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

#pragma once

#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_base.hpp"

namespace ck {

// Maximum Global Memory throughput pipeline with >=32KB data in fly
// GlobalPrefetchStages: >=2
// LocalPreFillStages: 1
// LocalPreFetchStages: 0
// LocalSharedMemoryBuffer: 1

template <BlockGemmPipelineScheduler BlkGemmPipelineVer,
          index_t BlockSize,
          typename ADataType,
          typename BDataType,
          typename ComputeDataType,
          typename AccDataType,
          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,
          index_t NRepeat,
          index_t KPacks,
          bool TransposeA,
          bool TransposeB>
struct BlockwiseGemmXdlops_pipeline_v2
{
};

template <index_t BlockSize,
          typename ADataType,
          typename BDataType,
          typename ComputeDataType,
          typename AccDataType,
          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,
          index_t NRepeat,
          index_t KPack,
          bool TransposeA,
          bool TransposeB
          // ,bool TransposeC //disable transposec right now...
          >
struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Intrawave,
                                       BlockSize,
                                       ADataType,
                                       BDataType,
                                       ComputeDataType,
                                       AccDataType,
                                       ATileDesc,
                                       BTileDesc,
                                       AMmaTileDesc,
                                       BMmaTileDesc,
                                       ABlockTransferSrcScalarPerVector,
                                       BBlockTransferSrcScalarPerVector,
                                       MPerBlock,
                                       NPerBlock,
                                       KPerBlock,
                                       MPerXDL,
                                       NPerXDL,
                                       MRepeat,
                                       NRepeat,
                                       KPack,
                                       TransposeA,
                                       TransposeB>
    : BlockwiseGemmXdlops_pipeline_base<BlockSize,
                                        ADataType,
                                        BDataType,
                                        ComputeDataType,
                                        AccDataType,
                                        ATileDesc,
                                        BTileDesc,
                                        AMmaTileDesc,
                                        BMmaTileDesc,
                                        ABlockTransferSrcScalarPerVector,
                                        BBlockTransferSrcScalarPerVector,
                                        MPerBlock,
                                        NPerBlock,
                                        KPerBlock,
                                        MPerXDL,
                                        NPerXDL,
                                        MRepeat,
                                        NRepeat,
                                        KPack,
                                        TransposeA,
                                        TransposeB>

{
    using Base = BlockwiseGemmXdlops_pipeline_base<BlockSize,
                                                   ADataType,
                                                   BDataType,
                                                   ComputeDataType,
                                                   AccDataType,
                                                   ATileDesc,
                                                   BTileDesc,
                                                   AMmaTileDesc,
                                                   BMmaTileDesc,
                                                   ABlockTransferSrcScalarPerVector,
                                                   BBlockTransferSrcScalarPerVector,
                                                   MPerBlock,
                                                   NPerBlock,
                                                   KPerBlock,
                                                   MPerXDL,
                                                   NPerXDL,
                                                   MRepeat,
                                                   NRepeat,
                                                   KPack,
                                                   TransposeA,
                                                   TransposeB>;
    using Base::I0;
    using Base::KRepeat;
    using Base::xdlops_gemm;

    using Base::CalculateCThreadOriginDataIndex;
    using Base::CalculateCThreadOriginDataIndex8D;
    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::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_k;
    using Base::b_block_desc_n0_n1_n2_k;

    using Base::AMmaKStride;
    using Base::BMmaKStride;

    static constexpr index_t WgpPerCU =
        (4 * warpSize / BlockSize) >= 1 ? 4 * warpSize / BlockSize : 1;
    static constexpr index_t FullMemBandPrefetchStages = math::integer_divide_ceil(
        32768 / WgpPerCU,
        (MPerBlock * sizeof(ADataType) + NPerBlock * sizeof(BDataType)) * KPerBlock);
    static constexpr index_t PrefetchStages =
        FullMemBandPrefetchStages >= 2
            ? FullMemBandPrefetchStages <= 8 ? FullMemBandPrefetchStages : 8
            : 2;

    static constexpr index_t PrefillStages   = 1;
    static constexpr index_t GlobalBufferNum = PrefetchStages;

    __host__ __device__ static constexpr bool BlockHasHotloop(index_t num_loop)
    {
        return num_loop > PrefetchStages;
    }

    __host__ __device__ static constexpr TailNumber BlockLoopTailNum(index_t num_loop)
    {
        if(num_loop % PrefetchStages == 1)
        {
            return TailNumber::One;
        }
        else if(num_loop % PrefetchStages == 2)
        {
            return TailNumber::Two;
        }
        else if(num_loop % PrefetchStages == 3)
        {
            return TailNumber::Three;
        }
        else if(num_loop % PrefetchStages == 4)
        {
            return TailNumber::Four;
        }
        else if(num_loop % PrefetchStages == 5)
        {
            return TailNumber::Five;
        }
        else if(num_loop % PrefetchStages == 6)
        {
            return TailNumber::Six;
        }
        else if(num_loop % PrefetchStages == 7)
        {
            return TailNumber::Seven;
        }
        else
        {
            return TailNumber::Full;
        }
    }

    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>
    __device__ void Run(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,
                        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,
                        CThreadBuffer& c_thread_buf,
                        index_t num_loop) const
    {
        auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
            a_thread_desc_.GetElementSpaceSize());
        auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
            b_thread_desc_.GetElementSpaceSize());

        // Global prefetch 1
        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, I0);
        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, I0);

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

        // Initialize C
        c_thread_buf.Clear();

        // Local prefill 1
        a_blockwise_copy.RunWrite(a_block_desc, a_block_buf, I0);
        b_blockwise_copy.RunWrite(b_block_desc, b_block_buf, I0);

        // Global prefetch [2, PrefetchStages]
        static_for<1, PrefetchStages, 1>{}([&](auto iprefetch) {
            a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, iprefetch);
            b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, iprefetch);

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

        // main body
        if constexpr(HasMainLoop)
        {
            index_t i = 0;
            do
            {
                static_for<0, PrefetchStages, 1>{}([&](auto iprefetch) {
                    // -------------------------------------------------------------------------------------------
                    block_sync_lds();
                    static_for<0, KRepeat, 1>{}([&](auto k) {
                        a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
                                           make_tuple(I0, I0, I0, Number<k * AMmaKStride>{}),
                                           a_block_buf,
                                           a_thread_desc_,
                                           make_tuple(I0, I0, k, I0),
                                           a_thread_buf);

                        b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
                                           make_tuple(I0, I0, I0, Number<k * BMmaKStride>{}),
                                           b_block_buf,
                                           b_thread_desc_,
                                           make_tuple(I0, I0, k, I0),
                                           b_thread_buf);
                    });

                    static_for<0, KRepeat, 1>{}([&](auto k0) {
                        static_for<0, MRepeat, 1>{}([&](auto m0) {
                            static_for<0, NRepeat, 1>{}([&](auto n0) {
                                vector_type<ComputeDataType, KPack> a_thread_vec;
                                vector_type<ComputeDataType, KPack> b_thread_vec;

                                static_for<0, KPack, 1>{}([&](auto ik) {
                                    a_thread_vec.template AsType<ComputeDataType>()(ik) =
                                        a_thread_buf[Number<a_thread_desc_.CalculateOffset(
                                            make_tuple(m0, I0, k0, ik))>{}];
                                    b_thread_vec.template AsType<ComputeDataType>()(ik) =
                                        b_thread_buf[Number<b_thread_desc_.CalculateOffset(
                                            make_tuple(n0, I0, k0, ik))>{}];
                                });

                                using mfma_input_type =
                                    typename vector_type<ComputeDataType,
                                                         xdlops_gemm.K1PerXdlops>::type;

                                constexpr index_t c_offset =
                                    c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));

                                xdlops_gemm.Run(
                                    a_thread_vec.template AsType<mfma_input_type>(),
                                    b_thread_vec.template AsType<mfma_input_type>(),
                                    c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
                            });
                        });
                    });

                    block_sync_lds();
                    a_blockwise_copy.RunWrite(
                        a_block_desc, a_block_buf, Number<(iprefetch + 1) % PrefetchStages>{});
                    b_blockwise_copy.RunWrite(
                        b_block_desc, b_block_buf, Number<(iprefetch + 1) % PrefetchStages>{});

                    a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, iprefetch);
                    b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, iprefetch);

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

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

        // tail

        auto LoopTailFunc = [&](auto tail_num) {
            static_for<1, tail_num, 1>{}([&](auto iprefetch) {
                block_sync_lds();
                static_for<0, KRepeat, 1>{}([&](auto k) {
                    a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
                                       make_tuple(I0, I0, I0, Number<k * AMmaKStride>{}),
                                       a_block_buf,
                                       a_thread_desc_,
                                       make_tuple(I0, I0, k, I0),
                                       a_thread_buf);

                    b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
                                       make_tuple(I0, I0, I0, Number<k * BMmaKStride>{}),
                                       b_block_buf,
                                       b_thread_desc_,
                                       make_tuple(I0, I0, k, I0),
                                       b_thread_buf);
                });

                static_for<0, KRepeat, 1>{}([&](auto k0) {
                    static_for<0, MRepeat, 1>{}([&](auto m0) {
                        static_for<0, NRepeat, 1>{}([&](auto n0) {
                            vector_type<ComputeDataType, KPack> a_thread_vec;
                            vector_type<ComputeDataType, KPack> b_thread_vec;

                            static_for<0, KPack, 1>{}([&](auto ik) {
                                a_thread_vec.template AsType<ComputeDataType>()(ik) =
                                    a_thread_buf[Number<a_thread_desc_.CalculateOffset(
                                        make_tuple(m0, I0, k0, ik))>{}];
                                b_thread_vec.template AsType<ComputeDataType>()(ik) =
                                    b_thread_buf[Number<b_thread_desc_.CalculateOffset(
                                        make_tuple(n0, I0, k0, ik))>{}];
                            });

                            using mfma_input_type =
                                typename vector_type<ComputeDataType,
                                                     xdlops_gemm.K1PerXdlops>::type;

                            constexpr index_t c_offset =
                                c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));

                            xdlops_gemm.Run(
                                a_thread_vec.template AsType<mfma_input_type>(),
                                b_thread_vec.template AsType<mfma_input_type>(),
                                c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
                        });
                    });
                });

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

            block_sync_lds();
            static_for<0, KRepeat, 1>{}([&](auto k) {
                a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
                                   make_tuple(I0, I0, I0, Number<k * AMmaKStride>{}),
                                   a_block_buf,
                                   a_thread_desc_,
                                   make_tuple(I0, I0, k, I0),
                                   a_thread_buf);

                b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
                                   make_tuple(I0, I0, I0, Number<k * BMmaKStride>{}),
                                   b_block_buf,
                                   b_thread_desc_,
                                   make_tuple(I0, I0, k, I0),
                                   b_thread_buf);
            });

            static_for<0, KRepeat, 1>{}([&](auto k0) {
                static_for<0, MRepeat, 1>{}([&](auto m0) {
                    static_for<0, NRepeat, 1>{}([&](auto n0) {
                        vector_type<ComputeDataType, KPack> a_thread_vec;
                        vector_type<ComputeDataType, KPack> b_thread_vec;

                        static_for<0, KPack, 1>{}([&](auto ik) {
                            a_thread_vec.template AsType<ComputeDataType>()(ik) =
                                a_thread_buf[Number<a_thread_desc_.CalculateOffset(
                                    make_tuple(m0, I0, k0, ik))>{}];
                            b_thread_vec.template AsType<ComputeDataType>()(ik) =
                                b_thread_buf[Number<b_thread_desc_.CalculateOffset(
                                    make_tuple(n0, I0, k0, ik))>{}];
                        });

                        using mfma_input_type =
                            typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;

                        constexpr index_t c_offset =
                            c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));

                        xdlops_gemm.Run(a_thread_vec.template AsType<mfma_input_type>(),
                                        b_thread_vec.template AsType<mfma_input_type>(),
                                        c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
                    });
                });
            });
        };

        if constexpr(TailNum == TailNumber::One)
        {
            block_sync_lds();
            static_for<0, KRepeat, 1>{}([&](auto k) {
                a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
                                   make_tuple(I0, I0, I0, Number<k * AMmaKStride>{}),
                                   a_block_buf,
                                   a_thread_desc_,
                                   make_tuple(I0, I0, k, I0),
                                   a_thread_buf);

                b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
                                   make_tuple(I0, I0, I0, Number<k * BMmaKStride>{}),
                                   b_block_buf,
                                   b_thread_desc_,
                                   make_tuple(I0, I0, k, I0),
                                   b_thread_buf);
            });

            static_for<0, KRepeat, 1>{}([&](auto k0) {
                static_for<0, MRepeat, 1>{}([&](auto m0) {
                    static_for<0, NRepeat, 1>{}([&](auto n0) {
                        vector_type<ComputeDataType, KPack> a_thread_vec;
                        vector_type<ComputeDataType, KPack> b_thread_vec;

                        static_for<0, KPack, 1>{}([&](auto ik) {
                            a_thread_vec.template AsType<ComputeDataType>()(ik) =
                                a_thread_buf[Number<a_thread_desc_.CalculateOffset(
                                    make_tuple(m0, I0, k0, ik))>{}];
                            b_thread_vec.template AsType<ComputeDataType>()(ik) =
                                b_thread_buf[Number<b_thread_desc_.CalculateOffset(
                                    make_tuple(n0, I0, k0, ik))>{}];
                        });

                        using mfma_input_type =
                            typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;

                        constexpr index_t c_offset =
                            c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));

                        xdlops_gemm.Run(a_thread_vec.template AsType<mfma_input_type>(),
                                        b_thread_vec.template AsType<mfma_input_type>(),
                                        c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
                    });
                });
            });
        }
        else if constexpr(TailNum == TailNumber::Two)
        {
            LoopTailFunc(Number<2>{});
        }
        else if constexpr(TailNum == TailNumber::Three)
        {
            LoopTailFunc(Number<3>{});
        }
        else if constexpr(TailNum == TailNumber::Four)
        {
            LoopTailFunc(Number<4>{});
        }
        else if constexpr(TailNum == TailNumber::Five)
        {
            LoopTailFunc(Number<5>{});
        }
        else if constexpr(TailNum == TailNumber::Six)
        {
            LoopTailFunc(Number<6>{});
        }
        else if constexpr(TailNum == TailNumber::Seven)
        {
            LoopTailFunc(Number<7>{});
        }
        else if constexpr(TailNum == TailNumber::Full)
        {
            LoopTailFunc(Number<PrefetchStages>{});
        }
    }

    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_;
};

template <index_t BlockSize,
          typename ADataType,
          typename BDataType,
          typename ComputeDataType,
          typename AccDataType,
          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,
          index_t NRepeat,
          index_t KPack,
          bool TransposeA,
          bool TransposeB
          // ,bool TransposeC //disable transposec right now...
          >
struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Interwave,
                                       BlockSize,
                                       ADataType,
                                       BDataType,
                                       ComputeDataType,
                                       AccDataType,
                                       ATileDesc,
                                       BTileDesc,
                                       AMmaTileDesc,
                                       BMmaTileDesc,
                                       ABlockTransferSrcScalarPerVector,
                                       BBlockTransferSrcScalarPerVector,
                                       MPerBlock,
                                       NPerBlock,
                                       KPerBlock,
                                       MPerXDL,
                                       NPerXDL,
                                       MRepeat,
                                       NRepeat,
                                       KPack,
                                       TransposeA,
                                       TransposeB>
    : BlockwiseGemmXdlops_pipeline_base<BlockSize,
                                        ADataType,
                                        BDataType,
                                        ComputeDataType,
                                        AccDataType,
                                        ATileDesc,
                                        BTileDesc,
                                        AMmaTileDesc,
                                        BMmaTileDesc,
                                        ABlockTransferSrcScalarPerVector,
                                        BBlockTransferSrcScalarPerVector,
                                        MPerBlock,
                                        NPerBlock,
                                        KPerBlock,
                                        MPerXDL,
                                        NPerXDL,
                                        MRepeat,
                                        NRepeat,
                                        KPack,
                                        TransposeA,
                                        TransposeB>

{
    using Base = BlockwiseGemmXdlops_pipeline_base<BlockSize,
                                                   ADataType,
                                                   BDataType,
                                                   ComputeDataType,
                                                   AccDataType,
                                                   ATileDesc,
                                                   BTileDesc,
                                                   AMmaTileDesc,
                                                   BMmaTileDesc,
                                                   ABlockTransferSrcScalarPerVector,
                                                   BBlockTransferSrcScalarPerVector,
                                                   MPerBlock,
                                                   NPerBlock,
                                                   KPerBlock,
                                                   MPerXDL,
                                                   NPerXDL,
                                                   MRepeat,
                                                   NRepeat,
                                                   KPack,
                                                   TransposeA,
                                                   TransposeB>;
    using Base::A_K1;
    using Base::B_K1;
    using Base::I0;
    using Base::I1;
    using Base::KPerThread;
    using Base::xdlops_gemm;

    using Base::CalculateCThreadOriginDataIndex;
    using Base::CalculateCThreadOriginDataIndex8D;
    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::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_k;
    using Base::b_block_desc_n0_n1_n2_k;

    static constexpr index_t NumMacClusters = CK_EXPERIMENTAL_INTER_WAVE_SCHEDULING_MAC_CLUSTERS;
    static constexpr index_t KPerInnerLoop  = math::max(KPerThread / NumMacClusters, KPack);
    static constexpr index_t KRepeat        = KPerThread / KPerInnerLoop;

    static constexpr index_t WgpPerCU =
        (4 * warpSize / BlockSize) >= 1 ? 4 * warpSize / BlockSize : 1;
    static constexpr index_t FullMemBandPrefetchStages = math::integer_divide_ceil(
        32768 / WgpPerCU,
        (MPerBlock * sizeof(ADataType) + NPerBlock * sizeof(BDataType)) * KPerBlock);
    static constexpr index_t PrefetchStages =
        FullMemBandPrefetchStages >= 2
            ? FullMemBandPrefetchStages <= 8 ? FullMemBandPrefetchStages : 8
            : 2;

    static constexpr index_t PrefillStages   = 1;
    static constexpr index_t GlobalBufferNum = PrefetchStages;

    __host__ __device__ static constexpr bool BlockHasHotloop(index_t num_loop)
    {
        return num_loop > PrefetchStages;
    }

    __host__ __device__ static constexpr TailNumber BlockLoopTailNum(index_t num_loop)
    {
        if(num_loop % PrefetchStages == 1)
        {
            return TailNumber::One;
        }
        else if(num_loop % PrefetchStages == 2)
        {
            return TailNumber::Two;
        }
        else if(num_loop % PrefetchStages == 3)
        {
            return TailNumber::Three;
        }
        else if(num_loop % PrefetchStages == 4)
        {
            return TailNumber::Four;
        }
        else if(num_loop % PrefetchStages == 5)
        {
            return TailNumber::Five;
        }
        else if(num_loop % PrefetchStages == 6)
        {
            return TailNumber::Six;
        }
        else if(num_loop % PrefetchStages == 7)
        {
            return TailNumber::Seven;
        }
        else
        {
            return TailNumber::Full;
        }
    }

    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>
    __device__ void Run(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,
                        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,
                        CThreadBuffer& c_thread_buf,
                        index_t num_loop) const
    {
        auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
            a_thread_desc_.GetElementSpaceSize());
        auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
            b_thread_desc_.GetElementSpaceSize());

        // Global prefetch 1
        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, I0);
        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, I0);

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

        // Initialize C
        c_thread_buf.Clear();

        // Local prefill 1
        a_blockwise_copy.RunWrite(a_block_desc, a_block_buf, I0);
        b_blockwise_copy.RunWrite(b_block_desc, b_block_buf, I0);

        // Global prefetch [2, PrefetchStages]
        static_for<1, PrefetchStages, 1>{}([&](auto iprefetch) {
            a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, iprefetch);
            b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, iprefetch);

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

        // main body
        if constexpr(HasMainLoop)
        {
            index_t i = 0;
            do
            {
                static_for<0, PrefetchStages, 1>{}([&](auto iprefetch) {
                    // -------------------------------------------------------------------------------------------
                    block_sync_lds();
                    static_for<0, KRepeat, 1>{}([&](auto k0) {
                        a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
                                           make_tuple(I0, I0, I0, Number<k0 * KPerInnerLoop>{}),
                                           a_block_buf,
                                           a_thread_desc_,
                                           make_tuple(I0, I0, k0, I0),
                                           a_thread_buf);
                        b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
                                           make_tuple(I0, I0, I0, Number<k0 * KPerInnerLoop>{}),
                                           b_block_buf,
                                           b_thread_desc_,
                                           make_tuple(I0, I0, k0, I0),
                                           b_thread_buf);
                        __builtin_amdgcn_sched_barrier(0);
                        // NOTE: Synchronize threads in a workgroup at the start of each MAC
                        // cluster, but except the first, as we can shorten non-MAC cluster a bit
                        // and there's no observable negative impact. The desired effect is waves in
                        // a workgroup executing MAC in sync. This avoids some out-of-sync waves
                        // hijacking MAC resource from other workgroups and reducing the chance of
                        // latency hiding by waiting for the rest of the workgroup at the eventual
                        // sync point.
                        if constexpr(k0.value != 0 || KRepeat == 1)
                        {
                            __builtin_amdgcn_s_barrier();
                            __builtin_amdgcn_sched_barrier(0);
                        }
                        static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
                            static_for<0, MRepeat, 1>{}([&](auto m0) {
                                static_for<0, NRepeat, 1>{}([&](auto n0) {
                                    vector_type<ComputeDataType, KPack> a_thread_vec;
                                    vector_type<ComputeDataType, KPack> b_thread_vec;

                                    static_for<0, KPack, 1>{}([&](auto ik) {
                                        a_thread_vec.template AsType<ComputeDataType>()(ik) =
                                            a_thread_buf[Number<a_thread_desc_.CalculateOffset(
                                                make_tuple(m0, I0, k0, k_ + ik))>{}];
                                        b_thread_vec.template AsType<ComputeDataType>()(ik) =
                                            b_thread_buf[Number<b_thread_desc_.CalculateOffset(
                                                make_tuple(n0, I0, k0, k_ + ik))>{}];
                                    });

                                    using mfma_input_type =
                                        typename vector_type<ComputeDataType,
                                                             xdlops_gemm.K1PerXdlops>::type;

                                    constexpr index_t c_offset =
                                        c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));

                                    // The block_sync_lds() here performs double duty:
                                    // A) safeguard against data hazard because barrier from
                                    // blockwise_gemm is moved here B) reduce VMEM FIFO congestion
                                    // by applying small delays to different wavefronts It is
                                    // performed near the end of MAC cluster to minimize lgkmcnt
                                    // penalty
                                    if constexpr(k0.value == KRepeat - 1 &&
                                                 k_.value == KPerInnerLoop - KPack &&
                                                 m0.value == MRepeat - 1 && n0.value == NRepeat - 1)
                                    {
                                        __builtin_amdgcn_sched_barrier(0);
                                        block_sync_lds();
                                        __builtin_amdgcn_sched_barrier(0);
                                    }
                                    xdlops_gemm.Run(
                                        a_thread_vec.template AsType<mfma_input_type>(),
                                        b_thread_vec.template AsType<mfma_input_type>(),
                                        c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
                                    if constexpr(k_.value == 0 && m0.value == 0 && n0.value == 0)
                                    {
                                        __builtin_amdgcn_sched_barrier(0);
                                        __builtin_amdgcn_s_setprio(1);
                                        __builtin_amdgcn_sched_barrier(0);
                                    }
                                });
                            });
                        });
                        __builtin_amdgcn_sched_barrier(0);
                        __builtin_amdgcn_s_setprio(0);
                        __builtin_amdgcn_sched_barrier(0);
                    });

                    // block_sync_lds();
                    a_blockwise_copy.RunWrite(
                        a_block_desc, a_block_buf, Number<(iprefetch + 1) % PrefetchStages>{});
                    b_blockwise_copy.RunWrite(
                        b_block_desc, b_block_buf, Number<(iprefetch + 1) % PrefetchStages>{});

                    a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, iprefetch);
                    b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, iprefetch);

                    a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
                    b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
                });
                i += PrefetchStages;
            } while(i < (num_loop - PrefetchStages));
        }

        // tail

        auto LoopTailFunc = [&](auto tail_num) {
            static_for<1, tail_num, 1>{}([&](auto iprefetch) {
                block_sync_lds();
                static_for<0, KRepeat, 1>{}([&](auto k0) {
                    a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
                                       make_tuple(I0, I0, I0, Number<k0 * KPerInnerLoop>{}),
                                       a_block_buf,
                                       a_thread_desc_,
                                       make_tuple(I0, I0, k0, I0),
                                       a_thread_buf);
                    b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
                                       make_tuple(I0, I0, I0, Number<k0 * KPerInnerLoop>{}),
                                       b_block_buf,
                                       b_thread_desc_,
                                       make_tuple(I0, I0, k0, I0),
                                       b_thread_buf);

                    __builtin_amdgcn_sched_barrier(0);
                    if constexpr(k0.value != 0 || KRepeat == 1)
                    {
                        __builtin_amdgcn_s_barrier();
                        __builtin_amdgcn_sched_barrier(0);
                    }
                    static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
                        static_for<0, MRepeat, 1>{}([&](auto m0) {
                            static_for<0, NRepeat, 1>{}([&](auto n0) {
                                vector_type<ComputeDataType, KPack> a_thread_vec;
                                vector_type<ComputeDataType, KPack> b_thread_vec;

                                static_for<0, KPack, 1>{}([&](auto ik) {
                                    a_thread_vec.template AsType<ComputeDataType>()(ik) =
                                        a_thread_buf[Number<a_thread_desc_.CalculateOffset(
                                            make_tuple(m0, I0, k0, k_ + ik))>{}];
                                    b_thread_vec.template AsType<ComputeDataType>()(ik) =
                                        b_thread_buf[Number<b_thread_desc_.CalculateOffset(
                                            make_tuple(n0, I0, k0, k_ + ik))>{}];
                                });

                                using mfma_input_type =
                                    typename vector_type<ComputeDataType,
                                                         xdlops_gemm.K1PerXdlops>::type;

                                constexpr index_t c_offset =
                                    c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));

                                if constexpr(k0.value == KRepeat - 1 &&
                                             k_.value == KPerInnerLoop - KPack &&
                                             m0.value == MRepeat - 1 && n0.value == NRepeat - 1)
                                {
                                    __builtin_amdgcn_sched_barrier(0);
                                    block_sync_lds();
                                    __builtin_amdgcn_sched_barrier(0);
                                }
                                xdlops_gemm.Run(
                                    a_thread_vec.template AsType<mfma_input_type>(),
                                    b_thread_vec.template AsType<mfma_input_type>(),
                                    c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
                                if constexpr(k_.value == 0 && m0.value == 0 && n0.value == 0)
                                {
                                    __builtin_amdgcn_sched_barrier(0);
                                    __builtin_amdgcn_s_setprio(1);
                                    __builtin_amdgcn_sched_barrier(0);
                                }
                            });
                        });
                    });
                    __builtin_amdgcn_sched_barrier(0);
                    __builtin_amdgcn_s_setprio(0);
                    __builtin_amdgcn_sched_barrier(0);
                });

                a_blockwise_copy.RunWrite(a_block_desc, a_block_buf, iprefetch);
                b_blockwise_copy.RunWrite(b_block_desc, b_block_buf, iprefetch);
            });
            block_sync_lds();
            static_for<0, KRepeat, 1>{}([&](auto k0) {
                a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
                                   make_tuple(I0, I0, I0, Number<k0 * KPerInnerLoop>{}),
                                   a_block_buf,
                                   a_thread_desc_,
                                   make_tuple(I0, I0, k0, I0),
                                   a_thread_buf);
                b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
                                   make_tuple(I0, I0, I0, Number<k0 * KPerInnerLoop>{}),
                                   b_block_buf,
                                   b_thread_desc_,
                                   make_tuple(I0, I0, k0, I0),
                                   b_thread_buf);

                __builtin_amdgcn_sched_barrier(0);
                if constexpr(k0.value != 0 || KRepeat == 1)
                {
                    __builtin_amdgcn_s_barrier();
                    __builtin_amdgcn_sched_barrier(0);
                }
                static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
                    static_for<0, MRepeat, 1>{}([&](auto m0) {
                        static_for<0, NRepeat, 1>{}([&](auto n0) {
                            vector_type<ComputeDataType, KPack> a_thread_vec;
                            vector_type<ComputeDataType, KPack> b_thread_vec;

                            static_for<0, KPack, 1>{}([&](auto ik) {
                                a_thread_vec.template AsType<ComputeDataType>()(ik) =
                                    a_thread_buf[Number<a_thread_desc_.CalculateOffset(
                                        make_tuple(m0, I0, k0, k_ + ik))>{}];
                                b_thread_vec.template AsType<ComputeDataType>()(ik) =
                                    b_thread_buf[Number<b_thread_desc_.CalculateOffset(
                                        make_tuple(n0, I0, k0, k_ + ik))>{}];
                            });

                            using mfma_input_type =
                                typename vector_type<ComputeDataType,
                                                     xdlops_gemm.K1PerXdlops>::type;

                            constexpr index_t c_offset =
                                c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));

                            if constexpr(k0.value == KRepeat - 1 &&
                                         k_.value == KPerInnerLoop - KPack &&
                                         m0.value == MRepeat - 1 && n0.value == NRepeat - 1)
                            {
                                __builtin_amdgcn_sched_barrier(0);
                                block_sync_lds();
                                __builtin_amdgcn_sched_barrier(0);
                            }
                            xdlops_gemm.Run(
                                a_thread_vec.template AsType<mfma_input_type>(),
                                b_thread_vec.template AsType<mfma_input_type>(),
                                c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
                            if constexpr(k_.value == 0 && m0.value == 0 && n0.value == 0)
                            {
                                __builtin_amdgcn_sched_barrier(0);
                                __builtin_amdgcn_s_setprio(1);
                                __builtin_amdgcn_sched_barrier(0);
                            }
                        });
                    });
                });
                __builtin_amdgcn_sched_barrier(0);
                __builtin_amdgcn_s_setprio(0);
                __builtin_amdgcn_sched_barrier(0);
            });
        };

        if constexpr(TailNum == TailNumber::One)
        {
            block_sync_lds();
            static_for<0, KRepeat, 1>{}([&](auto k0) {
                a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
                                   make_tuple(I0, I0, I0, Number<k0 * KPerInnerLoop>{}),
                                   a_block_buf,
                                   a_thread_desc_,
                                   make_tuple(I0, I0, k0, I0),
                                   a_thread_buf);
                b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
                                   make_tuple(I0, I0, I0, Number<k0 * KPerInnerLoop>{}),
                                   b_block_buf,
                                   b_thread_desc_,
                                   make_tuple(I0, I0, k0, I0),
                                   b_thread_buf);

                __builtin_amdgcn_sched_barrier(0);
                if constexpr(k0.value != 0 || KRepeat == 1)
                {
                    __builtin_amdgcn_s_barrier();
                    __builtin_amdgcn_sched_barrier(0);
                }
                static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
                    static_for<0, MRepeat, 1>{}([&](auto m0) {
                        static_for<0, NRepeat, 1>{}([&](auto n0) {
                            vector_type<ComputeDataType, KPack> a_thread_vec;
                            vector_type<ComputeDataType, KPack> b_thread_vec;

                            static_for<0, KPack, 1>{}([&](auto ik) {
                                a_thread_vec.template AsType<ComputeDataType>()(ik) =
                                    a_thread_buf[Number<a_thread_desc_.CalculateOffset(
                                        make_tuple(m0, I0, k0, k_ + ik))>{}];
                                b_thread_vec.template AsType<ComputeDataType>()(ik) =
                                    b_thread_buf[Number<b_thread_desc_.CalculateOffset(
                                        make_tuple(n0, I0, k0, k_ + ik))>{}];
                            });

                            using mfma_input_type =
                                typename vector_type<ComputeDataType,
                                                     xdlops_gemm.K1PerXdlops>::type;

                            constexpr index_t c_offset =
                                c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));

                            if constexpr(k0.value == KRepeat - 1 &&
                                         k_.value == KPerInnerLoop - KPack &&
                                         m0.value == MRepeat - 1 && n0.value == NRepeat - 1)
                            {
                                __builtin_amdgcn_sched_barrier(0);
                                block_sync_lds();
                                __builtin_amdgcn_sched_barrier(0);
                            }
                            xdlops_gemm.Run(
                                a_thread_vec.template AsType<mfma_input_type>(),
                                b_thread_vec.template AsType<mfma_input_type>(),
                                c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
                            if constexpr(k_.value == 0 && m0.value == 0 && n0.value == 0)
                            {
                                __builtin_amdgcn_sched_barrier(0);
                                __builtin_amdgcn_s_setprio(1);
                                __builtin_amdgcn_sched_barrier(0);
                            }
                        });
                    });
                });
                __builtin_amdgcn_sched_barrier(0);
                __builtin_amdgcn_s_setprio(0);
                __builtin_amdgcn_sched_barrier(0);
            });
        }
        else if constexpr(TailNum == TailNumber::Two)
        {
            LoopTailFunc(Number<2>{});
        }
        else if constexpr(TailNum == TailNumber::Three)
        {
            LoopTailFunc(Number<3>{});
        }
        else if constexpr(TailNum == TailNumber::Four)
        {
            LoopTailFunc(Number<4>{});
        }
        else if constexpr(TailNum == TailNumber::Five)
        {
            LoopTailFunc(Number<5>{});
        }
        else if constexpr(TailNum == TailNumber::Six)
        {
            LoopTailFunc(Number<6>{});
        }
        else if constexpr(TailNum == TailNumber::Seven)
        {
            LoopTailFunc(Number<7>{});
        }
        else if constexpr(TailNum == TailNumber::Full)
        {
            LoopTailFunc(Number<PrefetchStages>{});
        }
    }

    protected:
    // K->M loopover
    static constexpr auto a_thread_desc_ = make_naive_tensor_descriptor(
        make_tuple(Number<MRepeat>{}, I1, Number<KRepeat>{}, Number<KPerInnerLoop>{}),
        make_tuple(Number<KPerInnerLoop>{},
                   Number<KRepeat * MRepeat * KPerInnerLoop>{},
                   Number<MRepeat * KPerInnerLoop>{},
                   I1));

    static constexpr auto b_thread_desc_ = make_naive_tensor_descriptor(
        make_tuple(Number<NRepeat>{}, I1, Number<KRepeat>{}, Number<KPerInnerLoop>{}),
        make_tuple(Number<KPerInnerLoop>{},
                   Number<KRepeat * NRepeat * KPerInnerLoop>{},
                   Number<NRepeat * KPerInnerLoop>{},
                   I1));

    template <bool Transpose>
    struct AThreadCopySelector;

    template <>
    struct AThreadCopySelector<false>
    {
        using type = ThreadwiseTensorSliceTransfer_v5<ADataType,
                                                      ComputeDataType,
                                                      decltype(a_block_desc_m0_m1_m2_k),
                                                      decltype(a_thread_desc_),
                                                      Sequence<MRepeat, 1, 1, KPerInnerLoop>,
                                                      Sequence<0, 1, 2, 3>,
                                                      Sequence<0, 1, 2, 3>,
                                                      3,
                                                      3,
                                                      A_K1,
                                                      A_K1>;
    };

    template <>
    struct AThreadCopySelector<true>
    {
        using type = ThreadwiseTensorSliceTransfer_v5<ADataType,
                                                      ComputeDataType,
                                                      decltype(a_block_desc_m0_m1_m2_k),
                                                      decltype(a_thread_desc_),
                                                      Sequence<MRepeat, 1, 1, KPerInnerLoop>,
                                                      Sequence<3, 1, 2, 0>,
                                                      Sequence<0, 1, 2, 3>,
                                                      0,
                                                      3,
                                                      MRepeat,
                                                      A_K1>;
    };

    template <bool Transpose>
    struct BThreadCopySelector;

    template <>
    struct BThreadCopySelector<false>
    {
        using type = ThreadwiseTensorSliceTransfer_v5<BDataType,
                                                      ComputeDataType,
                                                      decltype(b_block_desc_n0_n1_n2_k),
                                                      decltype(b_thread_desc_),
                                                      Sequence<NRepeat, 1, 1, KPerInnerLoop>,
                                                      Sequence<0, 1, 2, 3>,
                                                      Sequence<0, 1, 2, 3>,
                                                      3,
                                                      3,
                                                      B_K1,
                                                      B_K1>;
    };

    template <>
    struct BThreadCopySelector<true>
    {
        using type = ThreadwiseTensorSliceTransfer_v5<BDataType,
                                                      ComputeDataType,
                                                      decltype(b_block_desc_n0_n1_n2_k),
                                                      decltype(b_thread_desc_),
                                                      Sequence<NRepeat, 1, 1, KPerInnerLoop>,
                                                      Sequence<3, 1, 2, 0>,
                                                      Sequence<0, 1, 2, 3>,
                                                      0,
                                                      3,
                                                      NRepeat,
                                                      B_K1>;
    };

    typename AThreadCopySelector<TransposeA>::type a_thread_copy_{
        Base::CalculateAThreadOriginDataIndex()};
    typename BThreadCopySelector<TransposeB>::type b_thread_copy_{
        Base::CalculateBThreadOriginDataIndex()};
    using Base::c_thread_desc_;
};

} // namespace ck
