// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.

#pragma once

#include <iostream>
#include <sstream>

#include "ck/utility/common_header.hpp"
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_fixed_nk.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_splitk_cshuffle.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp"

namespace ck {
namespace tensor_operation {
namespace device {

template <typename GridwiseGemm,
          typename GemmDesc,
          GemmSpecialization GemmSpec,
          typename ALayout,
          typename BLayout,
          typename DsLayout,
          typename ELayout,
          typename DsDataType,
          typename Block2ETileMap,
          typename GroupedGemmBlock2ETileMap,
          typename AElementwiseOperation,
          typename BElementwiseOperation,
          typename CDEElementwiseOperation,
          InMemoryDataOperationEnum EGlobalMemoryDataOperation,
          bool HasMainKBlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
        kernel_grouped_gemm_xdl_fixed_nk(const void CK_CONSTANT_ADDRESS_SPACE* gemm_descs_const,
                                         uint32_t* barrier_count,
                                         const index_t barrier_size_grp,
                                         const index_t group_count,
                                         const index_t grid_size_grp,
                                         const index_t KBatch,
                                         const AElementwiseOperation a_element_op,
                                         const BElementwiseOperation b_element_op,
                                         const CDEElementwiseOperation c_element_op)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
    defined(__gfx94__))
    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];

    const index_t block_id = get_block_1d_id();

    const auto gemm_desc_ptr =
        reinterpret_cast<const GemmDesc*>(cast_pointer_to_generic_address_space(gemm_descs_const));

    const index_t group_id = block_id / grid_size_grp;

    if(group_id >= group_count)
        return;

    const index_t M = gemm_desc_ptr[group_id].M;
    const index_t N = gemm_desc_ptr[group_id].N;
    const index_t K = gemm_desc_ptr[group_id].K;

    if(M * N * K == 0)
        return;

    const auto StrideA  = gemm_desc_ptr[group_id].StrideA;
    const auto StrideB  = gemm_desc_ptr[group_id].StrideB;
    const auto StrideDs = gemm_desc_ptr[group_id].StrideDs;
    const auto StrideE  = gemm_desc_ptr[group_id].StrideE;

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

    const index_t BlockStart = group_id * grid_size_grp;

    const auto local_b2e_tile_map = Block2ETileMap{e_grid_desc_m_n, KBatch};

    const auto local_grid_size = local_b2e_tile_map.CalculateGridSize(e_grid_desc_m_n);

    constexpr auto NumDTensor = DsDataType::Size();

    using DsGridPointer = decltype(GridwiseGemm::MakeDsGridPointer());

    DsGridPointer p_ds_grid_;

    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*>(gemm_desc_ptr[group_id].p_ds_grid[i]);
    });

    index_t id_off   = 0;
    index_t id_local = get_block_1d_id() - BlockStart;

    const index_t mn_blocks = local_grid_size / KBatch;

    while(id_local < local_grid_size)
    {
        const auto block_2_etile_map =
            GroupedGemmBlock2ETileMap(local_b2e_tile_map, BlockStart, id_off);

        auto barrier_count_finished =
            barrier_count + group_id * barrier_size_grp + id_local % mn_blocks;

        GridwiseGemm::template Run<HasMainKBlockLoop,
                                   EGlobalMemoryDataOperation,
                                   GemmSpec,
                                   ALayout,
                                   BLayout,
                                   DsLayout,
                                   ELayout>(gemm_desc_ptr[group_id].p_a_grid,
                                            gemm_desc_ptr[group_id].p_b_grid,
                                            p_ds_grid_,
                                            gemm_desc_ptr[group_id].p_e_grid,
                                            p_shared,
                                            barrier_count_finished,
                                            a_element_op,
                                            b_element_op,
                                            c_element_op,
                                            M,
                                            N,
                                            K,
                                            StrideA,
                                            StrideB,
                                            StrideDs,
                                            StrideE,
                                            KBatch,
                                            block_2_etile_map);

        id_off += grid_size_grp;
        id_local += grid_size_grp;
    }
#else
    ignore = gemm_descs_const;
    ignore = barrier_count;
    ignore = barrier_size_grp;
    ignore = group_count;
    ignore = grid_size_grp;
    ignore = KBatch;
    ignore = a_element_op;
    ignore = b_element_op;
    ignore = c_element_op;
#endif
}

template <typename ALayout,
          typename BLayout,
          typename DsLayout,
          typename ELayout,
          typename ADataType,
          typename BDataType,
          typename AccDataType,
          typename CShuffleDataType,
          typename DsDataType,
          typename EDataType,
          typename AElementwiseOperation,
          typename BElementwiseOperation,
          typename CDEElementwiseOperation,
          GemmSpecialization GemmSpec,
          ck::index_t NumPrefetch,
          ck::index_t BlockSize,
          ck::index_t MPerBlock,
          ck::index_t NPerBlock,
          ck::index_t KPerBlock,
          ck::index_t AK1,
          ck::index_t BK1,
          ck::index_t MPerXDL,
          ck::index_t NPerXDL,
          ck::index_t MXdlPerWave,
          ck::index_t NXdlPerWave,
          typename ABlockTransferThreadClusterLengths_K0_M_K1,
          typename ABlockTransferThreadClusterArrangeOrder,
          typename ABlockTransferSrcAccessOrder,
          ck::index_t ABlockTransferSrcVectorDim,
          ck::index_t ABlockTransferSrcScalarPerVector,
          ck::index_t ABlockTransferDstScalarPerVector_K1,
          bool ABlockLdsExtraM,
          typename BBlockTransferThreadClusterLengths_K0_N_K1,
          typename BBlockTransferThreadClusterArrangeOrder,
          typename BBlockTransferSrcAccessOrder,
          ck::index_t BBlockTransferSrcVectorDim,
          ck::index_t BBlockTransferSrcScalarPerVector,
          ck::index_t BBlockTransferDstScalarPerVector_K1,
          bool BBlockLdsExtraN,
          index_t CShuffleMXdlPerWavePerShuffle,
          index_t CShuffleNXdlPerWavePerShuffle,
          typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
          index_t CDEBlockTransferScalarPerVector_NPerBlock,
          typename ComputeType    = ADataType,
          LoopScheduler LoopSched = make_default_loop_scheduler()>
struct DeviceGroupedGemm_Xdl_Fixed_NK : public DeviceGroupedGemmFixedNK<ALayout,
                                                                        BLayout,
                                                                        DsLayout,
                                                                        ELayout,
                                                                        ADataType,
                                                                        BDataType,
                                                                        DsDataType,
                                                                        EDataType,
                                                                        AElementwiseOperation,
                                                                        BElementwiseOperation,
                                                                        CDEElementwiseOperation>
{
    using DeviceOp = DeviceGroupedGemm_Xdl_Fixed_NK;

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

    static constexpr auto I0 = Number<0>{};
    static constexpr auto I1 = Number<1>{};
    static constexpr auto I2 = Number<2>{};

    // GridwiseGemm
    using GridwiseGemm = GridwiseGemmMultipleD_xdl_splitk_cshuffle<
        ADataType, // TODO: distinguish A/B datatype
        BDataType,
        ComputeType,
        AccDataType,
        CShuffleDataType,
        DsDataType,
        EDataType,
        AElementwiseOperation,
        BElementwiseOperation,
        CDEElementwiseOperation,
        NumPrefetch, // NumGemmKPrefetchStage
        BlockSize,
        MPerBlock,
        NPerBlock,
        KPerBlock,
        AK1,
        BK1,
        MPerXDL,
        NPerXDL,
        MXdlPerWave,
        NXdlPerWave,
        ABlockTransferThreadClusterLengths_K0_M_K1,
        ABlockTransferThreadClusterArrangeOrder,
        ABlockTransferSrcAccessOrder,
        ABlockTransferSrcVectorDim,
        ABlockTransferSrcScalarPerVector,
        ABlockTransferDstScalarPerVector_K1,
        false, // AThreadTransferSrcResetCoordinateAfterRun,
        ABlockLdsExtraM,
        BBlockTransferThreadClusterLengths_K0_N_K1,
        BBlockTransferThreadClusterArrangeOrder,
        BBlockTransferSrcAccessOrder,
        BBlockTransferSrcVectorDim,
        BBlockTransferSrcScalarPerVector,
        BBlockTransferDstScalarPerVector_K1,
        false, // BThreadTransferSrcResetCoordinateAfterRun,
        BBlockLdsExtraN,
        CShuffleMXdlPerWavePerShuffle,
        CShuffleNXdlPerWavePerShuffle,
        CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
        CDEBlockTransferScalarPerVector_NPerBlock,
        LoopSched>;

    template <typename UnderlyingBlockToCTileMap>
    struct OffsettedBlockToCTileMapMLoops
    {
        using underlying_type = UnderlyingBlockToCTileMap;

        __host__ __device__ OffsettedBlockToCTileMapMLoops(
            UnderlyingBlockToCTileMap block_to_ctile_map, index_t block_start, index_t id_off = 0)
        {
            block_to_ctile_map_ = block_to_ctile_map;
            block_start_        = block_start;
            id_off_             = id_off;
        }

        template <typename TopIdx>
        __host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
        {
            auto idx_bot = block_to_ctile_map_.CalculateBottomIndex(
                make_multi_index(idx_top[Number<0>{}] - block_start_ + id_off_));

            return make_tuple(idx_bot[Number<0>{}], idx_bot[Number<1>{}], idx_bot[Number<2>{}]);
        }

        template <typename CTileIdx, typename CTileDim>
        __host__ __device__ bool ValidCTileIndex(const CTileIdx& c_tile_idx,
                                                 const CTileDim& c_tile_dim) const
        {
            return block_to_ctile_map_.ValidCTileIndex(c_tile_idx, c_tile_dim);
        }

        template <typename CGridDesc_M_N>
        __host__ bool CheckValidity(const CGridDesc_M_N& c_grid_desc_m_n) const
        {
            return block_to_ctile_map_.CheckValidity(c_grid_desc_m_n);
        }

        template <typename CGridDesc_M_N>
        __host__ constexpr index_t CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n) const
        {
            return block_to_ctile_map_.CalculateGridSize(c_grid_desc_m_n);
        }

        UnderlyingBlockToCTileMap block_to_ctile_map_;
        index_t block_start_;
        index_t id_off_;
    };

    template <index_t MPerBlock_, index_t NPerBlock_>
    struct BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops
    {
        static constexpr auto I0 = Number<0>{};
        static constexpr auto I1 = Number<1>{};

        __host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops() = default;

        __host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
            const BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&) = default;
        __host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
            BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&&) = default;
        __host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&
        operator=(const BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&) = default;
        __host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&
        operator=(BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&&) = default;

        __host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(index_t M,
                                                                          index_t N,
                                                                          index_t KBatch,
                                                                          index_t M01 = 8)
            : M_(M), N_(N), KBatch_(KBatch), M01_(M01)
        {
        }

        template <typename CGridDesc_M_N>
        __host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
            const CGridDesc_M_N& c_grid_desc_m_n, index_t KBatch, index_t M01 = 8)
            : BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
                  c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1), KBatch, M01)
        {
        }

        __host__ __device__ constexpr index_t CalculateGridSize(index_t M, index_t N) const
        {
            const auto M0 = math::integer_divide_ceil(M, MPerBlock);
            const auto N0 = math::integer_divide_ceil(N, NPerBlock);

            return M0 * N0 * KBatch_;
        }

        template <typename CGridDesc_M_N>
        __host__ __device__ constexpr index_t
        CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n) const
        {
            return CalculateGridSize(c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1));
        }

        template <typename CGridDesc_M_N>
        __host__ bool CheckValidity(const CGridDesc_M_N& /* c_grid_desc_m_n */) const
        {
            return true;
        }

        template <typename TopIdx>
        __host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
        {
            auto block_1d_id = idx_top[I0];

            const auto M0 = math::integer_divide_ceil(M_, MPerBlock_);
            const auto N0 = math::integer_divide_ceil(N_, NPerBlock_);

            block_1d_id = block_1d_id % (M0 * N0 * KBatch_); // hide groups

            const index_t idx_ksplit = block_1d_id / (M0 * N0);
            block_1d_id              = block_1d_id % (M0 * N0);

            index_t idx_N0 = block_1d_id % N0;
            index_t idx_M0 = block_1d_id / N0;

            const auto M01_adapt = (idx_M0 < M0 - M0 % M01_) ? M01_ : M0 % M01_;

            index_t idx_M00          = idx_M0 / M01_;
            index_t idx_M01          = idx_M0 % M01_;
            index_t idx_N0_M01_local = idx_N0 + idx_M01 * N0;

            return make_tuple(idx_ksplit,
                              idx_N0_M01_local % M01_adapt + idx_M00 * M01_,
                              idx_N0_M01_local / M01_adapt);
        }

        template <typename CTileIdx, typename CTileDim>
        __host__ __device__ bool ValidCTileIndex(const CTileIdx& /* c_tile_idx */,
                                                 const CTileDim& /* c_tile_dim */) const
        {
            return true; // always valid provided that user gets grid size from CalculateGridSize()
        }

        private:
        index_t M_;
        index_t N_;
        index_t KBatch_;
        index_t M01_;
    };

    using Block2ETileMap = BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops<MPerBlock, NPerBlock>;
    using GroupedGemmBlock2ETileMap = OffsettedBlockToCTileMapMLoops<Block2ETileMap>;

    struct GemmBiasTransKernelArg
    {
        // pointers
        const void* a_ptr_;
        const void* b_ptr_;
        std::array<const void*, NumDTensor> ds_ptr_;
        void* e_ptr_;

        index_t M_, N_, K_;
        index_t StrideA_, StrideB_;
        std::array<index_t, NumDTensor> StrideDs_;
        index_t StrideE_;
    };

    // Argument
    struct Argument : public BaseArgument
    {

        void UpdateKBatch(index_t k_batch)
        {
            k_batch_ = k_batch;

            if(k_batch_ < 1)
            {

                throw std::runtime_error("wrong! k_batch must be > 0");
            }

            const index_t AverM = math::integer_divide_ceil(sum_of_m, group_count_);

            const index_t StrideE = gemm_desc_kernel_arg_[0].StrideE_;
            const index_t N       = gemm_desc_kernel_arg_[0].N_;

            const auto e_grid_desc_m_n =
                GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(
                    AverM, N, StrideE);

            const auto local_b2c_tile_map = Block2ETileMap{e_grid_desc_m_n, k_batch_};

            grid_size_grp_ = local_b2c_tile_map.CalculateGridSize(e_grid_desc_m_n);

            grid_size_ = grid_size_grp_ * group_count_;
        }

        Argument(std::vector<const void*>&,
                 std::vector<const void*>&,
                 std::vector<std::array<const void*, NumDTensor>>&,
                 std::vector<void*>&,
                 std::vector<GemmDesc>& gemm_descs,
                 AElementwiseOperation a_element_op,
                 BElementwiseOperation b_element_op,
                 CDEElementwiseOperation c_element_op)
            : a_element_op_{a_element_op}, b_element_op_{b_element_op}, c_element_op_{c_element_op}
        {
            grid_size_ = 0;

            k_batch_ = 1;

            grouped_gemm_kernel_args_dev = nullptr;

            group_count_ = ck::type_convert<ck::index_t>(gemm_descs.size());

            gemm_desc_kernel_arg_.reserve(group_count_);

            index_t group_id = 0;

            sum_of_m            = gemm_descs[0].M_;
            const index_t AverM = math::integer_divide_ceil(sum_of_m, group_count_);
            const index_t N     = gemm_descs[0].N_;
            const index_t K     = gemm_descs[0].K_;

            for(std::size_t i = 0; i < gemm_descs.size(); i++)
            {
                if(sum_of_m != gemm_descs[i].M_ || N != gemm_descs[i].N_ || K != gemm_descs[i].K_)
                {
                    throw std::runtime_error("wrong! M/N/K is not identical");
                }

                a_mtx_mraw_kraw_.emplace_back(sum_of_m, K);
                b_mtx_nraw_kraw_.emplace_back(N, K);

                const index_t StrideA = gemm_descs[i].stride_A_;
                const index_t StrideB = gemm_descs[i].stride_B_;
                const index_t StrideE = gemm_descs[i].stride_C_;

                // pointer
                std::array<const void*, NumDTensor> p_ds_grid;

                static_for<0, NumDTensor, 1>{}([&](auto j) { p_ds_grid[j] = nullptr; });

                std::array<index_t, NumDTensor> StrideDs;

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

                    if(gemm_descs[i].stride_Ds_.size() != NumDTensor)
                    {
                        throw std::runtime_error(
                            "wrong! gemm_descs[i].stride_Ds_.size() does not match NumDTensor");
                    }

                    StrideDs[j] = gemm_descs[i].stride_Ds_[j];
                });

                const auto e_grid_desc_m_n =
                    GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(
                        AverM, N, StrideE);

                // block-to-e-tile map
                const auto local_b2c_tile_map = Block2ETileMap{e_grid_desc_m_n, k_batch_};

                grid_size_grp_ = local_b2c_tile_map.CalculateGridSize(e_grid_desc_m_n);

                if(group_id * grid_size_grp_ != grid_size_)
                {
                    throw std::runtime_error("wrong! grid_size_grp_ is not identical!");
                }

                grid_size_ += grid_size_grp_;

                // check block-to-E-tile
                if(!local_b2c_tile_map.CheckValidity(e_grid_desc_m_n))
                {
                    throw std::runtime_error("wrong! block_2_etile_map validation failed");
                }

                if(!GridwiseGemm::
                       template CheckValidity<ALayout, BLayout, DsLayout, ELayout, GemmSpec>(
                           AverM, N, K, StrideA, StrideB, StrideDs, StrideE, 1))
                {
                    throw std::runtime_error(
                        "wrong! GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3 has invalid setting");
                }

                gemm_desc_kernel_arg_.push_back(GemmBiasTransKernelArg{
                    nullptr,
                    nullptr,
                    p_ds_grid,
                    nullptr,
                    AverM,
                    N,
                    K,
                    StrideA,
                    StrideB,
                    StrideDs,
                    StrideE,
                });

                group_id++;
            }

            const auto e_grid_desc_sum_m_n =
                GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(
                    sum_of_m, gemm_desc_kernel_arg_[0].N_, gemm_desc_kernel_arg_[0].StrideE_);

            const auto local_b2c_tile_map = Block2ETileMap{e_grid_desc_sum_m_n, 1};

            barrier_size_grp_ = local_b2c_tile_map.CalculateGridSize(e_grid_desc_sum_m_n);
        }

        //  private:
        index_t group_count_;

        AElementwiseOperation a_element_op_;
        BElementwiseOperation b_element_op_;
        CDEElementwiseOperation c_element_op_;

        std::vector<GemmBiasTransKernelArg> gemm_desc_kernel_arg_;
        std::vector<Tuple<index_t, index_t>> a_mtx_mraw_kraw_;
        std::vector<Tuple<index_t, index_t>> b_mtx_nraw_kraw_;

        const void* grouped_gemm_kernel_args_dev;

        index_t grid_size_;
        index_t grid_size_grp_;
        index_t barrier_size_grp_;
        index_t sum_of_m;

        index_t k_batch_;
    };

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

        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
        {
            bool has_main_k_block_loop = true;

            for(std::size_t i = 0; i < arg.gemm_desc_kernel_arg_.size(); i++)
            {
                const auto KPad =
                    GridwiseGemm::CalculateKPadded(arg.gemm_desc_kernel_arg_[i].K_, arg.k_batch_);

                if(GridwiseGemm::CalculateHasMainKBlockLoop(KPad) != has_main_k_block_loop)
                {
                    throw std::runtime_error("wrong! not all gemm has_main_k_block_loop");
                }
            }

            if(arg.grouped_gemm_kernel_args_dev == nullptr)
            {
                throw std::runtime_error("wrong! grouped_gemm_kernel_args_dev is nullpr");
            }

            float ave_time = 0;

            auto launch_kernel = [&](auto has_main_k_block_loop_, auto e_global_memory_operation_) {
                const auto kernel =
                    kernel_grouped_gemm_xdl_fixed_nk<GridwiseGemm,
                                                     GroupedGemmKernelArgument<NumDTensor>,
                                                     GemmSpec,
                                                     ALayout,
                                                     BLayout,
                                                     DsLayout,
                                                     ELayout,
                                                     DsDataType,
                                                     Block2ETileMap,
                                                     GroupedGemmBlock2ETileMap,
                                                     AElementwiseOperation,
                                                     BElementwiseOperation,
                                                     CDEElementwiseOperation,
                                                     e_global_memory_operation_,
                                                     has_main_k_block_loop_>;

                return launch_and_time_kernel(
                    stream_config,
                    kernel,
                    dim3(arg.grid_size_),
                    dim3(BlockSize),
                    0,
                    cast_pointer_to_constant_address_space(arg.grouped_gemm_kernel_args_dev),
                    reinterpret_cast<uint32_t*>(arg.p_workspace_),
                    arg.barrier_size_grp_,
                    arg.gemm_desc_kernel_arg_.size(),
                    arg.grid_size_grp_,
                    arg.k_batch_,
                    arg.a_element_op_,
                    arg.b_element_op_,
                    arg.c_element_op_);
            };

            constexpr auto AtomicAdd = InMemoryDataOperationEnum::AtomicAdd;
            constexpr auto Set       = InMemoryDataOperationEnum::Set;

            // For bf16 datatype only kbatch = 1 scenario is supported. This condition is enforced
            // in IsSupportedArgument function
            if constexpr(std::is_same<ADataType, ck::bhalf_t>::value)
            {
                if(has_main_k_block_loop)
                {
                    ave_time = launch_kernel(integral_constant<bool, true>{},
                                             integral_constant<InMemoryDataOperationEnum, Set>{});
                }
                else
                {
                    ave_time = launch_kernel(integral_constant<bool, false>{},
                                             integral_constant<InMemoryDataOperationEnum, Set>{});
                }
            }
            else
            {
                if(arg.k_batch_ > 1)
                {
                    if(has_main_k_block_loop)
                    {
                        ave_time = launch_kernel(
                            integral_constant<bool, true>{},
                            integral_constant<InMemoryDataOperationEnum, AtomicAdd>{});
                    }
                    else
                    {
                        ave_time = launch_kernel(
                            integral_constant<bool, false>{},
                            integral_constant<InMemoryDataOperationEnum, AtomicAdd>{});
                    }
                }
                else
                {
                    if(has_main_k_block_loop)
                    {
                        ave_time =
                            launch_kernel(integral_constant<bool, true>{},
                                          integral_constant<InMemoryDataOperationEnum, Set>{});
                    }
                    else
                    {
                        ave_time =
                            launch_kernel(integral_constant<bool, false>{},
                                          integral_constant<InMemoryDataOperationEnum, Set>{});
                    }
                }
            }

            return ave_time;
        }

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

    static bool IsSupportedArgument(const Argument& arg)
    {
        if(ck::type_convert<ck::index_t>(arg.gemm_desc_kernel_arg_.size()) != arg.group_count_)
        {
            return false;
        }

        bool supported = true;

        // If we use padding we do not support vector loads for dimensions not divisible by vector
        // load size.
        if constexpr(GemmSpec != GemmSpecialization::Default)
        {
            // [A|B]BlockTransferSrcVectorDim value define dimension in the block {K0,M,K1} layout,
            // thus we have to adapt it to the {M,K} or {N,K} layout.
            const auto a_raw_vector_dim = ABlockTransferSrcVectorDim != 1 ? 1 : 0;
            const auto b_raw_vector_dim = BBlockTransferSrcVectorDim != 1 ? 1 : 0;

            for(index_t i = 0; i < arg.group_count_; ++i)
            {
                const auto a_vector_dim = arg.a_mtx_mraw_kraw_[i].At(Number<a_raw_vector_dim>{});
                const auto b_vector_dim = arg.b_mtx_nraw_kraw_[i].At(Number<b_raw_vector_dim>{});

                supported = supported & (a_vector_dim % ABlockTransferSrcScalarPerVector == 0);
                supported = supported & (b_vector_dim % BBlockTransferSrcScalarPerVector == 0);
            }
        }

        // For bf16 datatype only kbatch = 1 is supported since there is no AtomicAdd
        // instruction that supports bf16 and we cannot use splitk because of that
        if constexpr(std::is_same<ADataType, ck::bhalf_t>::value)
        {
            supported = supported & (arg.k_batch_ == 1);
        }

        return supported;
    }

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

    static auto MakeArgument(std::vector<const void*>& p_As,
                             std::vector<const void*>& p_Bs,
                             std::vector<std::array<const void*, NumDTensor>>& p_Ds,
                             std::vector<void*>& p_Es,
                             std::vector<GemmDesc> gemm_descs,
                             AElementwiseOperation a_element_op,
                             BElementwiseOperation b_element_op,
                             CDEElementwiseOperation c_element_op)
    {
        return Argument{
            p_As, p_Bs, p_Ds, p_Es, gemm_descs, a_element_op, b_element_op, c_element_op};
    }

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

    // polymorphic
    std::unique_ptr<BaseArgument>
    MakeArgumentPointer(std::vector<const void*>& p_As,
                        std::vector<const void*>& p_Bs,
                        std::vector<std::array<const void*, NumDTensor>>& p_Ds,
                        std::vector<void*>& p_Es,
                        std::vector<GemmDesc>& gemm_descs,
                        AElementwiseOperation a_element_op,
                        BElementwiseOperation b_element_op,
                        CDEElementwiseOperation c_element_op) override
    {
        return std::make_unique<Argument>(
            p_As, p_Bs, p_Ds, p_Es, gemm_descs, a_element_op, b_element_op, c_element_op);
    }

    // polymorphic
    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
    {
        return std::make_unique<Invoker>(Invoker{});
    }

    // polymorphic
    std::string GetTypeString() const override
    {
        auto str = std::stringstream();

        // clang-format off
        str << "DeviceGroupedGemm_Xdl_Fixed_NK"
            << "<"
            << BlockSize << ", "
            << MPerBlock << ", "
            << NPerBlock << ", "
            << KPerBlock << ", "
            << AK1 << ", "
            << BK1 << ", "
            << MPerXDL << ", "
            << NPerXDL << ", "
            << MXdlPerWave << ", "
            << NXdlPerWave << ", "
            << ABlockTransferSrcScalarPerVector << ", "
            << BBlockTransferSrcScalarPerVector << ", "
            << CShuffleMXdlPerWavePerShuffle << ", "
            << CShuffleNXdlPerWavePerShuffle << ", "
            << getGemmSpecializationString(GemmSpec)
            << ">";
        // clang-format on

        return str.str();
    }

    static void SetDeviceKernelArgs(Argument& arg, const void* kernel_args)
    {
        arg.grouped_gemm_kernel_args_dev = kernel_args;
    }

    // polymorphic
    void SetDeviceKernelArgs(BaseArgument* p_arg, const void* kernel_args) const override
    {
        return SetDeviceKernelArgs(*dynamic_cast<Argument*>(p_arg), kernel_args);
    }

    size_t GetWorkSpaceSize(const BaseArgument* p_arg) const override
    {
        auto arg = *dynamic_cast<const Argument*>(p_arg);

        return arg.group_count_ * arg.barrier_size_grp_ * sizeof(uint32_t);
    }

    size_t GetDeviceKernelArgSize(const BaseArgument* p_arg) const override
    {
        auto arg = *dynamic_cast<const Argument*>(p_arg);

        return arg.group_count_ * sizeof(GroupedGemmKernelArgument<NumDTensor>);
    }

    void SetWorkSpacePointer(BaseArgument* p_arg,
                             void* p_workspace,
                             const StreamConfig& stream_config = StreamConfig{}) const override
    {
        auto p_arg_          = dynamic_cast<Argument*>(p_arg);
        p_arg_->p_workspace_ = p_workspace;

        hip_check_error(
            hipMemsetAsync(p_workspace, 0, GetWorkSpaceSize(p_arg), stream_config.stream_id_));
    }

    static void SetKBatch(Argument& arg, index_t k_batch) { arg.UpdateKBatch(k_batch); }

    // polymorphic
    void SetKBatch(BaseArgument* p_arg, index_t k_batch) const override
    {
        return SetKBatch(*dynamic_cast<Argument*>(p_arg), k_batch);
    }
};

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