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 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
  
 #pragma once
  
 #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/grid/gridwise_gemm_pipeline_selector.hpp"
 #include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.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"
  
 namespace ck {
  
 template <typename GridwiseGemm, bool HasMainKBlockLoop>
 __global__ void
 #if CK_USE_LAUNCH_BOUNDS
     __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
 #endif
         kernel_gemm_xdl_cshuffle_v1(typename GridwiseGemm::Argument karg)
 {
 #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
     defined(__gfx94__))
     __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
  
     GridwiseGemm::template Run<HasMainKBlockLoop>(
         karg.p_a_grid, karg.p_b_grid, karg.p_c_grid, p_shared, karg);
 #else
     ignore = karg;
 #endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
 }
  
 template <typename GridwiseGemm,
           typename FloatA,
           typename FloatB,
           typename FloatC,
           bool HasMainKBlockLoop>
 __global__ void
 #if CK_USE_LAUNCH_BOUNDS
     __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
 #endif
         kernel_gemm_xdl_cshuffle_v1(const FloatA* __restrict__ p_a_grid,
                                     const FloatB* __restrict__ p_b_grid,
                                     FloatC* __restrict__ p_c_grid,
                                     typename GridwiseGemm::Problem problem)
 {
 #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
     defined(__gfx94__))
     __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
  
     GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid, p_b_grid, p_c_grid, p_shared, problem);
 #else
     ignore = p_a_grid;
     ignore = p_b_grid;
     ignore = p_c_grid;
     ignore = problem;
 #endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
 }
  
 template <typename ALayout,
           typename BLayout,
           typename CLayout,
           typename FloatA,
           typename FloatB,
           typename FloatGemmAcc,
           typename FloatCShuffle,
           typename FloatC,
           typename AElementwiseOperation,
           typename BElementwiseOperation,
           typename CElementwiseOperation,
           tensor_operation::device::GemmSpecialization GemmSpec,
           InMemoryDataOperationEnum CGlobalMemoryDataOperation,
           index_t NumGemmKPrefetchStage,
           index_t BlockSize,
           index_t MPerBlock,
           index_t NPerBlock,
           index_t KPerBlock,
           index_t AK1Value,
           index_t BK1Value,
           index_t MPerXdl,
           index_t NPerXdl,
           index_t MXdlPerWave,
           index_t NXdlPerWave,
           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 CShuffleMXdlPerWavePerShuffle,
           index_t CShuffleNXdlPerWavePerShuffle,
           typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
           index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
           LoopScheduler LoopSched,
           PipelineVersion PipelineVer = PipelineVersion::v1,
           typename ComputeTypeA       = FloatC,
           typename ComputeTypeB       = ComputeTypeA>
 struct GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1
 {
     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 AK0Number = Number<KPerBlock / AK1Value>{};
     static constexpr auto BK0Number = Number<KPerBlock / BK1Value>{};
     static constexpr auto AK1Number = Number<AK1Value>{};
     static constexpr auto BK1Number = Number<BK1Value>{};
  
     using ThisThreadBlock = ThisThreadBlock<BlockSize>;
  
     __host__ static auto CalculateGridSize(index_t M, index_t N)
     {
         return std::make_tuple(Block2CTileMap::CalculateGridSize(M, N), 1, 1);
     }
  
     __host__ static auto CalculateMPadded(index_t M)
     {
         return math::integer_divide_ceil(M, MPerBlock) * MPerBlock;
     }
  
     __host__ static auto CalculateNPadded(index_t N)
     {
         return math::integer_divide_ceil(N, NPerBlock) * NPerBlock;
     }
  
     __host__ static auto CalculateKPadded(index_t K)
     {
         return math::integer_divide_ceil(K, KPerBlock) * KPerBlock;
     }
  
     __host__ static auto CalculateAK0(index_t K)
     {
         using GemmSpecialization = tensor_operation::device::GemmSpecialization;
  
         if constexpr(GemmSpec == GemmSpecialization::MKPadding ||
                      GemmSpec == GemmSpecialization::MNKPadding ||
                      GemmSpec == GemmSpecialization::KPadding ||
                      GemmSpec == GemmSpecialization::NKPadding)
         {
             return CalculateKPadded(K) / AK1Value;
         }
         else
         {
             return K / AK1Value;
         }
     }
  
     __host__ static auto CalculateBK0(index_t K)
     {
         using GemmSpecialization = tensor_operation::device::GemmSpecialization;
  
         if constexpr(GemmSpec == GemmSpecialization::NKPadding ||
                      GemmSpec == GemmSpecialization::MNKPadding ||
                      GemmSpec == GemmSpecialization::KPadding ||
                      GemmSpec == GemmSpecialization::MKPadding)
         {
             return CalculateKPadded(K) / BK1Value;
         }
         else
         {
             return K / BK1Value;
         }
     }
  
     __host__ static auto CalculateMBlock(index_t M)
     {
         return math::integer_divide_floor(M, MPerBlock);
     }
  
     __host__ static auto CalculateNBlock(index_t N)
     {
         return math::integer_divide_floor(N, NPerBlock);
     }
  
     __device__ static auto MakeAGridDescriptor_AK0_M_AK1(
         index_t M, index_t MPad, index_t K, index_t KPad, index_t StrideA, index_t AK0)
     {
         const auto a_grid_desc_mraw_kraw = [&]() {
             if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
             {
                 return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));
             }
             else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
             {
                 return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, StrideA));
             }
         }();
  
         using GemmSpecialization = tensor_operation::device::GemmSpecialization;
  
         if constexpr(GemmSpec == GemmSpecialization::MKPadding ||
                      GemmSpec == GemmSpecialization::MNKPadding)
         {
             // pad both M and K
             const auto a_grid_desc_m_k =
                 transform_tensor_descriptor(a_grid_desc_mraw_kraw,
                                             make_tuple(make_right_pad_transform(M, MPad - M),
                                                        make_right_pad_transform(K, KPad - K)),
                                             make_tuple(Sequence<0>{}, Sequence<1>{}),
                                             make_tuple(Sequence<0>{}, Sequence<1>{}));
  
             const auto a_grid_desc_ak0_m_ak1 = transform_tensor_descriptor(
                 a_grid_desc_m_k,
                 make_tuple(make_unmerge_transform(make_tuple(AK0, AK1Value)),
                            make_pass_through_transform(MPad)),
                 make_tuple(Sequence<1>{}, Sequence<0>{}),
                 make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
  
             return a_grid_desc_ak0_m_ak1;
         }
         else if constexpr(GemmSpec == GemmSpecialization::MPadding ||
                           GemmSpec == GemmSpecialization::MNPadding)
         {
             // pad M, but not K
             const auto a_grid_desc_ak0_m_ak1 = transform_tensor_descriptor(
                 a_grid_desc_mraw_kraw,
                 make_tuple(make_unmerge_transform(make_tuple(AK0, AK1Value)),
                            make_right_pad_transform(M, MPad - M)),
                 make_tuple(Sequence<1>{}, Sequence<0>{}),
                 make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
  
             return a_grid_desc_ak0_m_ak1;
         }
         else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
                           GemmSpec == GemmSpecialization::NKPadding)
         {
             // pad K, but not M
             const auto a_grid_desc_m_k = transform_tensor_descriptor(
                 a_grid_desc_mraw_kraw,
                 make_tuple(make_pass_through_transform(M), make_right_pad_transform(K, KPad - K)),
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0>{}, Sequence<1>{}));
  
             const auto a_grid_desc_ak0_m_ak1 = transform_tensor_descriptor(
                 a_grid_desc_m_k,
                 make_tuple(make_unmerge_transform(make_tuple(AK0, AK1Value)),
                            make_pass_through_transform(M)),
                 make_tuple(Sequence<1>{}, Sequence<0>{}),
                 make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
  
             return a_grid_desc_ak0_m_ak1;
         }
         else
         {
             // not pad M or K
             const auto a_grid_desc_ak0_m_ak1 = transform_tensor_descriptor(
                 a_grid_desc_mraw_kraw,
                 make_tuple(make_unmerge_transform(make_tuple(AK0, AK1Value)),
                            make_pass_through_transform(M)),
                 make_tuple(Sequence<1>{}, Sequence<0>{}),
                 make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
  
             return a_grid_desc_ak0_m_ak1;
         }
     }
  
     __device__ static auto MakeBGridDescriptor_BK0_N_BK1(
         index_t K, index_t KPad, index_t N, index_t NPad, index_t StrideB, index_t BK0)
     {
         const auto b_grid_desc_nraw_kraw = [&]() {
             if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
             {
                 return make_naive_tensor_descriptor(make_tuple(N, K), make_tuple(I1, StrideB));
             }
             else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
             {
                 return make_naive_tensor_descriptor(make_tuple(N, K), make_tuple(StrideB, I1));
             }
         }();
  
         using GemmSpecialization = tensor_operation::device::GemmSpecialization;
  
         if constexpr(GemmSpec == GemmSpecialization::NKPadding ||
                      GemmSpec == GemmSpecialization::MNKPadding)
         {
             // pad both N and K
             const auto b_grid_desc_n_k =
                 transform_tensor_descriptor(b_grid_desc_nraw_kraw,
                                             make_tuple(make_right_pad_transform(N, NPad - N),
                                                        make_right_pad_transform(K, KPad - K)),
                                             make_tuple(Sequence<0>{}, Sequence<1>{}),
                                             make_tuple(Sequence<0>{}, Sequence<1>{}));
  
             const auto b_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(
                 b_grid_desc_n_k,
                 make_tuple(make_unmerge_transform(make_tuple(BK0, BK1Value)),
                            make_pass_through_transform(NPad)),
                 make_tuple(Sequence<1>{}, Sequence<0>{}),
                 make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
  
             return b_grid_desc_bk0_n_bk1;
         }
         else if constexpr(GemmSpec == GemmSpecialization::NPadding ||
                           GemmSpec == GemmSpecialization::MNPadding)
         {
             // pad N, but not K
             const auto b_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(
                 b_grid_desc_nraw_kraw,
                 make_tuple(make_unmerge_transform(make_tuple(BK0, BK1Value)),
                            make_right_pad_transform(N, NPad - N)),
                 make_tuple(Sequence<1>{}, Sequence<0>{}),
                 make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
  
             return b_grid_desc_bk0_n_bk1;
         }
         else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
                           GemmSpec == GemmSpecialization::MKPadding)
         {
             // pad K, but not N
             const auto b_grid_desc_n_k = transform_tensor_descriptor(
                 b_grid_desc_nraw_kraw,
                 make_tuple(make_pass_through_transform(N), make_right_pad_transform(K, KPad - K)),
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0>{}, Sequence<1>{}));
  
             const auto b_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(
                 b_grid_desc_n_k,
                 make_tuple(make_unmerge_transform(make_tuple(BK0, BK1Value)),
                            make_pass_through_transform(N)),
                 make_tuple(Sequence<1>{}, Sequence<0>{}),
                 make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
  
             return b_grid_desc_bk0_n_bk1;
         }
         else
         {
             // not pad N or K
             const auto b_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(
                 b_grid_desc_nraw_kraw,
                 make_tuple(make_unmerge_transform(make_tuple(BK0, BK1Value)),
                            make_pass_through_transform(N)),
                 make_tuple(Sequence<1>{}, Sequence<0>{}),
                 make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
  
             return b_grid_desc_bk0_n_bk1;
         }
     }
  
     __host__ __device__ static auto
     MakeCGridDescriptor_M_N(index_t M, index_t MPad, index_t N, index_t NPad, index_t StrideC)
     {
         const auto c_grid_desc_mraw_nraw = [&]() {
             if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
             {
                 return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideC, I1));
             }
             else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, CLayout>::value)
             {
                 return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, StrideC));
             }
         }();
  
         using GemmSpecialization = tensor_operation::device::GemmSpecialization;
  
         if constexpr(GemmSpec == GemmSpecialization::MNPadding ||
                      GemmSpec == GemmSpecialization::MNKPadding)
         {
             // pad M and N
             return transform_tensor_descriptor(c_grid_desc_mraw_nraw,
                                                make_tuple(make_right_pad_transform(M, MPad - M),
                                                           make_right_pad_transform(N, NPad - N)),
                                                make_tuple(Sequence<0>{}, Sequence<1>{}),
                                                make_tuple(Sequence<0>{}, Sequence<1>{}));
         }
         else if constexpr(GemmSpec == GemmSpecialization::MPadding ||
                           GemmSpec == GemmSpecialization::MKPadding)
         {
             // pad M, but not N
             return transform_tensor_descriptor(
                 c_grid_desc_mraw_nraw,
                 make_tuple(make_right_pad_transform(M, MPad - M), make_pass_through_transform(N)),
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0>{}, Sequence<1>{}));
         }
         else if constexpr(GemmSpec == GemmSpecialization::NPadding ||
                           GemmSpec == GemmSpecialization::NKPadding)
         {
             // pad N, but not M
             return transform_tensor_descriptor(
                 c_grid_desc_mraw_nraw,
                 make_tuple(make_pass_through_transform(M), make_right_pad_transform(N, NPad - N)),
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0>{}, Sequence<1>{}));
         }
         else
         {
             // not pad M or N
             return c_grid_desc_mraw_nraw;
         }
     }
  
     struct Problem
     {
         __host__ Problem(index_t M_,
                          index_t N_,
                          index_t K_,
                          index_t StrideA_,
                          index_t StrideB_,
                          index_t StrideC_)
             : M{M_},
               N{N_},
               K{K_},
               StrideA{StrideA_},
               StrideB{StrideB_},
               StrideC{StrideC_},
               MPadded{CalculateMPadded(M_)},
               NPadded{CalculateNPadded(N_)},
               KPadded{CalculateKPadded(K_)},
               AK0{CalculateAK0(K_)},
               BK0{CalculateBK0(K_)},
               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 << ", "
                       << "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 MPadded;
         index_t NPadded;
         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 FloatA* p_a_grid_,
                           const FloatB* p_b_grid_,
                           FloatC* p_c_grid_,
                           index_t M_,
                           index_t N_,
                           index_t K_,
                           index_t StrideA_,
                           index_t StrideB_,
                           index_t StrideC_)
             : Problem{M_, N_, K_, StrideA_, StrideB_, StrideC_},
               p_a_grid{p_a_grid_},
               p_b_grid{p_b_grid_},
               p_c_grid{p_c_grid_}
         {
         }
  
         const FloatA* p_a_grid;
         const FloatB* p_b_grid;
         FloatC* p_c_grid;
     };
  
     // FIXME: pass GridwiseGemmPipe as a template arguement into GridwiseGemm
     using GridwiseGemmPipe = remove_cvref_t<
         decltype(GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage, LoopSched>())>;
  
     __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
     {
         // A matrix in LDS memory, dst of blockwise copy
         return make_naive_tensor_descriptor(
             make_tuple(AK0Number, Number<MPerBlock>{}, AK1Number),
             make_tuple(Number<MPerBlock + ABlockLdsExtraM>{} * AK1Number, AK1Number, I1));
     }
  
     __device__ static constexpr auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()
     {
         // B matrix in LDS memory, dst of blockwise copy
         return make_naive_tensor_descriptor(
             make_tuple(BK0Number, Number<NPerBlock>{}, BK1Number),
             make_tuple(Number<NPerBlock + BBlockLdsExtraN>{} * BK1Number, BK1Number, I1));
     }
  
     __device__ static constexpr auto GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
     {
         constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
         constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
  
         constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
             make_naive_tensor_descriptor_packed(
                 make_tuple(I1,
                            Number<CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl>{},
                            I1,
                            Number<CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>{}));
  
         return c_shuffle_block_desc_mblock_mperblock_nblock_nperblock;
     }
  
     __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
     {
         // LDS allocation for A and B: be careful of alignment
         constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
         constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
  
         // lds max alignment
         constexpr auto max_lds_align = math::lcm(AK1Number, BK1Number);
  
         constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
             a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
  
         constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
             b_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);
  
         // LDS allocation for C shuffle in LDS
         constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
             GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
  
         constexpr auto c_block_size =
             c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();
  
         return math::max((a_block_space_size_aligned * sizeof(ComputeTypeA) +
                           b_block_space_size_aligned * sizeof(ComputeTypeB)),
                          c_block_size * sizeof(FloatCShuffle));
     }
  
     // block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
     __host__ static constexpr bool CheckValidity(const Problem& problem)
     {
         static_assert((MPerBlock % (MPerXdl * MXdlPerWave) == 0) &&
                           (NPerBlock % (NXdlPerWave * NPerXdl)) == 0,
                       "Invalid tuning param!");
  
         if constexpr(!(GemmSpec == tensor_operation::device::GemmSpecialization::MPadding ||
                        GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
                        GemmSpec == tensor_operation::device::GemmSpecialization::MKPadding ||
                        GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding))
         {
             if(!(problem.M % MPerBlock == 0))
             {
                 return false;
             }
         }
  
         if constexpr(!(GemmSpec == tensor_operation::device::GemmSpecialization::NPadding ||
                        GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
                        GemmSpec == tensor_operation::device::GemmSpecialization::NKPadding ||
                        GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding))
         {
             if(!(problem.N % NPerBlock == 0))
             {
                 return false;
             }
         }
  
         if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MKPadding ||
                      GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding ||
                      GemmSpec == tensor_operation::device::GemmSpecialization::KPadding ||
                      GemmSpec == tensor_operation::device::GemmSpecialization::NKPadding)
         {
             if(!(CalculateKPadded(problem.K) % AK1Value == 0) ||
                !(CalculateKPadded(problem.K) % BK1Value == 0))
             {
                 return false;
             }
         }
         else
         {
             if(!(problem.K % AK1Value == 0) || !(problem.K % BK1Value == 0))
             {
                 return false;
             }
         }
  
         if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
         {
             if(problem.K % ABlockTransferSrcScalarPerVector != 0)
             {
                 return false;
             }
         }
         else
         {
             if(problem.M % ABlockTransferSrcScalarPerVector != 0)
             {
                 return false;
             }
         }
  
         if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
         {
             if(problem.N % BBlockTransferSrcScalarPerVector != 0)
             {
                 return false;
             }
         }
         else
         {
             if(problem.K % BBlockTransferSrcScalarPerVector != 0)
             {
                 return false;
             }
         }
  
         if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
         {
             if(problem.N % CShuffleBlockTransferScalarPerVector_NPerBlock != 0)
             {
                 return false;
             }
         }
         else
         {
             if(problem.M % CShuffleBlockTransferScalarPerVector_NPerBlock != 0)
             {
                 return false;
             }
         }
  
         // check gridwise gemm pipeline
         const auto num_k_loop = (CalculateAK0(problem.K) * AK1Value) / KPerBlock;
  
         if(!GridwiseGemmPipe::IsSupported(num_k_loop))
         {
             return false;
         }
  
         // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
         return true;
     }
  
     __host__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
     {
         const index_t num_loop = K / KPerBlock;
  
         return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
     }
  
     template <typename CGridDesc>
     __device__ static constexpr auto MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
         const CGridDesc& c_grid_desc_m_n, index_t MBlock, index_t NBlock)
     {
         const auto c_grid_desc_mblock_mperblock_nblock_nperblock = transform_tensor_descriptor(
             c_grid_desc_m_n,
             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 c_grid_desc_mblock_mperblock_nblock_nperblock;
     }
  
     // return block_id to C matrix tile idx (m0, n0) mapping
     using Block2CTileMap = BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock>;
  
     template <bool HasMainKBlockLoop>
     __device__ static void Run(const FloatA* __restrict__ p_a_grid,
                                const FloatB* __restrict__ p_b_grid,
                                FloatC* __restrict__ p_c_grid,
                                void* __restrict__ 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);
  
         const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
             p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());
         const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
             p_b_grid, b_grid_desc_bk0_n_bk1.GetElementSpaceSize());
         auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
             p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
  
         const AElementwiseOperation a_element_op{};
         const BElementwiseOperation b_element_op{};
         const CElementwiseOperation c_element_op{};
  
         // divide block work by [M, N]
         const auto block_2_ctile_map = Block2CTileMap{problem.M, problem.N};
  
         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;
         }
  
         // 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[I0] * MPerBlock);
  
         const index_t n_block_data_idx_on_grid =
             __builtin_amdgcn_readfirstlane(block_work_idx[I1] * NPerBlock);
  
         // lds max alignment
         constexpr auto max_lds_align = math::lcm(AK1Number, BK1Number);
  
         // A matrix in LDS memory, dst of blockwise copy
         constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
  
         // B matrix in LDS memory, dst of blockwise copy
         constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
  
         // A matrix blockwise copy
         auto a_blockwise_copy =
             ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
                                                 AElementwiseOperation,
                                                 ck::tensor_operation::element_wise::PassThrough,
                                                 InMemoryDataOperationEnum::Set,
                                                 Sequence<AK0Number, MPerBlock, AK1Number>,
                                                 ABlockTransferThreadClusterLengths_AK0_M_AK1,
                                                 ABlockTransferThreadClusterArrangeOrder,
                                                 FloatA,
                                                 ComputeTypeA,
                                                 decltype(a_grid_desc_ak0_m_ak1),
                                                 decltype(a_block_desc_ak0_m_ak1),
                                                 ABlockTransferSrcAccessOrder,
                                                 Sequence<1, 0, 2>,
                                                 ABlockTransferSrcVectorDim,
                                                 2,
                                                 ABlockTransferSrcScalarPerVector,
                                                 ABlockTransferDstScalarPerVector_AK1,
                                                 1,
                                                 1,
                                                 AThreadTransferSrcResetCoordinateAfterRun,
                                                 true,
                                                 NumGemmKPrefetchStage>(
                 a_grid_desc_ak0_m_ak1,
                 make_multi_index(0, m_block_data_idx_on_grid, 0),
                 a_element_op,
                 a_block_desc_ak0_m_ak1,
                 make_multi_index(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<BK0Number, NPerBlock, BK1Number>,
                                                 BBlockTransferThreadClusterLengths_BK0_N_BK1,
                                                 BBlockTransferThreadClusterArrangeOrder,
                                                 FloatB,
                                                 ComputeTypeB,
                                                 decltype(b_grid_desc_bk0_n_bk1),
                                                 decltype(b_block_desc_bk0_n_bk1),
                                                 BBlockTransferSrcAccessOrder,
                                                 Sequence<1, 0, 2>,
                                                 BBlockTransferSrcVectorDim,
                                                 2,
                                                 BBlockTransferSrcScalarPerVector,
                                                 BBlockTransferDstScalarPerVector_BK1,
                                                 1,
                                                 1,
                                                 BThreadTransferSrcResetCoordinateAfterRun,
                                                 true,
                                                 NumGemmKPrefetchStage>(
                 b_grid_desc_bk0_n_bk1,
                 make_multi_index(0, n_block_data_idx_on_grid, 0),
                 b_element_op,
                 b_block_desc_bk0_n_bk1,
                 make_multi_index(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 auto lcm_AK1_BK1 = math::lcm(AK1Number, BK1Number);
         constexpr bool is_single_rate_mfma =
             (((is_same<ComputeTypeA, half_t>::value || is_same<ComputeTypeA, bhalf_t>::value) &&
               lcm_AK1_BK1 <= 4) ||
              (is_same<ComputeTypeA, int8_t>::value && lcm_AK1_BK1 <= 8) ||
              ((is_same<ComputeTypeA, f8_t>::value || is_same<ComputeTypeA, bf8_t>::value) &&
               lcm_AK1_BK1 < 32))
                 ? true
                 : false;
         constexpr auto is_scale_mfma = false;
         constexpr index_t KPack      = math::max(lcm_AK1_BK1,
                                             MfmaSelector<ComputeTypeA,
                                                          MPerXdl,
                                                          NPerXdl,
                                                          ComputeTypeB,
                                                          is_single_rate_mfma,
                                                          is_scale_mfma>::selected_mfma.k_per_blk);
  
         auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
             BlockSize,
             ComputeTypeA,
             ComputeTypeB,
             FloatGemmAcc,
             decltype(a_block_desc_ak0_m_ak1),
             decltype(b_block_desc_bk0_n_bk1),
             MPerXdl,
             NPerXdl,
             MXdlPerWave,
             NXdlPerWave,
             KPack,
             LoopSched>();
  
         auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
  
         // LDS allocation for A and B: be careful of alignment
         constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
             a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
  
         auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
             static_cast<ComputeTypeA*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
  
         auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
             static_cast<ComputeTypeB*>(p_shared) + a_block_space_size_aligned,
             b_block_desc_bk0_n_bk1.GetElementSpaceSize());
  
         constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1Number, 0, 0);
         constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock / BK1Number, 0, 0);
  
         // gridwise GEMM pipeline
         static_assert(std::is_default_constructible_v<GridwiseGemmPipe>);
         const auto gridwise_gemm_pipeline = GridwiseGemmPipe{};
  
         const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(
             (a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2)) /
             KPerBlock);
  
         gridwise_gemm_pipeline.template Run<HasMainKBlockLoop>(a_grid_desc_ak0_m_ak1,
                                                                a_block_desc_ak0_m_ak1,
                                                                a_blockwise_copy,
                                                                a_grid_buf,
                                                                a_block_buf,
                                                                a_block_slice_copy_step,
                                                                b_grid_desc_bk0_n_bk1,
                                                                b_block_desc_bk0_n_bk1,
                                                                b_blockwise_copy,
                                                                b_grid_buf,
                                                                b_block_buf,
                                                                b_block_slice_copy_step,
                                                                blockwise_gemm,
                                                                c_thread_buf,
                                                                num_k_block_main_loop);
  
         // shuffle C and write out
         {
             static_assert(MXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&
                               NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,
                           "wrong!");
  
             constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
             constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
  
             // TODO: hacky, fix it!
             constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
                 blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
  
             // TODO: hacky, fix it!
             // c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp is only used to get lengths
             constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp =
                 blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
  
             constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I0);
             constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I1);
             constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I2);
             constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I3);
             constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I4);
             constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I5);
             constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I6);
             constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I7);
  
             constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
                 GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
  
             auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
                 static_cast<FloatCShuffle*>(p_shared),
                 c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
  
             constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(
                 c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
                 make_tuple(
                     make_freeze_transform(I0),
                     make_unmerge_transform(make_tuple(
                         Number<CShuffleMXdlPerWavePerShuffle>{}, // M0 (MXdlPerWave) per shuffle
                         M1,                                      // M1 = MWave
                         M2,                                      // M2 * M3 * M4 = MPerXdl
                         M3,
                         M4)),
                     make_freeze_transform(I0),
                     make_unmerge_transform(make_tuple(
                         Number<CShuffleNXdlPerWavePerShuffle>{}, // N0 (NXdlPerWave) per shuffle
                         N1,                                      // N1 = NWave
                         N2))),                                   // N2 = NPerXdl
                 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));
  
             // shuffle: threadwise copy C from VGPR to LDS
             auto c_thread_copy_vgpr_to_lds =
                 ThreadwiseTensorSliceTransfer_v1r3<FloatGemmAcc,
                                                    FloatCShuffle,
                                                    decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
                                                    decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
                                                    ck::tensor_operation::element_wise::PassThrough,
                                                    Sequence<CShuffleMXdlPerWavePerShuffle,
                                                             CShuffleNXdlPerWavePerShuffle,
                                                             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{}};
  
             // shuffle: blockwise copy C from LDS to global
             auto c_shuffle_block_copy_lds_to_global = ThreadGroupTensorSliceTransfer_v6r1<
                 ThisThreadBlock,            // ThreadGroup
                 CElementwiseOperation,      // ElementwiseOperation,
                 CGlobalMemoryDataOperation, // DstInMemOp,
                 Sequence<1,
                          CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
                          1,
                          CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,
                 CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
                 Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
                 FloatCShuffle,        // typename SrcData,
                 FloatC,               // typename DstData,
                 decltype(c_shuffle_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,
                 CShuffleBlockTransferScalarPerVector_NPerBlock, // index_t ScalarPerVector,
                 true,  // bool ThreadTransferSrcResetCoordinateAfterRun,
                 false> // bool ThreadTransferDstResetCoordinateAfterRun>
                 {c_shuffle_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[I0], 0, block_work_idx[I1], 0),
                  c_element_op};
  
             // space filling curve for threadwise C in VGPR
             constexpr auto sfc_c_vgpr =
                 SpaceFillingCurve<Sequence<MXdlPerWave, NXdlPerWave, 1, 1, M2, 1, M4, 1>,
                                   Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
                                   Sequence<CShuffleMXdlPerWavePerShuffle,
                                            CShuffleNXdlPerWavePerShuffle,
                                            1,
                                            1,
                                            M2,
                                            1,
                                            M4,
                                            1>>{};
  
             // space filling curve for shuffled blockwise C in global mem
             constexpr auto sfc_c_global =
                 SpaceFillingCurve<Sequence<1, MPerBlock, 1, NPerBlock>,
                                   Sequence<0, 2, 1, 3>,
                                   Sequence<1,
                                            CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
                                            1,
                                            CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>>{};
  
             constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();
  
             static_assert(num_access == sfc_c_global.GetNumOfAccess(), "wrong!");
  
             static_for<0, num_access, 1>{}([&](auto access_id) {
                 // make sure it's safe to write to LDS
                 block_sync_lds();
  
                 // each thread write its data from VGPR to LDS
                 c_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,
                                               sfc_c_vgpr.GetIndexTupleOfNumber(access_id),
                                               c_thread_buf,
                                               c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
                                               c_shuffle_block_buf);
  
                 // make sure it's safe to read from LDS
                 block_sync_lds();
  
                 // each block copy its data from LDS to global
                 c_shuffle_block_copy_lds_to_global.Run(
                     c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
                     c_shuffle_block_buf,
                     c_grid_desc_mblock_mperblock_nblock_nperblock,
                     c_grid_buf);
  
                 if constexpr(access_id < num_access - 1)
                 {
                     constexpr auto c_global_step = sfc_c_global.GetForwardStep(access_id);
  
                     // move on C
                     c_shuffle_block_copy_lds_to_global.MoveDstSliceWindow(
                         c_grid_desc_mblock_mperblock_nblock_nperblock, c_global_step);
                 }
             });
         }
     }
 };
  
 } // namespace ck