// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT

#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: 1
// LocalPreFillStages: 1
// LocalPreFetchStages: 0
// 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_mx_moe_nbs_v1
{
};

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_mx_moe_nbs_v1<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::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  = 1;
    static constexpr index_t PrefillStages   = 1;
    static constexpr index_t GlobalBufferNum = 1;

    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;
    }

    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_buf,
        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_buf,
        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
    {
        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());

        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());

        // Global prefetch 1
        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf);

        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_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_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));

        // Local prefill 1
        a_blockwise_copy.RunWrite(a_block_desc, a_block_buf);
        b_blockwise_copy.RunWrite(b_block_desc, b_block_buf);

        // Initialize C
        c_thread_buf.Clear();

        // main body
        if constexpr(HasMainLoop)
        {
            // loop over k with the step KPerBlock
            index_t i = 0;
            do
            {
                // -------------------------------------------------------------------------------------------
                a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
                b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf);

                a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
                b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);

                block_sync_lds();

                static_for<0, KRepeat, 1>{}([&](auto k) {
                    constexpr auto k_step = k * xdlops_gemm.KPerXdlops / APackedSize *
                                            (APackedSize * KPack / xdlops_gemm.K1PerXdlops);
                    static_ford<Sequence<MRepeat,
                                         xdlops_gemm.K1PerXdlops / (APackedSize * KThreadChunk)>>{}(
                        [&](auto mc) {
                            constexpr auto m0    = Number<mc[Number<0>{}]>{};
                            constexpr auto chunk = Number<mc[Number<1>{}]>{};
                            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 / MXdlPack>{},
                                                          I0,
                                                          Number<m0 % MXdlPack>{},
                                                          I0,
                                                          Number<a_k_step_chunk>{}),
                                               a_block_buf,
                                               a_thread_desc_,
                                               make_tuple(Number<m0 / MXdlPack>{},
                                                          I0,
                                                          Number<m0 % MXdlPack>{},
                                                          k,
                                                          Number<chunk * KThreadChunk>{}),
                                               a_thread_buf);
                        });
                    // read block data in chunks to assemble correct thread vectors
                    static_ford<Sequence<NRepeat,
                                         xdlops_gemm.K1PerXdlops / (BPackedSize * KThreadChunk)>>{}(
                        [&](auto nc) {
                            constexpr auto n0    = Number<nc[Number<0>{}]>{};
                            constexpr auto chunk = Number<nc[Number<1>{}]>{};
                            constexpr auto b_k_step_chunk =
                                k_step +
                                chunk * KThreadChunk * xdlops_gemm.mfma_instr.num_input_blks;
                            b_thread_copy_.Run(b_block_desc_n0_n1_n2_n3_k,
                                               make_tuple(Number<n0 / NXdlPack>{},
                                                          I0,
                                                          Number<n0 % NXdlPack>{},
                                                          I0,
                                                          Number<b_k_step_chunk>{}),
                                               b_block_buf,
                                               b_thread_desc_,
                                               make_tuple(Number<n0 / NXdlPack>{},
                                                          I0,
                                                          Number<n0 % NXdlPack>{},
                                                          k,
                                                          Number<chunk * KThreadChunk>{}),
                                               b_thread_buf);
                        });
                });

                static_ford<Sequence<MRepeat / MXdlPack, NRepeat / NXdlPack, KRepeat / KXdlPack>>{}(
                    [&](auto mnk) {
                        constexpr auto m0 = mnk[Number<0>{}];
                        constexpr auto n0 = mnk[Number<1>{}];
                        constexpr auto k0 = mnk[Number<2>{}];

                        constexpr index_t a_scale_offset =
                            a_scale_thread_desc.CalculateOffset(make_tuple(m0, k0, I0));
                        constexpr index_t b_scale_offset =
                            b_scale_thread_desc.CalculateOffset(make_tuple(n0, k0, 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_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_buf[Number<b_scale_offset + s>{}];
                        });

                        static_ford<Sequence<KXdlPack, MXdlPack, NXdlPack>>{}([&](auto kmn_xdl) {
                            constexpr auto ikxdl = Number<kmn_xdl[Number<0>{}]>{};
                            constexpr auto imxdl = Number<kmn_xdl[Number<1>{}]>{};
                            constexpr auto inxdl = Number<kmn_xdl[Number<2>{}]>{};
                            constexpr auto kxdl  = ikxdl + k0 * KXdlPack;

                            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(m0, I0, imxdl, kxdl, ik))>{}];
                                b_thread_vec.template AsType<ComputeTypeB>()(ik) =
                                    b_thread_buf[Number<b_thread_desc_.CalculateOffset(
                                        make_tuple(n0, I0, inxdl, kxdl, 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(m0, n0, imxdl, inxdl, 0));

                            // MFMA accumulation
                            xdlops_gemm
                                .template Run<ikxdl * MXdlPack + imxdl, ikxdl * NXdlPack + inxdl>(
                                    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>{}));
                        });
                    });

                // 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_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_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));

                block_sync_lds();
                a_blockwise_copy.RunWrite(a_block_desc, a_block_buf);
                b_blockwise_copy.RunWrite(b_block_desc, b_block_buf);

                i += 1;
            } while(i < (num_loop - 1));
        }

        // tail
        if constexpr(TailNum == TailNumber::Full)
        {
            block_sync_lds();
            static_for<0, KRepeat, 1>{}([&](auto k) {
                constexpr auto k_step = k * xdlops_gemm.KPerXdlops / APackedSize *
                                        (APackedSize * KPack / xdlops_gemm.K1PerXdlops);
                static_ford<
                    Sequence<MRepeat, xdlops_gemm.K1PerXdlops / (APackedSize * KThreadChunk)>>{}(
                    [&](auto mc) {
                        constexpr auto m0    = Number<mc[Number<0>{}]>{};
                        constexpr auto chunk = Number<mc[Number<1>{}]>{};
                        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 / MXdlPack>{},
                                                      I0,
                                                      Number<m0 % MXdlPack>{},
                                                      I0,
                                                      Number<a_k_step_chunk>{}),
                                           a_block_buf,
                                           a_thread_desc_,
                                           make_tuple(Number<m0 / MXdlPack>{},
                                                      I0,
                                                      Number<m0 % MXdlPack>{},
                                                      k,
                                                      Number<chunk * KThreadChunk>{}),
                                           a_thread_buf);
                    });
                // read block data in chunks to assemble correct thread vectors
                static_ford<
                    Sequence<NRepeat, xdlops_gemm.K1PerXdlops / (BPackedSize * KThreadChunk)>>{}(
                    [&](auto nc) {
                        constexpr auto n0    = Number<nc[Number<0>{}]>{};
                        constexpr auto chunk = Number<nc[Number<1>{}]>{};
                        constexpr auto b_k_step_chunk =
                            k_step + chunk * KThreadChunk * xdlops_gemm.mfma_instr.num_input_blks;
                        b_thread_copy_.Run(b_block_desc_n0_n1_n2_n3_k,
                                           make_tuple(Number<n0 / NXdlPack>{},
                                                      I0,
                                                      Number<n0 % NXdlPack>{},
                                                      I0,
                                                      Number<b_k_step_chunk>{}),
                                           b_block_buf,
                                           b_thread_desc_,
                                           make_tuple(Number<n0 / NXdlPack>{},
                                                      I0,
                                                      Number<n0 % NXdlPack>{},
                                                      k,
                                                      Number<chunk * KThreadChunk>{}),
                                           b_thread_buf);
                    });
            });

            static_ford<Sequence<MRepeat / MXdlPack, NRepeat / NXdlPack, KRepeat / KXdlPack>>{}(
                [&](auto mnk) {
                    constexpr auto m0 = mnk[Number<0>{}];
                    constexpr auto n0 = mnk[Number<1>{}];
                    constexpr auto k0 = mnk[Number<2>{}];

                    constexpr index_t a_scale_offset =
                        a_scale_thread_desc.CalculateOffset(make_tuple(m0, k0, I0));
                    constexpr index_t b_scale_offset =
                        b_scale_thread_desc.CalculateOffset(make_tuple(n0, k0, 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_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_buf[Number<b_scale_offset + s>{}];
                    });

                    static_ford<Sequence<KXdlPack, MXdlPack, NXdlPack>>{}([&](auto kmn_xdl) {
                        constexpr auto ikxdl = Number<kmn_xdl[Number<0>{}]>{};
                        constexpr auto imxdl = Number<kmn_xdl[Number<1>{}]>{};
                        constexpr auto inxdl = Number<kmn_xdl[Number<2>{}]>{};
                        constexpr auto kxdl  = ikxdl + k0 * KXdlPack;

                        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(m0, I0, imxdl, kxdl, ik))>{}];
                            b_thread_vec.template AsType<ComputeTypeB>()(ik) =
                                b_thread_buf[Number<b_thread_desc_.CalculateOffset(
                                    make_tuple(n0, I0, inxdl, kxdl, 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(m0, n0, imxdl, inxdl, 0));

                        // MFMA accumulation
                        xdlops_gemm
                            .template Run<ikxdl * MXdlPack + imxdl, ikxdl * NXdlPack + inxdl>(
                                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>{}));
                    });
                });
        }
    }

    // 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
