// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.

#pragma once

#include "ck/utility/common_header.hpp"
#include "ck/tensor_description/multi_index_transform_helper.hpp"
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_selector.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v7.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"

#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"

namespace ck {

// 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 <typename ADataType, // FIXME: don't assume A/B have same datatype
          typename BDataType,
          typename AccDataType,
          typename CShuffleDataType,
          typename DsDataType,
          typename EDataType,
          typename ComputeType,
          typename AElementwiseOperation,
          typename BElementwiseOperation,
          typename CDEElementwiseOperation,
          index_t NumGemmKPrefetchStage,
          index_t BlockSize,
          index_t MPerBlock,
          index_t NPerBlock,
          index_t KPerBlock,
          index_t AK1Value,
          index_t BK1Value,
          index_t MPerXdl,
          index_t NPerXdl,
          index_t MXdlPerWave,
          index_t NXdlPerWave,
          typename ABlockTransferThreadClusterLengths_KBatch_AK0_M_AK1,
          typename ABlockTransferThreadClusterArrangeOrder,
          typename ABlockTransferSrcAccessOrder,
          index_t ABlockTransferSrcVectorDim,
          index_t ABlockTransferSrcScalarPerVector,
          index_t ABlockTransferDstScalarPerVector_AK1,
          bool AThreadTransferSrcResetCoordinateAfterRun,
          index_t ABlockLdsExtraM,
          typename BBlockTransferThreadClusterLengths_KBatch_BK0_N_BK1,
          typename BBlockTransferThreadClusterArrangeOrder,
          typename BBlockTransferSrcAccessOrder,
          index_t BBlockTransferSrcVectorDim,
          index_t BBlockTransferSrcScalarPerVector,
          index_t BBlockTransferDstScalarPerVector_BK1,
          bool BThreadTransferSrcResetCoordinateAfterRun,
          index_t BBlockLdsExtraN,
          index_t CShuffleMXdlPerWavePerShuffle,
          index_t CShuffleNXdlPerWavePerShuffle,
          typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
          index_t CDEShuffleBlockTransferScalarPerVector_NPerBlock,
          LoopScheduler LoopSched,
          PipelineVersion PipelineVer = PipelineVersion::v1>
struct GridwiseGemmMultipleD_xdl_splitk_cshuffle
{
    static constexpr index_t NumDTensor = DsDataType::Size();

    using GemmSpecialization = ck::tensor_operation::device::GemmSpecialization;

    static constexpr auto I0 = Number<0>{};
    static constexpr auto I1 = Number<1>{};
    static constexpr auto I2 = Number<2>{};
    static constexpr auto I3 = Number<3>{};
    static constexpr auto I4 = Number<4>{};
    static constexpr auto I5 = Number<5>{};
    static constexpr auto I6 = Number<6>{};
    static constexpr auto I7 = Number<7>{};

    // K1 should be Number<...>
    static constexpr auto AK1         = Number<AK1Value>{};
    static constexpr auto BK1         = Number<BK1Value>{};
    static constexpr auto AK0PerBlock = Number<KPerBlock / AK1Value>{};
    static constexpr auto BK0PerBlock = Number<KPerBlock / BK1Value>{};

    using ThisThreadBlock = ThisThreadBlock<BlockSize>;

    using GridwiseGemmPipe = remove_cvref_t<decltype(
        GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage, LoopSched>())>;

    __host__ __device__ static constexpr auto GetABlockDescriptor_KBatch_AK0PerBlock_MPerBlock_AK1()
    {
        // A matrix in LDS memory, dst of blockwise copy
        return make_naive_tensor_descriptor(
            make_tuple(I1, AK0PerBlock, Number<MPerBlock>{}, AK1),
            make_tuple(AK0PerBlock * Number<MPerBlock + ABlockLdsExtraM>{} * AK1,
                       Number<MPerBlock + ABlockLdsExtraM>{} * AK1,
                       AK1,
                       I1));
    }

    __host__ __device__ static constexpr auto GetBBlockDescriptor_KBatch_BK0PerBlock_NPerBlock_BK1()
    {
        // B matrix in LDS memory, dst of blockwise copy
        return make_naive_tensor_descriptor(
            make_tuple(I1, BK0PerBlock, Number<NPerBlock>{}, BK1),
            make_tuple(BK0PerBlock * Number<NPerBlock + BBlockLdsExtraN>{} * BK1,
                       Number<NPerBlock + BBlockLdsExtraN>{} * BK1,
                       BK1,
                       I1));
    }

    __host__ __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
    {
        // A matrix in LDS memory, dst of blockwise copy
        return make_naive_tensor_descriptor(
            make_tuple(AK0PerBlock, Number<MPerBlock>{}, AK1),
            make_tuple(Number<MPerBlock + ABlockLdsExtraM>{} * AK1, AK1, I1));
    }

    __host__ __device__ static constexpr auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()
    {
        // B matrix in LDS memory, dst of blockwise copy
        return make_naive_tensor_descriptor(
            make_tuple(BK0PerBlock, Number<NPerBlock>{}, BK1),
            make_tuple(Number<NPerBlock + BBlockLdsExtraN>{} * BK1, BK1, I1));
    }

    __host__ __device__ static constexpr auto
    GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
    {
        constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
        constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);

        constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
            make_naive_tensor_descriptor_packed(
                make_tuple(I1,
                           Number<CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl>{},
                           I1,
                           Number<CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>{}));

        return c_shuffle_block_desc_mblock_mperblock_nblock_nperblock;
    }

    // ck::Tuple<const D0DataType*, const D1DataType*, ...>
    static constexpr auto MakeDsGridPointer()
    {
        return generate_tuple(
            [&](auto i) {
                using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;

                return static_cast<const DDataType*>(nullptr);
            },
            Number<NumDTensor>{});
    }

    __host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
    {
        // LDS allocation for A and B: be careful of alignment
        constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
        constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();

        // lds max alignment
        constexpr auto max_lds_align = math::lcm(AK1, BK1);

        constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
            a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);

        constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
            b_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);

        // LDS allocation for C shuffle in LDS
        constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
            GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();

        constexpr auto c_block_size =
            c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();

        return math::max(a_block_space_size_aligned * sizeof(ADataType) +
                             b_block_space_size_aligned * sizeof(BDataType),
                         c_block_size * sizeof(CShuffleDataType));
    }

    __host__ __device__ static auto CalculateMPadded(index_t M)
    {
        return math::integer_least_multiple(M, MPerBlock);
    }

    __host__ __device__ static auto CalculateNPadded(index_t N)
    {
        return math::integer_least_multiple(N, NPerBlock);
    }

    __host__ __device__ static auto CalculateKPadded(index_t K, index_t K_Batch)
    {
        return math::integer_least_multiple(K, KPerBlock * K_Batch);
    }

    template <typename ALayout, GemmSpecialization GemmSpec>
    __host__ __device__ static auto
    MakeAGridDescriptor_KBatch_AK0_M_AK1(index_t M, index_t K, index_t StrideA, index_t KBatch)
    {
        const auto a_grid_desc_m_k = [&]() {
            if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));
            }
            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ALayout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, StrideA));
            }
        }();

        const auto MPad = CalculateMPadded(M);
        const auto KPad = CalculateKPadded(K, KBatch);

        const auto a_grid_desc_m_kpad = transform_tensor_descriptor(
            a_grid_desc_m_k,
            make_tuple(make_pass_through_transform(M), make_right_pad_transform(K, KPad - K)),
            make_tuple(Sequence<0>{}, Sequence<1>{}),
            make_tuple(Sequence<0>{}, Sequence<1>{}));

        const auto AK0 = KPad / (KBatch * AK1);

        if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MPadding ||
                     GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
                     GemmSpec == tensor_operation::device::GemmSpecialization::MKPadding ||
                     GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding)
        {
            // const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
            return transform_tensor_descriptor(
                a_grid_desc_m_kpad,
                make_tuple(make_unmerge_transform(make_tuple(KBatch, AK0, AK1)),
                           make_right_pad_transform(M, MPad - M)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
        }
        else
        {
            return transform_tensor_descriptor(
                a_grid_desc_m_kpad,
                make_tuple(make_unmerge_transform(make_tuple(KBatch, AK0, AK1)),
                           make_pass_through_transform(M)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
        }
    }

    template <typename BLayout, GemmSpecialization GemmSpec>
    __host__ __device__ static auto
    MakeBGridDescriptor_KBatch_BK0_N_BK1(index_t K, index_t N, index_t StrideB, index_t KBatch)
    {
        const auto b_grid_desc_k_n = [&]() {
            if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(StrideB, I1));
            }
            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(I1, StrideB));
            }
        }();

        const auto NPad = CalculateNPadded(N);
        const auto KPad = CalculateKPadded(K, KBatch);

        const auto b_grid_desc_kpad_n = transform_tensor_descriptor(
            b_grid_desc_k_n,
            make_tuple(make_right_pad_transform(K, KPad - K), make_pass_through_transform(N)),
            make_tuple(Sequence<0>{}, Sequence<1>{}),
            make_tuple(Sequence<0>{}, Sequence<1>{}));

        const auto BK0 = KPad / (KBatch * BK1);

        if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::NPadding ||
                     GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
                     GemmSpec == tensor_operation::device::GemmSpecialization::NKPadding ||
                     GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding)
        {
            // const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
            return transform_tensor_descriptor(
                b_grid_desc_kpad_n,
                make_tuple(make_unmerge_transform(make_tuple(KBatch, BK0, BK1)),
                           make_right_pad_transform(N, NPad - N)),
                make_tuple(Sequence<0>{}, Sequence<1>{}),
                make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
        }
        else
        {
            return transform_tensor_descriptor(
                b_grid_desc_kpad_n,
                make_tuple(make_unmerge_transform(make_tuple(KBatch, BK0, BK1)),
                           make_pass_through_transform(N)),
                make_tuple(Sequence<0>{}, Sequence<1>{}),
                make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
        }
    }

    // E desc for destination in blockwise copy
    template <typename EGridDesc_M_N>
    __host__ __device__ static constexpr auto
    MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const EGridDesc_M_N& e_grid_desc_m_n)
    {
        const auto M = e_grid_desc_m_n.GetLength(I0);
        const auto N = e_grid_desc_m_n.GetLength(I1);

        const auto MBlock = M / MPerBlock;
        const auto NBlock = N / NPerBlock;

        const auto e_grid_desc_mblock_mperblock_nblock_nperblock = transform_tensor_descriptor(
            e_grid_desc_m_n,
            make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
                       make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
            make_tuple(Sequence<0>{}, Sequence<1>{}),
            make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));

        return e_grid_desc_mblock_mperblock_nblock_nperblock;
    }

    // Ds desc for source in blockwise copy
    template <typename DsGridDesc_M_N>
    __host__ __device__ static constexpr auto
    MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const DsGridDesc_M_N& ds_grid_desc_m_n)
    {
        return generate_tuple(
            [&](auto i) {
                return MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(ds_grid_desc_m_n[i]);
            },
            Number<NumDTensor>{});
    }

    // return block_id to E matrix tile idx (m0, n0) mapping
    template <typename EGridDesc_M_N>
    __host__ __device__ static constexpr auto
    MakeDefaultBlock2ETileMap(const EGridDesc_M_N& e_grid_desc_m_n)
    {
        return BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock, EGridDesc_M_N>(
            e_grid_desc_m_n);
    }

    template <typename ALayout,
              typename BLayout,
              typename DsLayout,
              typename ELayout,
              GemmSpecialization GemmSpec>
    __host__ __device__ static constexpr bool
    CheckValidity(const index_t M,
                  const index_t N,
                  const index_t K,
                  const index_t StrideA,
                  const index_t StrideB,
                  const std::array<index_t, NumDTensor> StrideDs,
                  const index_t StrideE,
                  const index_t KBatch)
    {
        const auto a_grid_desc_kbatch_ak0_m_ak1 =
            MakeAGridDescriptor_KBatch_AK0_M_AK1<ALayout, GemmSpec>(M, K, StrideA, KBatch);
        const auto b_grid_desc_kbatch_bk0_n_bk1 =
            MakeBGridDescriptor_KBatch_BK0_N_BK1<BLayout, GemmSpec>(K, N, StrideB, KBatch);

        ignore = StrideDs;

        const auto e_grid_desc_m_n = MakeEGridDescriptor_M_N<ELayout, GemmSpec>(M, N, StrideE);

#if 0
        // check tile size
        if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K % KPerBlock == 0))
        {
            return false;
        }
#endif

        // check gridwise gemm pipeline
        const auto num_k_loop = K / KPerBlock;

        if(!GridwiseGemmPipe::IsSupported(num_k_loop))
        {
            return false;
        }

        // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
        // check tensor size: cannot be larger than 2GB each
        constexpr long_index_t TwoGB = (long_index_t{1} << 31);

        if(!(a_grid_desc_kbatch_ak0_m_ak1.GetElementSpaceSize() * sizeof(ADataType) <= TwoGB &&
             b_grid_desc_kbatch_bk0_n_bk1.GetElementSpaceSize() * sizeof(BDataType) <= TwoGB &&
             e_grid_desc_m_n.GetElementSpaceSize() * sizeof(EDataType) <= TwoGB))
        {
            return false;
        }

        return true;
    }

    __host__ __device__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
    {
        const index_t num_loop = K / KPerBlock;

        return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
    }

    using DsGridPointer = decltype(MakeDsGridPointer());

    template <typename ELayout, GemmSpecialization GemmSpec>
    __host__ __device__ static auto
    MakeEGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideE)
    {
        constexpr auto matrix_padder =
            ck::tensor_operation::device::MatrixPadder<GemmSpec, index_t, index_t, index_t>{
                MPerBlock, NPerBlock, KPerBlock};
        const auto e_grid_desc_mraw_nraw = [&]() {
            if constexpr(is_same<tensor_layout::gemm::RowMajor, ELayout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
                                                    make_tuple(StrideE, I1));
            }
            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ELayout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
                                                    make_tuple(I1, StrideE));
            }
        }();

        return matrix_padder.PadCDescriptor_M_N(e_grid_desc_mraw_nraw);
    }

    template <typename DsLayout, GemmSpecialization GemmSpec>
    __host__ __device__ static auto
    MakeDsGridDescriptor_M_N(const std::array<index_t, NumDTensor>& MRaws,
                             const std::array<index_t, NumDTensor>& NRaws,
                             const std::array<index_t, NumDTensor>& DsStride)
    {
        return generate_tuple(
            [&](auto i) {
                using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;

                return MakeEGridDescriptor_M_N<DLayout, GemmSpec>(MRaws[i], NRaws[i], DsStride[i]);
            },
            Number<NumDTensor>{});
    }

    __device__ __host__ static constexpr auto GetMPerBlock() { return MPerBlock; }

    template <bool HasMainKBlockLoop,
              InMemoryDataOperationEnum EGlobalMemoryDataOperation,
              index_t NumDTensor_,
              typename DsDataType_,
              typename AGridDesc_KBatch_AK0_M_AK1,
              typename BGridDesc_KBatch_BK0_N_BK1,
              typename DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
              typename EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
              typename CDEElementwiseOperation_,
              typename Block2ETileMap>
    __device__ static void Run(const ADataType* __restrict__ p_a_grid,
                               const BDataType* __restrict__ p_b_grid,
                               DsGridPointer p_ds_grid,
                               EDataType* __restrict__ p_e_grid,
                               void* __restrict__ p_shared,
                               uint32_t* barrier_count_finished,
                               const index_t KBatch,
                               const AElementwiseOperation& a_element_op,
                               const BElementwiseOperation& b_element_op,
                               const CDEElementwiseOperation_& cde_element_op,
                               const AGridDesc_KBatch_AK0_M_AK1& a_grid_desc_kbatch_ak0_m_ak1,
                               const BGridDesc_KBatch_BK0_N_BK1& b_grid_desc_kbatch_bk0_n_bk1,
                               const DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
                                   ds_grid_desc_mblock_mperblock_nblock_nperblock,
                               const EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
                                   e_grid_desc_mblock_mperblock_nblock_nperblock,
                               const Block2ETileMap& block_2_etile_map)
    {
        const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
            p_a_grid, a_grid_desc_kbatch_ak0_m_ak1.GetElementSpaceSize());

        const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
            p_b_grid, b_grid_desc_kbatch_bk0_n_bk1.GetElementSpaceSize());

        const auto ds_grid_buf = generate_tuple(
            [&](auto i) {
                return make_dynamic_buffer<AddressSpaceEnum::Global>(
                    p_ds_grid[i],
                    ds_grid_desc_mblock_mperblock_nblock_nperblock[i].GetElementSpaceSize());
            },
            Number<NumDTensor_>{});

        auto e_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
            p_e_grid, e_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());

        // divide block work by [M, N]
        const auto block_work_idx =
            block_2_etile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));

        // HACK: this force m/n_block_data_idx_on_grid into SGPR
        const index_t kbatch_id = __builtin_amdgcn_readfirstlane(block_work_idx[I0]);

        const index_t m_block_data_idx_on_grid =
            __builtin_amdgcn_readfirstlane(block_work_idx[I1] * MPerBlock);

        const index_t n_block_data_idx_on_grid =
            __builtin_amdgcn_readfirstlane(block_work_idx[I2] * NPerBlock);

        // lds max alignment
        constexpr auto max_lds_align = math::lcm(AK1, BK1);

        // A matrix in LDS memory, dst of blockwise copy
        constexpr auto a_block_desc_kbatch_ak0_m_ak1 =
            GetABlockDescriptor_KBatch_AK0PerBlock_MPerBlock_AK1();

        // B matrix in LDS memory, dst of blockwise copy
        constexpr auto b_block_desc_kbatch_bk0_n_bk1 =
            GetBBlockDescriptor_KBatch_BK0PerBlock_NPerBlock_BK1();

        // A matrix blockwise copy
        auto a_blockwise_copy =
            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
                                                AElementwiseOperation,
                                                ck::tensor_operation::element_wise::PassThrough,
                                                InMemoryDataOperationEnum::Set,
                                                Sequence<1, AK0PerBlock, MPerBlock, AK1>,
                                                ABlockTransferThreadClusterLengths_KBatch_AK0_M_AK1,
                                                ABlockTransferThreadClusterArrangeOrder,
                                                ADataType,
                                                ComputeType,
                                                decltype(a_grid_desc_kbatch_ak0_m_ak1),
                                                decltype(a_block_desc_kbatch_ak0_m_ak1),
                                                ABlockTransferSrcAccessOrder,
                                                Sequence<2, 0, 1, 3>,
                                                ABlockTransferSrcVectorDim,
                                                3,
                                                ABlockTransferSrcScalarPerVector,
                                                ABlockTransferDstScalarPerVector_AK1,
                                                1,
                                                1,
                                                AThreadTransferSrcResetCoordinateAfterRun,
                                                true,
                                                NumGemmKPrefetchStage>(
                a_grid_desc_kbatch_ak0_m_ak1,
                make_multi_index(kbatch_id, 0, m_block_data_idx_on_grid, 0),
                a_element_op,
                a_block_desc_kbatch_ak0_m_ak1,
                make_multi_index(0, 0, 0, 0),
                ck::tensor_operation::element_wise::PassThrough{});

        // B matrix blockwise copy
        auto b_blockwise_copy =
            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
                                                BElementwiseOperation,
                                                ck::tensor_operation::element_wise::PassThrough,
                                                InMemoryDataOperationEnum::Set,
                                                Sequence<1, BK0PerBlock, NPerBlock, BK1>,
                                                BBlockTransferThreadClusterLengths_KBatch_BK0_N_BK1,
                                                BBlockTransferThreadClusterArrangeOrder,
                                                BDataType,
                                                ComputeType,
                                                decltype(b_grid_desc_kbatch_bk0_n_bk1),
                                                decltype(b_block_desc_kbatch_bk0_n_bk1),
                                                BBlockTransferSrcAccessOrder,
                                                Sequence<2, 0, 1, 3>,
                                                BBlockTransferSrcVectorDim,
                                                3,
                                                BBlockTransferSrcScalarPerVector,
                                                BBlockTransferDstScalarPerVector_BK1,
                                                1,
                                                1,
                                                BThreadTransferSrcResetCoordinateAfterRun,
                                                true,
                                                NumGemmKPrefetchStage>(
                b_grid_desc_kbatch_bk0_n_bk1,
                make_multi_index(kbatch_id, 0, n_block_data_idx_on_grid, 0),
                b_element_op,
                b_block_desc_kbatch_bk0_n_bk1,
                make_multi_index(0, 0, 0, 0),
                ck::tensor_operation::element_wise::PassThrough{});

        // A matrix in LDS memory, dst of blockwise copy
        constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();

        // B matrix in LDS memory, dst of blockwise copy
        constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();

        // GEMM definition
        //   c_mtx += transpose(a_mtx) * b_mtx
        //     a_mtx[K0PerBlock, MPerBlock] is in LDS
        //     b_mtx[K0PerBlock, NPerBlock] is in LDS
        //     c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
        //       register
        // sanity check
        constexpr index_t KPack =
            math::max(math::lcm(AK1, BK1),
                      MfmaSelector<ComputeType, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);

        auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
            BlockSize,
            ComputeType,
            AccDataType,
            decltype(a_block_desc_ak0_m_ak1),
            decltype(b_block_desc_bk0_n_bk1),
            MPerXdl,
            NPerXdl,
            MXdlPerWave,
            NXdlPerWave,
            KPack,
            LoopSched>();

#if 1
        if(block_work_idx[I0] == 0)
        {
            const index_t nThreadSize = CDEShuffleBlockTransferScalarPerVector_NPerBlock;
            const index_t numNThreads = NPerBlock / nThreadSize;
            const index_t numMThreads = BlockSize / numNThreads;
            const index_t mThreadSize = MPerBlock / numMThreads;

            const index_t m_tid = get_thread_local_1d_id() / numNThreads;
            const index_t n_tid = get_thread_local_1d_id() % numNThreads;

            auto c_thread_desc_mblock_mperblock_nblock_nperblock =
                make_naive_tensor_descriptor_packed(
                    make_tuple(I1, Number<mThreadSize>{}, I1, Number<nThreadSize>{}));

            StaticBuffer<AddressSpaceEnum::Vgpr,
                         EDataType,
                         c_thread_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize(),
                         true>
                e_thread_zero_buf;

            auto c_thread_copy = ThreadwiseTensorSliceTransfer_v1r3<
                EDataType,
                EDataType,
                decltype(c_thread_desc_mblock_mperblock_nblock_nperblock),
                decltype(e_grid_desc_mblock_mperblock_nblock_nperblock),
                ck::tensor_operation::element_wise::PassThrough,
                Sequence<1, mThreadSize, 1, nThreadSize>,
                Sequence<0, 1, 2, 3>,
                3,
                CDEShuffleBlockTransferScalarPerVector_NPerBlock,
                InMemoryDataOperationEnum::Set,
                1,
                true>{e_grid_desc_mblock_mperblock_nblock_nperblock,
                      make_multi_index(block_work_idx[I1],
                                       m_tid * mThreadSize,
                                       block_work_idx[I2],
                                       n_tid * nThreadSize),
                      ck::tensor_operation::element_wise::PassThrough{}};

            c_thread_copy.Run(c_thread_desc_mblock_mperblock_nblock_nperblock,
                              make_tuple(I0, I0, I0, I0),
                              e_thread_zero_buf,
                              e_grid_desc_mblock_mperblock_nblock_nperblock,
                              e_grid_buf);

            __syncthreads();

            if(threadIdx.x == 0)
            {
                atomicAdd(barrier_count_finished, 1);
            }
        }
#endif

        auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();

        // LDS allocation for A and B: be careful of alignment
        constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
            a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);

        auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
            static_cast<ComputeType*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());

        auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
            static_cast<ComputeType*>(p_shared) + a_block_space_size_aligned,
            b_block_desc_bk0_n_bk1.GetElementSpaceSize());

        constexpr auto a_block_slice_copy_step = make_multi_index(0, KPerBlock / AK1, 0, 0);
        constexpr auto b_block_slice_copy_step = make_multi_index(0, KPerBlock / BK1, 0, 0);

        // gridwise GEMM pipeline
        const auto gridwise_gemm_pipeline =
            GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage, LoopSched>();

        const index_t num_k_block_main_loop =
            __builtin_amdgcn_readfirstlane((a_grid_desc_kbatch_ak0_m_ak1.GetLength(I1) *
                                            a_grid_desc_kbatch_ak0_m_ak1.GetLength(I3)) /
                                           KPerBlock);

        gridwise_gemm_pipeline.template Run<HasMainKBlockLoop>(a_grid_desc_kbatch_ak0_m_ak1,
                                                               a_block_desc_kbatch_ak0_m_ak1,
                                                               a_blockwise_copy,
                                                               a_grid_buf,
                                                               a_block_buf,
                                                               a_block_slice_copy_step,
                                                               b_grid_desc_kbatch_bk0_n_bk1,
                                                               b_block_desc_kbatch_bk0_n_bk1,
                                                               b_blockwise_copy,
                                                               b_grid_buf,
                                                               b_block_buf,
                                                               b_block_slice_copy_step,
                                                               blockwise_gemm,
                                                               c_thread_buf,
                                                               num_k_block_main_loop);

        // shuffle C and write out
        {
            if(threadIdx.x == 0)
            {
                while(__atomic_load_n(barrier_count_finished, __ATOMIC_RELAXED) == 0) {}
            }

            __syncthreads();

            static_assert(MXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&
                              NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,
                          "wrong!");

            constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
            constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);

            // TODO: hacky, fix it!
            constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
                blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();

            // TODO: hacky, fix it!
            // c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp is only used to get lengths
            constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp =
                blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();

            constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I0);
            constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I1);
            constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I2);
            constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I3);
            constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I4);
            constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I5);
            constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I6);
            constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I7);

            constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
                GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();

            auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
                static_cast<CShuffleDataType*>(p_shared),
                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());

            constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(
                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
                make_tuple(
                    make_freeze_transform(I0),
                    make_unmerge_transform(make_tuple(
                        Number<CShuffleMXdlPerWavePerShuffle>{}, // M0 (MXdlPerWave) per shuffle
                        M1,                                      // M1 = MWave
                        M2,                                      // M2 * M3 * M4 = MPerXdl
                        M3,
                        M4)),
                    make_freeze_transform(I0),
                    make_unmerge_transform(make_tuple(
                        Number<CShuffleNXdlPerWavePerShuffle>{}, // N0 (NXdlPerWave) per shuffle
                        N1,                                      // N1 = NWave
                        N2))),                                   // N2 = NPerXdl
                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
                make_tuple(
                    Sequence<>{}, Sequence<0, 2, 4, 5, 6>{}, Sequence<>{}, Sequence<1, 3, 7>{}));

            // calculate origin of thread output tensor on global memory
            //     blockwise GEMM c matrix starting index
            const auto c_thread_mtx_on_block =
                blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);

            const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
            const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];

            const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =
                make_single_stage_tensor_adaptor(
                    make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
                    make_tuple(Sequence<0, 1, 2, 3, 4>{}),
                    make_tuple(Sequence<0>{}));

            const auto m_thread_data_on_block_idx =
                m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
                    make_multi_index(m_thread_data_on_block));

            const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =
                make_single_stage_tensor_adaptor(
                    make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
                    make_tuple(Sequence<0, 1, 2>{}),
                    make_tuple(Sequence<0>{}));

            const auto n_thread_data_on_block_idx =
                n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
                    make_multi_index(n_thread_data_on_block));

            // shuffle: threadwise copy C from VGPR to LDS
            auto c_thread_copy_vgpr_to_lds =
                ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
                                                   CShuffleDataType,
                                                   decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
                                                   decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
                                                   ck::tensor_operation::element_wise::PassThrough,
                                                   Sequence<CShuffleMXdlPerWavePerShuffle,
                                                            CShuffleNXdlPerWavePerShuffle,
                                                            I1,
                                                            I1,
                                                            M2,
                                                            I1,
                                                            M4,
                                                            I1>,
                                                   Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
                                                   7,
                                                   1,
                                                   InMemoryDataOperationEnum::Set,
                                                   1,
                                                   true>{
                    c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
                    make_multi_index(0,
                                     0,
                                     m_thread_data_on_block_idx[I1],
                                     n_thread_data_on_block_idx[I1],
                                     m_thread_data_on_block_idx[I2],
                                     m_thread_data_on_block_idx[I3],
                                     m_thread_data_on_block_idx[I4],
                                     n_thread_data_on_block_idx[I2]),
                    ck::tensor_operation::element_wise::PassThrough{}};

            // tuple of reference to C/Ds tensor descriptors
            const auto c_ds_desc_refs = concat_tuple_of_reference(
                tie(c_shuffle_block_desc_mblock_mperblock_nblock_nperblock),
                generate_tie(
                    [&](auto i) -> const auto& // return type should be reference
                    { return ds_grid_desc_mblock_mperblock_nblock_nperblock[i]; },
                    Number<NumDTensor_>{}));

            // tuple of reference to C/Ds tensor descriptors
            const auto c_ds_buf_refs = concat_tuple_of_reference(
                tie(c_shuffle_block_buf),
                generate_tie(
                    [&](auto i) -> const auto& // return type should be reference
                    { return ds_grid_buf[i]; },
                    Number<NumDTensor_>{}));

            // tuple of starting index of C/Ds blockwise copy
            const auto idx_c_ds_block_begin = container_concat(
                make_tuple(make_multi_index(0, 0, 0, 0)),
                generate_tuple(
                    [&](auto) {
                        return make_multi_index(block_work_idx[I1], 0, block_work_idx[I2], 0);
                    },
                    Number<NumDTensor_>{}));

            // space filling curve for threadwise C in VGPR before shuffle
            constexpr auto sfc_c_vgpr =
                SpaceFillingCurve<Sequence<MXdlPerWave, NXdlPerWave, 1, 1, M2, 1, M4, 1>,
                                  Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
                                  Sequence<CShuffleMXdlPerWavePerShuffle,
                                           CShuffleNXdlPerWavePerShuffle,
                                           1,
                                           1,
                                           M2,
                                           1,
                                           M4,
                                           1>>{};

            // space filling curve for shuffled blockwise C/D/E
            constexpr auto sfc_cde_block =
                SpaceFillingCurve<Sequence<1, MPerBlock, 1, NPerBlock>,
                                  Sequence<0, 2, 1, 3>,
                                  Sequence<1,
                                           CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
                                           1,
                                           CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>>{};

            constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();

            static_assert(num_access == sfc_cde_block.GetNumOfAccess(), "wrong!");

            // blockwise copy C/D/E between LDS and global
            auto cde_block_copy_lds_and_global = ThreadGroupTensorSliceTransfer_v7<
                ThisThreadBlock,
                decltype(container_concat(make_tuple(CShuffleDataType{}), DsDataType_{})),
                Tuple<EDataType>,
                decltype(c_ds_desc_refs),
                decltype(tie(e_grid_desc_mblock_mperblock_nblock_nperblock)),
                CDEElementwiseOperation_,
                Sequence<static_cast<index_t>(EGlobalMemoryDataOperation)>, // FIXME: make
                                                                            // Sequence support
                                                                            // arbitray type
                Sequence<1,
                         CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
                         1,
                         CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,
                CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
                Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
                Sequence<0, 1, 2, 3>, // typename DimAccessOrder,
                3,                    // index_t VectorDim,
                CDEShuffleBlockTransferScalarPerVector_NPerBlock,
                sequence_merge_t<
                    Sequence<true>,
                    uniform_sequence_gen_t<NumDTensor_,
                                           false>>, // ThreadTransferSrcResetCoordinateAfterRunFlags
                Sequence<false>>                    // ThreadTransferDstResetCoordinateAfterRunFlags
                {c_ds_desc_refs,
                 idx_c_ds_block_begin,
                 tie(e_grid_desc_mblock_mperblock_nblock_nperblock),
                 make_tuple(make_multi_index(block_work_idx[I1], 0, block_work_idx[I2], 0)),
                 cde_element_op};

            static_for<0, num_access, 1>{}([&](auto access_id) {
                // make sure it's safe to write to LDS
                block_sync_lds();

                // each thread write its data from VGPR to LDS
                c_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,
                                              sfc_c_vgpr.GetIndexTupleOfNumber(access_id),
                                              c_thread_buf,
                                              c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
                                              c_shuffle_block_buf);

                // make sure it's safe to read from LDS
                block_sync_lds();

                // each block copy its data from LDS to global
                cde_block_copy_lds_and_global.Run(
                    c_ds_desc_refs,
                    c_ds_buf_refs,
                    tie(e_grid_desc_mblock_mperblock_nblock_nperblock),
                    tie(e_grid_buf));

                if constexpr(access_id < num_access - 1)
                {
                    constexpr auto cde_lds_and_global_step =
                        sfc_cde_block.GetForwardStep(access_id);

                    // move on Ds
                    static_for<0, NumDTensor_, 1>{}([&](auto i) {
                        cde_block_copy_lds_and_global.MoveSrcSliceWindow(
                            c_ds_desc_refs, i + I1, cde_lds_and_global_step);
                    });

                    // move on E
                    cde_block_copy_lds_and_global.MoveDstSliceWindow(
                        tie(e_grid_desc_mblock_mperblock_nblock_nperblock),
                        I0,
                        cde_lds_and_global_step);
                }
            });

            if(threadIdx.x == 0)
            {
                index_t k_id_finished_t = atomicAdd(barrier_count_finished, 1);

                if(k_id_finished_t == KBatch)
                {
                    *barrier_count_finished = 0;
                }
            }
        }
    }

    template <bool HasMainKBlockLoop,
              InMemoryDataOperationEnum EGlobalMemoryDataOperation,
              GemmSpecialization GemmSpec,
              typename ALayout,
              typename BLayout,
              typename DsLayout,
              typename ELayout,
              typename Block2ETileMap>
    __device__ static void Run(const void* __restrict__ p_a_grid_,
                               const void* __restrict__ p_b_grid_,
                               DsGridPointer p_ds_grid,
                               void* __restrict__ p_e_grid_,
                               void* __restrict__ p_shared,
                               uint32_t* barrier_count_finished,
                               const AElementwiseOperation& a_element_op,
                               const BElementwiseOperation& b_element_op,
                               const CDEElementwiseOperation& cde_element_op,
                               const index_t M,
                               const index_t N,
                               const index_t K,
                               const index_t StrideA,
                               const index_t StrideB,
                               const std::array<index_t, NumDTensor> StrideDs,
                               const index_t StrideE,
                               const index_t KBatch,
                               const Block2ETileMap& block_2_etile_map)
    {
        const auto p_a_grid = reinterpret_cast<const ADataType*>(p_a_grid_);
        const auto p_b_grid = reinterpret_cast<const BDataType*>(p_b_grid_);
        const auto p_e_grid = reinterpret_cast<EDataType*>(p_e_grid_);

        using DsGridDesc_M_N =
            remove_cvref_t<decltype(MakeDsGridDescriptor_M_N<DsLayout, GemmSpec>({}, {}, {}))>;

        DsGridDesc_M_N ds_grid_desc_m_n;

        static_for<0, NumDTensor, 1>{}([&](auto j) {
            using DLayout = remove_cvref_t<tuple_element_t<j.value, DsLayout>>;

            ds_grid_desc_m_n(j) = MakeEGridDescriptor_M_N<DLayout, GemmSpec>(M, N, StrideDs[j]);
        });

        const auto e_grid_desc_m_n = MakeEGridDescriptor_M_N<ELayout, GemmSpec>(M, N, StrideE);

        // tensor descriptors for block/thread-wise copy
        const auto a_grid_desc_kbatch_ak0_m_ak1 =
            MakeAGridDescriptor_KBatch_AK0_M_AK1<ALayout, GemmSpec>(M, K, StrideA, KBatch);

        const auto b_grid_desc_kbatch_bk0_n_bk1 =
            MakeBGridDescriptor_KBatch_BK0_N_BK1<BLayout, GemmSpec>(K, N, StrideB, KBatch);

        using DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<decltype(
            MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(DsGridDesc_M_N{}))>;

        DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock ds_grid_desc_mblock_mperblock_nblock_nperblock;

        static_for<0, NumDTensor, 1>{}([&](auto j) {
            ds_grid_desc_mblock_mperblock_nblock_nperblock(j) =
                MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(ds_grid_desc_m_n[j]);
        });

        const auto e_grid_desc_mblock_mperblock_nblock_nperblock =
            MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(e_grid_desc_m_n);

        const auto block_work_idx =
            block_2_etile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));

        const index_t kbatch_id = __builtin_amdgcn_readfirstlane(block_work_idx[I0]);

        if(kbatch_id == KBatch - 1)
        {
            Run<HasMainKBlockLoop, EGlobalMemoryDataOperation, NumDTensor, DsDataType>(
                p_a_grid,
                p_b_grid,
                p_ds_grid,
                p_e_grid,
                p_shared,
                barrier_count_finished,
                KBatch,
                a_element_op,
                b_element_op,
                cde_element_op,
                a_grid_desc_kbatch_ak0_m_ak1,
                b_grid_desc_kbatch_bk0_n_bk1,
                ds_grid_desc_mblock_mperblock_nblock_nperblock,
                e_grid_desc_mblock_mperblock_nblock_nperblock,
                block_2_etile_map);
        }
        else
        {
            Run<HasMainKBlockLoop, EGlobalMemoryDataOperation, 0, Tuple<>>(
                p_a_grid,
                p_b_grid,
                p_ds_grid,
                p_e_grid,
                p_shared,
                barrier_count_finished,
                KBatch,
                a_element_op,
                b_element_op,
                ck::tensor_operation::element_wise::PassThrough{},
                a_grid_desc_kbatch_ak0_m_ak1,
                b_grid_desc_kbatch_bk0_n_bk1,
                ds_grid_desc_mblock_mperblock_nblock_nperblock,
                e_grid_desc_mblock_mperblock_nblock_nperblock,
                block_2_etile_map);
        }
    }
};

} // namespace ck
