// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, 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/block/blockwise_gemm_pipeline_xdlops_b_preshuffle_selector.hpp"
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1_mod8.hpp"
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.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/block/thread_group_tensor_slice_transfer_v7r3_scatter.hpp"

#define DEBUG_LOG 0

namespace ck {

// Currently we do not have a elegant way to put single lds buffer & double lds buffer pipe in same
// kernel function Blockers:
// 1. Two separted declaration of __shared__ pointer is the key to make sure data access operate on
// two lds chunks.
// 2. Occupied __shared__ won't release until whole shader end, a.k.a AB and C may not use same lds
// buffer when we declare __shared__ inside blkgemmpipe
template <typename GridwiseGemm,
          bool HasMainKBlockLoop,
          InMemoryDataOperationEnum CGlobalMemoryDataOperation,
          index_t MinimumOccupancy = 1,
          TailNumber TailNum       = TailNumber::Even>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, MinimumOccupancy)
#endif
    // __attribute__((amdgpu_waves_per_eu(1, 1)))
    kernel_moe_gemm_scatter(typename GridwiseGemm::Argument karg)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))
    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];

    auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg, blockIdx.z);

    GridwiseGemm::template Run<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(
        karg.p_sorted_token_ids,
        karg.p_sorted_expert_ids,
        karg.p_max_token_id,
        karg.p_a_grid + splitk_batch_offset.a_k_split_offset,
        karg.p_b_grid + splitk_batch_offset.b_k_split_offset,
        karg.p_ds_grid,
        karg.p_c_grid,
        p_shared,
        karg,
        karg.a_element_op,
        karg.b_element_op,
        karg.c_element_op);
#else
    ignore = karg;
#endif // end of if (defined(__gfx9__))
}

// template <typename GridwiseGemm,
//           bool HasMainKBlockLoop,
//           InMemoryDataOperationEnum CGlobalMemoryDataOperation,
//           index_t MinimumOccupancy = 1,
//           TailNumber TailNum       = TailNumber::Even>
// __global__ void
// #if CK_USE_LAUNCH_BOUNDS
//     __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, MinimumOccupancy)
// #endif
//     // __attribute__((amdgpu_waves_per_eu(1, 1)))
//     kernel_moe_gemm_scatter_2lds(typename GridwiseGemm::Argument karg)
// {
// #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))
//     __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
//     __shared__ char p_shared1[GridwiseGemm::GetSharedMemoryNumberOfByte()];

//     auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg, blockIdx.z);

//     GridwiseGemm::template Run_2Lds<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(
//         karg.p_a_grid + splitk_batch_offset.a_k_split_offset,
//         karg.p_b_grid + splitk_batch_offset.b_k_split_offset,
//         karg.p_ds_grid,
//         karg.p_c_grid,
//         p_shared,
//         p_shared1,
//         karg,
//         karg.a_element_op,
//         karg.b_element_op,
//         karg.c_element_op);
// #else
//     ignore = karg;
// #endif // end of if (defined(__gfx9__))
// }

template <typename ALayout,
          typename BLayout,
          typename DsLayout,
          typename CLayout,
          typename ADataType,
          typename BDataType,
          typename AccDataType,
          typename CShuffleDataType,
          typename DsDataType,
          typename CDataType,
          typename AElementwiseOperation,
          typename BElementwiseOperation,
          typename CElementwiseOperation,
          tensor_operation::device::GemmSpecialization GemmSpec,
          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_AK0_M_AK1,
          typename ABlockTransferThreadClusterArrangeOrder,
          typename ABlockTransferSrcAccessOrder,
          index_t ABlockTransferSrcVectorDim,
          index_t ABlockTransferSrcScalarPerVector,
          index_t ABlockTransferDstScalarPerVector_AK1,
          bool AThreadTransferSrcResetCoordinateAfterRun,
          index_t ABlockLdsExtraM,
          typename BBlockTransferThreadClusterLengths_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 CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
          typename CDEShuffleBlockTransferScalarPerVectors,
          BlockGemmPipelineScheduler BlkGemmPipeSched = BlockGemmPipelineScheduler::Intrawave,
          BlockGemmPipelineVersion BlkGemmPipelineVer = BlockGemmPipelineVersion::v1,
          typename ComputeTypeA                       = CDataType,
          typename ComputeTypeB                       = ComputeTypeA,
          typename LDSTypeA                           = ADataType,
          typename LDSTypeB                           = BDataType>
struct GridwiseMoeGemmScatter
{
    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>{};

    static constexpr auto CShuffleBlockTransferScalarPerVector_NPerBlock =
        CDEShuffleBlockTransferScalarPerVectors{}[I0];
    // K1 should be Number<...>
    static constexpr auto AK0Number       = Number<KPerBlock / AK1Value>{};
    static constexpr auto BK0Number       = Number<KPerBlock / BK1Value>{};
    static constexpr auto AK1Number       = Number<AK1Value>{};
    static constexpr auto BK1Number       = Number<BK1Value>{};
    static constexpr auto BlockSizeNumber = Number<BlockSize>{};

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

    using mfma_selector = MfmaSelector<ComputeTypeA, MPerXdl, NPerXdl, ComputeTypeB>;
    static constexpr index_t KPack =
        math::max(math::lcm(AK1Number, BK1Number), mfma_selector::selected_mfma.k_per_blk);
    static constexpr index_t KLane =
        mfma_selector::GetKPerXdlops() / mfma_selector::GetK1PerXdlops();
    static constexpr index_t KRepeat = KPerBlock / KLane / KPack;
    static constexpr index_t NLane   = NPerXdl;
    static constexpr index_t NWave   = NPerBlock / NPerXdl / NXdlPerWave;
    static_assert(NWave * warpSize == BlockSize);
    // static constexpr index_t NumTokens = 1;
    static constexpr index_t SortedTileSize   = MPerBlock;


    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>{});
    }

    using DsGridPointer = decltype(MakeDsGridPointer());

    using ThisThreadBlock = ThisThreadBlock<BlockSize>;

    __host__ static auto CalculateGridSize(index_t M, index_t N)
    {
        return std::make_tuple(math::integer_divide_ceil(N, NPerBlock),
                               math::integer_divide_ceil(M, MPerBlock),
                               1);
    }

    __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 CalculateBN0Shuffled(index_t N)
    {
        return math::integer_divide_ceil(N, NLane);
    }
    __host__ __device__ static auto CalculateBK0Shuffled(index_t K)
    {
        return math::integer_divide_ceil(K, KLane * KPack);
    }

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

    __host__ __device__ static auto CalculateAK0Padded(index_t K, index_t K_Batch = 1)
    {
        auto K_t = K_Batch * KPerBlock;
        return (K + K_t - 1) / K_t * (KPerBlock / AK1Value);
    }

    __host__ __device__ static auto CalculateBK0Padded(index_t K, index_t K_Batch = 1)
    {
        auto K_t = K_Batch * KPerBlock;
        return (K + K_t - 1) / K_t * (KPerBlock / BK1Value);
    }

    __host__ __device__ static auto CalculateKPadded(index_t K, index_t K_Batch = 1)
    {
        auto K_t = K_Batch * KPerBlock;
        return (K + K_t - 1) / K_t * KPerBlock;
    }

    __host__ __device__ static auto CalculateKRead(index_t K, index_t K_Batch = 1)
    {
        constexpr auto KReadVec = math::lcm(AK1Number, BK1Number);
        auto K_t                = K_Batch * KReadVec;
        return (K + K_t - 1) / K_t * KReadVec;
    }

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

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

    template <index_t MNXdlPerWave, index_t MNWaves, index_t MNPerXdl, typename TileDesc_K0_MN_K1>
    __host__ __device__ static constexpr auto MakeGemmMmaTileDescriptor(const TileDesc_K0_MN_K1&)
    {
        constexpr index_t K0 = TileDesc_K0_MN_K1{}.GetLength(Number<0>{});
        constexpr index_t K1 = TileDesc_K0_MN_K1{}.GetLength(Number<2>{});

        return transform_tensor_descriptor(
            TileDesc_K0_MN_K1{},
            make_tuple(make_merge_transform_v3_division_mod(make_tuple(Number<K0>{}, Number<K1>{})),
                       make_unmerge_transform(make_tuple(
                           Number<MNXdlPerWave>{}, Number<MNWaves>{}, Number<MNPerXdl>{}))),
            make_tuple(Sequence<0, 2>{}, Sequence<1>{}),
            make_tuple(Sequence<3>{}, Sequence<0, 1, 2>{}));
    }

    __host__ __device__ static auto MakeAGridDescriptor_AK0_M_AK1(
        index_t M, index_t MPad, index_t K, index_t KPad, index_t StrideA, index_t AK0)
    {
        const auto a_grid_desc_mraw_kraw = [&]() {
            if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
            {
                return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));
            }
            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
            {
                return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, StrideA));
            }
        }();

        using GemmSpecialization = tensor_operation::device::GemmSpecialization;

        if constexpr(GemmSpec == GemmSpecialization::MKPadding ||
                     GemmSpec == GemmSpecialization::MNKPadding)
        {
            // pad both M and K
            const auto a_grid_desc_m_k =
                transform_tensor_descriptor(a_grid_desc_mraw_kraw,
                                            make_tuple(make_right_pad_transform(M, MPad - M),
                                                       make_right_pad_transform(K, KPad - K)),
                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
                                            make_tuple(Sequence<0>{}, Sequence<1>{}));

            const auto a_grid_desc_ak0_m_ak1 = transform_tensor_descriptor(
                a_grid_desc_m_k,
                make_tuple(make_unmerge_transform(make_tuple(AK0, AK1Value)),
                           make_pass_through_transform(MPad)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return a_grid_desc_ak0_m_ak1;
        }
        else if constexpr(GemmSpec == GemmSpecialization::MPadding ||
                          GemmSpec == GemmSpecialization::MNPadding)
        {
            // pad M, but not K
            const auto a_grid_desc_ak0_m_ak1 = transform_tensor_descriptor(
                a_grid_desc_mraw_kraw,
                make_tuple(make_unmerge_transform(make_tuple(AK0, AK1Value)),
                           make_right_pad_transform(M, MPad - M)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return a_grid_desc_ak0_m_ak1;
        }
        else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
                          GemmSpec == GemmSpecialization::NKPadding)
        {
            // pad K, but not M
            const auto a_grid_desc_m_k = transform_tensor_descriptor(
                a_grid_desc_mraw_kraw,
                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 a_grid_desc_ak0_m_ak1 = transform_tensor_descriptor(
                a_grid_desc_m_k,
                make_tuple(make_unmerge_transform(make_tuple(AK0, AK1Value)),
                           make_pass_through_transform(M)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return a_grid_desc_ak0_m_ak1;
        }
        else
        {
            // not pad M or K
            const auto a_grid_desc_ak0_m_ak1 = transform_tensor_descriptor(
                a_grid_desc_mraw_kraw,
                make_tuple(make_unmerge_transform(make_tuple(AK0, AK1Value)),
                           make_pass_through_transform(M)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return a_grid_desc_ak0_m_ak1;
        }
    }

    __host__ __device__ static auto MakeBGridDescriptor_Preshuffled(index_t N0, index_t K0)
    {
        constexpr index_t NkSwizzleNumber = Number<warpSize * KPack>{};
        return make_naive_tensor_descriptor(
            make_tuple(N0 / NWave, NWave, K0, NkSwizzleNumber),
            make_tuple(NWave * K0 * NkSwizzleNumber, K0 * NkSwizzleNumber, NkSwizzleNumber, I1));
    }

    __host__ __device__ static auto MakeBGridDescriptor_BK0_N_BK1(
        index_t K, index_t KPad, index_t N, index_t NPad, index_t StrideB, index_t BK0)
    {
        const auto b_grid_desc_nraw_kraw = [&]() {
            if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(N, K), make_tuple(I1, StrideB));
            }
            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(N, K), make_tuple(StrideB, I1));
            }
        }();

        using GemmSpecialization = tensor_operation::device::GemmSpecialization;

        if constexpr(GemmSpec == GemmSpecialization::NKPadding ||
                     GemmSpec == GemmSpecialization::MNKPadding)
        {
            // pad both N and K
            const auto b_grid_desc_n_k =
                transform_tensor_descriptor(b_grid_desc_nraw_kraw,
                                            make_tuple(make_right_pad_transform(N, NPad - N),
                                                       make_right_pad_transform(K, KPad - K)),
                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
                                            make_tuple(Sequence<0>{}, Sequence<1>{}));

            const auto b_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(
                b_grid_desc_n_k,
                make_tuple(make_unmerge_transform(make_tuple(BK0, BK1Value)),
                           make_pass_through_transform(NPad)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return b_grid_desc_bk0_n_bk1;
        }
        else if constexpr(GemmSpec == GemmSpecialization::NPadding ||
                          GemmSpec == GemmSpecialization::MNPadding)
        {
            // pad N, but not K
            const auto b_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(
                b_grid_desc_nraw_kraw,
                make_tuple(make_unmerge_transform(make_tuple(BK0, BK1Value)),
                           make_right_pad_transform(N, NPad - N)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return b_grid_desc_bk0_n_bk1;
        }
        else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
                          GemmSpec == GemmSpecialization::MKPadding)
        {
            // pad K, but not N
            const auto b_grid_desc_n_k = transform_tensor_descriptor(
                b_grid_desc_nraw_kraw,
                make_tuple(make_pass_through_transform(N), make_right_pad_transform(K, KPad - K)),
                make_tuple(Sequence<0>{}, Sequence<1>{}),
                make_tuple(Sequence<0>{}, Sequence<1>{}));

            const auto b_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(
                b_grid_desc_n_k,
                make_tuple(make_unmerge_transform(make_tuple(BK0, BK1Value)),
                           make_pass_through_transform(N)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return b_grid_desc_bk0_n_bk1;
        }
        else
        {
            // not pad N or K
            const auto b_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(
                b_grid_desc_nraw_kraw,
                make_tuple(make_unmerge_transform(make_tuple(BK0, BK1Value)),
                           make_pass_through_transform(N)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return b_grid_desc_bk0_n_bk1;
        }
    }

    template <typename ABlockDesc_AK0_M_AK1>
    __host__ __device__ static constexpr auto
    MakeAMmaTileDescriptor_M0_M1_M2_K(const ABlockDesc_AK0_M_AK1&)
    {
        constexpr index_t MWaves = MPerBlock / (MXdlPerWave * MPerXdl);

        return MakeGemmMmaTileDescriptor<MXdlPerWave, MWaves, MPerXdl>(ABlockDesc_AK0_M_AK1{});
    }

    template <typename BBlockDesc_BK0_N_BK1>
    __host__ __device__ static constexpr auto
    MakeBMmaTileDescriptor_N0_N1_N2_K(const BBlockDesc_BK0_N_BK1&)
    {
        return MakeGemmMmaTileDescriptor<NXdlPerWave, NWave, NPerXdl>(BBlockDesc_BK0_N_BK1{});
    }

    template <typename ELayout>
    __host__ __device__ static auto
    MakeCGridDescriptor_M_N(index_t M, index_t MPad, index_t N, index_t NPad, index_t StrideC)
    {
        const auto c_grid_desc_mraw_nraw = [&]() {
            if constexpr(is_same<tensor_layout::gemm::RowMajor, ELayout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideC, I1));
            }
            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ELayout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, StrideC));
            }
        }();

        // pad M and N
        return transform_tensor_descriptor(c_grid_desc_mraw_nraw,
                                           make_tuple(make_right_pad_transform(M, MPad - M),
                                                      make_right_pad_transform(N, NPad - N)),
                                           make_tuple(Sequence<0>{}, Sequence<1>{}),
                                           make_tuple(Sequence<0>{}, Sequence<1>{}));
    }

    template <typename DLayout>
    __host__ __device__ static auto
    MakeDGridDescriptor_M_N(index_t M, index_t MPad, index_t N, index_t NPad, index_t StrideC)
    {
        const auto c_grid_desc_mraw_nraw = [&]() {
            if constexpr(is_same<tensor_layout::gemm::RowMajor, DLayout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideC, I0));
            }
            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, DLayout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I0, StrideC));
            }
        }();

        // pad M and N
        return transform_tensor_descriptor(c_grid_desc_mraw_nraw,
                                           make_tuple(make_right_pad_transform(M, MPad - M),
                                                      make_right_pad_transform(N, NPad - N)),
                                           make_tuple(Sequence<0>{}, Sequence<1>{}),
                                           make_tuple(Sequence<0>{}, Sequence<1>{}));
    }

    __host__ __device__ static auto MakeDsGridDescriptor_M_N(
        index_t M, index_t MPad, index_t N, index_t NPad, std::array<index_t, NumDTensor> StrideDs)
    {
        return generate_tuple(
            [&](auto i) {
                using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
                return MakeDGridDescriptor_M_N<DLayout>(M, MPad, N, NPad, StrideDs[i]);
            },
            Number<NumDTensor>{});
    }

    template <typename DsGridDesc>
    __device__ static constexpr auto MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
        const DsGridDesc& ds_grid_desc_m_n, index_t MBlock, index_t NBlock)
    {
        return generate_tuple(
            [&](auto i) {
                return MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
                    ds_grid_desc_m_n[i], MBlock, NBlock);
            },
            Number<NumDTensor>{});
    }

    struct Problem
    {
        __host__ __device__ Problem(index_t NumTokens_,
                                    index_t TopK_,
                                    index_t M_,
                                    index_t N_,
                                    index_t K_,
                                    index_t StrideA_,
                                    index_t StrideB_,
                                    std::array<index_t, NumDTensor> StrideDs_,
                                    index_t StrideC_,
                                    index_t KBatch_)
            : 
              NumTokens{NumTokens_},
              TopK{TopK_},
              M{M_},
              N{N_},
              K{K_},
              StrideA{StrideA_},
              StrideB{StrideB_},
              StrideDs{StrideDs_},
              StrideC{StrideC_},
              KBatch{KBatch_},
              MPadded{CalculateMPadded(M_)},
              NPadded{CalculateNPadded(N_)},
              KRead{CalculateKRead(K_, KBatch_)},
              KPadded{CalculateKPadded(K_, KBatch_)},
              AK0{CalculateAK0Padded(K_, KBatch_)},
              BK0{CalculateBK0Padded(K_, KBatch_)},
              MBlock{CalculateMBlock(M_)},
              NBlock{CalculateNBlock(N_)},
              BN0Shuffled{CalculateBN0Shuffled(N_)},
              BK0Shuffled{CalculateBK0Shuffled(K_)}
        {
        }

        __host__ void Print() const
        {
            std::cout << "problem {"
                      << "NumTokens:" << NumTokens << ", "
                      << "TopK:" << TopK << ", "
                      << "M:" << M << ", "
                      << "N:" << N << ", "
                      << "K:" << K << ", "
                      << "SA:" << StrideA << ", "
                      << "SB:" << StrideB << ", "
                      << "SC:" << StrideC << ", "
                      << "MP:" << MPadded << ", "
                      << "NP:" << NPadded << ", "
                      << "KRead:" << KRead << ", "
                      << "KP:" << KPadded << ", "
                      << "AK0:" << AK0 << ", "
                      << "BK0:" << BK0 << ", "
                      << "MBlock: " << MBlock << ", "
                      << "NBlock: " << NBlock << "}" << std::endl;
        }

        index_t NumTokens;
        index_t TopK;
        index_t M;
        index_t N;
        index_t K;
        index_t StrideA;
        index_t StrideB;
        std::array<index_t, NumDTensor> StrideDs;
        index_t StrideC;
        index_t KBatch;
        index_t MPadded;
        index_t NPadded;
        index_t KRead;
        index_t KPadded;
        index_t AK0;
        index_t BK0;
        index_t MBlock;
        index_t NBlock;
        // FOR PRESHUFFLE ONLY
        index_t BN0Shuffled;
        index_t BK0Shuffled;
    };

    // Argument
    struct Argument : public tensor_operation::device::BaseArgument, public Problem
    {
        __host__ Argument(
                          const index_t* p_sorted_token_ids_,
                          const index_t* p_sorted_expert_ids_,
                          const index_t* p_max_token_id_,
                          const ADataType* p_a_grid_,
                          const BDataType* p_b_grid_,
                          std::array<const void*, NumDTensor> p_ds_grid_,
                          CDataType* p_c_grid_,
                          index_t NumTokens_,
                          index_t TopK_,
                          index_t M_,
                          index_t N_,
                          index_t K_,
                          index_t StrideA_,
                          index_t StrideB_,
                          std::array<index_t, NumDTensor> StrideDs_,
                          index_t StrideC_,
                          index_t k_batch_,
                          AElementwiseOperation a_element_op_,
                          BElementwiseOperation b_element_op_,
                          CElementwiseOperation c_element_op_)
            : Problem{NumTokens_, TopK_, M_, N_, K_, StrideA_, StrideB_, StrideDs_, StrideC_, k_batch_},
              p_sorted_token_ids{p_sorted_token_ids_},
              p_sorted_expert_ids{p_sorted_expert_ids_},
              p_max_token_id{p_max_token_id_},
              p_a_grid{p_a_grid_},
              p_b_grid{p_b_grid_},
              p_ds_grid{},
              p_c_grid{p_c_grid_},
              a_element_op{a_element_op_},
              b_element_op{b_element_op_},
              c_element_op{c_element_op_}
        {

            // populate pointer, desc for Ds
            static_for<0, NumDTensor, 1>{}([&](auto i) {
                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]);
            });
        }

        const index_t * p_sorted_token_ids;
        const index_t * p_sorted_expert_ids;
        const index_t * p_max_token_id;
        const ADataType* p_a_grid;
        const BDataType* p_b_grid;
        DsGridPointer p_ds_grid;
        CDataType* p_c_grid;

        const AElementwiseOperation a_element_op;
        const BElementwiseOperation b_element_op;
        const CElementwiseOperation c_element_op;
    };

    struct SplitKBatchOffset
    {
        __device__ SplitKBatchOffset(Argument& karg, index_t k_id)
        {
            if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
            {
                a_k_split_offset = k_id * karg.KRead;
            }
            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
            {
                a_k_split_offset = k_id * karg.KRead * karg.StrideA;
            }

            if constexpr(is_same_v<tensor_layout::gemm::RowMajor, BLayout>)
            {
                b_k_split_offset = k_id * karg.KRead * karg.StrideB;
            }
            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, BLayout>)
            {
                // KPack * NLane * KLane * K0 * N0
                b_k_split_offset = k_id * karg.KRead * NLane;
            }

            if(k_id < karg.KBatch - 1)
            {
                karg.K = karg.KRead;
            }
            else
            {
                karg.K = karg.K - karg.KRead * (karg.KBatch - 1);
            }
        }

        index_t a_k_split_offset;
        index_t b_k_split_offset;
    };

    __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
    {
        // A matrix in LDS memory, dst of blockwise copy
        if constexpr(ABlockLdsExtraM)
        {
            return make_naive_tensor_descriptor(
                make_tuple(AK0Number, Number<MPerBlock>{}, AK1Number),
                make_tuple(AK1Number, Number<KPerBlock + ABlockLdsExtraM>{}, I1));
        }
        // xor tensor transformation request more unnecessary vgpr usage, would cause register spill
        // in some cases.
        else if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
        {
            constexpr auto MLdsLayer        = 32 * 4 / KPerBlock / sizeof(LDSTypeA) < 1
                                                  ? 1
                                                  : 32 * 4 / KPerBlock / sizeof(LDSTypeA);
            constexpr auto a_lds_block_desc = make_naive_tensor_descriptor(
                make_tuple(
                    AK0Number * Number<MLdsLayer>{}, Number<MPerBlock / MLdsLayer>{}, AK1Number),
                make_tuple(AK1Number, Number<KPerBlock * MLdsLayer>{}, I1));

            constexpr auto a_lds_block_desc_permuted = transform_tensor_descriptor(
                a_lds_block_desc,
                make_tuple(make_xor_with_modulo_transform(make_tuple(
                               Number<MPerBlock / MLdsLayer>{}, Number<AK0Number * MLdsLayer>{})),
                           make_pass_through_transform(AK1Number)),
                make_tuple(Sequence<1, 0>{}, Sequence<2>{}),
                make_tuple(Sequence<1, 0>{}, Sequence<2>{}));

            constexpr auto a_lds_block_desc_ak0_mldslayer_m_ak1 = transform_tensor_descriptor(
                a_lds_block_desc_permuted,
                make_tuple(make_unmerge_transform(make_tuple(AK0Number, Number<MLdsLayer>{})),
                           make_pass_through_transform(Number<MPerBlock / MLdsLayer>{}),
                           make_pass_through_transform(AK1Number)),
                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}, Sequence<3>{}));

            constexpr auto a_lds_block_desc_ak0_m_ak1 = transform_tensor_descriptor(
                a_lds_block_desc_ak0_mldslayer_m_ak1,
                make_tuple(make_pass_through_transform(AK0Number),
                           make_merge_transform_v3_division_mod(
                               make_tuple(Number<MPerBlock / MLdsLayer>{}, Number<MLdsLayer>{})),
                           make_pass_through_transform(AK1Number)),
                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}),
                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));

            return a_lds_block_desc_ak0_m_ak1;
        }
        else // ColumnMajor A
        {
            // kfold and mpair dimension is not always required.
            // more dimension in merge_transform increase the difficulty of generating immarg offset
            // for compiler.
            constexpr auto M0 = ABlockTransferThreadClusterLengths_AK0_M_AK1{}.At(I1);
            constexpr auto M1 = MPerBlock / M0;

            constexpr auto KThreadWrite     = ABlockTransferThreadClusterLengths_AK0_M_AK1{}.At(I0);
            constexpr auto K0PerThreadWrite = AK0Number / KThreadWrite;
            constexpr auto KThreadRead      = 64 / MPerXdl;
            constexpr auto K0PerThreadRead  = AK0Number / KThreadRead;

            constexpr auto kfold = (AK1Number * M0 * sizeof(LDSTypeA) > 128)
                                       ? 1
                                       : 128 / (AK1Number * M0 * sizeof(LDSTypeA));
            constexpr auto KThreadReadPerm =
                (kfold * K0PerThreadWrite / K0PerThreadRead) > 1
                    ? KThreadRead / (kfold * K0PerThreadWrite / K0PerThreadRead)
                    : KThreadRead;

            // 1<=mpair<=n0
            constexpr auto mpair = (AK1Number * MPerXdl * sizeof(LDSTypeA) > 128)
                                       ? 1
                                       : ((128 / (AK1Number * MPerXdl * sizeof(LDSTypeA))) > M0
                                              ? M0
                                              : 128 / (AK1Number * MPerXdl * sizeof(LDSTypeA)));

            constexpr auto a_lds_block_desc = make_naive_tensor_descriptor_packed(
                make_tuple(Number<KThreadWrite / kfold / KThreadReadPerm>{},
                           Number<K0PerThreadWrite>{},
                           Number<KThreadReadPerm * M1>{},
                           Number<kfold * M0 / mpair>{},
                           Number<mpair>{},
                           AK1Number));

            constexpr auto a_lds_block_desc_permuted = transform_tensor_descriptor(
                a_lds_block_desc,
                make_tuple(
                    make_pass_through_transform(Number<KThreadWrite / kfold / KThreadReadPerm>{}),
                    make_pass_through_transform(Number<K0PerThreadWrite>{}),
                    make_xor_with_modulo_transform(
                        make_tuple(Number<KThreadReadPerm * M1>{}, Number<kfold * M0 / mpair>{})),
                    make_pass_through_transform(Number<mpair>{}),
                    make_pass_through_transform(AK1Number)),
                make_tuple(
                    Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}, Sequence<5>{}),
                make_tuple(
                    Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}, Sequence<5>{}));

            constexpr auto a_lds_block_desc_unmerged = transform_tensor_descriptor(
                a_lds_block_desc_permuted,
                make_tuple(
                    make_pass_through_transform(Number<KThreadWrite / kfold / KThreadReadPerm>{}),
                    make_pass_through_transform(Number<K0PerThreadWrite>{}),
                    make_unmerge_transform(make_tuple(Number<KThreadReadPerm>{}, Number<M1>{})),
                    make_unmerge_transform(make_tuple(Number<kfold>{}, Number<M0 / mpair>{})),
                    make_pass_through_transform(Number<mpair>{}),
                    make_pass_through_transform(AK1Number)),
                make_tuple(Sequence<0>{},
                           Sequence<1>{},
                           Sequence<2>{},
                           Sequence<3>{},
                           Sequence<4>{},
                           Sequence<5>{}),
                make_tuple(Sequence<1>{},
                           Sequence<2>{},
                           Sequence<0, 3>{},
                           Sequence<4, 5>{},
                           Sequence<6>{},
                           Sequence<7>{}));

            constexpr auto a_lds_block_desc_ak0_m_ak1 = transform_tensor_descriptor(
                a_lds_block_desc_unmerged,
                make_tuple(make_merge_transform_v3_division_mod(
                               make_tuple(Number<KThreadReadPerm>{},
                                          Number<KThreadWrite / kfold / KThreadReadPerm>{},
                                          Number<kfold>{},
                                          Number<K0PerThreadWrite>{})),
                           make_merge_transform_v3_division_mod(
                               make_tuple(Number<M0 / mpair>{}, Number<mpair>{}, Number<M1>{})),
                           make_pass_through_transform(AK1Number)),
                make_tuple(Sequence<0, 1, 4, 2>{}, Sequence<5, 6, 3>{}, Sequence<7>{}),
                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));

            return a_lds_block_desc_ak0_m_ak1;
        }
    }

    __device__ static constexpr auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()
    {
        // K0 -> N0/NWave -> NWave -> KLane -> NLane -> KPack
        return make_naive_tensor_descriptor_packed(
            make_tuple(Number<NXdlPerWave>{}, I1, Number<KRepeat>{}, Number<BK1Value>{}));
    }

    __device__ static constexpr auto GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
    {
        constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);

        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;
    }

    using BlockwiseGemmPipe =
        remove_cvref_t<decltype(BlockGemmBPreshufflePipeline_Selector<
                                BlkGemmPipelineVer,
                                BlkGemmPipeSched,
                                BlockSize,
                                LDSTypeA,
                                LDSTypeB,
                                ComputeTypeA,
                                AccDataType,
                                decltype(GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()),
                                decltype(GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()),
                                decltype(MakeAMmaTileDescriptor_M0_M1_M2_K(
                                    GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1())),
                                decltype(MakeBMmaTileDescriptor_N0_N1_N2_K(
                                    GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1())),
                                ABlockTransferSrcScalarPerVector,
                                BBlockTransferSrcScalarPerVector,
                                MPerBlock,
                                NPerBlock,
                                KPerBlock,
                                MPerXdl,
                                NPerXdl,
                                MXdlPerWave,
                                NXdlPerWave,
                                KPack>())>;

    __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();
        // lds max alignment
        constexpr auto max_lds_align = math::lcm(AK1Number, BK1Number);

        constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
            a_block_desc_ak0_m_ak1.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(LDSTypeA),
                         c_block_size * sizeof(CShuffleDataType));
    }

    // block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
    __host__ static constexpr bool CheckValidity(const Argument& karg)
    {
        static_assert((MPerBlock % (MPerXdl * MXdlPerWave) == 0) &&
                          (NPerBlock % (NXdlPerWave * NPerXdl)) == 0,
                      "Invalid tuning param!");

        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) &&
                     !(is_same<tensor_layout::gemm::RowMajor, ALayout>::value))
        {
            if(!(karg.M % MPerBlock == 0))
            {
#if DEBUG_LOG
                std::cout << "Arg M value is not a multiple of MPerBlock! M: " << karg.M << " "
                          << __FILE__ << ":" << __LINE__ << ", in function: " << __func__
                          << std::endl;

#endif // DEBUG_LOG
                return false;
            }
        }

        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) &&
                     (is_same<tensor_layout::gemm::RowMajor, BLayout>::value))
        {
            if(!(karg.N % NPerBlock == 0))
            {
#if DEBUG_LOG
                std::cout << "Arg N value is not a multiple of NPerBlock! N: " << karg.N << " "
                          << __FILE__ << ":" << __LINE__ << ", in function: " << __func__
                          << std::endl;

#endif // DEBUG_LOG
                return false;
            }
        }

        if constexpr(!(GemmSpec == tensor_operation::device::GemmSpecialization::KPadding ||
                       GemmSpec == tensor_operation::device::GemmSpecialization::MKPadding ||
                       GemmSpec == tensor_operation::device::GemmSpecialization::NKPadding ||
                       GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding))
        {

            auto K_t = karg.KBatch * KPerBlock;
            if(!(karg.K % K_t == 0))
            {
#if DEBUG_LOG
                std::cout << "Arg K value is not a multiple of K_Batch * K0PerBlock * K1! K: "
                          << karg.K << " " << __FILE__ << ":" << __LINE__
                          << ", in function: " << __func__ << std::endl;

#endif // DEBUG_LOG
                return false;
            }
        }
        else
        {
            constexpr auto KReadVec = math::lcm(AK1Number, BK1Number);
            auto K_t                = karg.KBatch * KReadVec;
            auto KReadPadSplited    = math::integer_divide_ceil(karg.K, K_t) * KReadVec;
            if((KReadPadSplited * (karg.KBatch - 1)) >= karg.K)
            {
                return false;
            }
        }

        if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
        {
            if(karg.K % ABlockTransferSrcScalarPerVector != 0)
            {
#if DEBUG_LOG
                std::cout << "Arg K (" << karg.K
                          << ") value is not a multiple of ABlockTransferSrcScalarPerVector ("
                          << ABlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"
                          << __LINE__ << ", in function: " << __func__ << std::endl;

#endif // DEBUG_LOG
                return false;
            }
        }
        else
        {
            if(karg.M % ABlockTransferSrcScalarPerVector != 0)
            {
#if DEBUG_LOG
                std::cout << "Arg M (" << karg.M
                          << ") value is not a multiple of ABlockTransferSrcScalarPerVector ("
                          << ABlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"
                          << __LINE__ << ", in function: " << __func__ << std::endl;

#endif // DEBUG_LOG
                return false;
            }
        }

        if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
        {
            if(karg.N % BBlockTransferSrcScalarPerVector != 0)
            {
#if DEBUG_LOG
                std::cout << "Arg N (" << karg.N
                          << ") value is not a multiple of BBlockTransferSrcScalarPerVector ("
                          << BBlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"
                          << __LINE__ << ", in function: " << __func__ << std::endl;

#endif // DEBUG_LOG
                return false;
            }
        }
        else
        {
            if(karg.K % BBlockTransferSrcScalarPerVector != 0)
            {
#if DEBUG_LOG
                std::cout << "Arg K (" << karg.K
                          << ") value is not a multiple of BBlockTransferSrcScalarPerVector ("
                          << BBlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"
                          << __LINE__ << ", in function: " << __func__ << std::endl;

#endif // DEBUG_LOG
                return false;
            }
        }

        if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
        {
            if(karg.N % CShuffleBlockTransferScalarPerVector_NPerBlock != 0)
            {
#if DEBUG_LOG
                std::cout << "Arg N (" << karg.N
                          << ") value is not a multiple of "
                             "CShuffleBlockTransferScalarPerVector_NPerBlock ("
                          << CShuffleBlockTransferScalarPerVector_NPerBlock << " )! " << __FILE__
                          << ":" << __LINE__ << ", in function: " << __func__ << std::endl;

#endif // DEBUG_LOG
                return false;
            }
        }
        else
        {
            if(karg.M % CShuffleBlockTransferScalarPerVector_NPerBlock != 0)
            {
#if DEBUG_LOG
                std::cout << "Arg M (" << karg.M
                          << ") value is not a multiple of "
                             "CShuffleBlockTransferScalarPerVector_NPerBlock ("
                          << CShuffleBlockTransferScalarPerVector_NPerBlock << " )! " << __FILE__
                          << ":" << __LINE__ << ", in function: " << __func__ << std::endl;

#endif // DEBUG_LOG
                return false;
            }
        }

        // check gridwise gemm pipeline
#if 1
        const auto num_k_loop = karg.AK0 / (KPerBlock / AK1Value);

        if(num_k_loop <= BlockwiseGemmPipe::PrefetchStages)
        {
            return false;
        }
#endif
        // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
        return true;
    }

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

        return BlockwiseGemmPipe::BlockHasHotloop(num_loop);
    }

    __host__ __device__ static constexpr TailNumber CalculateKBlockLoopTailNum(index_t K)
    {
        const index_t num_loop = K / KPerBlock;

        return BlockwiseGemmPipe::BlockLoopTailNum(num_loop);
    }

    template <typename CGridDesc>
    __device__ static constexpr auto MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
        const CGridDesc& c_grid_desc_m_n, index_t MBlock, index_t NBlock)
    {
        const auto c_grid_desc_mblock_mperblock_nblock_nperblock = transform_tensor_descriptor(
            c_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 c_grid_desc_mblock_mperblock_nblock_nperblock;
    }

    // return block_id to C matrix tile idx (m0, n0) mapping
    // if arch = gfx942
    // using Block2CTileMapDefault = BlockToCTileMap_Grouped_M00_N0_M01Adapt<8, MPerBlock, NPerBlock>;

    template <bool HasMainKBlockLoop,
              InMemoryDataOperationEnum CGlobalMemoryDataOperation,
              TailNumber TailNum = TailNumber::Odd>
    __device__ static void Run(
                               const index_t* p_sorted_token_ids,
                               const index_t* p_sorted_expert_ids,
                               const index_t* p_max_token_id,
                               const ADataType* p_a_grid,
                               const BDataType* p_b_grid,
                               DsGridPointer& p_ds_grid,
                               CDataType* p_c_grid,
                               void* p_shared,
                               const Problem& problem,
                               AElementwiseOperation a_element_op,
                               BElementwiseOperation b_element_op,
                               CElementwiseOperation c_element_op)
    {
        ignore                           = b_element_op;
        const auto a_grid_desc_ak0_m_ak1 = MakeAGridDescriptor_AK0_M_AK1(
            problem.NumTokens * problem.TopK, problem.MPadded, problem.K, problem.KPadded, problem.StrideA, problem.AK0);

        const auto b_grid_desc_bpreshuffled =
            MakeBGridDescriptor_Preshuffled(problem.BN0Shuffled, problem.BK0Shuffled);
        const auto c_grid_desc_m_n = MakeCGridDescriptor_M_N<CLayout>(
            problem.NumTokens, problem.MPadded, problem.N, problem.NPadded, problem.StrideC);
        const auto c_grid_desc_mblock_mperblock_nblock_nperblock =
            MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
                c_grid_desc_m_n, problem.MBlock, problem.NBlock);

        const index_t block_n_id = __builtin_amdgcn_readfirstlane(blockIdx.x);
        const index_t block_m_id = __builtin_amdgcn_readfirstlane(blockIdx.y);
        const index_t expert_id = __builtin_amdgcn_readfirstlane(p_sorted_expert_ids[block_m_id]);
        const index_t max_token_id = __builtin_amdgcn_readfirstlane(p_max_token_id[0]);     
        const index_t token0 = __builtin_amdgcn_readfirstlane(p_sorted_token_ids[block_m_id * MPerBlock] & 0xffffff);
        
        // constexpr auto M0 = ABlockTransferThreadClusterLengths_AK0_M_AK1{}.At(I1);
        constexpr auto AMThreads = ABlockTransferThreadClusterLengths_AK0_M_AK1{}.At(I1);
        constexpr auto AK0Threads = ABlockTransferThreadClusterLengths_AK0_M_AK1{}.At(I0);
        constexpr auto AK1Threads = ABlockTransferThreadClusterLengths_AK0_M_AK1{}.At(I2);
        constexpr auto AKThreads = AK0Threads * AK1Threads;
        constexpr auto AMRepeats = MPerBlock / AMThreads;
        const index_t token_pos = block_m_id * MPerBlock + threadIdx.x / AKThreads * AMRepeats;
                
        if(token_pos >= max_token_id || token0 >= problem.NumTokens)
            return;
        StaticallyIndexedArray<index_t, AMRepeats> gather_offsets; //= p_sorted_token_ids[token_pos];
        static_for<0, AMRepeats, 1>{}([&](auto m0) {
            const index_t fused_token = p_sorted_token_ids[token_pos + m0];
            const index_t token_offset = (fused_token & 0xffffff) * problem.TopK + (fused_token >> 24);
            gather_offsets(m0) = token_offset * problem.K;
            // printf("init off tid %d m %d off %d\n", threadIdx.x, m0(), gather_offsets(m0));
        });
        const index_t expert_stride = __builtin_amdgcn_readfirstlane(problem.N * problem.K);

        // N0, K0, Blocksize*KPack
        const index_t n_block_data_idx_on_grid =
            __builtin_amdgcn_readfirstlane(block_n_id * NXdlPerWave);

        const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
            p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());
        const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
            p_b_grid + expert_id * expert_stride, b_grid_desc_bpreshuffled.GetElementSpaceSize());
        // if(threadIdx.x==0)
        // printf("tid %d eid %d expert_stride %d bufsize %d\n",
        // threadIdx.x, expert_id, expert_stride, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());

        // 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
        // dummy
        constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
        // A matrix blockwise copy
        auto a_blockwise_copy =
            ThreadGroupTensorSliceTransfer_v4r1_mod8<ThisThreadBlock,
                                                AElementwiseOperation,
                                                ck::tensor_operation::element_wise::PassThrough,
                                                InMemoryDataOperationEnum::Set,
                                                Sequence<AK0Number, MPerBlock, AK1Number>,
                                                ABlockTransferThreadClusterLengths_AK0_M_AK1,
                                                ABlockTransferThreadClusterArrangeOrder,
                                                ADataType,
                                                LDSTypeA,
                                                decltype(a_grid_desc_ak0_m_ak1),
                                                decltype(a_block_desc_ak0_m_ak1),
                                                ABlockTransferSrcAccessOrder,
                                                Sequence<0, 1, 2>,
                                                ABlockTransferSrcVectorDim,
                                                2,
                                                ABlockTransferSrcScalarPerVector,
                                                ABlockTransferDstScalarPerVector_AK1,
                                                1,
                                                1,
                                                AThreadTransferSrcResetCoordinateAfterRun,
                                                true,
                                                1,
                                                BlockwiseGemmPipe::GlobalBufferNum>(
                a_grid_desc_ak0_m_ak1,
                make_multi_index(0, 0, 0),
                a_element_op,
                a_block_desc_ak0_m_ak1,
                make_multi_index(0, 0, 0),
                ck::tensor_operation::element_wise::PassThrough{},
                gather_offsets);

        // Thread-wise copy
        // K0 -> N0/NWave -> NWave -> KLane -> NLane -> KPack
        auto b_block_buf = make_static_buffer<AddressSpaceEnum::Vgpr, BDataType>(
            b_block_desc_bk0_n_bk1.GetElementSpaceSize());

        auto b_blockwise_copy = ThreadwiseTensorSliceTransfer_v2<
            BDataType,
            BDataType,
            decltype(b_grid_desc_bpreshuffled),
            decltype(b_block_desc_bk0_n_bk1),
            Sequence<Number<NXdlPerWave>{}, I1, Number<KRepeat>{}, Number<BK1Value>{}>,
            Sequence<0, 1, 2, 3>,
            3,
            BBlockTransferSrcScalarPerVector,
            BThreadTransferSrcResetCoordinateAfterRun,
            true>(b_grid_desc_bpreshuffled,
                  make_multi_index(n_block_data_idx_on_grid,
                                   get_warp_local_1d_id(),
                                   0,
                                   KPack * (get_thread_local_1d_id() % warpSize)));

        // LDS allocation for A and B: be careful of alignment
        // Cast after lds
        auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
            static_cast<LDSTypeA*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());

        constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1Number, 0, 0);
        constexpr auto b_block_slice_copy_step = make_multi_index(0, 0, KRepeat, 0);

        // Blockwise GEMM pipeline
        static_assert(std::is_default_constructible_v<BlockwiseGemmPipe>);
        auto blockwise_gemm_pipeline = BlockwiseGemmPipe{};
        auto c_thread_buf            = blockwise_gemm_pipeline.GetCThreadBuffer();

        const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(
            (a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2)) /
            KPerBlock);

        blockwise_gemm_pipeline.template Run<HasMainKBlockLoop, TailNum>(a_grid_desc_ak0_m_ak1,
                                                                         a_block_desc_ak0_m_ak1,
                                                                         a_blockwise_copy,
                                                                         a_grid_buf,
                                                                         a_block_buf,
                                                                         a_block_slice_copy_step,
                                                                         b_grid_desc_bpreshuffled,
                                                                         b_blockwise_copy,
                                                                         b_grid_buf,
                                                                         b_block_buf,
                                                                         b_block_slice_copy_step,
                                                                         c_thread_buf,
                                                                         num_k_block_main_loop);

        // shuffle C and write out
        {
            static_assert(MXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&
                              NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,
                          "wrong!");

            constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);

            // TODO: hacky, fix it!
            constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
                blockwise_gemm_pipeline.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_pipeline.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_pipeline.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{}};

            using EDataType = CDataType;

            const auto ds_grid_desc_m_n = MakeDsGridDescriptor_M_N(
                problem.M, problem.MPadded, problem.N, problem.NPadded, problem.StrideDs);

            const auto ds_grid_desc_mblock_mperblock_nblock_nperblock =
                MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
                    ds_grid_desc_m_n, problem.MBlock, problem.NBlock);

            const auto ds_grid_buf = generate_tuple(
                [&](auto i) {
                    using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
                    const DDataType *ptr_ = p_ds_grid[i];
                    // hack logic here to support different kind of strides. todo fix it.
                    // ascale t, 1; bscale E, N, 1, move ptr to E
                    if (i.value == 1) 
                    {
                        ptr_ += expert_id * (problem.StrideDs[1]? problem.StrideDs[1] * problem.N : 1);
                        // if ( threadIdx.x  % 16 ==0)
                        //     printf("bid %d eid %d b eoff %d %f\n", blockIdx.y, expert_id, expert_id * (problem.StrideDs[1]? problem.StrideDs[1] * problem.N : 1), ptr_[0]);
                    }
                    return make_dynamic_buffer<AddressSpaceEnum::Global>(
                        ptr_, ds_grid_desc_m_n[i].GetElementSpaceSize());
                },
                Number<NumDTensor>{});

            // 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_m_id, 0, block_n_id, 0);
                        // return make_multi_index(block_work_idx[I0], 0, block_work_idx[I1], 0);
                    },
                    Number<NumDTensor>{}));

            const auto e_grid_desc_mblock_mperblock_nblock_nperblock =
                c_grid_desc_mblock_mperblock_nblock_nperblock;

            using CDEBlockTransferCluster =
                CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock;
            const auto EGlobalMemoryDataOperation = CGlobalMemoryDataOperation;
            constexpr auto EMThreads = CDEBlockTransferCluster{}.At(I0) * CDEBlockTransferCluster{}.At(I1);
            constexpr auto EMRepeats = MPerBlock / EMThreads;
            constexpr auto ENThreads = CDEBlockTransferCluster{}.At(I2) * CDEBlockTransferCluster{}.At(I3);
            const index_t c_token_pos = block_m_id * MPerBlock + threadIdx.x / ENThreads * EMRepeats;
            StaticallyIndexedArray<index_t, EMRepeats> scatter_offsets; //= p_sorted_token_ids[c_token_pos];
            StaticallyIndexedArray<float, EMRepeats> scatter_weights; //= for topk
            // too hack here, 2 specific for topk weights, fixme
            const float *p_sorted_weights_2 = p_ds_grid[I2];
            const float *p_sorted_weights_0 = p_ds_grid[I0];
            static_for<0, EMRepeats, 1>{}([&](auto m0) {
                scatter_offsets(m0) = (p_sorted_token_ids[c_token_pos + m0] & 0xffffff) * problem.N;
                scatter_weights(m0) = p_sorted_weights_2[c_token_pos + m0] 
                     * p_sorted_weights_0[(c_token_pos + m0) * problem.StrideDs[0]];
                // printf("init off bid %d tid %d m %d off %d\n", blockIdx.y, threadIdx.x, m0(), scatter_offsets(m0));
            });

            // printf("tid %d pos %d offset %d size %d\n", threadIdx.x, token_pos, scatter_offsets(I0), c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
            auto cde_block_copy_lds_and_global = ThreadGroupTensorSliceTransfer_v7r3_scatter<
                ThisThreadBlock,
                decltype(container_concat(make_tuple(CShuffleDataType{}), DsDataType{})),
                Tuple<EDataType>,
                decltype(c_ds_desc_refs),
                decltype(tie(e_grid_desc_mblock_mperblock_nblock_nperblock)),
                CElementwiseOperation,
                Sequence<static_cast<index_t>(EGlobalMemoryDataOperation)>, // FIXME: make Sequence
                                                                            // support arbitray type
                Sequence<1,
                         CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
                         1,
                         CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,
                CDEBlockTransferCluster,
                Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
                Sequence<0, 1, 2, 3>, // typename SrcDimAccessOrder,
                Sequence<0, 1, 2, 3>, // typename DstDimAccessOrder,
                3,                    // index_t SrcVectorDim,
                3,                    // index_t DstVectorDim,
                CDEShuffleBlockTransferScalarPerVectors,
                CShuffleBlockTransferScalarPerVector_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(0, 0, block_n_id, 0)),
                 c_element_op,
                 scatter_offsets,
                 scatter_weights};
            // if(threadIdx.x== 0)
            auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
                p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
            // space filling curve for threadwise C in VGPR
            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>>{};

            constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();

            // 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>>{};

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

    // template <bool HasMainKBlockLoop,
    //           InMemoryDataOperationEnum CGlobalMemoryDataOperation,
    //           TailNumber TailNum = TailNumber::Odd>
    // __device__ static void Run_2Lds(const ADataType* p_a_grid,
    //                                 const BDataType* p_b_grid,
    //                                 DsGridPointer& p_ds_grid,
    //                                 CDataType* p_c_grid,
    //                                 void* p_shared,
    //                                 void* p_shared1,
    //                                 const Problem& problem,
    //                                 AElementwiseOperation a_element_op,
    //                                 BElementwiseOperation b_element_op,
    //                                 CElementwiseOperation c_element_op)
    // {
    //     // const auto block_2_ctile_map = Block2CTileMapDefault{problem.M, problem.N, 4};
    //     // Run_2Lds<Block2CTileMapDefault, HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(
    //     //     p_a_grid,
    //     //     p_b_grid,
    //     //     p_ds_grid,
    //     //     p_c_grid,
    //     //     p_shared,
    //     //     p_shared1,
    //     //     problem,
    //     //     a_element_op,
    //     //     b_element_op,
    //     //     c_element_op,
    //     //     block_2_ctile_map);
    // }

    // template <typename Block2CTileMap,
    //           bool HasMainKBlockLoop,
    //           InMemoryDataOperationEnum CGlobalMemoryDataOperation,
    //           TailNumber TailNum = TailNumber::Odd>
    // __device__ static void Run_2Lds(const ADataType* p_a_grid,
    //                                 const BDataType* p_b_grid,
    //                                 DsGridPointer& p_ds_grid,
    //                                 CDataType* p_c_grid,
    //                                 void* p_shared,
    //                                 void* p_shared1,
    //                                 const Problem& problem,
    //                                 AElementwiseOperation a_element_op,
    //                                 BElementwiseOperation b_element_op,
    //                                 CElementwiseOperation c_element_op,
    //                                 const Block2CTileMap& block_2_ctile_map)
    // {
    // }
};

} // namespace ck
