsType_::NumDTensor;
 
    static constexpr index_t kBlockSize = GemmPipeline::BlockSize;
 
    using InDataType  = remove_cvref_t<typename GemmPipeline::ADataType>;
    using WeiDataType = remove_cvref_t<typename GemmPipeline::BDataType>;
    using DsDataType  = remove_cvref_t<typename EpiloguePipeline::DsDataType>;
 
    using OutDataType = remove_cvref_t<typename EpiloguePipeline::ODataType>;
 
    using GroupedConvBwdDataKernelArgsSpecialized =
        GroupedConvBwdDataKernelArgs<GroupedConvTraitsType_, TilePartitioner>;
    static constexpr index_t MaxGroupedGemmGroupsNum =
        GroupedConvBwdDataKernelArgsSpecialized::MaxGroupedGemmGroupsNum;
 
    // TODO: Enable this
    static constexpr bool IsSplitKSupported = false;
 
    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_assert(GemmPipeline::kPadM && GemmPipeline::kPadN && GemmPipeline::kPadK,
                  "Not supported!");
    static_assert(std::is_same_v<GemmALayout, tensor_layout::gemm::RowMajor>, "Not supported!");
    static_assert(std::is_same_v<GemmBLayout, tensor_layout::gemm::RowMajor>, "Not supported!");
    static_assert(std::is_same_v<GemmCLayout, tensor_layout::gemm::RowMajor>,
                  "Not supported C GEMM layout!");
 
    [[nodiscard]] CK_TILE_HOST static const std::string GetName()
    {
        // clang-format off
        return concat('_', "grouped_convolution_backward_data", 
            gemm_prec_str<InDataType, WeiDataType>(), 
            "gemm",
            GemmPipeline::GetName(),
            "epilogue",
            EpiloguePipeline::GetName());
        // clang-format on
    }
 
    CK_TILE_HOST static auto GridSize(const GroupedConvBwdDataKernelArgsSpecialized& kargs)
    {
        // enable batched grouped gemm
        return dim3(kargs.grid_size_, kargs.GemmBatch, kargs.n_splits * kargs.k_batch);
    }
 
    CK_TILE_HOST static constexpr auto BlockSize()
    {
        return is_wave32() ? dim3(kBlockSize / 2) : dim3(kBlockSize);
    }
 
    CK_TILE_HOST static constexpr GroupedConvBwdDataKernelArgsSpecialized
    MakeKernelArgs(const GroupedConvBwdDataHostArgs& hostArgs)
    {
        return GroupedConvBwdDataKernelArgsSpecialized(hostArgs);
    }
 
    CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
    {
        return max(GemmPipeline::GetSmemSize(), EpiloguePipeline::GetSmemSize());
    }
 
    CK_TILE_HOST static bool
    IsSupportedArgument(const GroupedConvBwdDataKernelArgsSpecialized& kargs)
    {
        if constexpr((GroupedConvTraitsType_::VectorSizeC % 2 != 0 &&
                      is_any_of<OutDataType, fp16_t, bf16_t>::value) ||
                     !IsSplitKSupported)
        {
            if(kargs.k_batch != 1)
            {
                if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
                {
                    CK_TILE_ERROR("Conditions not met for Kbatch >1 !");
                }
                return false;
            }
        }
 
        if(kargs.gemm_count > MaxGroupedGemmGroupsNum)
        {
            return false;
        }
 
        const index_t ConvK = kargs.wei_g_k_c_xs_lengths[number<1>{}];
        const index_t ConvC = kargs.wei_g_k_c_xs_lengths[number<2>{}];
 
        // check ConvSpecialization
        if constexpr(ConvSpecialization == ConvolutionSpecialization::Filter1x1Stride1Pad0)
        {
            // check if it's 1x1, stride=1 conv
            for(index_t i = 0; i < NDimSpatial; ++i)
            {
                const index_t SpatialDim = kargs.wei_g_k_c_xs_lengths[i + 3];
                const index_t ConvStride = kargs.conv_filter_strides[i];
                const index_t LeftPad    = kargs.input_left_pads[i];
                const index_t RightPad   = kargs.input_right_pads[i];
 
                if(!(SpatialDim == 1 && ConvStride == 1 && LeftPad == 0 && RightPad == 0))
                {
                    return false;
                }
            }
        }
        else if constexpr(ConvSpecialization == ConvolutionSpecialization::Filter1x1Pad0)
        {
            // check if it's 1x1 conv
            for(index_t i = 0; i < NDimSpatial; ++i)
            {
                const index_t SpatialDim = kargs.wei_g_k_c_xs_lengths[i + 3];
                const index_t LeftPad    = kargs.input_left_pads[i];
                const index_t RightPad   = kargs.input_right_pads[i];
 
                if(!(SpatialDim == 1 && LeftPad == 0 && RightPad == 0))
                {
                    return false;
                }
            }
        }
        else if constexpr(ConvSpecialization == ConvolutionSpecialization::Filter3x3)
        {
            if(ConvC != 1)
            {
                return false;
            }
            for(index_t i = 0; i < NDimSpatial; ++i)
            {
                const index_t filter_spatial_dim = kargs.wei_g_k_c_xs_lengths[i + I3];
 
                if(filter_spatial_dim != I3)
                {
                    return false;
                }
            }
        }
 
        namespace ctc = tensor_layout::convolution;
 
        if constexpr(std::is_same_v<InLayout, ctc::NWGC> || std::is_same_v<InLayout, ctc::NHWGC> ||
                     std::is_same_v<InLayout, ctc::NDHWGC>)
        {
            // Check access per C
            if(ConvC % GroupedConvTraitsType_::VectorSizeB != 0)
            {
                CK_TILE_ERROR("Conv C is not a multiple of vector load size for input image!");
                return false;
            }
        }
        else
        {
            CK_TILE_ERROR("Not supported input layout!");
            return false;
        }
 
        // FIXME: layout
        if constexpr(std::is_same_v<WeiLayout, ctc::GKXC> ||
                     std::is_same_v<WeiLayout, ctc::GKYXC> ||
                     std::is_same_v<WeiLayout, ctc::GKZYXC>)
        {
            if(ConvC % GroupedConvTraitsType_::VectorSizeC != 0)
            {
                CK_TILE_ERROR("Conv C is not a multiple of vector load size for weight!");
                return false;
            }
        }
        else
        {
            CK_TILE_ERROR("Not supported weight layout!");
            return false;
        }
 
        if constexpr(std::is_same_v<OutLayout, ctc::NWGK> ||
                     std::is_same_v<OutLayout, ctc::NHWGK> ||
                     std::is_same_v<OutLayout, ctc::NDHWGK>)
        {
            if(ConvK % GroupedConvTraitsType_::VectorSizeA != 0)
            {
                CK_TILE_ERROR("Conv K is not a multiple of vector store size for output image!");
                return false;
            }
        }
        else
        {
            CK_TILE_ERROR("Not supported output layout!");
            return false;
        }
 
        return true;
    }
 
    template <memory_operation_enum DstInMemOp = memory_operation_enum::set>
    CK_TILE_DEVICE static auto
    MakeGemmTensorViews(const OutDataType* a_ptr,
                        const InDataType* b_ptr,
                        const std::array<const void*, NumDTensor>& ds_ptr,
                        WeiDataType* c_ptr,
                        const GroupedConvBwdDataKernelArgsSpecialized& kargs,
                        const index_t group_id)
    {
        static_assert(!TilePartitioner::BlockGemmShape::PermuteA, "Not implemented!");
        static_assert(!TilePartitioner::BlockGemmShape::PermuteB, "Not implemented!");
        const auto& a_tensor_view = [&]() {
            return make_tensor_view<address_space_enum::global>(
                a_ptr,
                kargs.a_grid_descs_m_k[group_id]); // A: out
        }();
 
        const auto& b_tensor_view = [&]() {
            return make_tensor_view<address_space_enum::global>(
                b_ptr,
                kargs.b_grid_descs_n_k[group_id]); // B: weight
        }();
 
        const auto& c_tensor_view = [&]() {
            return make_tensor_view<address_space_enum::global>(c_ptr,
                                                                kargs.c_grid_descs_m_n[group_id]);
        }();
 
        const auto& ds_tensor_view = generate_tuple(
            [&](auto i) {
                static_assert(std::is_same_v<std::tuple_element_t<i, DsLayout>, OutLayout>,
                              "Not supported!");
                static_assert(std::is_same_v<GemmCLayout, tensor_layout::gemm::RowMajor>,
                              "Not supported!");
                static_assert(std::is_same_v<std::tuple_element_t<i, DsDataType>, OutDataType>,
                              "Not supported!");
 
                return make_tensor_view<address_space_enum::global>(
                    static_cast<OutDataType*>(ds_ptr[i]), kargs.c_grid_descs_m_n[group_id]);
            },
            number<NumDTensor>{});
 
        return make_tuple(a_tensor_view, b_tensor_view, ds_tensor_view, c_tensor_view);
    }
 
    template <typename TensorView>
    CK_TILE_DEVICE static auto MakeGemmPadViews(const TensorView& views)
    {
        const auto& a_pad_view = [&]() {
            const auto& a_tensor_view = views.at(I0);
            return pad_tensor_view(a_tensor_view,
                                   make_tuple(number<TilePartitioner::MPerBlock>{},
                                              number<TilePartitioner::KPerBlock>{}),
                                   sequence<true, true>{});
        }();
 
        const auto& b_pad_view = [&]() {
            const auto& b_tensor_view = views.at(I1);
            return pad_tensor_view(b_tensor_view,
                                   make_tuple(number<TilePartitioner::KPerBlock>{},
                                              number<TilePartitioner::NPerBlock>{}),
                                   sequence<true, true>{});
        }();
 
        const auto& ds_tensor_view = views.at(I2);
        const auto& ds_pad_view    = generate_tuple(
            [&](auto i) {
                return pad_tensor_view(ds_tensor_view[i],
                                       make_tuple(number<TilePartitioner::MPerBlock>{},
                                                  number<TilePartitioner::NPerBlock>{}),
                                       sequence<true, true>{});
            },
            number<NumDTensor>{});
 
        const auto& c_pad_view = [&]() {
            const auto& c_tensor_view = views.at(I3);
            return pad_tensor_view(c_tensor_view,
                                   make_tuple(number<TilePartitioner::MPerBlock>{},
                                              number<TilePartitioner::NPerBlock>{}),
                                   sequence<true, true>{});
        }();
 
        return make_tuple(a_pad_view, b_pad_view, ds_pad_view, c_pad_view);
    }
 
    template <typename PadView>
    CK_TILE_DEVICE static auto MakeGemmTileWindows(const PadView& views,
                                                   const index_t i_m,
                                                   const index_t i_n,
                                                   const index_t i_k = 0)
    {
        const auto& a_pad_view  = views.at(I0);
        const auto& b_pad_view  = views.at(I1);
        const auto& ds_pad_view = views.at(I2);
        const auto& c_pad_view  = views.at(I3);
 
        const auto& a_block_window = [&]() {
            return make_tile_window(a_pad_view,
                                    make_tuple(number<TilePartitioner::MPerBlock>{},
                                               number<TilePartitioner::KPerBlock>{}),
                                    {i_m, i_k});
        }();
 
        const auto& b_block_window = [&]() {
            return make_tile_window(b_pad_view,
                                    make_tuple(number<TilePartitioner::KPerBlock>{},
                                               number<TilePartitioner::NPerBlock>{}),
                                    {i_k, i_n});
        }();
 
        const auto ds_block_window = generate_tuple(
            [&](auto i) {
                return make_tile_window(ds_pad_view[i],
                                        make_tuple(number<TilePartitioner::MPerBlock>{},
                                                   number<TilePartitioner::NPerBlock>{}),
                                        {i_m, i_n});
            },
            number<NumDTensor>{});
 
        auto c_block_window = make_tile_window(
            c_pad_view,
            make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
            {i_m, i_n});
 
        return make_tuple(a_block_window, b_block_window, ds_block_window, c_block_window);
    }
    CK_TILE_DEVICE static void RunGemm(const OutDataType* a_ptr,
                                       const InDataType* b_ptr,
                                       const std::array<const void*, NumDTensor>& ds_ptr,
                                       WeiDataType* c_ptr,
                                       void* smem_ptr_0,
                                       const GroupedConvBwdDataKernelArgsSpecialized& kargs,
                                       const index_t block_idx_m,
                                       const index_t block_idx_n,
                                       const index_t group_id)
    {
        // Create Gemm tensor views, pad views and tile windows
        const auto& gemm_tensor_views_tuple =
            MakeGemmTensorViews<EpiloguePipeline::MemoryOperation>(
                a_ptr, b_ptr, ds_ptr, c_ptr, kargs, group_id);
 
        const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
        auto gemm_tile_windows     = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
 
        const index_t num_loop = amd_wave_read_first_lane(TilePartitioner::GetLoopNum(
            gemm_pad_views.at(I0).get_tensor_descriptor().get_length(I1)));
 
        // Run GEMM cooperatively by whole workgroup.
        const auto& a_block_window = gemm_tile_windows.at(I0);
        const auto& b_block_window = gemm_tile_windows.at(I1);
        const auto& d_block_window = gemm_tile_windows.at(I2);
 
        const auto& c_block_tile = GemmPipeline{}.template operator()(
            a_block_window, b_block_window, num_loop, smem_ptr_0);
 
        // Run Epilogue Pipeline
        auto& c_block_window = gemm_tile_windows.at(I3);
 
        EpiloguePipeline{}.template operator()<decltype(c_block_window), decltype(c_block_tile)>(
            c_block_window, c_block_tile, d_block_window, smem_ptr_0);
    }
    CK_TILE_DEVICE static void RunGemm2LDS(const OutDataType* a_ptr,
                                           const InDataType* b_ptr,
                                           const std::array<const void*, NumDTensor>& ds_ptr,
                                           WeiDataType* c_ptr,
                                           void* __restrict__ smem_ptr_0,
                                           void* __restrict__ smem_ptr_1,
                                           const GroupedConvBwdDataKernelArgsSpecialized& kargs,
                                           const index_t block_idx_m,
                                           const index_t block_idx_n,
                                           const index_t group_id)
    {
        // Create Gemm tensor views, pad views and tile windows
        const auto& gemm_tensor_views_tuple =
            MakeGemmTensorViews<EpiloguePipeline::MemoryOperation>(
                a_ptr, b_ptr, ds_ptr, c_ptr, kargs, group_id);
        const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
        auto gemm_tile_windows     = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
 
        const index_t num_loop = amd_wave_read_first_lane(
            TilePartitioner::GetLoopNum(gemm_tile_windows.at(I0).get_length(I1)));
 
        // Run GEMM cooperatively by whole workgroup.
        const auto& a_block_window = gemm_tile_windows.at(I0);
        const auto& b_block_window = gemm_tile_windows.at(I1);
        const auto& d_block_window = gemm_tile_windows.at(I2);
 
        const auto& c_block_tile = GemmPipeline{}.template operator()(
            a_block_window, b_block_window, num_loop, smem_ptr_0, smem_ptr_1);
 
        // Run Epilogue Pipeline
        auto& c_block_window = gemm_tile_windows.at(I3);
 
        EpiloguePipeline{}.template operator()<decltype(c_block_window), decltype(c_block_tile)>(
            c_block_window, c_block_tile, d_block_window, smem_ptr_0);
    }
 
    CK_TILE_DEVICE index_t FindGroupId(const GroupedConvBwdDataKernelArgsSpecialized& kargs,
                                       index_t block_id) const
    {
        index_t left     = 0;
        index_t right    = kargs.gemm_count;
        index_t group_id = index_t((left + right) >> 1);
 
        while((!(block_id >= kargs.block_starts[group_id] &&
                 block_id < kargs.block_ends[group_id])) &&
              left <= right)
        {
            if(block_id < kargs.block_starts[group_id])
            {
                right = group_id;
            }
            else
            {
                left = group_id;
            }
            group_id = index_t((left + right) >> 1);
        }
 
        return group_id;
    }
 
    CK_TILE_DEVICE void operator()(GroupedConvBwdDataKernelArgsSpecialized kargs) const
    {
        const auto blockIdX    = amd_wave_read_first_lane(blockIdx.x);
        const index_t group_id = FindGroupId(kargs, blockIdX);
 
        const auto [iM, iN] = OffsettedTile1DPartitioner<TilePartitioner>::GetOffsetedTileIndex(
            kargs.block_starts[group_id],
            kargs.c_grid_descs_m_n[group_id].get_length(I0),
            kargs.c_grid_descs_m_n[group_id].get_length(I1));
 
        const index_t i_m = amd_wave_read_first_lane(iM * TilePartitioner::MPerBlock);
        const index_t i_n = amd_wave_read_first_lane(iN * TilePartitioner::NPerBlock);
 
        const auto blockIdY       = amd_wave_read_first_lane(blockIdx.y);
        const auto group_offset_a = amd_wave_read_first_lane(kargs.group_stride_a * blockIdY);
        const auto group_offset_b = amd_wave_read_first_lane(kargs.group_stride_b * blockIdY);
        const auto group_offset_c = amd_wave_read_first_lane(kargs.group_stride_c * blockIdY);
 
        const auto blockIdZ = amd_wave_read_first_lane(blockIdx.z);
 
        // SplitN
        const index_t split_n_idx = __builtin_amdgcn_readfirstlane(blockIdZ / kargs.k_batch);
        const index_t split_n_offset =
            __builtin_amdgcn_readfirstlane(split_n_idx * kargs.n_per_split);
 
        const long_index_t output_batch_offset =
            static_cast<long_index_t>(split_n_offset) *
            static_cast<long_index_t>(kargs.output_batch_stride);
        const long_index_t input_batch_offset = static_cast<long_index_t>(split_n_offset) *
                                                static_cast<long_index_t>(kargs.input_batch_stride);
 
        // SplitK
        // TODO: Implement SplitK support
        // const index_t split_k_idx =
        //     __builtin_amdgcn_readfirstlane(blockIdZ - split_n_idx * kargs.k_batch);
 
        // options
        // conv_bwd_data = Out * Weight = In
        const OutDataType* a_ptr =
            static_cast<const OutDataType*>(kargs.out_ptr) + group_offset_a + output_batch_offset;
        const WeiDataType* b_ptr = static_cast<const WeiDataType*>(kargs.wei_ptr) + group_offset_b;
        InDataType* c_ptr =
            static_cast<InDataType*>(kargs.in_ptr) + group_offset_c + input_batch_offset;
 
        // allocate LDS
        __shared__ char smem_ptr_0[GetSmemSize()];
 
        if constexpr(GemmPipeline::DoubleSmemBuffer == true)
        {
            __shared__ char smem_ptr_1[GetSmemSize()];
            if constexpr(!(EpiloguePipeline::MemoryOperation == memory_operation_enum::atomic_add &&
                           GroupedConvTraitsType_::VectorSizeC % 2 != 0 &&
                           is_any_of<OutDataType, fp16_t, bf16_t>::value))
            {
                RunGemm2LDS(a_ptr,
                            b_ptr,
                            kargs.ds_ptr,
                            c_ptr,
                            smem_ptr_0,
                            smem_ptr_1,
                            kargs,
                            i_m,
                            i_n,
                            group_id);
            }
        }
        else
        {
            if constexpr(!(EpiloguePipeline::MemoryOperation == memory_operation_enum::atomic_add &&
                           GroupedConvTraitsType_::VectorSizeC % 2 != 0 &&
                           is_any_of<OutDataType, fp16_t, bf16_t>::value))
            {
                RunGemm(a_ptr, b_ptr, kargs.ds_ptr, c_ptr, smem_ptr_0, kargs, i_m, i_n, group_id);
            }
        }
    }
};
 
} // namespace ck_tile