// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.

#pragma once

#include "ck/utility/env.hpp"
#include "ck/utility/common_header.hpp"
#include "ck/tensor_description/multi_index_transform_helper.hpp"
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_wmma_selector.hpp"
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_wmma_cshuffle_v3_common.hpp"

namespace ck {

/// @brief \"Universal\" GEMM kernel with SplitK support.
///
/// @par Overview
///         This GEMM kernel is carrying out following mathematical equation:
///         C{M,N} = C_op(A_op(A{M,K}) * B_op(B{K,N}))
///         Where A, B are input tensors and C is the output tensor. The A/B/C_op are
///         elementwise operations that could be applied on each tensor respectively.
///         The \"universal\" gemm comes with multiple pipelines optimized for different usage
///         scenarios. That's why it's called \"universal\". It's universal through it's design
///         and versatilty.
///
/// @note   This Kernel implementation supports SplitK algorithm. It can be configured
///         to split the dot product accumulated over the K dimension into multiple working groups.
///         The partial products of different workgroups are then reduced using the AtomicAdd
///         operation.
///
/// @tparam ALayout     A tensor data layout.
/// @tparam BLayout     B tensor data layout.
/// @tparam CLayout     C tensor data layout.
/// @tparam ADataType   A tensor data type.
/// @tparam BDataType   B tensor data type.
/// @tparam AccDataType The accumulation data type related to the hardware
///                         matrix-multiplication instruction.
/// @tparam CShuffleDataType The data type used to store matrix-multiplication results into
///                          LDS memory during \"CShuffle\" data layout optimization.
/// @tparam CDataType   C tensor data type.
/// @tparam AElementwiseOperation Elementwise operation applied to the A input tensor elements.
/// @tparam BElementwiseOperation Elementwise operation applied to the B input tensor elements.
/// @tparam CElementwiseOperation Elementwise operation applied to the C output tensor
///                               (after GEMM).
/// @tparam GemmSpec    Determines used "padding" version.
/// @tparam BlockSize   The number of threads within workgroup.
/// @tparam MPerBlock   The input/output data tile size in the M dimension.
/// @tparam NPerBlock   The input/output data tile size in the N dimension.
/// @tparam KPerBlock   The input data tile size in the K dimension.
/// @tparam AK1Value    The vector load size from global memory for A tensor.
/// @tparam BK1Value    The vector load size from global memory for B tensor.
/// @tparam MPerWmma    M size of Wave Matrix Multiply Accumulate (WMMA) instruction.
/// @tparam NPerWmma    N size of Wave Matrix Multiply Accumulate (WMMA) instruction.
/// @tparam MRepeat     The number of iterations in the M dimension over output tile per wavefront.
/// @tparam NRepeat     The number of iterations in the N dimension over output tile per wavefront.
/// @tparam ABlockTransferThreadClusterLengths_AK0_M_AK1 Spatial thread distribution over the input
///                                                      data. Can be interpreted as the answer
///                                                      to the question, "How many threads can be
///                                                      arranged on each input data axis?"
/// @tparam ABlockTransferThreadClusterArrangeOrder The order of thread spatial distribution over
///                                                 the input tensor dimension. Can be interpreted
///                                                 as the answer to the question: "In which
///                                                 order to spread threads through tensor axes?".
/// @tparam ABlockTransferSrcAccessOrder The order of accessing input tensor axes. Can be
///                                      interpreted as the answer to the question "Which dimension
///                                      to read first? And which next?" etc.
/// @tparam ABlockTransferSrcVectorDim   The index of axis on which we could do vectorized memory
///                                      access - the one with contiguous memory.
/// @tparam ABlockTransferSrcScalarPerVector The size of vector access instruction - the number of
///                                          elements accessed per thread per instruction.
/// @tparam ABlockTransferDstScalarPerVector_AK1 The size of vectorized store into LDS memory.
/// @tparam AThreadTransferSrcResetCoordinateAfterRun   Decides whether we reset thread coordinate
///                          (return back to the window origin) after all thread finish data copy.
/// @tparam ABlockLdsExtraM                      Whether to use padding for LDS or not. With
///                                              universal GEMM there's no need for padding.
/// @tparam BBlockTransferThreadClusterLengths_BK0_N_BK1 Spatial thread distribution over the input
///                                                      data. Can be interpreted as the answer
///                                                      to the question: "How many threads to
///                                                      arrange on each input data axis?"
/// @tparam BBlockTransferThreadClusterArrangeOrder The order of thread spatial distribution over
///                                                 the input tensor dimension. Can be interpreted
///                                                 as the answer to the question: "In which
///                                                 order to spread threads through tensor axes?".
/// @tparam BBlockTransferSrcAccessOrder he order of accessing input tensor axes. Can be
///                                      interpreted as the answer to the question "Which dimension
///                                      to read first? And which next?" etc.
/// @tparam BBlockTransferSrcVectorDim  The index of axis on which we could do vectorized memory
///                                      access - the one with contiguous memory.
/// @tparam BBlockTransferSrcScalarPerVector The size of vector access instruction - the number of
///                                          elements accessed per thread per instruction.
/// @tparam BBlockTransferDstScalarPerVector_BK1 The size of vectorized store into LDS memory.
/// @tparam BThreadTransferSrcResetCoordinateAfterRun   Decides whether we reset thread coordinate
///                          (return back to the window origin) after all thread finish data copy.
/// @tparam BBlockLdsExtraN             Whether to use padding for LDS or not. With universal GEMM
///                                         there's no need for padding.
/// @tparam CShuffleMRepeatPerShuffle   The number of matrix-multiplication instructions
///                                         results to process per wave per iteration of CShuffle
///                                         in M dimension.
/// @tparam CShuffleNRepeatPerShuffle   The number of matrix-multiplication instructions
///                                         results to process per wave per iteration of CShuffle
///                                         in N dimension.
/// @tparam CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock The spatial
///                                         thread distribution used for storing data into output
///                                         tensor across output data layout dimensions.
/// @tparam CShuffleBlockTransferScalarPerVector_NPerBlock The size of vectorized memory access.
///                                         Used when storing data to output tensor.
/// @tparam BlkGemmPipeSched    The version of blockwise-gemm pipeline scheduler (interwave or
///                             intrawave).
/// @tparam BlkGemmPipelineVer  The version of blockwise-gemm pipeline.
/// @tparam ComputeTypeA    Data type used for A input of hardware matrix-multiplication
///                         instructions.
/// @tparam ComputeTypeB    Data type used for B input of hardware matrix-multiplication
///                         instructions.
/// @tparam PermuteA            Whether the A input tensor has gridwise-gemm friendly data layout
///                             in global memory. Currently not supported!
/// @tparam PermuteB            Whether the B input tensor has gridwise-gemm friendly data layout
///                             in global memory (pre-shuffled).
template <typename ALayout,
          typename BLayout,
          typename CLayout,
          typename ADataType,
          typename BDataType,
          typename AccDataType,
          typename CShuffleDataType,
          typename CDataType,
          typename AElementwiseOperation,
          typename BElementwiseOperation,
          typename CElementwiseOperation,
          tensor_operation::device::GemmSpecialization GemmSpec,
          index_t BlockSize,
          index_t MPerBlock,
          index_t NPerBlock,
          index_t KPerBlock,
          index_t AK1Value,
          index_t BK1Value,
          index_t MPerWmma,
          index_t NPerWmma,
          index_t MRepeat,
          index_t NRepeat,
          typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
          typename ABlockTransferThreadClusterArrangeOrder,
          typename ABlockTransferSrcAccessOrder,
          index_t ABlockTransferSrcVectorDim,
          index_t ABlockTransferSrcScalarPerVector,
          index_t ABlockTransferDstScalarPerVector_AK1,
          bool AThreadTransferSrcResetCoordinateAfterRun,
          index_t ABlockLdsExtraM,
          typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
          typename BBlockTransferThreadClusterArrangeOrder,
          typename BBlockTransferSrcAccessOrder,
          index_t BBlockTransferSrcVectorDim,
          index_t BBlockTransferSrcScalarPerVector,
          index_t BBlockTransferDstScalarPerVector_BK1,
          bool BThreadTransferSrcResetCoordinateAfterRun,
          index_t BBlockLdsExtraN,
          index_t CShuffleMRepeatPerShuffle,
          index_t CShuffleNRepeatPerShuffle,
          typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
          index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
          BlockGemmPipelineScheduler BlkGemmPipeSched,
          BlockGemmPipelineVersion BlkGemmPipelineVer,
          typename ComputeTypeA,
          typename ComputeTypeB,
          bool PermuteA,
          bool PermuteB>
struct GridwiseGemm_wmma_cshuffle_v3
    : GridwiseGemm_wmma_cshuffle_v3_base<
          ALayout,
          BLayout,
          CLayout,
          ADataType,
          BDataType,
          AccDataType,
          CShuffleDataType,
          CDataType,
          AElementwiseOperation,
          BElementwiseOperation,
          CElementwiseOperation,
          GemmSpec,
          BlockSize,
          MPerBlock,
          NPerBlock,
          KPerBlock,
          AK1Value,
          BK1Value,
          MPerWmma,
          NPerWmma,
          MRepeat,
          NRepeat,
          ABlockTransferThreadClusterLengths_AK0_M_AK1,
          ABlockTransferThreadClusterArrangeOrder,
          ABlockTransferSrcAccessOrder,
          ABlockTransferSrcVectorDim,
          ABlockTransferSrcScalarPerVector,
          ABlockTransferDstScalarPerVector_AK1,
          AThreadTransferSrcResetCoordinateAfterRun,
          ABlockLdsExtraM,
          BBlockTransferThreadClusterLengths_BK0_N_BK1,
          BBlockTransferThreadClusterArrangeOrder,
          BBlockTransferSrcAccessOrder,
          BBlockTransferSrcVectorDim,
          BBlockTransferSrcScalarPerVector,
          BBlockTransferDstScalarPerVector_BK1,
          BThreadTransferSrcResetCoordinateAfterRun,
          BBlockLdsExtraN,
          CShuffleMRepeatPerShuffle,
          CShuffleNRepeatPerShuffle,
          CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
          CShuffleBlockTransferScalarPerVector_NPerBlock,
          BlkGemmPipeSched,
          BlkGemmPipelineVer,
          ComputeTypeA,
          ComputeTypeB,
          PermuteA,
          PermuteB>
{
    using Base = GridwiseGemm_wmma_cshuffle_v3_base<
        ALayout,
        BLayout,
        CLayout,
        ADataType,
        BDataType,
        AccDataType,
        CShuffleDataType,
        CDataType,
        AElementwiseOperation,
        BElementwiseOperation,
        CElementwiseOperation,
        GemmSpec,
        BlockSize,
        MPerBlock,
        NPerBlock,
        KPerBlock,
        AK1Value,
        BK1Value,
        MPerWmma,
        NPerWmma,
        MRepeat,
        NRepeat,
        ABlockTransferThreadClusterLengths_AK0_M_AK1,
        ABlockTransferThreadClusterArrangeOrder,
        ABlockTransferSrcAccessOrder,
        ABlockTransferSrcVectorDim,
        ABlockTransferSrcScalarPerVector,
        ABlockTransferDstScalarPerVector_AK1,
        AThreadTransferSrcResetCoordinateAfterRun,
        ABlockLdsExtraM,
        BBlockTransferThreadClusterLengths_BK0_N_BK1,
        BBlockTransferThreadClusterArrangeOrder,
        BBlockTransferSrcAccessOrder,
        BBlockTransferSrcVectorDim,
        BBlockTransferSrcScalarPerVector,
        BBlockTransferDstScalarPerVector_BK1,
        BThreadTransferSrcResetCoordinateAfterRun,
        BBlockLdsExtraN,
        CShuffleMRepeatPerShuffle,
        CShuffleNRepeatPerShuffle,
        CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
        CShuffleBlockTransferScalarPerVector_NPerBlock,
        BlkGemmPipeSched,
        BlkGemmPipelineVer,
        ComputeTypeA,
        ComputeTypeB,
        PermuteA,
        PermuteB>;

    using Base::I0;
    using Base::I1;
    using Base::I2;
    using Base::I3;
    using Base::I4;
    using Base::I5;
    using Base::I6;
    using Base::I7;

    using Base::AK0Number;
    using Base::AK1Number;
    using Base::BK0Number;
    using Base::BK1Number;

    using Base::APackedSize;
    using Base::BPackedSize;

    using Base::CalculateAK0Padded;
    using Base::CalculateBK0Padded;
    using Base::CalculateKPadded;
    using Base::CalculateKRead;
    using Base::CalculateMBlock;
    using Base::CalculateMPadded;
    using Base::CalculateNBlock;
    using Base::CalculateNPadded;
    using Base::MakeAGridDescriptor_AK0_M_AK1;
    using Base::MakeBGridDescriptor_BK0_N_BK1;
    using Base::MakeCGridDescriptor_M_N;

    using Base::GetCShuffleBlockDescriptor_MShRepeat_MPerShRepeat_NShRepeat_NPerShRepeat;

    using Base::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock;

    using ThisThreadBlock = ThisThreadBlock<BlockSize>;

    using Base::GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1;
    using Base::GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1;

    struct Problem
    {
        __host__ Problem(index_t M_,
                         index_t N_,
                         index_t K_,
                         index_t StrideA_,
                         index_t StrideB_,
                         index_t StrideC_,
                         index_t KBatch_)
            : M{M_},
              N{N_},
              K{K_},
              StrideA{StrideA_},
              StrideB{StrideB_},
              StrideC{StrideC_},
              KBatch{KBatch_},
              MPadded{CalculateMPadded(M_)},
              NPadded{CalculateNPadded(N_)},
              KRead{CalculateKRead(K_, KBatch_)},
              KPadded{CalculateKPadded(K_, KBatch_)},
              AK0{CalculateAK0Padded(K_, KBatch_)},
              BK0{CalculateBK0Padded(K_, KBatch_)},
              MBlock{CalculateMBlock(M_)},
              NBlock{CalculateNBlock(N_)}
        {
        }

        __host__ void Print() const
        {
            std::cout << "problem {" << "M:" << M << ", " << "N:" << N << ", " << "K:" << K << ", "
                      << "SA:" << StrideA << ", " << "SB:" << StrideB << ", " << "SC:" << StrideC
                      << ", " << "MP:" << MPadded << ", " << "NP:" << NPadded << ", "
                      << "KRead:" << KRead << ", " << "KP:" << KPadded << ", " << "AK0:" << AK0
                      << ", " << "BK0:" << BK0 << ", " << "MBlock: " << MBlock << ", "
                      << "NBlock: " << NBlock << "}" << std::endl;
        }

        index_t M;
        index_t N;
        index_t K;
        index_t StrideA;
        index_t StrideB;
        index_t StrideC;
        index_t KBatch;
        index_t MPadded;
        index_t NPadded;
        index_t KRead;
        index_t KPadded;
        index_t AK0;
        index_t BK0;
        index_t MBlock;
        index_t NBlock;
    };

    // Argument
    struct Argument : public tensor_operation::device::BaseArgument, public Problem
    {
        __host__ Argument(const ADataType* p_a_grid_,
                          const BDataType* p_b_grid_,
                          CDataType* p_c_grid_,
                          index_t M_,
                          index_t N_,
                          index_t K_,
                          index_t StrideA_,
                          index_t StrideB_,
                          index_t StrideC_,
                          index_t k_batch_,
                          bool is_reduce_ = false)
            : Problem{M_, N_, K_, StrideA_, StrideB_, StrideC_, k_batch_},
              p_a_grid{p_a_grid_},
              p_b_grid{p_b_grid_},
              p_c_grid{p_c_grid_},
              is_reduce(is_reduce_)
        {
        }

        __host__ __device__ inline bool IsReduceAdd() const
        {
            return (Problem::KBatch > 1) && is_reduce;
        }

        __host__ __device__ inline bool IsAtomicAdd() const
        {
            return (Problem::KBatch > 1) && (!is_reduce);
        }

        const ADataType* p_a_grid;
        const BDataType* p_b_grid;
        CDataType* p_c_grid;
        bool is_reduce;
    };

    struct SplitKBatchOffset
    {

        __device__ SplitKBatchOffset(Argument& karg)
        {
            if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
            {
                a_k_split_offset = blockIdx.z * karg.KRead / APackedSize;
            }
            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
            {
                a_k_split_offset = blockIdx.z * karg.KRead * karg.StrideA;
            }

            if constexpr(is_same_v<tensor_layout::gemm::RowMajor, BLayout>)
            {
                b_k_split_offset = blockIdx.z * karg.KRead * karg.StrideB;
            }
            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, BLayout>)
            {
                if constexpr(!PermuteB)
                {
                    b_k_split_offset = blockIdx.z * karg.KRead / BPackedSize;
                }
                else
                {
                    const int k0_offset = karg.KRead * karg.N;
                    b_k_split_offset    = blockIdx.z * k0_offset / BPackedSize;
                }
            }

            if(blockIdx.z < static_cast<uint32_t>(karg.KBatch - 1))
            {
                karg.K = karg.KRead;
            }
            else
            {
                karg.K = karg.K - karg.KRead * (karg.KBatch - 1);
            }

            if(karg.IsReduceAdd())
            {
                c_reduce_offset = blockIdx.z * karg.M * karg.N;
            }
            else
            {
                c_reduce_offset = 0;
            }
        }

        index_t a_k_split_offset;
        index_t b_k_split_offset;
        index_t c_reduce_offset;
    };

    using BlockwiseGemmPipe = typename Base::BlockwiseGemmPipe;

    // return block_id to C matrix tile idx (m0, n0) mapping
    // if arch = gfx942
    using Block2CTileMap = BlockToCTileMap_Grouped_M00_N0_M01Adapt<8, MPerBlock, NPerBlock>;
    // using Block2CTileMap = BlockToCTileMap_3DGrid_KSplit<MPerBlock, NPerBlock>;

    __device__ static index_t GetKBlockPerScale() { return 1; }

    template <bool HasMainKBlockLoop,
              InMemoryDataOperationEnum CGlobalMemoryDataOperation,
              TailNumber TailNum = TailNumber::Odd>
    __device__ static void Run(const ADataType* p_a_grid,
                               const BDataType* p_b_grid,
                               CDataType* p_c_grid,
                               void* p_shared,
                               const Problem& problem)
    {
        const auto a_grid_desc_ak0_m_ak1 = MakeAGridDescriptor_AK0_M_AK1(
            problem.M, problem.MPadded, problem.K, problem.KPadded, problem.StrideA, problem.AK0);
        const auto b_grid_desc_bk0_n_bk1 = MakeBGridDescriptor_BK0_N_BK1(
            problem.K, problem.KPadded, problem.N, problem.NPadded, problem.StrideB, problem.BK0);
        const auto c_grid_desc_m_n = MakeCGridDescriptor_M_N(
            problem.M, problem.MPadded, problem.N, problem.NPadded, problem.StrideC);
        const auto c_grid_desc_mblock_mperblock_nblock_nperblock =
            MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
                c_grid_desc_m_n, problem.MBlock, problem.NBlock);

        // divide block work by [M, N]
        const auto block_2_ctile_map = Block2CTileMap{problem.M, problem.N, 4};

        const auto block_work_idx =
            block_2_ctile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));

        if(!block_2_ctile_map.ValidCTileIndex(
               block_work_idx,
               make_tuple(c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
                          c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))
        {
            return;
        }

        const index_t block_m_id = __builtin_amdgcn_readfirstlane(block_work_idx[I0]);
        const index_t block_n_id = __builtin_amdgcn_readfirstlane(block_work_idx[I1]);

        // BScale struct (Empty)
        using BScale        = typename BlockwiseGemmPipe::Empty;
        auto b_scale_struct = BScale{};

        const index_t num_k_block_per_scale = GetKBlockPerScale();

        Base::template Run<decltype(a_grid_desc_ak0_m_ak1),
                           decltype(b_grid_desc_bk0_n_bk1),
                           decltype(c_grid_desc_mblock_mperblock_nblock_nperblock),
                           decltype(b_scale_struct),
                           HasMainKBlockLoop,
                           CGlobalMemoryDataOperation,
                           TailNum>(p_a_grid,
                                    p_b_grid,
                                    p_c_grid,
                                    p_shared,
                                    a_grid_desc_ak0_m_ak1,
                                    b_grid_desc_bk0_n_bk1,
                                    c_grid_desc_mblock_mperblock_nblock_nperblock,
                                    block_m_id,
                                    block_n_id,
                                    num_k_block_per_scale,
                                    b_scale_struct);
    }

    // Wrapper function to have __global__ function in common
    // between gemm_universal, b_scale, ab_scale, etc.
    template <bool HasMainKBlockLoop,
              InMemoryDataOperationEnum CGlobalMemoryDataOperation,
              TailNumber TailNum = TailNumber::Odd>
    __device__ static void
    Run(void* p_shared, const SplitKBatchOffset& splitk_batch_offset, const Argument& karg)
    {
        Run<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(
            karg.p_a_grid + splitk_batch_offset.a_k_split_offset,
            karg.p_b_grid + splitk_batch_offset.b_k_split_offset,
            karg.p_c_grid + splitk_batch_offset.c_reduce_offset,
            p_shared,
            karg);
    }
};

} // namespace ck
