gridwise_gemm_wmma_cshuffle_v3_b_scale.hpp Source File

gridwise_gemm_wmma_cshuffle_v3_b_scale.hpp Source File#

Composable Kernel: gridwise_gemm_wmma_cshuffle_v3_b_scale.hpp Source File
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
 
#pragma once
 
#include "ck/utility/env.hpp"
#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_wmma_selector.hpp"
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_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/grid/gridwise_gemm_wmma_cshuffle_v3_common.hpp"
 
namespace ck {
 
template <typename ALayout,
          typename BLayout,
          typename DsLayout,
          typename ELayout,
          typename AsDataType,
          typename BsDataType,
          typename BScaleType,
          typename AccDataType,
          typename CShuffleDataType,
          typename DsDataType,
          typename EDataType,
          typename AElementwiseOperation,
          typename BElementwiseOperation,
          typename CDEElementwiseOperation,
          tensor_operation::device::GemmSpecialization GemmSpec,
          index_t BlockSize,
          index_t ScaleBlockN, // scale N
          index_t ScaleBlockK, // scale K
          index_t MPerBlock,
          index_t NPerBlock,
          index_t KPerBlock,
          index_t AK1Value,
          index_t BK1Value,
          index_t MPerWmma,
          index_t NPerWmma,
          index_t MRepeat,
          index_t NRepeat,
          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 CShuffleMRepeatPerShuffle,
          index_t CShuffleNRepeatPerShuffle,
          typename CDEShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
          typename CDEShuffleBlockTransferScalarPerVectors,
          BlockGemmPipelineScheduler BlkGemmPipeSched = BlockGemmPipelineScheduler::Intrawave,
          BlockGemmPipelineVersion BlkGemmPipelineVer = BlockGemmPipelineVersion::v4,
          typename ComputeTypeA                       = EDataType,
          typename ComputeTypeB                       = ComputeTypeA,
          bool PermuteA                               = false,
          bool PermuteB                               = false>
struct GridwiseGemm_wmma_cshuffle_v3_b_scale
    : GridwiseGemm_wmma_cshuffle_v3_base<
          ALayout,
          BLayout,
          DsLayout,
          ELayout,
          AsDataType,
          BsDataType,
          AccDataType,
          CShuffleDataType,
          DsDataType,
          EDataType,
          AElementwiseOperation,
          BElementwiseOperation,
          CDEElementwiseOperation,
          GemmSpec,
          BlockSize,
          MPerBlock,
          NPerBlock,
          KPerBlock,
          AK1Value,
          BK1Value,
          MPerWmma,
          NPerWmma,
          MRepeat,
          NRepeat,
          ABlockTransferThreadClusterLengths_AK0_M_AK1,
          ABlockTransferThreadClusterArrangeOrder,
          ABlockTransferSrcAccessOrder,
          ABlockTransferSrcVectorDim,
          ABlockTransferSrcScalarPerVector,
          ABlockTransferDstScalarPerVector_AK1,
          AThreadTransferSrcResetCoordinateAfterRun,
          ABlockLdsExtraM,
          BBlockTransferThreadClusterLengths_BK0_N_BK1,
          BBlockTransferThreadClusterArrangeOrder,
          BBlockTransferSrcAccessOrder,
          BBlockTransferSrcVectorDim,
          BBlockTransferSrcScalarPerVector,
          BBlockTransferDstScalarPerVector_BK1,
          BThreadTransferSrcResetCoordinateAfterRun,
          BBlockLdsExtraN,
          CShuffleMRepeatPerShuffle,
          CShuffleNRepeatPerShuffle,
          CDEShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
          CDEShuffleBlockTransferScalarPerVectors,
          BlkGemmPipeSched,
          BlkGemmPipelineVer,
          ComputeTypeA,
          ComputeTypeB,
          PermuteA,
          PermuteB,
          true>
{
    using Base = GridwiseGemm_wmma_cshuffle_v3_base<
        ALayout,
        BLayout,
        DsLayout,
        ELayout,
        AsDataType,
        BsDataType,
        AccDataType,
        CShuffleDataType,
        DsDataType,
        EDataType,
        AElementwiseOperation,
        BElementwiseOperation,
        CDEElementwiseOperation,
        GemmSpec,
        BlockSize,
        MPerBlock,
        NPerBlock,
        KPerBlock,
        AK1Value,
        BK1Value,
        MPerWmma,
        NPerWmma,
        MRepeat,
        NRepeat,
        ABlockTransferThreadClusterLengths_AK0_M_AK1,
        ABlockTransferThreadClusterArrangeOrder,
        ABlockTransferSrcAccessOrder,
        ABlockTransferSrcVectorDim,
        ABlockTransferSrcScalarPerVector,
        ABlockTransferDstScalarPerVector_AK1,
        AThreadTransferSrcResetCoordinateAfterRun,
        ABlockLdsExtraM,
        BBlockTransferThreadClusterLengths_BK0_N_BK1,
        BBlockTransferThreadClusterArrangeOrder,
        BBlockTransferSrcAccessOrder,
        BBlockTransferSrcVectorDim,
        BBlockTransferSrcScalarPerVector,
        BBlockTransferDstScalarPerVector_BK1,
        BThreadTransferSrcResetCoordinateAfterRun,
        BBlockLdsExtraN,
        CShuffleMRepeatPerShuffle,
        CShuffleNRepeatPerShuffle,
        CDEShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
        CDEShuffleBlockTransferScalarPerVectors,
        BlkGemmPipeSched,
        BlkGemmPipelineVer,
        ComputeTypeA,
        ComputeTypeB,
        PermuteA,
        PermuteB,
        true>;
 
    using Base::I0;
    using Base::I1;
    using Base::I2;
    using Base::I3;
    using Base::I4;
    using Base::I5;
    using Base::I6;
    using Base::I7;
 
    using Base::AK0Number;
    using Base::AK1Number;
    using Base::BK0Number;
    using Base::BK1Number;
 
    using Base::APackedSize;
    using Base::BPackedSize;
 
    using Base::CalculateAK0Padded;
    using Base::CalculateBK0Padded;
    using Base::CalculateKPadded;
    using Base::CalculateKRead;
    using Base::CalculateMBlock;
    using Base::CalculateMPadded;
    using Base::CalculateNBlock;
    using Base::CalculateNPadded;
    using Base::MakeAsGridDescriptor_AK0_M_AK1;
    using Base::MakeBsGridDescriptor_BK0_N_BK1;
    using Base::MakeDEGridDescriptor_M_N;
    using Base::MakeDsGridDescriptor_M_N;
    using Base::MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock;
 
    using Base::MakeDEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock;
 
    using ThisThreadBlock = ThisThreadBlock<BlockSize>;
 
    using Base::NumATensor;
    using Base::NumBTensor;
    using Base::NumDTensor;
    using typename Base::AsGridPointer;
    using typename Base::BsGridPointer;
    using typename Base::DsGridPointer;
    using AsDataType_ = AsDataType;
    using BsDataType_ = BsDataType;
 
    struct Problem
    {
        __host__ Problem(index_t M_,
                         index_t N_,
                         index_t K_,
                         std::array<index_t, NumATensor> StrideAs_,
                         std::array<index_t, NumBTensor> StrideBs_,
                         std::array<index_t, NumDTensor> StrideDs_,
                         index_t StrideE_,
                         index_t StrideScaleB_,
                         index_t KBatch_)
            : M{M_},
              N{N_},
              K{K_},
              StrideAs{StrideAs_},
              StrideBs{StrideBs_},
              StrideDs{StrideDs_},
              StrideE{StrideE_},
              StrideScaleB{StrideScaleB_},
              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_)}
        {
        }
 
        __host__ void Print() const
        {
            std::cout << "problem {" << "M:" << M << ", " << "N:" << N << ", " << "K:" << K << ", "
                      << "SAs: {";
            static_for<0, NumATensor, 1>{}([&](auto i) {
                std::cout << StrideAs[i] << (i.value < NumATensor - 1 ? ", " : "");
            });
            std::cout << "}, " << "SBs: {";
            static_for<0, NumBTensor, 1>{}([&](auto i) {
                std::cout << StrideBs[i] << (i.value < NumBTensor - 1 ? ", " : "");
            });
            std::cout << "}, ";
            if constexpr(NumDTensor > 0)
            {
                std::cout << "SDs: { ";
                static_for<0, NumDTensor, 1>{}([&](auto i) {
                    std::cout << StrideDs[i] << (i.value < NumDTensor - 1 ? ", " : "");
                });
                std::cout << " }, ";
            }
            std::cout << "SE:" << StrideE << ", " << "SScaleB:" << StrideScaleB << ", "
                      << "MP:" << MPadded << ", " << "NP:" << NPadded << ", " << "KRead:" << KRead
                      << ", " << "KP:" << KPadded << ", " << "AK0:" << AK0 << ", " << "BK0:" << BK0
                      << ", " << "MBlock: " << MBlock << ", " << "NBlock: " << NBlock << "}"
                      << std::endl;
        }
 
        index_t M;
        index_t N;
        index_t K;
        std::array<index_t, NumATensor> StrideAs;
        std::array<index_t, NumBTensor> StrideBs;
        std::array<index_t, NumDTensor> StrideDs;
        index_t StrideE;
        index_t StrideScaleB;
        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;
    };
 
    // Argument
    struct Argument : public tensor_operation::device::BaseArgument, public Problem
    {
        __host__ Argument(std::array<const void*, NumATensor> p_as_grid_,
                          std::array<const void*, NumBTensor> p_bs_grid_,
                          std::array<const void*, NumDTensor> p_ds_grid_,
                          EDataType* p_e_grid_,
                          index_t M_,
                          index_t N_,
                          index_t K_,
                          std::array<index_t, NumATensor> StrideAs_,
                          std::array<index_t, NumBTensor> StrideBs_,
                          std::array<index_t, NumDTensor> StrideDs_,
                          index_t StrideE_,
                          index_t StrideScaleB_,
                          const BScaleType* p_b_scale_grid_,
                          index_t k_batch_,
                          AElementwiseOperation a_element_op_,
                          BElementwiseOperation b_element_op_,
                          CDEElementwiseOperation cde_element_op_,
                          bool is_reduce_ = false)
            : Problem{M_,
                      N_,
                      K_,
                      StrideAs_,
                      StrideBs_,
                      StrideDs_,
                      StrideE_,
                      StrideScaleB_,
                      k_batch_},
              p_as_grid{},
              p_bs_grid{},
              p_ds_grid{},
              p_e_grid{p_e_grid_},
              p_b_scale_grid{p_b_scale_grid_},
              a_element_op{a_element_op_},
              b_element_op{b_element_op_},
              cde_element_op{cde_element_op_},
              is_reduce(is_reduce_)
        {
            // populate pointer, desc for As
            static_for<0, NumATensor, 1>{}([&](auto i) {
                using ADataType_ = remove_cvref_t<tuple_element_t<i.value, AsDataType>>;
 
                // A pointer
                p_as_grid(i) = static_cast<const ADataType_*>(p_as_grid_[i]);
            });
 
            // populate pointer, desc for Bs
            static_for<0, NumBTensor, 1>{}([&](auto i) {
                using BDataType_ = remove_cvref_t<tuple_element_t<i.value, BsDataType>>;
 
                // B pointer
                p_bs_grid(i) = static_cast<const BDataType_*>(p_bs_grid_[i]);
            });
 
            static_for<0, NumDTensor, 1>{}([&](auto i) {
                using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
 
                p_ds_grid(i) = static_cast<const DDataType*>(p_ds_grid_[i]);
            });
        }
 
        __host__ __device__ inline bool IsReduceAdd() const
        {
            return (Problem::KBatch > 1) && is_reduce;
        }
 
        __host__ __device__ inline bool IsAtomicAdd() const
        {
            return (Problem::KBatch > 1) && (!is_reduce);
        }
 
        AsGridPointer p_as_grid;
        BsGridPointer p_bs_grid;
        DsGridPointer p_ds_grid;
        EDataType* p_e_grid;
 
        const BScaleType* p_b_scale_grid;
        const AElementwiseOperation a_element_op;
        const BElementwiseOperation b_element_op;
        const CDEElementwiseOperation cde_element_op;
        bool is_reduce;
    };
 
    struct SplitKBatchOffset
    {
 
        __device__ SplitKBatchOffset(Argument& karg, index_t k_id)
        {
            // Note: in xdl implementation multiple AB supports one layout
            // but multiple strides, so we create an array of offsets with
            // the same values.
            // It should be fixed later on. Once we will have a thread transfer
            // more flexible.
            if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
            {
                static_for<0, NumATensor, 1>{}(
                    [&](auto i) { a_k_split_offset[i] = k_id * karg.KRead / APackedSize; });
            }
            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
            {
                static_for<0, NumATensor, 1>{}(
                    [&](auto i) { a_k_split_offset[i] = k_id * karg.KRead * karg.StrideAs[i]; });
            }
 
            if constexpr(is_same_v<tensor_layout::gemm::RowMajor, BLayout>)
            {
                static_for<0, NumBTensor, 1>{}(
                    [&](auto i) { b_k_split_offset[i] = k_id * karg.KRead * karg.StrideBs[i]; });
            }
            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, BLayout>)
            {
                if constexpr(!PermuteB)
                {
                    static_for<0, NumBTensor, 1>{}(
                        [&](auto i) { b_k_split_offset[i] = k_id * karg.KRead / BPackedSize; });
                }
                else
                {
                    const int k0_offset = karg.KRead * karg.N;
                    static_for<0, NumBTensor, 1>{}(
                        [&](auto i) { b_k_split_offset[i] = k_id * k0_offset / BPackedSize; });
                }
            }
 
            // Calculate B scale offset
            if constexpr(is_same_v<tensor_layout::gemm::RowMajor, BLayout>)
            {
                scale_k_split_offset = k_id * (karg.KRead / ScaleBlockK) * karg.StrideB;
            }
            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, BLayout>)
            {
                scale_k_split_offset = k_id * (karg.KRead / ScaleBlockK);
            }
 
            if(k_id < karg.KBatch - 1)
            {
                karg.K = karg.KRead;
            }
            else
            {
                karg.K = karg.K - karg.KRead * (karg.KBatch - 1);
            }
 
            if(karg.IsReduceAdd())
            {
                c_reduce_offset = k_id * karg.M * karg.N;
            }
            else
            {
                c_reduce_offset = 0;
            }
        }
 
        std::array<index_t, NumATensor> a_k_split_offset;
        std::array<index_t, NumBTensor> b_k_split_offset;
        index_t scale_k_split_offset; // New member for scale matrix offset
        index_t c_reduce_offset;
    };
 
    using BlockwiseGemmPipe = typename Base::BlockwiseGemmPipe;
 
    // return block_id to C matrix tile idx (m0, n0) mapping
    // if arch = gfx942
    using Block2CTileMap = BlockToCTileMap_Grouped_M00_N0_M01Adapt<8, MPerBlock, NPerBlock>;
    // using Block2CTileMap = BlockToCTileMap_3DGrid_KSplit<MPerBlock, NPerBlock>;
 
    template <index_t NumberOfBuffers, typename BScaleGridDesc_BN_AK>
    __device__ static auto MakeBScale(const BScaleGridDesc_BN_AK& b_scale_grid_desc_bn_ak,
                                      const BScaleType* p_b_scale_grid,
                                      index_t block_n_id)
    {
        const auto b_scale_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
            p_b_scale_grid, b_scale_grid_desc_bn_ak.GetElementSpaceSize());
 
        static constexpr auto wmma =
            WmmaSelector<ComputeTypeA, ComputeTypeB, AccDataType, MPerWmma, NPerWmma>{};
        static constexpr auto KPerThread = wmma.selected_wmma.k_per_wmma;
 
        static constexpr auto ScaleSliceSizeN = NRepeat;
        static constexpr auto ScaleSliceSizeK = (KPerThread + ScaleBlockK - 1) / ScaleBlockK;
 
        constexpr auto b_scale_thread_desc = make_naive_tensor_descriptor_packed(
            make_tuple(Number<ScaleSliceSizeN>{}, Number<ScaleSliceSizeK>{}));
 
        constexpr index_t NWaves = NPerBlock / (NRepeat * NPerWmma);
 
        auto b_thread_offset_n = get_thread_local_1d_id() % NPerWmma +
                                 (get_thread_local_1d_id() / 32) % NWaves * NPerWmma;
        auto b_thread_offset_k = (get_thread_local_1d_id() % 32) / NPerWmma * KPerThread;
 
        auto b_scale_thread_copy =
            ThreadwiseTensorSliceTransfer_v2<BScaleType,
                                             BScaleType,
                                             decltype(b_scale_grid_desc_bn_ak),
                                             decltype(b_scale_thread_desc),
                                             Sequence<1, ScaleSliceSizeK>,
                                             Sequence<0, 1>,
                                             1,
                                             ScaleSliceSizeK,
                                             1,
                                             false>(
                b_scale_grid_desc_bn_ak,
                make_multi_index(block_n_id * NPerBlock / ScaleBlockN + b_thread_offset_n,
                                 b_thread_offset_k / ScaleBlockK));
 
        auto b_scale_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeTypeB>(
            b_scale_thread_desc.GetElementSpaceSize());
 
        using BScale =
            typename BlockwiseGemmPipe::template BScale<ScaleSliceSizeN,
                                                        ScaleSliceSizeK,
                                                        NWaves,
                                                        ScaleBlockK,
                                                        NumberOfBuffers,
                                                        decltype(b_scale_grid_desc_bn_ak),
                                                        decltype(b_scale_thread_copy),
                                                        decltype(b_scale_grid_buf),
                                                        decltype(b_scale_thread_buf),
                                                        decltype(b_scale_thread_desc)>;
 
        return BScale{b_scale_grid_desc_bn_ak, b_scale_thread_copy, b_scale_grid_buf};
    }
 
    __device__ static index_t GetKBlockPerScale()
    {
        return (ScaleBlockK + KPerBlock - 1) / KPerBlock;
    }
 
    template <bool HasMainKBlockLoop,
              InMemoryDataOperationEnum EGlobalMemoryDataOperation,
              TailNumber TailNum,
              typename EpilogueArgument>
    __device__ static void Run(AsGridPointer& p_as_grid,
                               BsGridPointer& p_bs_grid,
                               DsGridPointer& p_ds_grid,
                               EDataType* p_e_grid,
                               const BScaleType* p_b_scale_grid,
                               void* p_shared,
                               const Problem& problem,
                               AElementwiseOperation a_element_op,
                               BElementwiseOperation b_element_op,
                               CDEElementwiseOperation cde_element_op,
                               EpilogueArgument& epilogue_args)
    {
        const auto as_grid_desc_ak0_m_ak1 = MakeAsGridDescriptor_AK0_M_AK1(
            problem.M, problem.MPadded, problem.K, problem.KPadded, problem.StrideAs, problem.AK0);
        const auto bs_grid_desc_bk0_n_bk1 = MakeBsGridDescriptor_BK0_N_BK1(
            problem.K, problem.KPadded, problem.N, problem.NPadded, problem.StrideBs, problem.BK0);
        const auto ds_grid_desc_m_n = MakeDsGridDescriptor_M_N(
            problem.M, problem.MPadded, problem.N, problem.NPadded, problem.StrideDs);
        const auto e_grid_desc_m_n = Base::template MakeDEGridDescriptor_M_N<ELayout>(
            problem.M, problem.MPadded, problem.N, problem.NPadded, problem.StrideE);
        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 e_grid_desc_mblock_mperblock_nblock_nperblock =
            MakeDEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
                e_grid_desc_m_n, problem.MBlock, problem.NBlock);
 
        // B Scale grid
        const auto b_scale_grid_desc_bn_ak = make_naive_tensor_descriptor(
            make_tuple(math::integer_divide_ceil(problem.N, ScaleBlockN),
                       math::integer_divide_ceil(problem.K, ScaleBlockK)),
            make_tuple(problem.StrideScaleB, 1));
 
        // divide block work by [M, N]
        const auto block_2_ctile_map = Block2CTileMap{problem.M, problem.N, 4};
 
        const auto block_work_idx =
            block_2_ctile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
 
        if(!block_2_ctile_map.ValidCTileIndex(
               block_work_idx,
               make_tuple(e_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
                          e_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))
        {
            return;
        }
 
        const index_t block_m_id = __builtin_amdgcn_readfirstlane(block_work_idx[I0]);
        const index_t block_n_id = __builtin_amdgcn_readfirstlane(block_work_idx[I1]);
 
        // BScale struct
        auto b_scale_struct = MakeBScale<1>(b_scale_grid_desc_bn_ak, p_b_scale_grid, block_n_id);
 
        const index_t num_k_block_per_scale = GetKBlockPerScale();
 
        Base::template Run<decltype(as_grid_desc_ak0_m_ak1),
                           decltype(bs_grid_desc_bk0_n_bk1),
                           decltype(ds_grid_desc_mblock_mperblock_nblock_nperblock),
                           decltype(e_grid_desc_mblock_mperblock_nblock_nperblock),
                           decltype(b_scale_struct),
                           decltype(epilogue_args),
                           HasMainKBlockLoop,
                           EGlobalMemoryDataOperation,
                           TailNum>(p_as_grid,
                                    p_bs_grid,
                                    p_ds_grid,
                                    p_e_grid,
                                    p_shared,
                                    as_grid_desc_ak0_m_ak1,
                                    bs_grid_desc_bk0_n_bk1,
                                    ds_grid_desc_mblock_mperblock_nblock_nperblock,
                                    e_grid_desc_mblock_mperblock_nblock_nperblock,
                                    a_element_op,
                                    b_element_op,
                                    cde_element_op,
                                    block_m_id,
                                    block_n_id,
                                    num_k_block_per_scale,
                                    b_scale_struct,
                                    epilogue_args);
    }
 
    // NOTE: Wrapper function to have __global__ function in common
    // between gemm_universal, b_scale, ab_scale, etc.
    template <bool HasMainKBlockLoop,
              InMemoryDataOperationEnum EGlobalMemoryDataOperation,
              TailNumber TailNum,
              typename EpilogueArgument>
    __device__ static void Run(void* p_shared,
                               const SplitKBatchOffset& splitk_batch_offset,
                               Argument& karg,
                               EpilogueArgument& epilogue_args)
    {
        // shift A matrices pointer for splitk
        AsGridPointer p_as_grid_splitk;
        static_for<0, NumATensor, 1>{}([&](auto i) {
            using ADataType_    = remove_cvref_t<tuple_element_t<i.value, AsDataType>>;
            p_as_grid_splitk(i) = static_cast<const ADataType_*>(karg.p_as_grid[i]) +
                                  splitk_batch_offset.a_k_split_offset[i];
        });
 
        // shift B matrices pointer for splitk
        BsGridPointer p_bs_grid_splitk;
        static_for<0, NumBTensor, 1>{}([&](auto i) {
            using BDataType_    = remove_cvref_t<tuple_element_t<i.value, BsDataType>>;
            p_bs_grid_splitk(i) = static_cast<const BDataType_*>(karg.p_bs_grid[i]) +
                                  splitk_batch_offset.b_k_split_offset[i];
        });
 
        Run<HasMainKBlockLoop, EGlobalMemoryDataOperation, TailNum>(
            p_as_grid_splitk,
            p_bs_grid_splitk,
            karg.p_ds_grid,
            karg.p_e_grid + splitk_batch_offset.c_reduce_offset,
            karg.p_b_scale_grid + splitk_batch_offset.scale_k_split_offset,
            p_shared,
            karg,
            karg.a_element_op,
            karg.b_element_op,
            karg.cde_element_op,
            epilogue_args);
    }
};
 
} // namespace ck