blockwise_gemm_pipeline_xdlops_v3_mx_bpreshuffle.hpp Source File

blockwise_gemm_pipeline_xdlops_v3_mx_bpreshuffle.hpp Source File#

Composable Kernel: blockwise_gemm_pipeline_xdlops_v3_mx_bpreshuffle.hpp Source File
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
 
#pragma once
 
#include "ck/tensor_operation/gpu/block/blockwise_gemm_mx_pipeline_xdlops_base.hpp"
 
namespace ck {
 
// Naive pipeline with lowest resource request per WGP
// GlobalPrefetchStages: 2
// LocalPreFillStages: 1
// LocalPreFetchStages: 1
// LocalSharedMemoryBuffer: 1
 
template <BlockGemmPipelineScheduler BlkGemmPipelineVer,
          index_t ThreadBlockSize,
          index_t ScaleBlockSize,
          typename ADataType,
          typename AScaleDataType,
          typename BDataType,
          typename BScaleDataType,
          typename ATileDesc,
          typename BTileDesc,
          typename AMmaTileDesc,
          typename BMmaTileDesc,
          index_t ABlockTransferSrcScalarPerVector,
          index_t BBlockTransferSrcScalarPerVector,
          index_t MPerBlock,
          index_t NPerBlock,
          index_t KPerBlock,
          index_t MPerXDL,
          index_t NPerXDL,
          index_t MRepeat, // MXdlPerWave
          index_t NRepeat, // NXdlPerWave
          index_t KPack>
struct BlockwiseGemmXdlops_pipeline_v3_mx_bprehuffle
{
};
 
template <index_t ThreadBlockSize,
          index_t ScaleBlockSize,
          typename ADataType,
          typename AScaleDataType,
          typename BDataType,
          typename BScaleDataType,
          typename ATileDesc,
          typename BTileDesc,
          typename AMmaTileDesc,
          typename BMmaTileDesc,
          index_t ABlockTransferSrcScalarPerVector,
          index_t BBlockTransferSrcScalarPerVector,
          index_t MPerBlock,
          index_t NPerBlock,
          index_t KPerBlock,
          index_t MPerXDL,
          index_t NPerXDL,
          index_t MRepeat, // MXdlPerWave
          index_t NRepeat, // NXdlPerWave
          index_t KPack>
struct BlockwiseGemmXdlops_pipeline_v3_mx_bprehuffle<BlockGemmPipelineScheduler::Intrawave,
                                                     ThreadBlockSize,
                                                     ScaleBlockSize,
                                                     ADataType,
                                                     AScaleDataType,
                                                     BDataType,
                                                     BScaleDataType,
                                                     ATileDesc,
                                                     BTileDesc,
                                                     AMmaTileDesc,
                                                     BMmaTileDesc,
                                                     ABlockTransferSrcScalarPerVector,
                                                     BBlockTransferSrcScalarPerVector,
                                                     MPerBlock,
                                                     NPerBlock,
                                                     KPerBlock,
                                                     MPerXDL,
                                                     NPerXDL,
                                                     MRepeat,
                                                     NRepeat,
                                                     KPack>
    : BlockwiseGemmXdlops_mx_pipeline_base<ThreadBlockSize,
                                           ADataType,
                                           BDataType,
                                           ATileDesc,
                                           BTileDesc,
                                           AMmaTileDesc,
                                           BMmaTileDesc,
                                           ABlockTransferSrcScalarPerVector,
                                           BBlockTransferSrcScalarPerVector,
                                           MPerBlock,
                                           NPerBlock,
                                           KPerBlock,
                                           MPerXDL,
                                           NPerXDL,
                                           MRepeat,
                                           NRepeat,
                                           KPack>
 
{
 
    using Base = BlockwiseGemmXdlops_mx_pipeline_base<ThreadBlockSize,
                                                      ADataType,
                                                      BDataType,
                                                      ATileDesc,
                                                      BTileDesc,
                                                      AMmaTileDesc,
                                                      BMmaTileDesc,
                                                      ABlockTransferSrcScalarPerVector,
                                                      BBlockTransferSrcScalarPerVector,
                                                      MPerBlock,
                                                      NPerBlock,
                                                      KPerBlock,
                                                      MPerXDL,
                                                      NPerXDL,
                                                      MRepeat,
                                                      NRepeat,
                                                      KPack>;
    using Base::A_K1;
    using Base::I0;
    using Base::I1;
    using Base::KRepeat;
    using Base::MWaves;
    using Base::NWaves;
    using Base::WaveSize;
    using Base::xdlops_gemm;
    using typename Base::HotLoopInstList;
 
    using Base::CalculateCThreadOriginDataIndex;
    using Base::GetCBlockDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
    using Base::GetCThreadBuffer;
    using Base::GetCThreadDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
    using Base::GetWaveIdx;
    using Base::MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
    using Base::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
 
    using Base::a_block_desc_m0_m1_m2_m3_k;
    using Base::b_block_desc_n0_n1_n2_n3_k;
 
    using Base::AMmaKStride;
    using Base::APackedSize;
    using Base::BMmaKStride;
    using Base::BPackedSize;
    using Base::KThreadChunk;
 
    using Base::KXdlPack;
    using Base::MXdlPack;
    using Base::NXdlPack;
 
    using AccType      = typename Base::AccType;
    using Tuple5       = typename Base::Tuple5;
    using ComputeTypeA = typename Base::ComputeTypeA;
    using ComputeTypeB = typename Base::ComputeTypeB;
 
    static constexpr index_t PrefetchStages        = 2;
    static constexpr index_t LocalPrefetchStages   = 2;
    static constexpr index_t PrefillStages         = 1;
    static constexpr index_t GlobalBufferNum       = 1;
    static constexpr index_t HotloopLocalBufSwitch = MRepeat % 2 == 0 ? 0 : 1;
 
    static constexpr auto num_buffer_load_a_scale = MRepeat / MXdlPack * KRepeat / KXdlPack;
    static constexpr auto num_buffer_load_b_scale = NRepeat / NXdlPack * KRepeat / KXdlPack;
    static constexpr auto async_vmcnt =
        num_buffer_load_a_scale + num_buffer_load_b_scale + HotLoopInstList::B_Buffer_Load_Inst_Num;
    static constexpr auto async_vmcnt_encoding = 3952 + async_vmcnt % 16 + async_vmcnt / 16 * 16384;
 
    static constexpr auto ScalesPerKBlockSize =
        KPerBlock / ScaleBlockSize; // How many mx-vectors per K block
 
    //> How many mx-vectors in each row/col is processed in one call to xdlops_gemm.Run()
    static constexpr auto ScalesPerXdlopsRun =
        (APackedSize * KPack * xdlops_gemm.K0PerXdlops) / ScaleBlockSize;
 
    //> How many scales a thread must read to accommodate one call to xdlops_gemm.Run()
    static constexpr auto ScalesPerXdlopsRunPerThread =
        ScalesPerXdlopsRun / xdlops_gemm.mfma_instr.num_input_blks;
 
    using mx_scale_t                        = e8m0_bexp_t;
    static constexpr auto scale_pack_size_a = sizeof(AScaleDataType) / sizeof(mx_scale_t);
    static constexpr auto scale_pack_size_b = sizeof(BScaleDataType) / sizeof(mx_scale_t);
    static_assert(KXdlPack * MXdlPack % scale_pack_size_a == 0,
                  "A scale pack data type too large!");
    static_assert(KXdlPack * NXdlPack % scale_pack_size_b == 0,
                  "B scale pack data type too large!");
    static constexpr auto a_scale_thread_vec_size = KXdlPack * MXdlPack / scale_pack_size_a;
    static constexpr auto b_scale_thread_vec_size = KXdlPack * NXdlPack / scale_pack_size_b;
 
    __host__ static constexpr bool BlockHasHotloop(index_t num_loop)
    {
        return num_loop > PrefetchStages;
    }
 
    __host__ static constexpr TailNumber BlockLoopTailNum(index_t num_loop)
    {
        return num_loop % 2 == 0 ? TailNumber::Even : TailNumber::Odd;
    }
 
    __device__ static constexpr auto HotLoopScheduler()
    {
        // A/B split schedule
        // compiler is likely to use ds_read2 when instruction width smaller than 16bytes
        constexpr auto num_ds_read_inst_a =
            HotLoopInstList::A_LDS_Read_Width * sizeof(ADataType) == 16
                ? HotLoopInstList::A_LDS_Read_Inst_Num
                : HotLoopInstList::A_LDS_Read_Inst_Num / 2;
 
        constexpr auto num_buffer_load_inst_a = HotLoopInstList::A_Buffer_Load_Inst_Num;
        constexpr auto num_buffer_load_inst_b = HotLoopInstList::B_Buffer_Load_Inst_Num;
        constexpr auto num_buffer_load_stage1 =
            num_buffer_load_inst_b + num_buffer_load_a_scale + num_buffer_load_b_scale;
 
        constexpr auto num_buffer_load_stage2 = num_buffer_load_inst_a;
 
        constexpr auto num_mfma_inst = HotLoopInstList::C_MFMA_Inst_Num * APackedSize;
        constexpr auto mfma_cycle    = HotLoopInstList::C_MFMA_Inst_Cycle;
 
        constexpr auto ds_read_a_issue_cycle =
            HotLoopInstList::A_LDS_Read_Width * sizeof(ADataType) == 16 ? 8 : 4;
        constexpr auto ds_read_a_mfma_rate =
            math::integer_divide_ceil(mfma_cycle - 8, 2 * ds_read_a_issue_cycle);
 
        // constexpr auto num_dsread_a_mfma =
        //     (num_ds_read_inst_a + ds_read_a_mfma_rate - 1) / ds_read_a_mfma_rate;
 
        constexpr auto num_total_stages = std::max(2, MRepeat);
 
        if constexpr(num_total_stages > 2)
        {
            // Group num_mfma_perstage num_ds_read_a_perstage
            // since we want to reuse a local register buffer
            constexpr auto num_mfma_perstage      = num_mfma_inst / num_total_stages;
            constexpr auto num_ds_read_a_perstage = num_ds_read_inst_a / num_total_stages;
 
            constexpr auto num_ds_read_a_mfma_perstage =
                math::integer_divide_ceil(num_ds_read_a_perstage, ds_read_a_mfma_rate);
 
            constexpr auto num_ds_read_a_prefetch_stages = 2;
 
            constexpr auto buffer_load_perstage_more =
                math::integer_divide_ceil((num_buffer_load_stage1), (num_total_stages - 2));
            constexpr auto buffer_load_perstage_less =
                math::integer_divide_floor((num_buffer_load_stage1), (num_total_stages - 2));
            constexpr auto buffer_load_perstage_stage2 =
                math::integer_divide_floor((num_buffer_load_stage2), 2);
 
            constexpr auto buffer_load_stages_more =
                num_buffer_load_stage1 -
                math::integer_divide_floor(num_buffer_load_stage1, (num_total_stages - 2)) *
                    ((num_total_stages - 2));
 
            constexpr auto buffer_load_issue_point_interval_more =
                num_mfma_perstage / buffer_load_perstage_more;
            constexpr auto buffer_load_issue_point_interval_less =
                num_mfma_perstage / buffer_load_perstage_less;
            constexpr auto buffer_load_issue_point_interval_stage2 =
                num_mfma_perstage / buffer_load_perstage_stage2;
 
            // Stage 1
            // global read more
            static_for<0, buffer_load_stages_more, 1>{}([&](auto /*i*/) {
                static_for<0, num_mfma_perstage, 1>{}([&](auto imfma) {
                    __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
 
                    if constexpr(imfma % buffer_load_issue_point_interval_more == 0)
                    {
                        __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
                    }
 
                    if constexpr(imfma >= (num_mfma_perstage - num_ds_read_a_mfma_perstage))
                    {
                        __builtin_amdgcn_sched_group_barrier(
                            0x100, ds_read_a_mfma_rate, 0); // DS read
                    }
                });
            });
 
            // global read less
            static_for<0, (num_total_stages - 2 - buffer_load_stages_more), 1>{}([&](auto /*i*/) {
                static_for<0, num_mfma_perstage, 1>{}([&](auto imfma) {
                    __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
                    if constexpr(imfma % buffer_load_issue_point_interval_less == 0)
                    {
                        __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
                    }
                    if constexpr(imfma >= (num_mfma_perstage - num_ds_read_a_mfma_perstage))
                    {
                        __builtin_amdgcn_sched_group_barrier(
                            0x100, ds_read_a_mfma_rate, 0); // DS read
                    }
                });
            });
 
            // Stage 2, Sync
            // lds synchronization, prefetch next loop local A
            static_for<0, num_ds_read_a_prefetch_stages, 1>{}([&](auto /*i*/) {
                static_for<0, num_mfma_perstage, 1>{}([&](auto imfma) {
                    __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
                    if constexpr(imfma % buffer_load_issue_point_interval_stage2 == 0)
                    {
                        __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
                    }
                    if constexpr(imfma >= (num_mfma_perstage - num_ds_read_a_mfma_perstage))
                    {
                        __builtin_amdgcn_sched_group_barrier(
                            0x100, ds_read_a_mfma_rate, 0); // DS read
                    }
                });
            });
        }
        else
        {
            constexpr auto num_buffer_load_total = num_buffer_load_inst_a + num_buffer_load_inst_b +
                                                   num_buffer_load_a_scale +
                                                   num_buffer_load_b_scale;
            constexpr auto num_dsread_a_mfma = math::integer_divide_ceil(
                num_ds_read_inst_a, ds_read_a_mfma_rate); // how many mfma per dsread_a
 
            // stage 1
            constexpr auto num_mfma_stage1 = num_mfma_inst - num_dsread_a_mfma;
 
            constexpr auto mfma_perstage_more =
                math::integer_divide_ceil(num_mfma_stage1, num_buffer_load_total);
            constexpr auto mfma_perstage_less =
                math::integer_divide_floor(num_mfma_stage1, num_buffer_load_total);
 
            constexpr auto mfma_stages_more =
                num_mfma_stage1 - mfma_perstage_less * num_buffer_load_total;
 
            static_for<0, num_buffer_load_inst_a, 1>{}([&](auto i) {
                if constexpr(i < mfma_stages_more)
                {
                    static_for<0, mfma_perstage_more, 1>{}([&](auto) {
                        __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
                    });
                    __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
                }
                else
                {
                    static_for<0, mfma_perstage_less, 1>{}([&](auto) {
                        __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
                    });
                    __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
                }
            });
 
            static_for<0, num_buffer_load_inst_b, 1>{}([&](auto i) {
                if constexpr((i + num_buffer_load_inst_a) < mfma_stages_more)
                {
                    static_for<0, mfma_perstage_more, 1>{}([&](auto /*imfma*/) {
                        __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
                    });
                    __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
                }
                else
                {
                    static_for<0, mfma_perstage_less, 1>{}([&](auto /*imfma*/) {
                        __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
                    });
                    __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
                }
            });
 
            static_for<0, num_buffer_load_a_scale, 1>{}([&](auto i) {
                if constexpr((i + num_buffer_load_inst_a + num_buffer_load_inst_b) <
                             mfma_stages_more)
                {
                    static_for<0, mfma_perstage_more, 1>{}([&](auto /*imfma*/) {
                        __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
                    });
                    __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
                }
                else
                {
                    static_for<0, mfma_perstage_less, 1>{}([&](auto /*imfma*/) {
                        __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
                    });
                    __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
                }
            });
 
            static_for<0, num_buffer_load_b_scale, 1>{}([&](auto i) {
                if constexpr((i + num_buffer_load_inst_a + num_buffer_load_inst_b +
                              num_buffer_load_a_scale) < mfma_stages_more)
                {
                    static_for<0, mfma_perstage_more, 1>{}([&](auto /*imfma*/) {
                        __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
                    });
                    __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
                }
                else
                {
                    static_for<0, mfma_perstage_less, 1>{}([&](auto /*imfma*/) {
                        __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
                    });
                    __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
                }
            });
 
            // stage 2
            static_for<0, num_dsread_a_mfma, 1>{}([&](auto i) {
                __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
                if constexpr((num_ds_read_inst_a - (i + 1) * ds_read_a_mfma_rate) >=
                             ds_read_a_mfma_rate)
                {
                    __builtin_amdgcn_sched_group_barrier(0x100, ds_read_a_mfma_rate, 0); // DS read
                }
                else
                {
                    __builtin_amdgcn_sched_group_barrier(
                        0x100,
                        num_ds_read_inst_a - (num_dsread_a_mfma - 1) * ds_read_a_mfma_rate,
                        0); // DS read
                }
            });
        }
    }
 
    template <bool HasMainLoop,
              TailNumber TailNum,
              typename AGridDesc,
              typename ABlockDesc,
              typename ABlockTransfer,
              typename AGridBuffer,
              typename ABlockBuffer,
              typename ABlockTransferStep,
              typename BGridDesc,
              typename BBlockDesc,
              typename BBlockTransfer,
              typename BGridBuffer,
              typename BBlockBuffer,
              typename BBlockTransferStep,
              typename CThreadBuffer,
              typename AScaleGridBuffer,
              typename AScaleGridDesc,
              typename AScaleThreadTransfer,
              typename BScaleGridBuffer,
              typename BScaleGridDesc,
              typename BScaleThreadTransfer>
    __device__ void Run(
        // ABlockCopy
        const AGridDesc& a_grid_desc,
        const ABlockDesc& a_block_desc,
        ABlockTransfer& a_blockwise_copy,
        const AGridBuffer& a_grid_buf,
        ABlockBuffer& a_block_bufs,
        const ABlockTransferStep& a_block_copy_step,
        // BBlockCopy
        const BGridDesc& b_grid_desc,
        const BBlockDesc& b_block_desc,
        BBlockTransfer& b_blockwise_copy,
        const BGridBuffer& b_grid_buf,
        BBlockBuffer& b_block_bufs,
        const BBlockTransferStep& b_block_copy_step,
        // CThread
        CThreadBuffer& c_thread_buf,
        // A and B scales
        const AScaleGridDesc& a_scale_grid_desc,
        AScaleThreadTransfer& a_scale_thread_copy,
        const AScaleGridBuffer& a_scale_grid_buf,
        const BScaleGridDesc& b_scale_grid_desc,
        BScaleThreadTransfer& b_scale_thread_copy,
        const BScaleGridBuffer& b_scale_grid_buf,
        index_t num_loop) const
    {
        ignore            = b_block_bufs;
        auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeTypeA>(
            a_thread_desc_.GetElementSpaceSize());
        auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeTypeB>(
            b_thread_desc_.GetElementSpaceSize());
        StaticallyIndexedArray<decltype(b_thread_buf), Number<2>{}> b_thread_bufs;
        constexpr auto b_block_origin_idx = make_tuple(I0, I0, I0, I0, I0);
 
        auto a_scale_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, AScaleDataType>(
            a_scale_thread_desc.GetElementSpaceSize());
 
        auto b_scale_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, BScaleDataType>(
            b_scale_thread_desc.GetElementSpaceSize());
 
        StaticallyIndexedArray<decltype(a_scale_thread_buf), Number<2>{}> a_scale_thread_bufs;
        StaticallyIndexedArray<decltype(b_scale_thread_buf), Number<2>{}> b_scale_thread_bufs;
 
        // Global prefetch 1
        a_blockwise_copy.Run(a_grid_desc, a_grid_buf, a_block_desc, a_block_bufs(I0));
        b_blockwise_copy.Run(
            b_grid_desc, b_grid_buf, b_block_desc, b_block_origin_idx, b_thread_bufs(I0));
 
        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
 
        // Prefetch a_scales
        static_for<0, MRepeat / MXdlPack, 1>{}([&](auto m0) {
            static_for<0, KRepeat / KXdlPack, 1>{}([&](auto k0) {
                a_scale_thread_copy.Run(a_scale_grid_desc,
                                        a_scale_grid_buf,
                                        a_scale_thread_desc,
                                        make_tuple(m0, k0, I0),
                                        a_scale_thread_bufs(I0));
 
                a_scale_thread_copy.MoveSrcSliceWindow(a_scale_grid_desc,
                                                       make_multi_index(0, I1, 0));
            });
            a_scale_thread_copy.MoveSrcSliceWindow(
                a_scale_grid_desc, make_multi_index(MWaves, -KRepeat / KXdlPack, 0));
        });
 
        // restore row id and advance to the next set of scales
        a_scale_thread_copy.MoveSrcSliceWindow(
            a_scale_grid_desc,
            make_multi_index(-MWaves * MRepeat / MXdlPack, KRepeat / KXdlPack, 0));
 
        // Prefetch b_scales
        static_for<0, NRepeat / NXdlPack, 1>{}([&](auto n0) {
            static_for<0, KRepeat / KXdlPack, 1>{}([&](auto k0) {
                b_scale_thread_copy.Run(b_scale_grid_desc,
                                        b_scale_grid_buf,
                                        b_scale_thread_desc,
                                        make_tuple(n0, k0, I0),
                                        b_scale_thread_bufs(I0));
 
                b_scale_thread_copy.MoveSrcSliceWindow(b_scale_grid_desc,
                                                       make_multi_index(0, I1, 0));
            });
            b_scale_thread_copy.MoveSrcSliceWindow(
                b_scale_grid_desc, make_multi_index(NWaves, -KRepeat / KXdlPack, 0));
        });
 
        // restore col id and advance to the next set of scales
        // NWaves * NPerXDL * NRepeat == NPerBlock
        b_scale_thread_copy.MoveSrcSliceWindow(
            b_scale_grid_desc,
            make_multi_index(-NWaves * NRepeat / NXdlPack, KRepeat / KXdlPack, 0));
 
        // Local prefetch 1, sync the async load
        __builtin_amdgcn_s_waitcnt(async_vmcnt_encoding);
        block_sync_lds();
        static_for<0, LocalPrefetchStages, 1>{}([&](auto m0) {
            static_for<0, KRepeat, 1>{}([&](auto k) {
                constexpr auto k_step = k * xdlops_gemm.KPerXdlops / APackedSize *
                                        (APackedSize * KPack / xdlops_gemm.K1PerXdlops);
                static_for<0, xdlops_gemm.K1PerXdlops / (APackedSize * KThreadChunk), 1>{}(
                    [&](auto chunk) {
                        constexpr auto a_k_step_chunk =
                            k_step + chunk * KThreadChunk * xdlops_gemm.mfma_instr.num_input_blks;
                        a_thread_copy_.Run(
                            a_block_desc_m0_m1_m2_m3_k,
                            make_tuple(
                                I0, I0, Number<m0 % MXdlPack>{}, I0, Number<a_k_step_chunk>{}),
                            a_block_bufs(I0),
                            a_thread_desc_,
                            make_tuple(
                                I0, I0, Number<m0 % MXdlPack>{}, k, Number<chunk * KThreadChunk>{}),
                            a_thread_buf);
                    });
            });
        });
 
        // Global prefetch 2
        a_blockwise_copy.Run(a_grid_desc, a_grid_buf, a_block_desc, a_block_bufs(I1));
        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
 
        // Initialize C
        c_thread_buf.Clear();
        __builtin_amdgcn_sched_barrier(0);
        constexpr index_t SwitchM = MRepeat - LocalPrefetchStages;
        // main body
        if constexpr(HasMainLoop)
        {
            // loop over k with the step KPerBlock
            index_t i = 0;
            do
            {
                auto LoopFunc = [&](auto scale_comp_buf, auto scale_mem_buf) {
                    b_blockwise_copy.Run(b_grid_desc,
                                         b_grid_buf,
                                         b_block_desc,
                                         b_block_origin_idx,
                                         b_thread_bufs(scale_mem_buf));
 
                    // Prefetch a_scales
                    static_for<0, MRepeat / MXdlPack, 1>{}([&](auto m0) {
                        static_for<0, KRepeat / KXdlPack, 1>{}([&](auto k0) {
                            a_scale_thread_copy.Run(a_scale_grid_desc,
                                                    a_scale_grid_buf,
                                                    a_scale_thread_desc,
                                                    make_tuple(m0, k0, I0),
                                                    a_scale_thread_bufs(scale_mem_buf));
 
                            a_scale_thread_copy.MoveSrcSliceWindow(a_scale_grid_desc,
                                                                   make_multi_index(0, I1, 0));
                        });
                        a_scale_thread_copy.MoveSrcSliceWindow(
                            a_scale_grid_desc, make_multi_index(MWaves, -KRepeat / KXdlPack, 0));
                    });
 
                    // restore row id and advance to the next set of scales
                    a_scale_thread_copy.MoveSrcSliceWindow(
                        a_scale_grid_desc,
                        make_multi_index(-MWaves * MRepeat / MXdlPack, KRepeat / KXdlPack, 0));
 
                    // Prefetch b_scales
                    static_for<0, NRepeat / NXdlPack, 1>{}([&](auto n0) {
                        static_for<0, KRepeat / KXdlPack, 1>{}([&](auto k0) {
                            b_scale_thread_copy.Run(b_scale_grid_desc,
                                                    b_scale_grid_buf,
                                                    b_scale_thread_desc,
                                                    make_tuple(n0, k0, I0),
                                                    b_scale_thread_bufs(scale_mem_buf));
 
                            b_scale_thread_copy.MoveSrcSliceWindow(b_scale_grid_desc,
                                                                   make_multi_index(0, I1, 0));
                        });
                        b_scale_thread_copy.MoveSrcSliceWindow(
                            b_scale_grid_desc, make_multi_index(NWaves, -KRepeat / KXdlPack, 0));
                    });
 
                    // restore col id and advance to the next set of scales
                    // NWaves * NPerXDL * NRepeat == NPerBlock
                    b_scale_thread_copy.MoveSrcSliceWindow(
                        b_scale_grid_desc,
                        make_multi_index(-NWaves * NRepeat / NXdlPack, KRepeat / KXdlPack, 0));
 
                    // a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
                    b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
 
                    static_for<0, MRepeat, 1>{}([&](auto m0) {
                        constexpr auto im_major = m0 / MXdlPack;
                        constexpr auto im_minor = m0 % MXdlPack;
                        static_for<0, KRepeat, 1>{}([&](auto k0) {
                            constexpr auto ik_major = k0 / KXdlPack;
                            constexpr auto ik_minor = k0 % KXdlPack;
                            static_for<0, NRepeat, 1>{}([&](auto n0) {
                                constexpr auto in_major = n0 / NXdlPack;
                                constexpr auto in_minor = n0 % NXdlPack;
 
                                constexpr index_t a_scale_offset =
                                    a_scale_thread_desc.CalculateOffset(
                                        make_tuple(im_major, ik_major, I0));
                                constexpr index_t b_scale_offset =
                                    b_scale_thread_desc.CalculateOffset(
                                        make_tuple(in_major, ik_major, I0));
 
                                static_assert(0 < ScalesPerXdlopsRunPerThread,
                                              "Must have at least one scale per Xdlops "
                                              "per Thread.");
 
                                vector_type<AScaleDataType, a_scale_thread_vec_size>
                                    a_scale_thread_vec;
                                vector_type<BScaleDataType, b_scale_thread_vec_size>
                                    b_scale_thread_vec;
 
                                // Pack scale_thread_buf into scale_thread_vec
                                static_for<0, a_scale_thread_vec_size, 1>{}([&](auto s) {
                                    a_scale_thread_vec.template AsType<AScaleDataType>()(s) =
                                        a_scale_thread_bufs(
                                            scale_comp_buf)[Number<a_scale_offset + s>{}];
                                });
 
                                static_for<0, b_scale_thread_vec_size, 1>{}([&](auto s) {
                                    b_scale_thread_vec.template AsType<BScaleDataType>()(s) =
                                        b_scale_thread_bufs(
                                            scale_comp_buf)[Number<b_scale_offset + s>{}];
                                });
 
                                vector_type<ComputeTypeA, KPack> a_thread_vec;
                                vector_type<ComputeTypeB, KPack> b_thread_vec;
 
                                static_for<0, KPack, 1>{}([&](auto ik) {
                                    a_thread_vec.template AsType<ComputeTypeA>()(ik) =
                                        a_thread_buf[Number<a_thread_desc_.CalculateOffset(
                                            make_tuple(I0, I0, im_minor, k0, ik))>{}];
                                    b_thread_vec.template AsType<ComputeTypeB>()(ik) = b_thread_bufs
                                        [scale_comp_buf][Number<b_thread_desc_.CalculateOffset(
                                            make_tuple(in_major, I0, in_minor, k0, ik))>{}];
                                });
 
                                using mfma_input_type_a =
                                    typename vector_type<ComputeTypeA,
                                                         xdlops_gemm.K1PerXdlops /
                                                             APackedSize>::type;
 
                                using mfma_input_type_b =
                                    typename vector_type<ComputeTypeB,
                                                         xdlops_gemm.K1PerXdlops /
                                                             BPackedSize>::type;
 
                                using mfma_scale_input_type_a =
                                    typename vector_type<AScaleDataType,
                                                         a_scale_thread_vec_size>::type;
                                using mfma_scale_input_type_b =
                                    typename vector_type<BScaleDataType,
                                                         b_scale_thread_vec_size>::type;
 
                                constexpr index_t c_offset = c_thread_desc_.CalculateOffset(
                                    make_tuple(im_major, in_major, im_minor, in_minor, 0));
 
                                // MFMA accumulation
                                xdlops_gemm.template Run<ik_minor * MXdlPack + im_minor,
                                                         ik_minor * NXdlPack + in_minor>(
                                    a_thread_vec.template AsType<mfma_input_type_a>(),
                                    a_scale_thread_vec.template AsType<mfma_scale_input_type_a>(),
                                    b_thread_vec.template AsType<mfma_input_type_b>(),
                                    b_scale_thread_vec.template AsType<mfma_scale_input_type_b>(),
                                    c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
                            });
                        });
 
                        if constexpr(m0.value == SwitchM)
                        {
                            __builtin_amdgcn_s_waitcnt(async_vmcnt_encoding);
                            block_sync_lds();
                            a_blockwise_copy.Run(a_grid_desc,
                                                 a_grid_buf,
                                                 a_block_desc,
                                                 a_block_bufs(scale_comp_buf));
                            a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
                        }
 
                        constexpr auto lds_buf =
                            m0.value >= SwitchM ? scale_mem_buf : scale_comp_buf;
 
                        static_for<0, KRepeat, 1>{}([&](auto k) {
                            constexpr auto k_step = k * xdlops_gemm.KPerXdlops / APackedSize *
                                                    (APackedSize * KPack / xdlops_gemm.K1PerXdlops);
                            static_for<0,
                                       xdlops_gemm.K1PerXdlops / (APackedSize * KThreadChunk),
                                       1>{}([&](auto chunk) {
                                constexpr auto a_k_step_chunk =
                                    k_step +
                                    chunk * KThreadChunk * xdlops_gemm.mfma_instr.num_input_blks;
                                a_thread_copy_.Run(
                                    a_block_desc_m0_m1_m2_m3_k,
                                    make_tuple(Number<((m0 + LocalPrefetchStages) / MXdlPack) %
                                                      (MRepeat / MXdlPack)>{},
                                               I0,
                                               Number<im_minor>{},
                                               I0,
                                               Number<a_k_step_chunk>{}),
                                    a_block_bufs(Number<lds_buf>{}),
                                    a_thread_desc_,
                                    make_tuple(I0,
                                               I0,
                                               Number<im_minor>{},
                                               k,
                                               Number<chunk * KThreadChunk>{}),
                                    a_thread_buf);
                            });
                        });
                    });
 
                    HotLoopScheduler();
                    __builtin_amdgcn_sched_barrier(0);
                };
 
                LoopFunc(I0, I1);
                LoopFunc(I1, I0);
 
                i += 2;
            } while(i < (num_loop - 2));
        }
 
        // tail
        if constexpr(TailNum == TailNumber::Even)
        {
            b_blockwise_copy.Run(
                b_grid_desc, b_grid_buf, b_block_desc, b_block_origin_idx, b_thread_bufs(I1));
 
            // Prefetch a_scales
            static_for<0, MRepeat / MXdlPack, 1>{}([&](auto m0) {
                static_for<0, KRepeat / KXdlPack, 1>{}([&](auto k0) {
                    a_scale_thread_copy.Run(a_scale_grid_desc,
                                            a_scale_grid_buf,
                                            a_scale_thread_desc,
                                            make_tuple(m0, k0, I0),
                                            a_scale_thread_bufs(I1));
 
                    a_scale_thread_copy.MoveSrcSliceWindow(a_scale_grid_desc,
                                                           make_multi_index(0, I1, 0));
                });
                a_scale_thread_copy.MoveSrcSliceWindow(
                    a_scale_grid_desc, make_multi_index(MWaves, -KRepeat / KXdlPack, 0));
            });
 
            // Prefetch b_scales
            static_for<0, NRepeat / NXdlPack, 1>{}([&](auto n0) {
                static_for<0, KRepeat / KXdlPack, 1>{}([&](auto k0) {
                    b_scale_thread_copy.Run(b_scale_grid_desc,
                                            b_scale_grid_buf,
                                            b_scale_thread_desc,
                                            make_tuple(n0, k0, I0),
                                            b_scale_thread_bufs(I1));
 
                    b_scale_thread_copy.MoveSrcSliceWindow(b_scale_grid_desc,
                                                           make_multi_index(0, I1, 0));
                });
                b_scale_thread_copy.MoveSrcSliceWindow(
                    b_scale_grid_desc, make_multi_index(NWaves, -KRepeat / KXdlPack, 0));
            });
 
            static_for<0, MRepeat, 1>{}([&](auto m0) {
                constexpr auto im_major = m0 / MXdlPack;
                constexpr auto im_minor = m0 % MXdlPack;
                static_for<0, KRepeat, 1>{}([&](auto k0) {
                    constexpr auto ik_major = k0 / KXdlPack;
                    constexpr auto ik_minor = k0 % KXdlPack;
                    static_for<0, NRepeat, 1>{}([&](auto n0) {
                        constexpr auto in_major = n0 / NXdlPack;
                        constexpr auto in_minor = n0 % NXdlPack;
 
                        constexpr index_t a_scale_offset =
                            a_scale_thread_desc.CalculateOffset(make_tuple(im_major, ik_major, I0));
                        constexpr index_t b_scale_offset =
                            b_scale_thread_desc.CalculateOffset(make_tuple(in_major, ik_major, I0));
 
                        static_assert(0 < ScalesPerXdlopsRunPerThread,
                                      "Must have at least one scale per Xdlops "
                                      "per Thread.");
 
                        vector_type<AScaleDataType, a_scale_thread_vec_size> a_scale_thread_vec;
                        vector_type<BScaleDataType, b_scale_thread_vec_size> b_scale_thread_vec;
 
                        // Pack scale_thread_buf into scale_thread_vec
                        static_for<0, a_scale_thread_vec_size, 1>{}([&](auto s) {
                            a_scale_thread_vec.template AsType<AScaleDataType>()(s) =
                                a_scale_thread_bufs(I0)[Number<a_scale_offset + s>{}];
                        });
 
                        static_for<0, b_scale_thread_vec_size, 1>{}([&](auto s) {
                            b_scale_thread_vec.template AsType<BScaleDataType>()(s) =
                                b_scale_thread_bufs(I0)[Number<b_scale_offset + s>{}];
                        });
 
                        vector_type<ComputeTypeA, KPack> a_thread_vec;
                        vector_type<ComputeTypeB, KPack> b_thread_vec;
 
                        static_for<0, KPack, 1>{}([&](auto ik) {
                            a_thread_vec.template AsType<ComputeTypeA>()(ik) =
                                a_thread_buf[Number<a_thread_desc_.CalculateOffset(
                                    make_tuple(I0, I0, im_minor, k0, ik))>{}];
                            b_thread_vec.template AsType<ComputeTypeB>()(ik) =
                                b_thread_bufs[I0][Number<b_thread_desc_.CalculateOffset(
                                    make_tuple(in_major, I0, in_minor, k0, ik))>{}];
                        });
 
                        using mfma_input_type_a =
                            typename vector_type<ComputeTypeA,
                                                 xdlops_gemm.K1PerXdlops / APackedSize>::type;
 
                        using mfma_input_type_b =
                            typename vector_type<ComputeTypeB,
                                                 xdlops_gemm.K1PerXdlops / BPackedSize>::type;
 
                        using mfma_scale_input_type_a =
                            typename vector_type<AScaleDataType, a_scale_thread_vec_size>::type;
                        using mfma_scale_input_type_b =
                            typename vector_type<BScaleDataType, b_scale_thread_vec_size>::type;
 
                        constexpr index_t c_offset = c_thread_desc_.CalculateOffset(
                            make_tuple(im_major, in_major, im_minor, in_minor, 0));
 
                        // MFMA accumulation
                        xdlops_gemm.template Run<ik_minor * MXdlPack + im_minor,
                                                 ik_minor * NXdlPack + in_minor>(
                            a_thread_vec.template AsType<mfma_input_type_a>(),
                            a_scale_thread_vec.template AsType<mfma_scale_input_type_a>(),
                            b_thread_vec.template AsType<mfma_input_type_b>(),
                            b_scale_thread_vec.template AsType<mfma_scale_input_type_b>(),
                            c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
                    });
                });
                if constexpr(m0.value == SwitchM)
                {
                    __builtin_amdgcn_s_waitcnt(async_vmcnt_encoding);
                    block_sync_lds();
                }
 
                constexpr auto lds_buf = m0.value >= SwitchM ? I1 : I0;
 
                static_for<0, KRepeat, 1>{}([&](auto k) {
                    constexpr auto k_step = k * xdlops_gemm.KPerXdlops / APackedSize *
                                            (APackedSize * KPack / xdlops_gemm.K1PerXdlops);
                    static_for<0, xdlops_gemm.K1PerXdlops / (APackedSize * KThreadChunk), 1>{}(
                        [&](auto chunk) {
                            constexpr auto a_k_step_chunk =
                                k_step +
                                chunk * KThreadChunk * xdlops_gemm.mfma_instr.num_input_blks;
                            a_thread_copy_.Run(
                                a_block_desc_m0_m1_m2_m3_k,
                                make_tuple(Number<((m0 + LocalPrefetchStages) / MXdlPack) %
                                                  (MRepeat / MXdlPack)>{},
                                           I0,
                                           Number<im_minor>{},
                                           I0,
                                           Number<a_k_step_chunk>{}),
                                a_block_bufs(Number<lds_buf>{}),
                                a_thread_desc_,
                                make_tuple(
                                    I0, I0, Number<im_minor>{}, k, Number<chunk * KThreadChunk>{}),
                                a_thread_buf);
                        });
                });
            });
 
            static_for<0, MRepeat, 1>{}([&](auto m0) {
                constexpr auto im_major = m0 / MXdlPack;
                constexpr auto im_minor = m0 % MXdlPack;
                static_for<0, KRepeat, 1>{}([&](auto k0) {
                    constexpr auto ik_major = k0 / KXdlPack;
                    constexpr auto ik_minor = k0 % KXdlPack;
                    static_for<0, NRepeat, 1>{}([&](auto n0) {
                        constexpr auto in_major = n0 / NXdlPack;
                        constexpr auto in_minor = n0 % NXdlPack;
 
                        constexpr index_t a_scale_offset =
                            a_scale_thread_desc.CalculateOffset(make_tuple(im_major, ik_major, I0));
                        constexpr index_t b_scale_offset =
                            b_scale_thread_desc.CalculateOffset(make_tuple(in_major, ik_major, I0));
 
                        static_assert(0 < ScalesPerXdlopsRunPerThread,
                                      "Must have at least one scale per Xdlops "
                                      "per Thread.");
 
                        vector_type<AScaleDataType, a_scale_thread_vec_size> a_scale_thread_vec;
                        vector_type<BScaleDataType, b_scale_thread_vec_size> b_scale_thread_vec;
 
                        // Pack scale_thread_buf into scale_thread_vec
                        static_for<0, a_scale_thread_vec_size, 1>{}([&](auto s) {
                            a_scale_thread_vec.template AsType<AScaleDataType>()(s) =
                                a_scale_thread_bufs(I1)[Number<a_scale_offset + s>{}];
                        });
 
                        static_for<0, b_scale_thread_vec_size, 1>{}([&](auto s) {
                            b_scale_thread_vec.template AsType<BScaleDataType>()(s) =
                                b_scale_thread_bufs(I1)[Number<b_scale_offset + s>{}];
                        });
 
                        vector_type<ComputeTypeA, KPack> a_thread_vec;
                        vector_type<ComputeTypeB, KPack> b_thread_vec;
 
                        static_for<0, KPack, 1>{}([&](auto ik) {
                            a_thread_vec.template AsType<ComputeTypeA>()(ik) =
                                a_thread_buf[Number<a_thread_desc_.CalculateOffset(
                                    make_tuple(I0, I0, im_minor, k0, ik))>{}];
                            b_thread_vec.template AsType<ComputeTypeB>()(ik) =
                                b_thread_bufs[I1][Number<b_thread_desc_.CalculateOffset(
                                    make_tuple(in_major, I0, in_minor, k0, ik))>{}];
                        });
 
                        using mfma_input_type_a =
                            typename vector_type<ComputeTypeA,
                                                 xdlops_gemm.K1PerXdlops / APackedSize>::type;
 
                        using mfma_input_type_b =
                            typename vector_type<ComputeTypeB,
                                                 xdlops_gemm.K1PerXdlops / BPackedSize>::type;
 
                        using mfma_scale_input_type_a =
                            typename vector_type<AScaleDataType, a_scale_thread_vec_size>::type;
                        using mfma_scale_input_type_b =
                            typename vector_type<BScaleDataType, b_scale_thread_vec_size>::type;
 
                        constexpr index_t c_offset = c_thread_desc_.CalculateOffset(
                            make_tuple(im_major, in_major, im_minor, in_minor, 0));
 
                        // MFMA accumulation
                        xdlops_gemm.template Run<ik_minor * MXdlPack + im_minor,
                                                 ik_minor * NXdlPack + in_minor>(
                            a_thread_vec.template AsType<mfma_input_type_a>(),
                            a_scale_thread_vec.template AsType<mfma_scale_input_type_a>(),
                            b_thread_vec.template AsType<mfma_input_type_b>(),
                            b_scale_thread_vec.template AsType<mfma_scale_input_type_b>(),
                            c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
                    });
                });
                if constexpr(m0.value < (MRepeat - LocalPrefetchStages))
                {
                    static_for<0, KRepeat, 1>{}([&](auto k) {
                        constexpr auto k_step = k * xdlops_gemm.KPerXdlops / APackedSize *
                                                (APackedSize * KPack / xdlops_gemm.K1PerXdlops);
                        static_for<0, xdlops_gemm.K1PerXdlops / (APackedSize * KThreadChunk), 1>{}(
                            [&](auto chunk) {
                                constexpr auto a_k_step_chunk =
                                    k_step +
                                    chunk * KThreadChunk * xdlops_gemm.mfma_instr.num_input_blks;
                                a_thread_copy_.Run(
                                    a_block_desc_m0_m1_m2_m3_k,
                                    make_tuple(Number<((m0 + LocalPrefetchStages) / MXdlPack) %
                                                      (MRepeat / MXdlPack)>{},
                                               I0,
                                               Number<im_minor>{},
                                               I0,
                                               Number<a_k_step_chunk>{}),
                                    a_block_bufs(I1),
                                    a_thread_desc_,
                                    make_tuple(I0,
                                               I0,
                                               Number<im_minor>{},
                                               k,
                                               Number<chunk * KThreadChunk>{}),
                                    a_thread_buf);
                            });
                    });
                }
            });
        }
        else if constexpr(TailNum == TailNumber::Odd)
        {
            static_for<0, MRepeat, 1>{}([&](auto m0) {
                constexpr auto im_major = m0 / MXdlPack;
                constexpr auto im_minor = m0 % MXdlPack;
                static_for<0, KRepeat, 1>{}([&](auto k0) {
                    constexpr auto ik_major = k0 / KXdlPack;
                    constexpr auto ik_minor = k0 % KXdlPack;
                    static_for<0, NRepeat, 1>{}([&](auto n0) {
                        constexpr auto in_major = n0 / NXdlPack;
                        constexpr auto in_minor = n0 % NXdlPack;
 
                        constexpr index_t a_scale_offset =
                            a_scale_thread_desc.CalculateOffset(make_tuple(im_major, ik_major, I0));
                        constexpr index_t b_scale_offset =
                            b_scale_thread_desc.CalculateOffset(make_tuple(in_major, ik_major, I0));
 
                        static_assert(0 < ScalesPerXdlopsRunPerThread,
                                      "Must have at least one scale per Xdlops "
                                      "per Thread.");
 
                        vector_type<AScaleDataType, a_scale_thread_vec_size> a_scale_thread_vec;
                        vector_type<BScaleDataType, b_scale_thread_vec_size> b_scale_thread_vec;
 
                        // Pack scale_thread_buf into scale_thread_vec
                        static_for<0, a_scale_thread_vec_size, 1>{}([&](auto s) {
                            a_scale_thread_vec.template AsType<AScaleDataType>()(s) =
                                a_scale_thread_bufs(I0)[Number<a_scale_offset + s>{}];
                        });
 
                        static_for<0, b_scale_thread_vec_size, 1>{}([&](auto s) {
                            b_scale_thread_vec.template AsType<BScaleDataType>()(s) =
                                b_scale_thread_bufs(I0)[Number<b_scale_offset + s>{}];
                        });
 
                        vector_type<ComputeTypeA, KPack> a_thread_vec;
                        vector_type<ComputeTypeB, KPack> b_thread_vec;
 
                        static_for<0, KPack, 1>{}([&](auto ik) {
                            a_thread_vec.template AsType<ComputeTypeA>()(ik) =
                                a_thread_buf[Number<a_thread_desc_.CalculateOffset(
                                    make_tuple(I0, I0, im_minor, k0, ik))>{}];
                            b_thread_vec.template AsType<ComputeTypeB>()(ik) =
                                b_thread_bufs[I0][Number<b_thread_desc_.CalculateOffset(
                                    make_tuple(in_major, I0, in_minor, k0, ik))>{}];
                        });
 
                        using mfma_input_type_a =
                            typename vector_type<ComputeTypeA,
                                                 xdlops_gemm.K1PerXdlops / APackedSize>::type;
 
                        using mfma_input_type_b =
                            typename vector_type<ComputeTypeB,
                                                 xdlops_gemm.K1PerXdlops / BPackedSize>::type;
 
                        using mfma_scale_input_type_a =
                            typename vector_type<AScaleDataType, a_scale_thread_vec_size>::type;
                        using mfma_scale_input_type_b =
                            typename vector_type<BScaleDataType, b_scale_thread_vec_size>::type;
 
                        constexpr index_t c_offset = c_thread_desc_.CalculateOffset(
                            make_tuple(im_major, in_major, im_minor, in_minor, 0));
 
                        // MFMA accumulation
                        xdlops_gemm.template Run<ik_minor * MXdlPack + im_minor,
                                                 ik_minor * NXdlPack + in_minor>(
                            a_thread_vec.template AsType<mfma_input_type_a>(),
                            a_scale_thread_vec.template AsType<mfma_scale_input_type_a>(),
                            b_thread_vec.template AsType<mfma_input_type_b>(),
                            b_scale_thread_vec.template AsType<mfma_scale_input_type_b>(),
                            c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
                    });
                });
                if constexpr(m0.value < (MRepeat - LocalPrefetchStages))
                {
                    static_for<0, KRepeat, 1>{}([&](auto k) {
                        constexpr auto k_step = k * xdlops_gemm.KPerXdlops / APackedSize *
                                                (APackedSize * KPack / xdlops_gemm.K1PerXdlops);
                        static_for<0, xdlops_gemm.K1PerXdlops / (APackedSize * KThreadChunk), 1>{}(
                            [&](auto chunk) {
                                constexpr auto a_k_step_chunk =
                                    k_step +
                                    chunk * KThreadChunk * xdlops_gemm.mfma_instr.num_input_blks;
                                a_thread_copy_.Run(
                                    a_block_desc_m0_m1_m2_m3_k,
                                    make_tuple(Number<((m0 + LocalPrefetchStages) / MXdlPack) %
                                                      (MRepeat / MXdlPack)>{},
                                               I0,
                                               Number<im_minor>{},
                                               I0,
                                               Number<a_k_step_chunk>{}),
                                    a_block_bufs(I0),
                                    a_thread_desc_,
                                    make_tuple(I0,
                                               I0,
                                               Number<im_minor>{},
                                               k,
                                               Number<chunk * KThreadChunk>{}),
                                    a_thread_buf);
                            });
                    });
                }
            });
        }
    }
 
    //  Length:  A[ARegBuf, MWave, MXdlPack, KRepeat, KPack]
    //  Order:     1        0      3         2        4
    static constexpr auto ARegBuf        = 2;
    static constexpr auto a_thread_desc_ = make_naive_tensor_descriptor_packed(
        make_tuple(Number<ARegBuf>{}, I1, Number<MXdlPack>{}, Number<KRepeat>{}, Number<KPack>{}));
 
    using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<ADataType,
                                                         ComputeTypeA,
                                                         decltype(a_block_desc_m0_m1_m2_m3_k),
                                                         decltype(a_thread_desc_),
                                                         Sequence<1, 1, 1, 1, KThreadChunk>,
                                                         Sequence<0, 1, 2, 3, 4>,
                                                         4,
                                                         A_K1,
                                                         A_K1>;
    AThreadCopy a_thread_copy_{Base::CalculateAThreadOriginDataIndex()};
 
    // TODO: make this field protected when a_scale_thread_copy_ is moved
    // here
    static constexpr auto a_scale_thread_desc = make_naive_tensor_descriptor_packed(
        make_tuple(Number<MRepeat / MXdlPack>{},
                   Number<KRepeat / KXdlPack>{},
                   Number<ScalesPerXdlopsRunPerThread * a_scale_thread_vec_size>{}));
 
    // TODO: make this field protected when b_scale_thread_copy_ is moved
    // here
    static constexpr auto b_scale_thread_desc = make_naive_tensor_descriptor_packed(
        make_tuple(Number<NRepeat / NXdlPack>{},
                   Number<KRepeat / KXdlPack>{},
                   Number<ScalesPerXdlopsRunPerThread * b_scale_thread_vec_size>{}));
 
    protected:
    // using Base::a_thread_copy_;
    // using Base::a_thread_desc_;
    using Base::b_thread_copy_;
    using Base::b_thread_desc_;
    using Base::c_thread_desc_;
};
 
} // namespace ck