#pragma once

#include "common_header.hpp"
#include "multi_index_transform_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "tensor_operation/gpu/grid/block_to_ctile_map.hpp"
#include "blockwise_gemm_xdlops.hpp"
#include "thread_group_tensor_slice_transfer_v4r1.hpp"
#include "thread_group_tensor_slice_transfer_v6r1.hpp"
#include "threadwise_tensor_slice_transfer.hpp"
#include "gridwise_gemm_pipeline_v1.hpp"

namespace ck {

// Implementation of "Merge" transformation primitive that uses division and mod. It is supposed to
// be used for low_lengths that are known at compile time and are power of 2, otherwise performance
// will be very bad
template <typename LowLengths>
struct Merge_v4_no_carry
{
    static constexpr index_t NDimLow = LowLengths::Size();

    using LowerIndex = MultiIndex<NDimLow>;
    using UpperIndex = MultiIndex<1>;

    using LowLengthsScan =
        decltype(container_reverse_exclusive_scan(LowLengths{}, math::multiplies{}, Number<1>{}));

    using UpLengths =
        decltype(make_tuple(container_reduce(LowLengths{}, math::multiplies{}, Number<1>{})));

    LowLengths low_lengths_;
    LowLengthsScan low_lengths_scan_;
    UpLengths up_lengths_;

    __host__ __device__ constexpr Merge_v4_no_carry() = default;

    __host__ __device__ constexpr Merge_v4_no_carry(const LowLengths& low_lengths)
        : low_lengths_{low_lengths},
          low_lengths_scan_{
              container_reverse_exclusive_scan(low_lengths, math::multiplies{}, Number<1>{})},
          up_lengths_{make_tuple(container_reduce(low_lengths, math::multiplies{}, Number<1>{}))}
    {
        static_assert(LowerIndex::Size() == NDimLow, "wrong!");
    }

    __host__ __device__ static constexpr index_t GetNumOfLowerDimension() { return NDimLow; }

    __host__ __device__ static constexpr index_t GetNumOfUpperDimension() { return 1; }

    __host__ __device__ constexpr const auto& GetUpperLengths() const { return up_lengths_; }

    template <typename LowIdx, typename UpIdx>
    __host__ __device__ constexpr void CalculateLowerIndex(LowIdx& idx_low,
                                                           const UpIdx& idx_up) const
    {
        static_assert(LowIdx::Size() == NDimLow && UpIdx::Size() == 1,
                      "wrong! inconsistent # of dimension");

        index_t tmp = idx_up[Number<0>{}];

        // division and mod
        static_for<0, NDimLow - 1, 1>{}([&](auto i) {
            idx_low(i) = tmp / this->low_lengths_scan_[i];
            tmp %= this->low_lengths_scan_[i];
        });

        idx_low(Number<NDimLow - 1>{}) = tmp;
    }

    template <typename LowIdxDiff,
              typename UpIdxDiff,
              typename LowIdx,
              typename UpIdx,
              index_t Hack>
    __host__ __device__ void UpdateLowerIndex(LowIdxDiff& idx_diff_low,
                                              const UpIdxDiff& idx_up_diff,
                                              LowIdx& idx_low,
                                              const UpIdx& idx_up_new,
                                              Number<Hack>) const
    {
        static_assert(LowIdxDiff::Size() == NDimLow && UpIdxDiff::Size() == 1 &&
                          LowIdx::Size() == NDimLow && UpIdx::Size() == 1,
                      "wrong! inconsistent # of dimension");

        constexpr auto I0   = Number<0>{};
        constexpr auto INm1 = Number<NDimLow - 1>{};

        index_t tmp = idx_up_new[I0];

        idx_low(INm1)      = tmp;
        idx_diff_low(INm1) = idx_up_diff[I0];
    }

    __host__ __device__ static constexpr bool IsLinearTransform() { return false; }

    __host__ __device__ static constexpr bool IsValidUpperIndexAlwaysMappedToValidLowerIndex()
    {
        return true;
    }

    __host__ __device__ static constexpr bool IsKnownAtCompileTime()
    {
        return is_known_at_compile_time<LowLengths>::value &&
               is_known_at_compile_time<LowLengthsScan>::value &&
               is_known_at_compile_time<UpLengths>::value;
    }

    template <typename UpIdx>
    __host__ __device__ static constexpr bool
    IsValidUpperIndexMappedToValidLowerIndex(const UpIdx& /* idx_up */)
    {
        return true;
    }

    __host__ __device__ void Print() const
    {
        printf("{");
        printf("Merge_v3_direct_division_mod_wrw, ");
        printf("low_lengths_ ");
        print_multi_index(low_lengths_);
        printf("low_lengths_scan_ ");
        print_multi_index(low_lengths_scan_);
        printf("up_lengths_ ");
        print_multi_index(up_lengths_);
        printf("}");
    }
};

template <typename LowLengths>
__host__ __device__ constexpr auto make_merge_transform_v4_no_carry(const LowLengths& low_lengths)
{
    return Merge_v4_no_carry<LowLengths>{low_lengths};
}

template <typename GridwiseGemm,
          typename FloatAB,
          typename FloatC,
          typename AGridDesc_B_K0_M_K1,
          typename BGridDesc_B_K0_N_K1,
          typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
          typename AElementwiseOperation,
          typename BElementwiseOperation,
          typename CElementwiseOperation,
          typename CBlockClusterAdaptor,
          bool HasMainKBlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
        kernel_gemm_xdlops_bwd_weight(const FloatAB* __restrict__ p_a_grid,
                                      const FloatAB* __restrict__ p_b_grid,
                                      FloatC* __restrict__ p_c_grid,
                                      const AGridDesc_B_K0_M_K1 a_b_k0_m_k1_grid_desc,
                                      const BGridDesc_B_K0_N_K1 b_b_k0_n_k1_grid_desc,
                                      const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
                                          c_grid_desc_mblock_mperblock_nblock_nperblock,
                                      const AElementwiseOperation a_element_op,
                                      const BElementwiseOperation b_element_op,
                                      const CElementwiseOperation c_element_op,
                                      const CBlockClusterAdaptor c_block_cluster_adaptor)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
    constexpr index_t shared_block_size =
        GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);

    __shared__ FloatAB p_shared_block[shared_block_size];

    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
                                                  p_b_grid,
                                                  p_c_grid,
                                                  p_shared_block,
                                                  a_b_k0_m_k1_grid_desc,
                                                  b_b_k0_n_k1_grid_desc,
                                                  c_grid_desc_mblock_mperblock_nblock_nperblock,
                                                  a_element_op,
                                                  b_element_op,
                                                  c_element_op,
                                                  c_block_cluster_adaptor);
#else
    ignore = p_a_grid;
    ignore = p_b_grid;
    ignore = p_c_grid;
    ignore = a_b_k0_m_k1_grid_desc;
    ignore = b_b_k0_n_k1_grid_desc;
    ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
    ignore = a_element_op;
    ignore = b_element_op;
    ignore = c_element_op;
    ignore = c_block_cluster_adaptor;
#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
}

template <index_t BlockSize,
          typename FloatAB,
          typename FloatAcc,
          typename FloatC,
          InMemoryDataOperationEnum CGlobalMemoryDataOperation,
          typename AGridDesc_B_K0_M_K1,
          typename BGridDesc_B_K0_N_K1,
          typename CMNGridDesc,
          typename AElementwiseOperation,
          typename BElementwiseOperation,
          typename CElementwiseOperation,
          index_t MPerBlock,
          index_t NPerBlock,
          index_t K0PerBlock,
          index_t MPerXDL,
          index_t NPerXDL,
          index_t K1Value,
          index_t MRepeat,
          index_t NRepeat,
          typename ABlockTransferThreadClusterLengths_K0_M_K1,
          typename ABlockTransferThreadClusterArrangeOrder,
          typename ABlockTransferSrcAccessOrder,
          index_t ABlockTransferSrcVectorDim,
          index_t ABlockTransferSrcScalarPerVector,
          index_t ABlockTransferDstScalarPerVector_K1,
          bool AThreadTransferSrcResetCoordinateAfterRun,
          bool ABlockLdsExtraM,
          index_t ABlockLdsM1PerBlock,
          index_t ABlockLdsM0PerBlock,
          index_t ABlockLdsM1Padding,
          typename BBlockTransferThreadClusterLengths_K0_N_K1,
          typename BBlockTransferThreadClusterArrangeOrder,
          typename BBlockTransferSrcAccessOrder,
          index_t BBlockTransferSrcVectorDim,
          index_t BBlockTransferSrcScalarPerVector,
          index_t BBlockTransferDstScalarPerVector_K1,
          bool BThreadTransferSrcResetCoordinateAfterRun,
          bool BBlockLdsExtraN,
          index_t BBlockLdsN1PerBlock,
          index_t BBlockLdsN0PerBlock,
          index_t BBlockLdsN1Padding,
          index_t CShuffleMRepeatPerShuffle,
          index_t CShuffleNRepeatPerShuffle,
          index_t CBlockTransferScalarPerVector_NWaveNPerXDL,
          typename CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
          bool ABlockLdsExtraM1Wrw      = false,
          bool BBlockLdsExtraN1Wrw      = false,
          index_t NumGemmKPrefetchStage = 1>
struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight
{
    static constexpr auto I0 = Number<0>{};
    static constexpr auto I1 = Number<1>{};
    static constexpr auto I2 = Number<2>{};
    static constexpr auto I3 = Number<3>{};
    static constexpr auto I4 = Number<4>{};
    static constexpr auto I5 = Number<5>{};
    static constexpr auto I6 = Number<6>{};
    static constexpr auto I7 = Number<7>{};

    // K1 should be Number<...>
    static constexpr auto K1 = Number<K1Value>{};

    using ThisThreadBlock  = ThisThreadBlock<BlockSize>;
    using GridwiseGemmPipe = GridwiseGemmPipeline_v1<NumGemmKPrefetchStage>;

    // M0/M1/M1Padding
    static constexpr auto M1PerBlock = Number<ABlockLdsM1PerBlock>{};
    static constexpr auto M0PerBlock = Number<ABlockLdsM0PerBlock>{};
    static constexpr auto M1Padding  = Number<ABlockLdsM1Padding>{};

    // N0/N1/N1Padding
    static constexpr auto N1PerBlock = Number<BBlockLdsN1PerBlock>{};
    static constexpr auto N0PerBlock = Number<BBlockLdsN0PerBlock>{};
    static constexpr auto N1Padding  = Number<BBlockLdsN1Padding>{};

    __host__ __device__ static constexpr auto GetABlockDescriptor_K0PerBlock_MPerBlock_K1()
    {
        constexpr auto max_lds_align = K1;

        // A matrix in LDS memory, dst of blockwise copy
        constexpr auto a_block_desc_k0_m_k1 = [&]() {
            if constexpr(ABlockLdsExtraM)
            {
                if constexpr(ABlockLdsExtraM1Wrw)
                {
                    constexpr auto a_block_desc_k0_m0_m1_k1 = make_naive_tensor_descriptor(
                        make_tuple(
                            Number<K0PerBlock>{}, Number<M0PerBlock>{}, Number<M1PerBlock>{}, K1),
                        make_tuple(Number<M0PerBlock>{} * (Number<M1PerBlock>{} * K1 + M1Padding),
                                   Number<M1PerBlock>{} * K1 + M1Padding,
                                   K1,
                                   I1));

                    constexpr auto a_block_desc_k0_m_k1_tmp = transform_tensor_descriptor(
                        a_block_desc_k0_m0_m1_k1,
                        make_tuple(make_pass_through_transform(Number<K0PerBlock>{}),
                                   make_merge_transform_v3_division_mod(
                                       make_tuple(Number<M0PerBlock>{}, Number<M1PerBlock>{})),
                                   make_pass_through_transform(K1)),
                        make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}),
                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));

                    return a_block_desc_k0_m_k1_tmp;
                }
                else
                {
                    return make_naive_tensor_descriptor(
                        make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
                        make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
                }
            }
            else
            {
                return make_naive_tensor_descriptor_aligned(
                    make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
            }
        }();

        return a_block_desc_k0_m_k1;
    }

    __host__ __device__ static constexpr auto GetABlockDescriptor_Batch_K0PerBlock_MPerBlock_K1()
    {
        constexpr auto max_lds_align = K1;

        // A matrix in LDS memory, dst of blockwise copy
        constexpr auto a_block_desc_b_k0_m_k1 = [&]() {
            if constexpr(ABlockLdsExtraM)
            {
                if constexpr(ABlockLdsExtraM1Wrw)
                {
                    constexpr auto a_block_desc_b_k0_m0_m1_k1 = make_naive_tensor_descriptor(
                        make_tuple(Number<1>{},
                                   Number<K0PerBlock>{},
                                   Number<M0PerBlock>{},
                                   Number<M1PerBlock>{},
                                   K1),
                        make_tuple(Number<K0PerBlock>{} * Number<M0PerBlock>{} *
                                       (Number<M1PerBlock>{} * K1 + M1Padding),
                                   Number<M0PerBlock>{} * (Number<M1PerBlock>{} * K1 + M1Padding),
                                   Number<M1PerBlock>{} * K1 + M1Padding,
                                   K1,
                                   I1));

                    constexpr auto a_block_desc_b_k0_m_k1_tmp = transform_tensor_descriptor(
                        a_block_desc_b_k0_m0_m1_k1,
                        make_tuple(make_pass_through_transform(Number<1>{}),
                                   make_pass_through_transform(Number<K0PerBlock>{}),
                                   make_merge_transform_v4_no_carry(
                                       make_tuple(Number<M0PerBlock>{}, Number<M1PerBlock>{})),
                                   make_pass_through_transform(K1)),
                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}),
                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));

                    return a_block_desc_b_k0_m_k1_tmp;
                }
                else
                {
                    return make_naive_tensor_descriptor(
                        make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
                        make_tuple(Number<K0PerBlock>{} * Number<MPerBlock + 1>{} * K1,
                                   Number<MPerBlock + 1>{} * K1,
                                   K1,
                                   I1));
                }
            }
            else
            {
                return make_naive_tensor_descriptor_aligned(
                    make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
                    max_lds_align);
            }
        }();

        return a_block_desc_b_k0_m_k1;
    }

    __host__ __device__ static constexpr auto GetBBlockDescriptor_K0PerBlock_NPerBlock_K1()
    {
        constexpr auto max_lds_align = K1;

        // B matrix in LDS memory, dst of blockwise copy
        constexpr auto b_block_desc_k0_n_k1 = [&]() {
            if constexpr(BBlockLdsExtraN)
            {
                if constexpr(BBlockLdsExtraN1Wrw)
                {
                    constexpr auto b_block_desc_k0_n0_n1_k1 = make_naive_tensor_descriptor(
                        make_tuple(
                            Number<K0PerBlock>{}, Number<N0PerBlock>{}, Number<N1PerBlock>{}, K1),
                        make_tuple(Number<N0PerBlock>{} * (Number<N1PerBlock>{} * K1 + N1Padding),
                                   Number<N1PerBlock>{} * K1 + N1Padding,
                                   K1,
                                   I1));

                    constexpr auto b_block_desc_k0_n_k1_tmp = transform_tensor_descriptor(
                        b_block_desc_k0_n0_n1_k1,
                        make_tuple(make_pass_through_transform(Number<K0PerBlock>{}),
                                   make_merge_transform_v3_division_mod(
                                       make_tuple(Number<N0PerBlock>{}, Number<N1PerBlock>{})),
                                   make_pass_through_transform(K1)),
                        make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}),
                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));

                    return b_block_desc_k0_n_k1_tmp;
                }
                else
                {
                    return make_naive_tensor_descriptor(
                        make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
                        make_tuple(Number<NPerBlock + 1>{} * K1, K1, I1));
                }
            }
            else
            {
                return make_naive_tensor_descriptor_aligned(
                    make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
            }
        }();

        return b_block_desc_k0_n_k1;
    }

    __host__ __device__ static constexpr auto GetBBlockDescriptor_Batch_K0PerBlock_NPerBlock_K1()
    {
        constexpr auto max_lds_align = K1;

        // B matrix in LDS memory, dst of blockwise copy
        constexpr auto b_block_desc_b_k0_n_k1 = [&]() {
            if constexpr(BBlockLdsExtraN)
            {
                if constexpr(BBlockLdsExtraN1Wrw)
                {
                    constexpr auto b_block_desc_b_k0_n0_n1_k1 = make_naive_tensor_descriptor(
                        make_tuple(Number<1>{},
                                   Number<K0PerBlock>{},
                                   Number<N0PerBlock>{},
                                   Number<N1PerBlock>{},
                                   K1),
                        make_tuple(Number<K0PerBlock>{} * Number<N0PerBlock>{} *
                                       (Number<N1PerBlock>{} * K1 + N1Padding),
                                   Number<N0PerBlock>{} * (Number<N1PerBlock>{} * K1 + N1Padding),
                                   Number<N1PerBlock>{} * K1 + N1Padding,
                                   K1,
                                   I1));

                    constexpr auto b_block_desc_b_k0_n_k1_tmp = transform_tensor_descriptor(
                        b_block_desc_b_k0_n0_n1_k1,
                        make_tuple(make_pass_through_transform(Number<1>{}),
                                   make_pass_through_transform(Number<K0PerBlock>{}),
                                   make_merge_transform_v4_no_carry(
                                       make_tuple(Number<N0PerBlock>{}, Number<N1PerBlock>{})),
                                   make_pass_through_transform(K1)),
                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}),
                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));

                    return b_block_desc_b_k0_n_k1_tmp;
                }
                else
                {
                    return make_naive_tensor_descriptor(
                        make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
                        make_tuple(Number<K0PerBlock>{} * Number<NPerBlock + 1>{} * K1,
                                   Number<NPerBlock + 1>{} * K1,
                                   K1,
                                   I1));
                }
            }
            else
            {
                return make_naive_tensor_descriptor_aligned(
                    make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
                    max_lds_align);
            }
        }();

        return b_block_desc_b_k0_n_k1;
    }

    __host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
    {
        constexpr auto max_lds_align = K1;

        // A matrix in LDS memory, dst of blockwise copy
        constexpr auto a_b_k0_m_k1_block_desc = GetABlockDescriptor_Batch_K0PerBlock_MPerBlock_K1();

        // B matrix in LDS memory, dst of blockwise copy
        constexpr auto b_b_k0_n_k1_block_desc = GetBBlockDescriptor_Batch_K0PerBlock_NPerBlock_K1();

        // LDS allocation for A and B: be careful of alignment
        constexpr auto a_block_space_size = math::integer_least_multiple(
            a_b_k0_m_k1_block_desc.GetElementSpaceSize(), max_lds_align);

        constexpr auto b_block_space_size = math::integer_least_multiple(
            b_b_k0_n_k1_block_desc.GetElementSpaceSize(), max_lds_align);

        constexpr auto c_block_size =
            GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock().GetElementSpaceSize();

        return math::max((a_block_space_size + b_block_space_size) * sizeof(FloatAB),
                         c_block_size * sizeof(FloatC));
    }

    // block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
    template <typename Block2CTileMap>
    __host__ __device__ static constexpr bool
    CheckValidity(const AGridDesc_B_K0_M_K1& a_b_k0_m_k1_grid_desc,
                  const BGridDesc_B_K0_N_K1& b_b_k0_n_k1_grid_desc,
                  const CMNGridDesc& c_m_n_grid_desc,
                  const Block2CTileMap& block_2_ctile_map)
    {
        static_assert(is_known_at_compile_time<remove_cv_t<decltype(K1)>>::value,
                      "wrong! K1 need to be known at compile-time");

        static_assert((MPerBlock % (MPerXDL * MRepeat) == 0) &&
                          (NPerBlock % (NRepeat * NPerXDL)) == 0,
                      "Invalid tuning param!");

        const auto M      = a_b_k0_m_k1_grid_desc.GetLength(I2);
        const auto N      = b_b_k0_n_k1_grid_desc.GetLength(I2);
        const auto K0     = a_b_k0_m_k1_grid_desc.GetLength(I1);
        const auto KBatch = a_b_k0_m_k1_grid_desc.GetLength(I0);

        // check gridwise gemm pipeline
        const auto num_k_loop = K0 / K0PerBlock;

        if(!GridwiseGemmPipe::IsSupported(num_k_loop))
        {
            return false;
        }

        if(!(M == c_m_n_grid_desc.GetLength(I0) && N == c_m_n_grid_desc.GetLength(I1) &&
             K0 == b_b_k0_n_k1_grid_desc.GetLength(I1) &&
             K1 == a_b_k0_m_k1_grid_desc.GetLength(I3) &&
             K1 == b_b_k0_n_k1_grid_desc.GetLength(I3) &&
             KBatch == b_b_k0_n_k1_grid_desc.GetLength(I0)))
            return false;

        if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K0 % K0PerBlock == 0))
            return false;

        if(!block_2_ctile_map.CheckValidity(c_m_n_grid_desc))
        {
            return false;
        }

        // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
        return true;
    }

    __host__ __device__ static constexpr bool CalculateHasMainK0BlockLoop(index_t K0)
    {
        // const bool has_main_k0_block_loop = K0 > K0PerBlock;
        const index_t num_loop = K0 / K0PerBlock;

        return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);

        // return has_main_k0_block_loop;
    }

    __host__ __device__ static constexpr auto
    MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(const CMNGridDesc& c_m_n_grid_desc)
    {
        const auto M = c_m_n_grid_desc.GetLength(I0);
        const auto N = c_m_n_grid_desc.GetLength(I1);

        const auto MBlock = M / MPerBlock;
        const auto NBlock = N / NPerBlock;

        return transform_tensor_descriptor(
            c_m_n_grid_desc,
            make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
                       make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
            make_tuple(Sequence<0>{}, Sequence<1>{}),
            make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
    }

    // return block_id to C matrix tile idx (m0, n0) mapping
    __host__ __device__ static constexpr auto MakeCBlockClusterAdaptor(
        const CMNGridDesc& c_m_n_grid_desc, index_t M01, index_t N01, index_t KBatch)
    {
        return BlockToCTileMap_KSplit_M00_N00_M01_N01<MPerBlock, NPerBlock, CMNGridDesc>(
            c_m_n_grid_desc, M01, N01, KBatch);
    }

    __host__ __device__ static constexpr auto
    GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
    {
        constexpr index_t MWave = MPerBlock / (MRepeat * MPerXDL);
        constexpr index_t NWave = NPerBlock / (NRepeat * NPerXDL);

        return make_naive_tensor_descriptor_packed(
            make_tuple(I1,
                       Number<CShuffleMRepeatPerShuffle * MWave * MPerXDL>{},
                       I1,
                       Number<CShuffleNRepeatPerShuffle * NWave * NPerXDL>{}));
    }

    using CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
        decltype(MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(CMNGridDesc{}));
    using CBlockClusterAdaptor = decltype(MakeCBlockClusterAdaptor(CMNGridDesc{}, 1, 1, 1));

    template <bool HasMainKBlockLoop>
    __device__ static void Run(const FloatAB* __restrict__ p_a_grid,
                               const FloatAB* __restrict__ p_b_grid,
                               FloatC* __restrict__ p_c_grid,
                               FloatAB* __restrict__ p_shared_block,
                               const AGridDesc_B_K0_M_K1& a_b_k0_m_k1_grid_desc,
                               const BGridDesc_B_K0_N_K1& b_b_k0_n_k1_grid_desc,
                               const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
                                   c_grid_desc_mblock_mperblock_nblock_nperblock,
                               const AElementwiseOperation& a_element_op,
                               const BElementwiseOperation& b_element_op,
                               const CElementwiseOperation& c_element_op,
                               const CBlockClusterAdaptor& c_block_cluster_adaptor)
    {
        const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
            p_a_grid, a_b_k0_m_k1_grid_desc.GetElementSpaceSize());
        const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
            p_b_grid, b_b_k0_n_k1_grid_desc.GetElementSpaceSize());
        auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
            p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());

        const auto K0 = a_b_k0_m_k1_grid_desc.GetLength(I1);

        // divide block work by [M, N]
        const auto block_work_idx =
            c_block_cluster_adaptor.CalculateBottomIndex(make_multi_index(get_block_1d_id()));

        const index_t k_batch_id = block_work_idx[I0];

        if(!c_block_cluster_adaptor.ValidCTileIndex(
               make_tuple(block_work_idx[I1], block_work_idx[I2]),
               make_tuple(c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
                          c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))
        {
            return;
        }

        // HACK: this force m/n_block_data_idx_on_grid into SGPR
        const index_t m_block_data_idx_on_grid =
            __builtin_amdgcn_readfirstlane(block_work_idx[I1] * MPerBlock);

        const index_t n_block_data_idx_on_grid =
            __builtin_amdgcn_readfirstlane(block_work_idx[I2] * NPerBlock);

        // lds max alignment
        constexpr auto max_lds_align = K1;

        // A matrix in LDS memory, dst of blockwise copy
        constexpr auto a_k0_m_k1_block_desc = GetABlockDescriptor_K0PerBlock_MPerBlock_K1();

        constexpr auto a_b_k0_m_k1_block_desc = GetABlockDescriptor_Batch_K0PerBlock_MPerBlock_K1();
        // B matrix in LDS memory, dst of blockwise copy
        constexpr auto b_k0_n_k1_block_desc = GetBBlockDescriptor_K0PerBlock_NPerBlock_K1();

        constexpr auto b_b_k0_n_k1_block_desc = GetBBlockDescriptor_Batch_K0PerBlock_NPerBlock_K1();
        // A matrix blockwise copy
        auto a_blockwise_copy =
            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
                                                AElementwiseOperation,
                                                ck::tensor_operation::element_wise::PassThrough,
                                                InMemoryDataOperationEnum::Set,
                                                Sequence<1, K0PerBlock, MPerBlock, K1>,
                                                ABlockTransferThreadClusterLengths_K0_M_K1,
                                                ABlockTransferThreadClusterArrangeOrder,
                                                FloatAB,
                                                FloatAB,
                                                decltype(a_b_k0_m_k1_grid_desc),
                                                decltype(a_b_k0_m_k1_block_desc),
                                                ABlockTransferSrcAccessOrder,
                                                Sequence<0, 2, 1, 3>,
                                                ABlockTransferSrcVectorDim,
                                                3,
                                                ABlockTransferSrcScalarPerVector,
                                                ABlockTransferDstScalarPerVector_K1,
                                                1,
                                                1,
                                                AThreadTransferSrcResetCoordinateAfterRun,
                                                true>(
                a_b_k0_m_k1_grid_desc,
                make_multi_index(k_batch_id, 0, m_block_data_idx_on_grid, 0),
                a_element_op,
                a_b_k0_m_k1_block_desc,
                make_multi_index(0, 0, 0, 0),
                ck::tensor_operation::element_wise::PassThrough{});

        // B matrix blockwise copy
        auto b_blockwise_copy =
            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
                                                BElementwiseOperation,
                                                ck::tensor_operation::element_wise::PassThrough,
                                                InMemoryDataOperationEnum::Set,
                                                Sequence<1, K0PerBlock, NPerBlock, K1>,
                                                BBlockTransferThreadClusterLengths_K0_N_K1,
                                                BBlockTransferThreadClusterArrangeOrder,
                                                FloatAB,
                                                FloatAB,
                                                decltype(b_b_k0_n_k1_grid_desc),
                                                decltype(b_b_k0_n_k1_block_desc),
                                                BBlockTransferSrcAccessOrder,
                                                Sequence<0, 2, 1, 3>,
                                                BBlockTransferSrcVectorDim,
                                                3,
                                                BBlockTransferSrcScalarPerVector,
                                                BBlockTransferDstScalarPerVector_K1,
                                                1,
                                                1,
                                                BThreadTransferSrcResetCoordinateAfterRun,
                                                true>(
                b_b_k0_n_k1_grid_desc,
                make_multi_index(k_batch_id, 0, n_block_data_idx_on_grid, 0),
                b_element_op,
                b_b_k0_n_k1_block_desc,
                make_multi_index(0, 0, 0, 0),
                ck::tensor_operation::element_wise::PassThrough{});

        // GEMM definition
        //   c_mtx += transpose(a_mtx) * b_mtx
        //     a_mtx[K0PerBlock, MPerBlock] is in LDS
        //     b_mtx[K0PerBlock, NPerBlock] is in LDS
        //     c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
        //       register
        // sanity check

        constexpr index_t KPack =
            math::max(K1, MfmaSelector<FloatAB, MPerXDL, NPerXDL>::selected_mfma.k_per_blk);

        auto blockwise_gemm =
            BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
                                                                FloatAB,
                                                                FloatAcc,
                                                                decltype(a_k0_m_k1_block_desc),
                                                                decltype(b_k0_n_k1_block_desc),
                                                                MPerXDL,
                                                                NPerXDL,
                                                                MRepeat,
                                                                NRepeat,
                                                                KPack>{};

        auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();

        // LDS allocation for A and B: be careful of alignment
        constexpr auto a_block_space_size =
            math::integer_least_multiple(a_k0_m_k1_block_desc.GetElementSpaceSize(), max_lds_align);

        FloatAB* p_a_block = p_shared_block;
        FloatAB* p_b_block = p_shared_block + a_block_space_size;

        constexpr auto a_block_slice_copy_step = make_multi_index(0, K0PerBlock, 0, 0);
        constexpr auto b_block_slice_copy_step = make_multi_index(0, K0PerBlock, 0, 0);

        auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
            p_a_block, a_k0_m_k1_block_desc.GetElementSpaceSize());
        auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
            p_b_block, b_k0_n_k1_block_desc.GetElementSpaceSize());

        // gridwise GEMM pipeline
        const index_t K0BlockMainLoop = __builtin_amdgcn_readfirstlane(K0 / K0PerBlock);

        GridwiseGemmPipe::template Run<HasMainKBlockLoop>(a_b_k0_m_k1_grid_desc,
                                                          a_b_k0_m_k1_block_desc,
                                                          a_blockwise_copy,
                                                          a_grid_buf,
                                                          a_block_buf,
                                                          a_block_slice_copy_step,
                                                          b_b_k0_n_k1_grid_desc,
                                                          b_b_k0_n_k1_block_desc,
                                                          b_blockwise_copy,
                                                          b_grid_buf,
                                                          b_block_buf,
                                                          b_block_slice_copy_step,
                                                          blockwise_gemm,
                                                          c_thread_buf,
                                                          K0BlockMainLoop);

        // output: register to global memory
        {
            constexpr index_t MWave = MPerBlock / (MRepeat * MPerXDL);
            constexpr index_t NWave = NPerBlock / (NRepeat * NPerXDL);

            constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc =
                blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();

            constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc =
                blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();

            constexpr auto M0 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I0);
            constexpr auto N0 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I1);
            constexpr auto M1 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I2);
            constexpr auto N1 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I3);
            constexpr auto M2 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I4);
            constexpr auto M3 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I5);
            constexpr auto M4 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I6);
            constexpr auto N2 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I7);

            constexpr auto c_block_desc_mblock_mperblock_nblock_nperblock =
                GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();

            void* p_shared = static_cast<void*>(p_shared_block);

            auto c_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
                static_cast<FloatC*>(p_shared),
                c_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());

            static_assert(M1 == MWave, "");
            static_assert(N1 == NWave, "");
            static_assert(M2 * M3 * M4 == MPerXDL, "");
            static_assert(N2 == NPerXDL, "");

            constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(
                c_block_desc_mblock_mperblock_nblock_nperblock,
                make_tuple(
                    make_freeze_transform(I0), // freeze mblock
                    make_unmerge_transform(make_tuple(CShuffleMRepeatPerShuffle,
                                                      M1,
                                                      M2,
                                                      M3,
                                                      M4)), // M1 = MWave, M2 * M3 * M4 = MPerXDL
                    make_freeze_transform(I0),              // freeze nblock
                    make_unmerge_transform(make_tuple(CShuffleNRepeatPerShuffle,
                                                      N1,
                                                      N2))), // M1 = MWave, M2 * M3 * M4 = MPerXDL
                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
                make_tuple(
                    Sequence<>{}, Sequence<0, 2, 4, 5, 6>{}, Sequence<>{}, Sequence<1, 3, 7>{}));

            // calculate origin of thread output tensor on global memory
            //     blockwise GEMM c matrix starting index
            const auto c_thread_mtx_on_block =
                blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);

            const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
            const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];

            const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =
                make_single_stage_tensor_adaptor(
                    make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
                    make_tuple(Sequence<0, 1, 2, 3, 4>{}),
                    make_tuple(Sequence<0>{}));

            const auto m_thread_data_on_block_idx =
                m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
                    make_multi_index(m_thread_data_on_block));

            const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =
                make_single_stage_tensor_adaptor(
                    make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
                    make_tuple(Sequence<0, 1, 2>{}),
                    make_tuple(Sequence<0>{}));

            const auto n_thread_data_on_block_idx =
                n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
                    make_multi_index(n_thread_data_on_block));

            // VGPR to LDS
            auto c_thread_copy_vgpr_to_lds =
                ThreadwiseTensorSliceTransfer_v1r3<FloatAcc,
                                                   FloatC,
                                                   decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc),
                                                   decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
                                                   ck::tensor_operation::element_wise::PassThrough,
                                                   Sequence<CShuffleMRepeatPerShuffle,
                                                            CShuffleNRepeatPerShuffle,
                                                            I1,
                                                            I1,
                                                            M2,
                                                            I1,
                                                            M4,
                                                            I1>,
                                                   Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
                                                   7,
                                                   1,
                                                   InMemoryDataOperationEnum::Set,
                                                   1,
                                                   true>{
                    c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
                    make_multi_index(0,
                                     0,
                                     m_thread_data_on_block_idx[I1],
                                     n_thread_data_on_block_idx[I1],
                                     m_thread_data_on_block_idx[I2],
                                     m_thread_data_on_block_idx[I3],
                                     m_thread_data_on_block_idx[I4],
                                     n_thread_data_on_block_idx[I2]),
                    ck::tensor_operation::element_wise::PassThrough{}};

            // LDS to global
            auto c_block_copy_lds_to_global = ThreadGroupTensorSliceTransfer_v6r1<
                ThisThreadBlock,            // index_t BlockSize,
                CElementwiseOperation,      // ElementwiseOperation,
                CGlobalMemoryDataOperation, // DstInMemOp,
                Sequence<1,
                         CShuffleMRepeatPerShuffle * MWave * MPerXDL,
                         1,
                         CShuffleNRepeatPerShuffle * NWave * NPerXDL>, // BlockSliceLengths,
                CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
                Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
                FloatC,               // typename SrcData,
                FloatC,               // typename DstData,
                decltype(c_block_desc_mblock_mperblock_nblock_nperblock),
                decltype(c_grid_desc_mblock_mperblock_nblock_nperblock),
                Sequence<0, 1, 2, 3>,                       // typename DimAccessOrder,
                3,                                          // index_t VectorDim,
                CBlockTransferScalarPerVector_NWaveNPerXDL, // index_t ScalarPerVector,
                true,  // bool ThreadTransferSrcResetCoordinateAfterRun,
                false> // bool ThreadTransferDstResetCoordinateAfterRun
                {c_block_desc_mblock_mperblock_nblock_nperblock,
                 make_multi_index(0, 0, 0, 0),
                 c_grid_desc_mblock_mperblock_nblock_nperblock,
                 make_multi_index(block_work_idx[I1], 0, block_work_idx[I2], 0),
                 c_element_op};

            constexpr auto mxdlperwave_forward_step =
                make_multi_index(0, CShuffleMRepeatPerShuffle * MWave * MPerXDL, 0, 0);
            constexpr auto nxdlperwave_forward_step =
                make_multi_index(0, 0, 0, CShuffleNRepeatPerShuffle * NWave * NPerXDL);
            constexpr auto nxdlperwave_backward_step =
                make_multi_index(0, 0, 0, -CShuffleNRepeatPerShuffle * NWave * NPerXDL);

            static_for<0, MRepeat, CShuffleMRepeatPerShuffle>{}([&](auto mxdlperwave_iter) {
                constexpr auto mxdlperwave = mxdlperwave_iter;

                static_for<0, NRepeat, CShuffleNRepeatPerShuffle>{}([&](auto nxdlperwave_iter) {
                    constexpr bool nxdlperwave_forward_sweep =
                        (mxdlperwave % (2 * CShuffleMRepeatPerShuffle) == 0);

                    constexpr index_t nxdlperwave_value =
                        nxdlperwave_forward_sweep
                            ? nxdlperwave_iter
                            : (NRepeat - nxdlperwave_iter - CShuffleNRepeatPerShuffle);

                    constexpr auto nxdlperwave = Number<nxdlperwave_value>{};

                    // make sure it's safe to do ds_write
                    block_sync_lds();

                    // VGPR to LDS
                    c_thread_copy_vgpr_to_lds.Run(
                        c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc,
                        make_tuple(mxdlperwave, nxdlperwave, I0, I0, I0, I0, I0, I0),
                        c_thread_buf,
                        c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
                        c_block_buf);

                    // make sure it's safe to do ds_read
                    block_sync_lds();

                    // LDS to global
                    c_block_copy_lds_to_global.Run(c_block_desc_mblock_mperblock_nblock_nperblock,
                                                   c_block_buf,
                                                   c_grid_desc_mblock_mperblock_nblock_nperblock,
                                                   c_grid_buf);

                    // move on nxdlperwave dimension
                    if constexpr(nxdlperwave_forward_sweep &&
                                 (nxdlperwave < NRepeat - CShuffleNRepeatPerShuffle))
                    {
                        c_block_copy_lds_to_global.MoveDstSliceWindow(
                            c_grid_desc_mblock_mperblock_nblock_nperblock,
                            nxdlperwave_forward_step);
                    }
                    else if constexpr((!nxdlperwave_forward_sweep) && (nxdlperwave > 0))
                    {
                        c_block_copy_lds_to_global.MoveDstSliceWindow(
                            c_grid_desc_mblock_mperblock_nblock_nperblock,
                            nxdlperwave_backward_step);
                    }
                });

                // move on mxdlperwave dimension
                if constexpr(mxdlperwave < MRepeat - CShuffleMRepeatPerShuffle)
                {
                    c_block_copy_lds_to_global.MoveDstSliceWindow(
                        c_grid_desc_mblock_mperblock_nblock_nperblock, mxdlperwave_forward_step);
                }
            });
        }
    }
}; // namespace ck

} // namespace ck
