// 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 {

// Compute optimized pipeline
// GlobalPrefetchStages: 2
// LocalPreFillStages: 1
// LocalPreFetchStages: 1
// 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>
struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v1
{
};

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 TransposeC //disable transposec right now...
          >
struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v1<BlockGemmPipelineScheduler::Intrawave,
                                                   BlockSize,
                                                   ADataType,
                                                   BDataType,
                                                   ComputeDataType,
                                                   AccDataType,
                                                   ATileDesc,
                                                   BTileDesc,
                                                   AMmaTileDesc,
                                                   BMmaTileDesc,
                                                   ABlockTransferSrcScalarPerVector,
                                                   BBlockTransferSrcScalarPerVector,
                                                   MPerBlock,
                                                   NPerBlock,
                                                   KPerBlock,
                                                   MPerXDL,
                                                   NPerXDL,
                                                   MRepeat,
                                                   NRepeat,
                                                   KPack>
    : BlockwiseGemmXdlops_pipeline_base<BlockSize,
                                        ADataType,
                                        BDataType,
                                        ComputeDataType,
                                        AccDataType,
                                        ATileDesc,
                                        BTileDesc,
                                        AMmaTileDesc,
                                        BMmaTileDesc,
                                        ABlockTransferSrcScalarPerVector,
                                        BBlockTransferSrcScalarPerVector,
                                        MPerBlock,
                                        NPerBlock,
                                        KPerBlock,
                                        MPerXDL,
                                        NPerXDL,
                                        MRepeat,
                                        NRepeat,
                                        KPack>

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

    using Base::a_block_desc_m0_m1_m2_k;
    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::AMmaKStride;
    using Base::BMmaKStride;

    static constexpr index_t PrefetchStages  = 2;
    static constexpr index_t PrefillStages   = 1;
    static constexpr index_t GlobalBufferNum = 2;

    template <typename TileDesc_M0_M1_M2_K>
    __host__ __device__ static constexpr auto MakeAGemmMmaTileDescriptor(const TileDesc_M0_M1_M2_K&)
    {
        constexpr index_t M0 = TileDesc_M0_M1_M2_K{}.GetLength(Number<0>{});
        constexpr index_t M1 = TileDesc_M0_M1_M2_K{}.GetLength(Number<1>{});
        constexpr index_t M2 = TileDesc_M0_M1_M2_K{}.GetLength(Number<2>{});
        constexpr index_t K2 = KPack;
        constexpr index_t K1 = 64 / NPerXDL;
        constexpr index_t K0 = KRepeat;

        return transform_tensor_descriptor(
            TileDesc_M0_M1_M2_K{},
            make_tuple(
                make_pass_through_transform(Number<M0>{}),
                make_pass_through_transform(Number<M1>{}),
                make_pass_through_transform(Number<M2>{}),
                make_unmerge_transform(make_tuple(Number<K0>{}, Number<K1>{}, Number<K2>{}))),
            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3, 4, 5>{}));
    }

    static constexpr auto a_block_desc_m0_m1_m2_k0_k1_k2 =
        MakeAGemmMmaTileDescriptor(a_block_desc_m0_m1_m2_k);

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

    __host__ __device__ static constexpr TailNumber BlockLoopTailNum(index_t num_loop)
    {
        return num_loop % 2 == 0 ? TailNumber::Even : TailNumber::Odd;
    }

    __device__ static constexpr auto HotLoopScheduler()
    {
        constexpr auto num_ds_read_inst_a     = HotLoopInstList::A_LDS_Read_Inst_Num;
        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;

        // B global
        static_for<0, num_buffer_load_inst_b, 1>{}([&](auto i) {
            ignore = i;
            __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
            __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
        });

        // A global
        static_for<0, num_buffer_load_inst_a, 1>{}([&](auto i) {
            ignore = i;
            __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
            __builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS write
            __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
            __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
        });

        // A local
        static_for<0, num_ds_read_inst_a / 2, 1>{}([&](auto i) {
            ignore = i;
            __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
            __builtin_amdgcn_sched_group_barrier(0x100, 2, 0); // DS read
        });
    }

    template <bool HasMainLoop,
              TailNumber TailNum,
              typename AGridDesc,
              typename ABlockDesc,
              typename ABlockTransfer,
              typename AGridBuffer,
              typename ABlockBuffer,
              typename ABlockTransferStep,
              typename BGridDesc,
              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,
                        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
    {
        ignore = b_block_buf;
        __builtin_amdgcn_sched_barrier(0);
        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());

        StaticallyIndexedArray<decltype(b_thread_buf), Number<2>{}> b_thread_bufs;
        constexpr auto b_block_origin_idx = make_tuple(I0, I0, I0, I0);

        // Global prefetch A1 B1
        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, I0);
        b_blockwise_copy.Run(b_grid_desc,
                             b_grid_buf,
                             b_block_desc_n0_n1_k0_k1,
                             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);
        __builtin_amdgcn_sched_barrier(0);

        // // Local prefill A1
        a_blockwise_copy.RunWrite(a_block_desc, a_block_buf, I0);

        // // Global prefetch A2
        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, I0);
        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);

        // Local prefetch A1
        block_sync_lds();
        static_for<0, MRepeat, 1>{}([&](auto m0) {
            static_for<0, KRepeat, 1>{}([&](auto k0) {
                a_thread_copy_.Run(a_block_desc_m0_m1_m2_k0_k1_k2,
                                   make_tuple(m0, I0, I0, k0, I0, I0),
                                   a_block_buf,
                                   a_thread_desc_,
                                   make_tuple(m0, I0, I0, k0, I0, I0),
                                   a_thread_buf);
            });
        });

        // Initialize C
        c_thread_buf.Clear();

        __builtin_amdgcn_sched_barrier(0);

        // main body
        if constexpr(HasMainLoop)
        {
            index_t i = 0;
            do
            {
                auto LoopFunc = [&](auto mfma_reg_buf, auto local_read_buf) {
                    b_blockwise_copy.Run(b_grid_desc,
                                         b_grid_buf,
                                         b_block_desc_n0_n1_k0_k1,
                                         b_block_origin_idx,
                                         b_thread_bufs(local_read_buf));
                    b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);

                    block_sync_lds();
                    a_blockwise_copy.RunWrite(a_block_desc, a_block_buf, mfma_reg_buf);

                    a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, local_read_buf);
                    a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);

                    static_for<0, MRepeat, 1>{}([&](auto m0) {
                        static_for<0, NRepeat, 1>{}([&](auto n0) {
                            static_for<0, KRepeat, 1>{}([&](auto k0) {
                                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, I0, k0, I0, ik))>{}];
                                    b_thread_vec.template AsType<ComputeDataType>()(ik) =
                                        b_thread_bufs[mfma_reg_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();

                    static_for<0, MRepeat, 1>{}([&](auto m0) {
                        static_for<0, KRepeat, 1>{}([&](auto k0) {
                            a_thread_copy_.Run(a_block_desc_m0_m1_m2_k0_k1_k2,
                                               make_tuple(m0, I0, I0, k0, I0, I0),
                                               a_block_buf,
                                               a_thread_desc_,
                                               make_tuple(m0, I0, I0, k0, I0, I0),
                                               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_n0_n1_k0_k1,
                                 b_block_origin_idx,
                                 b_thread_bufs(I1));

            block_sync_lds();
            a_blockwise_copy.RunWrite(a_block_desc, a_block_buf);

            static_for<0, MRepeat, 1>{}([&](auto m0) {
                static_for<0, NRepeat, 1>{}([&](auto n0) {
                    static_for<0, KRepeat, 1>{}([&](auto k0) {
                        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, I0, k0, I0, ik))>{}];
                            b_thread_vec.template AsType<ComputeDataType>()(ik) =
                                b_thread_bufs[I0][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();

            static_for<0, MRepeat, 1>{}([&](auto m0) {
                static_for<0, KRepeat, 1>{}([&](auto k0) {
                    a_thread_copy_.Run(a_block_desc_m0_m1_m2_k0_k1_k2,
                                       make_tuple(m0, I0, I0, k0, I0, I0),
                                       a_block_buf,
                                       a_thread_desc_,
                                       make_tuple(m0, I0, I0, k0, I0, I0),
                                       a_thread_buf);
                });
            });

            __builtin_amdgcn_sched_barrier(0);

            static_for<0, MRepeat, 1>{}([&](auto m0) {
                static_for<0, NRepeat, 1>{}([&](auto n0) {
                    static_for<0, KRepeat, 1>{}([&](auto k0) {
                        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, I0, k0, I0, ik))>{}];
                            b_thread_vec.template AsType<ComputeDataType>()(ik) =
                                b_thread_bufs[I1][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>{}));
                    });
                });
            });
            // Let's leak last MFMA block to epilogue region, cover the potential lds-shuffle
            // latency
            // __builtin_amdgcn_sched_barrier(0);
        }
        else
        {
            static_for<0, MRepeat, 1>{}([&](auto m0) {
                static_for<0, NRepeat, 1>{}([&](auto n0) {
                    static_for<0, KRepeat, 1>{}([&](auto k0) {
                        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, I0, k0, I0, ik))>{}];
                            b_thread_vec.template AsType<ComputeDataType>()(ik) =
                                b_thread_bufs[I0][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>{}));
                    });
                });
            });
        }
    }

    protected:
    // MRepeat MWave MLane KRepeat KLane KPack
    // KRepeat -> MRepeat-> Mwave->KLane->MLane->KPack
    static constexpr auto a_thread_desc_ = make_naive_tensor_descriptor_packed(
        make_tuple(Number<MRepeat>{}, I1, I1, Number<KRepeat>{}, I1, Number<KPack>{}));

    using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<ADataType,
                                                         ComputeDataType,
                                                         decltype(a_block_desc_m0_m1_m2_k0_k1_k2),
                                                         decltype(a_thread_desc_),
                                                         Sequence<1, 1, 1, 1, 1, KPack>,
                                                         Sequence<0, 1, 2, 3, 4, 5>,
                                                         5,
                                                         A_K1,
                                                         A_K1>;

    AThreadCopy a_thread_copy_{Base::CalculateAThreadOriginDataIndex6D()};

    static constexpr auto b_thread_desc_ = make_naive_tensor_descriptor_packed(
        make_tuple(Number<NRepeat>{}, I1, Number<KRepeat>{}, Number<KPack>{}));

    static constexpr BTileDesc b_block_desc_n0_n1_k0_k1;

    using Base::c_thread_desc_;
};

} // namespace ck
