device_contraction_multiple_abd_xdl_cshuffle.hpp Source File

device_contraction_multiple_abd_xdl_cshuffle.hpp Source File#

Composable Kernel: device_contraction_multiple_abd_xdl_cshuffle.hpp Source File
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2025, Advanced Micro Devices, Inc. All rights reserved.
 
#pragma once
 
#include <iostream>
#include <sstream>
#include <vector>
 
#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_contraction_multiple_abd.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_contraction_utils.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_abd_xdl_cshuffle.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp"
 
namespace ck {
 
template <typename GridwiseGemm,
          typename AsPointer,
          typename BsPointer,
          typename DsPointer,
          typename EDataType,
          typename AElementwiseOperation,
          typename BElementwiseOperation,
          typename CDEElementwiseOperation,
          typename AsGridDesc_AK0_M_AK1,
          typename BsGridDesc_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_contraction_multiple_abd_xdl_cshuffle(
        AsPointer p_as_grid,
        BsPointer p_bs_grid,
        DsPointer p_ds_grid,
        EDataType* __restrict__ p_e_grid,
        const AElementwiseOperation a_element_op,
        const BElementwiseOperation b_element_op,
        const CDEElementwiseOperation cde_element_op,
        const AsGridDesc_AK0_M_AK1 as_grid_desc_ak0_m_ak1,
        const BsGridDesc_BK0_N_BK1 bs_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(__gfx9__) || defined(__gfx11__) || defined(__gfx12__)
    if constexpr(GridwiseGemm::template IsValidCompilationParameter<>())
    {
        __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
 
        GridwiseGemm::template Run<HasMainKBlockLoop>(
            p_as_grid,
            p_bs_grid,
            p_ds_grid,
            p_e_grid,
            p_shared,
            a_element_op,
            b_element_op,
            cde_element_op,
            as_grid_desc_ak0_m_ak1,
            bs_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_as_grid;
    ignore = p_bs_grid;
    ignore = p_ds_grid;
    ignore = p_e_grid;
    ignore = a_element_op;
    ignore = b_element_op;
    ignore = cde_element_op;
    ignore = as_grid_desc_ak0_m_ak1;
    ignore = bs_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 ck
 
namespace ck {
namespace tensor_operation {
namespace device {
 
// GEMM:
//   input : A[M, K]
//   input : B[N, K]
//   input : D0[M, N], D1[M, N], ...
//   output : E[M, N]
//   C = a_op(A) * b_op(B)
//   E = cde_op(C, D0, D1, ...)
// Assume:
//   D0, D1, ... and E have the same layout
template <index_t NumDimM,
          index_t NumDimN,
          index_t NumDimK,
          typename AsDataType,
          typename BsDataType,
          typename AccDataType,
          typename CShuffleDataType,
          typename DsDataType,
          typename EDataType,
          typename AElementwiseOperation,
          typename BElementwiseOperation,
          typename CDEElementwiseOperation,
          GemmSpecialization GemmSpec,
          index_t NumGemmKPrefetchStage,
          index_t BlockSize,
          index_t MPerBlock,
          index_t NPerBlock,
          index_t KPerBlock,
          index_t AK1,
          index_t BK1,
          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(),
          PipelineVersion PipelineVer = PipelineVersion::v1>
struct DeviceContractionMultipleABD_Xdl_CShuffle
    : public DeviceContractionMultipleABD<NumDimM,
                                          NumDimN,
                                          NumDimK,
                                          AsDataType,
                                          BsDataType,
                                          DsDataType,
                                          EDataType,
                                          AElementwiseOperation,
                                          BElementwiseOperation,
                                          CDEElementwiseOperation>
{
    using DeviceOp = DeviceContractionMultipleABD_Xdl_CShuffle;
 
    GET_NXDL_PER_WAVE_IMPL
    static constexpr auto NXdlPerWave64 = GetNXdlPerWave<true>();
    static constexpr auto NXdlPerWave32 = GetNXdlPerWave<false>();
 
    static constexpr index_t NumATensor = AsDataType::Size();
    static constexpr index_t NumBTensor = BsDataType::Size();
    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>{};
 
    using ComputeDataType = EDataType;
 
    // GridwiseGemm
    template <index_t NXdlPerWave_>
    using GridwiseGemmBase = GridwiseGemmMultipleABD_xdl_cshuffle<
        AsDataType,
        BsDataType,
        ComputeDataType,
        AccDataType,
        CShuffleDataType,
        DsDataType,
        EDataType,
        AElementwiseOperation,
        BElementwiseOperation,
        CDEElementwiseOperation,
        InMemoryDataOperationEnum::Set,
        NumGemmKPrefetchStage,
        BlockSize,
        MPerBlock,
        NPerBlock,
        KPerBlock,
        AK1,
        BK1,
        MPerXDL,
        NPerXDL,
        MXdlPerWave,
        NXdlPerWave_,
        ABlockTransferThreadClusterLengths_AK0_M_AK1,
        ABlockTransferThreadClusterArrangeOrder,
        ABlockTransferSrcAccessOrder,
        ABlockTransferSrcVectorDim,
        ABlockTransferSrcScalarPerVector,
        ABlockTransferDstScalarPerVector_AK1,
        false,
        ABlockLdsExtraM,
        BBlockTransferThreadClusterLengths_BK0_N_BK1,
        BBlockTransferThreadClusterArrangeOrder,
        BBlockTransferSrcAccessOrder,
        BBlockTransferSrcVectorDim,
        BBlockTransferSrcScalarPerVector,
        BBlockTransferDstScalarPerVector_BK1,
        false,
        BBlockLdsExtraN,
        CShuffleMXdlPerWavePerShuffle,
        CShuffleNXdlPerWavePerShuffle,
        CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
        CDEBlockTransferScalarPerVector_NPerBlock,
        LoopSched,
        PipelineVer>;
    using GridwiseGemm64 = GridwiseGemmBase<math::max(NXdlPerWave64, 1)>;
    using GridwiseGemm32 = GridwiseGemmBase<NXdlPerWave32>;
 
    static constexpr auto matrix_padder =
        ck::tensor_operation::device::MatrixPadder<GemmSpec, index_t, index_t, index_t>{
            MPerBlock, NPerBlock, KPerBlock};
 
    static auto MakeAGridDescriptor_M_K(const std::vector<index_t>& a_ms_ks_lengths_,
                                        const std::vector<index_t>& a_ms_ks_strides_)
    {
        assert(a_ms_ks_lengths_.size() == NumDimM + NumDimK &&
               a_ms_ks_strides_.size() == NumDimM + NumDimK);
 
        const auto to_tuple = [&](auto& vec, auto num) {
            return generate_tuple([&](auto i) { return vec[i]; }, num);
        };
 
        const auto a_ms_ks_lengths = to_tuple(a_ms_ks_lengths_, Number<NumDimM + NumDimK>{});
        const auto a_ms_ks_strides = to_tuple(a_ms_ks_strides_, Number<NumDimM + NumDimK>{});
 
        // dimension Ids for M0, M1, ...
        constexpr auto mDimIds = typename arithmetic_sequence_gen<0, NumDimM, 1>::type{};
 
        // dimension Ids for K0, K1, ...
        constexpr auto kDimIds =
            typename arithmetic_sequence_gen<NumDimM, NumDimM + NumDimK, 1>::type{};
 
        // lengths for M0, M1, ...
        const auto mLengths = get_container_subset(a_ms_ks_lengths, mDimIds);
 
        // lengths for K0, K1, ...
        const auto kLengths = get_container_subset(a_ms_ks_lengths, kDimIds);
 
        // naive tensor A[M0, M1, M2, ..., K0, K1, K2...]
        const auto a_grid_desc_ms_ks =
            make_naive_tensor_descriptor(a_ms_ks_lengths, a_ms_ks_strides);
 
        // transformed tensor A[MRaw = M0 * M1 * M2 * ... , KRaw = K0 * K1 * K2 * ...]
        const auto a_grid_desc_mraw_kraw = transform_tensor_descriptor(
            a_grid_desc_ms_ks,
            make_tuple(make_merge_transform(mLengths), make_merge_transform(kLengths)),
            make_tuple(mDimIds, kDimIds),
            make_tuple(Sequence<0>{}, Sequence<1>{}));
 
        return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
    }
 
    __host__ __device__ static auto
    MakeAsGridDescriptor_M_K(const std::array<std::vector<index_t>, NumATensor>& as_ms_ks_lengths,
                             const std::array<std::vector<index_t>, NumATensor>& as_ms_ks_strides)
    {
        return generate_tuple(
            [&](auto i) {
                return MakeAGridDescriptor_M_K(as_ms_ks_lengths[i], as_ms_ks_strides[i]);
            },
            Number<NumATensor>{});
    }
 
    // Assume: B[N0, N1, N2, ..., K0, K1, K2, ...]
    static auto MakeBGridDescriptor_N_K(const std::vector<index_t>& b_ns_ks_lengths_,
                                        const std::vector<index_t>& b_ns_ks_strides_)
    {
        assert(b_ns_ks_lengths_.size() == NumDimN + NumDimK &&
               b_ns_ks_strides_.size() == NumDimN + NumDimK);
 
        const auto to_tuple = [&](auto& vec, auto num) {
            return generate_tuple([&](auto i) { return vec[i]; }, num);
        };
 
        const auto b_ns_ks_lengths = to_tuple(b_ns_ks_lengths_, Number<NumDimN + NumDimK>{});
        const auto b_ns_ks_strides = to_tuple(b_ns_ks_strides_, Number<NumDimN + NumDimK>{});
 
        // dimension Ids for N0, N1, ...
        constexpr auto nDimIds = typename arithmetic_sequence_gen<0, NumDimN, 1>::type{};
 
        // dimension Ids for K0, K1, ...
        constexpr auto kDimIds =
            typename arithmetic_sequence_gen<NumDimN, NumDimN + NumDimK, 1>::type{};
 
        // lengths for K0, K1, ...
        const auto kLengths = get_container_subset(b_ns_ks_lengths, kDimIds);
 
        // lengths for N0, N1, ...
        const auto nLengths = get_container_subset(b_ns_ks_lengths, nDimIds);
 
        // naive tensor B[N0, N1, N2, ..., K0, K1, K2, ...]
        const auto b_grid_desc_ns_ks =
            make_naive_tensor_descriptor(b_ns_ks_lengths, b_ns_ks_strides);
 
        // transformed tensor B[NRaw = N0 * N1 * N2 * ..., KRaw = K0 * K1 * K2 * ...]
        const auto b_grid_desc_nraw_kraw = transform_tensor_descriptor(
            b_grid_desc_ns_ks,
            make_tuple(make_merge_transform(nLengths), make_merge_transform(kLengths)),
            make_tuple(nDimIds, kDimIds),
            make_tuple(Sequence<0>{}, Sequence<1>{}));
 
        return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
    }
 
    __host__ __device__ static auto
    MakeBsGridDescriptor_N_K(const std::array<std::vector<index_t>, NumBTensor>& bs_ns_ks_lengths,
                             const std::array<std::vector<index_t>, NumBTensor>& bs_ns_ks_strides)
    {
        return generate_tuple(
            [&](auto i) {
                return MakeBGridDescriptor_N_K(bs_ns_ks_lengths[i], bs_ns_ks_strides[i]);
            },
            Number<NumBTensor>{});
    }
 
    // assume E[M0, M1, M2, ..., N0, N1, N2...]
    static auto MakeEGridDescriptor_M_N(const std::vector<index_t>& e_ms_ns_lengths_,
                                        const std::vector<index_t>& e_ms_ns_strides_)
    {
        assert(e_ms_ns_lengths_.size() == NumDimM + NumDimN &&
               e_ms_ns_strides_.size() == NumDimM + NumDimN);
 
        const auto to_tuple = [&](auto& vec, auto num) {
            return generate_tuple([&](auto i) { return vec[i]; }, num);
        };
 
        const auto e_ms_ns_lengths = to_tuple(e_ms_ns_lengths_, Number<NumDimM + NumDimN>{});
        const auto e_ms_ns_strides = to_tuple(e_ms_ns_strides_, Number<NumDimM + NumDimN>{});
 
        // dimension Ids for M0, M1, ...
        constexpr auto mDimIds = typename arithmetic_sequence_gen<0, NumDimM, 1>::type{};
 
        // dimension Ids for N0, N1, ...
        constexpr auto nDimIds =
            typename arithmetic_sequence_gen<NumDimM, NumDimM + NumDimN, 1>::type{};
 
        // lengths for M0, M1, ...
        const auto mLengths = get_container_subset(e_ms_ns_lengths, mDimIds);
 
        // lengths for K0, K1, ...
        const auto nLengths = get_container_subset(e_ms_ns_lengths, nDimIds);
 
        // naive tensor E[M0, M1, M2, ..., N0, N1, N2...]
        const auto e_grid_desc_ms_ns =
            make_naive_tensor_descriptor(e_ms_ns_lengths, e_ms_ns_strides);
 
        // transformed tensor E[MRaw = M0 * M1 * M2 * ... , NRaw = N0 * N1 * N2 * ...]
        const auto e_grid_desc_mraw_nraw = transform_tensor_descriptor(
            e_grid_desc_ms_ns,
            make_tuple(make_merge_transform(mLengths), make_merge_transform(nLengths)),
            make_tuple(mDimIds, nDimIds),
            make_tuple(Sequence<0>{}, Sequence<1>{}));
 
        return matrix_padder.PadCDescriptor_M_N(e_grid_desc_mraw_nraw);
    }
 
    static auto
    MakeDsGridDescriptor_M_N(const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_lengths,
                             const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_strides)
    {
        return generate_tuple(
            [&](auto i) {
                return MakeEGridDescriptor_M_N(ds_ms_ns_lengths[i], ds_ms_ns_strides[i]);
            },
            Number<NumDTensor>{});
    }
 
    // desc for problem definition
    using AsGridDesc_M_K = remove_cvref_t<decltype(MakeAsGridDescriptor_M_K({}, {}))>;
    using BsGridDesc_N_K = remove_cvref_t<decltype(MakeBsGridDescriptor_N_K({}, {}))>;
    using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({}, {}))>;
    using EGridDesc_M_N  = remove_cvref_t<decltype(MakeEGridDescriptor_M_N({}, {}))>;
 
    // desc for blockwise copy
    using AsGridDesc_AK0_M_AK1 =
        remove_cvref_t<decltype(GridwiseGemm64::MakeDefaultAsGridDescriptor_AK0_M_AK1(
            AsGridDesc_M_K{}))>;
    using BsGridDesc_BK0_N_BK1 =
        remove_cvref_t<decltype(GridwiseGemm64::MakeDefaultBsGridDescriptor_BK0_N_BK1(
            BsGridDesc_N_K{}))>;
    using DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<
        decltype(GridwiseGemm64::MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
            DsGridDesc_M_N{}))>;
    using EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<
        decltype(GridwiseGemm64::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
            EGridDesc_M_N{}))>;
 
    // block-to-e-tile map
    using Block2ETileMap =
        remove_cvref_t<decltype(GridwiseGemm64::MakeDefaultBlock2ETileMap(EGridDesc_M_N{}))>;
 
    // Argument
    struct Argument : public BaseArgument
    {
        Argument(std::array<const void*, NumATensor> p_as_grid,
                 std::array<const void*, NumBTensor> p_bs_grid,
                 std::array<const void*, NumDTensor> p_ds_grid,
                 void* p_e_grid,
                 const std::array<std::vector<index_t>, NumATensor>& a_ms_ks_lengths,
                 const std::array<std::vector<index_t>, NumATensor>& a_ms_ks_strides,
                 const std::array<std::vector<index_t>, NumBTensor>& b_ns_ks_lengths,
                 const std::array<std::vector<index_t>, NumBTensor>& b_ns_ks_strides,
                 const std::array<std::vector<index_t>, NumDTensor>& d_ms_ns_lengths,
                 const std::array<std::vector<index_t>, NumDTensor>& d_ms_ns_strides,
                 const std::vector<index_t>& e_ms_ns_length,
                 const std::vector<index_t>& e_ms_ns_stride,
                 AElementwiseOperation a_element_op,
                 BElementwiseOperation b_element_op,
                 CDEElementwiseOperation cde_element_op)
            : p_as_grid_{},
              p_bs_grid_{},
              p_ds_grid_{},
              p_e_grid_{static_cast<EDataType*>(p_e_grid)},
              as_grid_desc_m_k_{},
              bs_grid_desc_n_k_{},
              ds_grid_desc_m_n_{},
              e_grid_desc_m_n_{MakeEGridDescriptor_M_N(e_ms_ns_length, e_ms_ns_stride)},
              block_2_etile_map_{GridwiseGemm64::MakeDefaultBlock2ETileMap(e_grid_desc_m_n_)},
              a_element_op_{a_element_op},
              b_element_op_{b_element_op},
              cde_element_op_{cde_element_op}
        {
            // populate pointer, desc for As
            static_for<0, NumATensor, 1>{}([&](auto i) {
                // using ALayout   = remove_cvref_t<tuple_element_t<i.value, AsLayout>>;
                using ADataType = remove_cvref_t<tuple_element_t<i.value, AsDataType>>;
 
                // A pointer
                p_as_grid_(i) = static_cast<const ADataType*>(p_as_grid[i]);
 
                // A desc
                as_grid_desc_m_k_(i) =
                    MakeAGridDescriptor_M_K(a_ms_ks_lengths[i], a_ms_ks_strides[i]);
            });
 
            // populate pointer, desc for Bs
            static_for<0, NumBTensor, 1>{}([&](auto i) {
                // using BLayout   = remove_cvref_t<tuple_element_t<i.value, BsLayout>>;
                using BDataType = remove_cvref_t<tuple_element_t<i.value, BsDataType>>;
 
                // B pointer
                p_bs_grid_(i) = static_cast<const BDataType*>(p_bs_grid[i]);
 
                // B desc
                bs_grid_desc_n_k_(i) =
                    MakeBGridDescriptor_N_K(b_ns_ks_lengths[i], b_ns_ks_strides[i]);
            });
 
            // populate pointer, desc for Ds
            static_for<0, NumDTensor, 1>{}([&](auto i) {
                // using DLayout   = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
                using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
 
                // D pointer
                p_ds_grid_(i) = static_cast<const DDataType*>(p_ds_grid[i]);
 
                // D desc
                ds_grid_desc_m_n_(i) =
                    MakeEGridDescriptor_M_N(d_ms_ns_lengths[i], d_ms_ns_strides[i]);
            });
 
            // for sanity check of vector memory access
            for(index_t i = 0; i < NumATensor; ++i)
            {
                tie(as_continous_dim_[i], as_max_read_elems_[i]) =
                    CalculateMaxRead<NumDimM, NumDimK>(a_ms_ks_lengths[i], a_ms_ks_strides[i]);
            }
 
            for(index_t i = 0; i < NumBTensor; ++i)
            {
                tie(bs_continous_dim_[i], bs_max_read_elems_[i]) =
                    CalculateMaxRead<NumDimN, NumDimK>(b_ns_ks_lengths[i], b_ns_ks_strides[i]);
            }
 
            for(index_t i = 0; i < NumDTensor; ++i)
            {
                tie(ds_continous_dim_[i], ds_max_read_elems_[i]) =
                    CalculateMaxRead<NumDimM, NumDimN>(d_ms_ns_lengths[i], d_ms_ns_strides[i]);
            }
 
            tie(e_continous_dim_, e_max_write_elems_) =
                CalculateMaxRead<NumDimM, NumDimN>(e_ms_ns_length, e_ms_ns_stride);
        }
 
        // pointers
        typename GridwiseGemm64::AsGridPointer p_as_grid_;
        typename GridwiseGemm64::BsGridPointer p_bs_grid_;
        typename GridwiseGemm64::DsGridPointer p_ds_grid_;
        EDataType* p_e_grid_;
 
        // tensor descriptors for problem definiton
        AsGridDesc_M_K as_grid_desc_m_k_;
        BsGridDesc_N_K bs_grid_desc_n_k_;
        DsGridDesc_M_N ds_grid_desc_m_n_;
        EGridDesc_M_N e_grid_desc_m_n_;
 
        // 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_;
 
        // Describe whether the last part of a given dimension of A/B/D/E is continues dim.
        std::array<index_t, NumATensor> as_continous_dim_;
        std::array<index_t, NumATensor> bs_continous_dim_;
        std::array<index_t, NumBTensor> ds_continous_dim_;
        index_t e_continous_dim_;
 
        std::array<index_t, NumATensor> as_max_read_elems_;
        std::array<index_t, NumBTensor> bs_max_read_elems_;
        std::array<index_t, NumDTensor> ds_max_read_elems_;
        index_t e_max_write_elems_;
    };
 
    // Invoker
    struct Invoker : public BaseInvoker
    {
        using Argument = DeviceOp::Argument;
 
        template <typename GridwiseGemm>
        float RunImp(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
        {
            if(!GridwiseGemm::CheckValidity(arg.as_grid_desc_m_k_,
                                            arg.bs_grid_desc_n_k_,
                                            arg.ds_grid_desc_m_n_,
                                            arg.e_grid_desc_m_n_,
                                            arg.block_2_etile_map_))
            {
                throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
            }
            auto as_grid_desc_ak0_m_ak1 =
                GridwiseGemm::MakeDefaultAsGridDescriptor_AK0_M_AK1(arg.as_grid_desc_m_k_);
 
            auto bs_grid_desc_bk0_n_bk1 =
                GridwiseGemm::MakeDefaultBsGridDescriptor_BK0_N_BK1(arg.bs_grid_desc_n_k_);
 
            auto ds_grid_desc_mblock_mperblock_nblock_nperblock =
                GridwiseGemm::MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
                    arg.ds_grid_desc_m_n_);
 
            auto e_grid_desc_mblock_mperblock_nblock_nperblock =
                GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
                    arg.e_grid_desc_m_n_);
            const index_t grid_size =
                arg.block_2_etile_map_.CalculateGridSize(arg.e_grid_desc_m_n_);
 
            auto launch_kernel = [&](auto has_main_k_block_loop) {
                constexpr bool has_main_loop = has_main_k_block_loop.value;
 
                const auto kernel = kernel_contraction_multiple_abd_xdl_cshuffle<
                    GridwiseGemm,
                    typename GridwiseGemm::AsGridPointer,
                    typename GridwiseGemm::BsGridPointer,
                    typename GridwiseGemm::DsGridPointer,
                    EDataType,
                    AElementwiseOperation,
                    BElementwiseOperation,
                    CDEElementwiseOperation,
                    DeviceOp::AsGridDesc_AK0_M_AK1,
                    DeviceOp::BsGridDesc_BK0_N_BK1,
                    DeviceOp::DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
                    DeviceOp::EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
                    DeviceOp::Block2ETileMap,
                    has_main_loop>;
 
                return launch_and_time_kernel(stream_config,
                                              kernel,
                                              dim3(grid_size),
                                              dim3(BlockSize),
                                              0,
                                              arg.p_as_grid_,
                                              arg.p_bs_grid_,
                                              arg.p_ds_grid_,
                                              arg.p_e_grid_,
                                              arg.a_element_op_,
                                              arg.b_element_op_,
                                              arg.cde_element_op_,
                                              as_grid_desc_ak0_m_ak1,
                                              bs_grid_desc_bk0_n_bk1,
                                              ds_grid_desc_mblock_mperblock_nblock_nperblock,
                                              e_grid_desc_mblock_mperblock_nblock_nperblock,
                                              arg.block_2_etile_map_);
            };
 
            const auto K = arg.as_grid_desc_m_k_[I0].GetLength(I1);
 
            if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
            {
                return launch_kernel(integral_constant<bool, true>{});
            }
            else
            {
                return launch_kernel(integral_constant<bool, false>{});
            }
        }
 
        INVOKER_RUN_IMPL
 
        // polymorphic
        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)
    {
        using A0DataType = remove_cvref_t<tuple_element_t<0, AsDataType>>;
        using B0DataType = remove_cvref_t<tuple_element_t<0, BsDataType>>;
        if(!ck::is_xdl_wmma_supported<A0DataType, B0DataType, MPerXDL, NPerXDL>())
        {
            return false;
        }
        // check vector load/store
        {
            bool valid_as_access = true;
            static_for<0, NumATensor, 1>{}([&](auto i) {
                const bool valid_a_vector_size =
                    arg.as_max_read_elems_[i] % ABlockTransferSrcScalarPerVector == 0;
                const bool valid_a_access_dim_m =
                    ABlockTransferSrcVectorDim == 1 && arg.as_continous_dim_[i] == 0;
                const bool valid_a_access_dim_k =
                    ABlockTransferSrcVectorDim == 2 && arg.as_continous_dim_[i] == 1;
                const bool valid_a_access_dim = valid_a_access_dim_m || valid_a_access_dim_k;
                if(!((valid_a_vector_size && valid_a_access_dim) ||
                     ABlockTransferSrcScalarPerVector == 1))
                {
                    valid_as_access = false;
                }
            });
            if(!valid_as_access)
            {
                return false;
            }
 
            bool valid_bs_access = true;
            static_for<0, NumBTensor, 1>{}([&](auto i) {
                const bool valid_b_vector_size =
                    arg.bs_max_read_elems_[i] % BBlockTransferSrcScalarPerVector == 0;
                const bool valid_b_access_dim_n =
                    BBlockTransferSrcVectorDim == 1 && arg.bs_continous_dim_[i] == 0;
                const bool valid_b_access_dim_k =
                    BBlockTransferSrcVectorDim == 2 && arg.bs_continous_dim_[i] == 1;
                const bool valid_b_access_dim = valid_b_access_dim_n || valid_b_access_dim_k;
                if(!((valid_b_vector_size && valid_b_access_dim) ||
                     BBlockTransferSrcScalarPerVector == 1))
                {
                    valid_bs_access = false;
                }
            });
            if(!valid_bs_access)
            {
                return false;
            }
 
            bool valid_ds_access = true;
            static_for<0, NumDTensor, 1>{}([&](auto i) {
                const bool valid_d_vector_size =
                    arg.ds_max_read_elems_[i] % CDEBlockTransferScalarPerVector_NPerBlock == 0;
                // Vector read of Ds is always on N dimension.
                const bool valid_d_access_dim = arg.ds_continous_dim_[i] == 1;
                if(!((valid_d_vector_size && valid_d_access_dim) ||
                     CDEBlockTransferScalarPerVector_NPerBlock == 1))
                {
                    valid_ds_access = false;
                }
            });
            if(!valid_ds_access)
            {
                return false;
            }
 
            const bool valid_e_vector_size =
                arg.e_max_write_elems_ % CDEBlockTransferScalarPerVector_NPerBlock == 0;
            // Vector write of E is always on N dimension.
            const bool valid_e_access_dim = arg.e_continous_dim_ == 1;
            if(!((valid_e_vector_size && valid_e_access_dim) ||
                 CDEBlockTransferScalarPerVector_NPerBlock == 1))
            {
                return false;
            }
        }
 
        if(get_warp_size() > 0)
        {
            if constexpr(NXdlPerWave64 > 0)
            {
                return GridwiseGemm64::CheckValidity(arg.as_grid_desc_m_k_,
                                                     arg.bs_grid_desc_n_k_,
                                                     arg.ds_grid_desc_m_n_,
                                                     arg.e_grid_desc_m_n_,
                                                     arg.block_2_etile_map_);
            }
        }
        else
        {
            if constexpr(NXdlPerWave32 > 0)
            {
                return GridwiseGemm32::CheckValidity(arg.as_grid_desc_m_k_,
                                                     arg.bs_grid_desc_n_k_,
                                                     arg.ds_grid_desc_m_n_,
                                                     arg.e_grid_desc_m_n_,
                                                     arg.block_2_etile_map_);
            }
        }
 
        return false;
    }
 
    // polymorphic
    bool IsSupportedArgument(const BaseArgument* p_arg) override
    {
        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
    }
 
    static auto MakeArgument(std::array<const void*, NumATensor> p_as,
                             std::array<const void*, NumBTensor> p_bs,
                             std::array<const void*, NumDTensor> p_ds,
                             void* p_e,
                             const std::array<std::vector<index_t>, NumATensor>& a_ms_ks_lengths,
                             const std::array<std::vector<index_t>, NumATensor>& a_ms_ks_strides,
                             const std::array<std::vector<index_t>, NumBTensor>& b_ns_ks_lengths,
                             const std::array<std::vector<index_t>, NumBTensor>& b_ns_ks_strides,
                             const std::array<std::vector<index_t>, NumDTensor>& d_ms_ns_lengths,
                             const std::array<std::vector<index_t>, NumDTensor>& d_ms_ns_strides,
                             const std::vector<index_t>& e_ms_ns_length,
                             const std::vector<index_t>& e_ms_ns_stride,
                             AElementwiseOperation a_element_op,
                             BElementwiseOperation b_element_op,
                             CDEElementwiseOperation cde_element_op)
    {
        return Argument{p_as,
                        p_bs,
                        p_ds,
                        p_e,
                        a_ms_ks_lengths,
                        a_ms_ks_strides,
                        b_ns_ks_lengths,
                        b_ns_ks_strides,
                        d_ms_ns_lengths,
                        d_ms_ns_strides,
                        e_ms_ns_length,
                        e_ms_ns_stride,
                        a_element_op,
                        b_element_op,
                        cde_element_op};
    }
 
    static auto MakeInvoker() { return Invoker{}; }
 
    // polymorphic
    std::unique_ptr<BaseArgument>
    MakeArgumentPointer(std::array<const void*, NumATensor> p_as,
                        std::array<const void*, NumBTensor> p_bs,
                        std::array<const void*, NumDTensor> p_ds,
                        void* p_e,
                        const std::array<std::vector<index_t>, NumATensor>& as_ms_ks_lengths,
                        const std::array<std::vector<index_t>, NumATensor>& as_ms_ks_strides,
                        const std::array<std::vector<index_t>, NumBTensor>& bs_ns_ks_lengths,
                        const std::array<std::vector<index_t>, NumBTensor>& bs_ns_ks_strides,
                        const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_lengths,
                        const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_strides,
                        const std::vector<index_t>& e_ms_ns_length,
                        const std::vector<index_t>& e_ms_ns_stride,
                        AElementwiseOperation a_element_op,
                        BElementwiseOperation b_element_op,
                        CDEElementwiseOperation cde_element_op) override
    {
        return std::make_unique<Argument>(p_as,
                                          p_bs,
                                          p_ds,
                                          p_e,
                                          as_ms_ks_lengths,
                                          as_ms_ks_strides,
                                          bs_ns_ks_lengths,
                                          bs_ns_ks_strides,
                                          ds_ms_ns_lengths,
                                          ds_ms_ns_strides,
                                          e_ms_ns_length,
                                          e_ms_ns_stride,
                                          a_element_op,
                                          b_element_op,
                                          cde_element_op);
    }
 
    // polymorphic
    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
    {
        return std::make_unique<Invoker>(Invoker{});
    }
 
    // polymorphic
    std::string GetTypeString() const override
    {
        auto str = std::stringstream();
 
        std::map<LoopScheduler, std::string> LoopSchedToString{
            {LoopScheduler::Default, "Default"}, {LoopScheduler::Interwave, "Interwave"}};
 
        std::map<PipelineVersion, std::string> PipelineVersionToString{{PipelineVersion::v1, "v1"},
                                                                       {PipelineVersion::v2, "v2"}};
 
        // clang-format off
        str << "DeviceContractionMultipleABD_Xdl_CShuffle"
            << "<"
            << BlockSize << ", "
            << MPerBlock << ", "
            << NPerBlock << ", "
            << KPerBlock << ", "
            << AK1 << ", "
            << BK1 << ", "
            << MPerXDL << ", "
            << NPerXDL << ", "
            << MXdlPerWave << ", "
            << NXdlPerWave << ", "
            << ABlockTransferSrcScalarPerVector << ", "
            << BBlockTransferSrcScalarPerVector << ", "
            << CShuffleMXdlPerWavePerShuffle << ", "
            << CShuffleNXdlPerWavePerShuffle << ", "
            << getGemmSpecializationString(GemmSpec)
            << ">"
            << " LoopScheduler: "
            << LoopSchedToString[LoopSched] << ", "
            << "PipelineVersion: "
            << PipelineVersionToString[PipelineVer];
        // clang-format on
 
        return str.str();
    }
};
 
} // namespace device
} // namespace tensor_operation
} // namespace ck