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 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
  
 #pragma once
  
 #include "ck/utility/common_header.hpp"
 #include "ck/utility/env.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"
 #include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
  
 namespace ck {
  
 template <typename GridwiseGemm,
           bool HasMainKBlockLoop,
           InMemoryDataOperationEnum CGlobalMemoryDataOperation,
           typename Block2CTileMap,
           typename AElementwiseOperation,
           typename BElementwiseOperation,
           typename CElementwiseOperation>
 __global__ void
 #if CK_USE_LAUNCH_BOUNDS
 __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
 #endif
     kernel_gemm_xdlops_v2r4r2_simplified(typename GridwiseGemm::Argument karg,
                                          const Block2CTileMap& b2c_map,
                                          const AElementwiseOperation a_element_op,
                                          const BElementwiseOperation b_element_op,
                                          const CElementwiseOperation c_element_op)
 {
 #if(defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx94__))
     constexpr index_t shared_size = GridwiseGemm::GetSharedMemoryNumberOfByte();
  
     __shared__ uint8_t p_shared[shared_size];
  
     GridwiseGemm::template Run<HasMainKBlockLoop, CGlobalMemoryDataOperation>(
         karg, static_cast<void*>(p_shared), b2c_map, a_element_op, b_element_op, c_element_op);
 #else
     ignore = karg;
     ignore = b2c_map;
     ignore = a_element_op;
     ignore = b_element_op;
     ignore = c_element_op;
 #endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
 }
  
 template <index_t BlockSize,
           typename FloatA,
           typename FloatB,
           typename FloatAcc,
           typename FloatC,
           typename ALayout,
           typename BLayout,
           typename CLayout,
           typename AElementwiseOperation,
           typename BElementwiseOperation,
           typename CElementwiseOperation,
           tensor_operation::device::GemmSpecialization GemmSpec,
           index_t NumGemmKPrefetchStage,
           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,
           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 CShuffleMRepeatPerShuffle,
           index_t CShuffleNRepeatPerShuffle,
           index_t CBlockTransferScalarPerVector_NWaveNPerXDL,
           typename CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
           LoopScheduler LoopSched     = make_default_loop_scheduler(),
           PipelineVersion PipelineVer = PipelineVersion::v1,
           typename ComputeTypeA       = FloatC,
           typename ComputeTypeB       = ComputeTypeA,
           typename LDSTypeA           = ComputeTypeA,
           typename LDSTypeB           = ComputeTypeB>
 struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
 {
     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>{};
     static constexpr auto M01 = 1;
     static constexpr auto N01 = 1;
  
     static constexpr auto gemm_padder =
         tensor_operation::device::GemmPadder<GemmSpec, index_t, index_t, index_t>{
             MPerBlock, NPerBlock, K1* K0PerBlock};
  
     using ThisThreadBlock = ThisThreadBlock<BlockSize>;
  
     using GridwiseGemmPipe = remove_cvref_t<
         decltype(GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage, LoopSched>())>;
  
     struct Argument : public ck::tensor_operation::device::BaseArgument
     {
         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;
         index_t MPadded;
         index_t NPadded;
         index_t KPadded;
         index_t K0Padded;
         index_t k_batch;
  
         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_,
                  index_t MPadded_,
                  index_t NPadded_,
                  index_t KPadded_,
                  index_t K0Padded_,
                  index_t k_batch_)
             : p_a_grid(p_a_grid_),
               p_b_grid(p_b_grid_),
               p_c_grid(p_c_grid_),
               M(M_),
               N(N_),
               K(K_),
               StrideA(StrideA_),
               StrideB(StrideB_),
               StrideC(StrideC_),
               MPadded(MPadded_),
               NPadded(NPadded_),
               KPadded(KPadded_),
               K0Padded(K0Padded_),
               k_batch(k_batch_)
         {
         }
  
         void Print() const
         {
             std::cout << "arg {" << "M:" << M << ", " << "N:" << N << ", " << "K:" << K << ", "
                       << "SA:" << StrideA << ", " << "SB:" << StrideB << ", " << "SC:" << StrideC
                       << ", " << "MP:" << MPadded << ", " << "NP:" << NPadded << ", "
                       << "KP:" << KPadded << ", " << "K0Padded:" << K0Padded << ", "
                       << "KB:" << k_batch << "}" << std::endl;
         }
     };
  
     __host__ __device__ static auto CalculateGridSize(const Argument& karg)
     {
         return std::make_tuple(math::integer_divide_ceil(karg.N, NPerBlock),
                                math::integer_divide_ceil(karg.M, MPerBlock),
                                karg.k_batch);
     }
  
     // prefer this to be called on host
     __host__ __device__ static auto CalculateMPadded(index_t M)
     {
         return math::integer_least_multiple(M, MPerBlock);
     }
  
     __host__ __device__ static auto CalculateNPadded(index_t N)
     {
         return math::integer_least_multiple(N, NPerBlock);
     }
  
     __host__ __device__ static auto CalculateK0Padded(index_t K, index_t K_Batch = 1)
     {
         // k_batch * k0 * k0_per_block * k1
         auto K_t = K_Batch * K0PerBlock * K1;
         return (K + K_t - 1) / K_t * K0PerBlock;
     }
  
     __host__ __device__ static auto CalculateKPadded(index_t K, index_t K_Batch = 1)
     {
         auto K0Padded = CalculateK0Padded(K, K_Batch);
         return K_Batch * K0Padded * K1;
     }
  
     __host__ __device__ static auto MakeAGridDescriptor_KBatch_K0_M_K1(index_t M,
                                                                        index_t MPad,
                                                                        index_t K,
                                                                        index_t StrideA,
                                                                        index_t KBatch,
                                                                        index_t K0Padded,
                                                                        index_t KPad)
     {
         const auto a_grid_desc_m_k = [&]() {
             if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
             {
                 return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));
             }
             else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ALayout>::value)
             {
                 return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, StrideA));
             }
         }();
  
         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)
         {
  
             const auto a_grid_desc_m_kpad = transform_tensor_descriptor(
                 a_grid_desc_m_k,
                 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 PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
             return transform_tensor_descriptor(
                 a_grid_desc_m_kpad,
                 make_tuple(make_unmerge_transform(make_tuple(KBatch, K0Padded, K1)),
                            make_right_pad_transform(M, MPad - M)),
                 make_tuple(Sequence<1>{}, Sequence<0>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
         }
         else if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MPadding ||
                           GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding)
         {
             // const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
             return transform_tensor_descriptor(
                 a_grid_desc_m_k,
                 make_tuple(make_unmerge_transform(make_tuple(KBatch, K0Padded, K1)),
                            make_right_pad_transform(M, MPad - M)),
                 make_tuple(Sequence<1>{}, Sequence<0>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
         }
         else if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::KPadding)
         {
             const auto a_grid_desc_m_kpad = transform_tensor_descriptor(
                 a_grid_desc_m_k,
                 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>{}));
  
             return transform_tensor_descriptor(
                 a_grid_desc_m_kpad,
                 make_tuple(make_unmerge_transform(make_tuple(KBatch, K0Padded, K1)),
                            make_pass_through_transform(M)),
                 make_tuple(Sequence<1>{}, Sequence<0>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
         }
         else
         {
             return transform_tensor_descriptor(
                 a_grid_desc_m_k,
                 make_tuple(make_unmerge_transform(make_tuple(KBatch, K0Padded, K1)),
                            make_pass_through_transform(M)),
                 make_tuple(Sequence<1>{}, Sequence<0>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
         }
     }
  
     __host__ __device__ static auto MakeBGridDescriptor_KBatch_K0_N_K1(index_t K,
                                                                        index_t NPad,
                                                                        index_t N,
                                                                        index_t StrideB,
                                                                        index_t KBatch,
                                                                        index_t K0Padded,
                                                                        index_t KPad)
     {
         const auto b_grid_desc_k_n = [&]() {
             if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
             {
                 return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(StrideB, I1));
             }
             else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
             {
                 return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(I1, StrideB));
             }
         }();
  
         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)
         {
  
             const auto b_grid_desc_kpad_n = transform_tensor_descriptor(
                 b_grid_desc_k_n,
                 make_tuple(make_right_pad_transform(K, KPad - K), make_pass_through_transform(N)),
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0>{}, Sequence<1>{}));
  
             // const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
             return transform_tensor_descriptor(
                 b_grid_desc_kpad_n,
                 make_tuple(make_unmerge_transform(make_tuple(KBatch, K0Padded, K1)),
                            make_right_pad_transform(N, NPad - N)),
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
         }
         else if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::NPadding ||
                           GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding)
         {
             // const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
             return transform_tensor_descriptor(
                 b_grid_desc_k_n,
                 make_tuple(make_unmerge_transform(make_tuple(KBatch, K0Padded, K1)),
                            make_right_pad_transform(N, NPad - N)),
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
         }
         else if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::KPadding)
         {
             const auto b_grid_desc_kpad_n = transform_tensor_descriptor(
                 b_grid_desc_k_n,
                 make_tuple(make_right_pad_transform(K, KPad - K), make_pass_through_transform(N)),
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0>{}, Sequence<1>{}));
  
             return transform_tensor_descriptor(
                 b_grid_desc_kpad_n,
                 make_tuple(make_unmerge_transform(make_tuple(KBatch, K0Padded, K1)),
                            make_pass_through_transform(N)),
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
         }
         else
         {
             return transform_tensor_descriptor(
                 b_grid_desc_k_n,
                 make_tuple(make_unmerge_transform(make_tuple(KBatch, K0Padded, K1)),
                            make_pass_through_transform(N)),
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
         }
     }
  
     __host__ __device__ static auto MakeCGridDescriptor_M_N(index_t M, index_t N, index_t StrideC)
     {
         const auto c_grid_desc_m_n = [&]() {
             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));
             }
         }();
  
         return gemm_padder.PadCDescriptor_M_N(c_grid_desc_m_n);
     }
  
     __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_k0_m_k1_block_desc = [&]() {
             if constexpr(ABlockLdsExtraM)
             {
                 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);
             }
         }();
  
         // B matrix in LDS memory, dst of blockwise copy
         constexpr auto b_k0_n_k1_block_desc = [&]() {
             if constexpr(BBlockLdsExtraN)
             {
                 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);
             }
         }();
  
         // 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);
  
         constexpr auto b_block_space_size =
             math::integer_least_multiple(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 * sizeof(LDSTypeA) +
                              b_block_space_size * sizeof(LDSTypeB),
                          c_block_size * sizeof(FloatC));
     }
  
     __host__ __device__ static constexpr bool CheckValidity(const Argument& karg)
     {
         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(!(karg.M % MPerBlock == 0))
             {
                 if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
                 {
                     std::cout << "Arg M value is not a multiple of MPerBlock! M: " << karg.M << " "
                               << __FILE__ << ":" << __LINE__ << ", in function: " << __func__
                               << std::endl;
                 }
                 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(!(karg.N % NPerBlock == 0))
             {
                 if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
                 {
                     std::cout << "Arg N value is not a multiple of NPerBlock! N: " << karg.N << " "
                               << __FILE__ << ":" << __LINE__ << ", in function: " << __func__
                               << std::endl;
                 }
                 return false;
             }
         }
  
         if constexpr(!(GemmSpec == tensor_operation::device::GemmSpecialization::KPadding ||
                        GemmSpec == tensor_operation::device::GemmSpecialization::MKPadding ||
                        GemmSpec == tensor_operation::device::GemmSpecialization::NKPadding ||
                        GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding))
         {
  
             auto K_t = karg.k_batch * K0PerBlock * K1;
             if(!(karg.K % K_t == 0))
             {
                 if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
                 {
                     std::cout << "Arg K value is not a multiple of K_Batch * K0PerBlock * K1! K: "
                               << karg.K << " " << __FILE__ << ":" << __LINE__
                               << ", in function: " << __func__ << std::endl;
                 }
                 return false;
             }
         }
  
         if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
         {
             if(karg.K % ABlockTransferSrcScalarPerVector != 0)
             {
                 if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
                 {
                     std::cout << "Arg K (" << karg.K
                               << ") value is not a multiple of ABlockTransferSrcScalarPerVector ("
                               << ABlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"
                               << __LINE__ << ", in function: " << __func__ << std::endl;
                 }
                 return false;
             }
         }
         else
         {
             if(karg.M % ABlockTransferSrcScalarPerVector != 0)
             {
                 if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
                 {
                     std::cout << "Arg M (" << karg.M
                               << ") value is not a multiple of ABlockTransferSrcScalarPerVector ("
                               << ABlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"
                               << __LINE__ << ", in function: " << __func__ << std::endl;
                 }
                 return false;
             }
         }
  
         if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
         {
             if(karg.N % BBlockTransferSrcScalarPerVector != 0)
             {
                 if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
                 {
                     std::cout << "Arg N (" << karg.N
                               << ") value is not a multiple of BBlockTransferSrcScalarPerVector ("
                               << BBlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"
                               << __LINE__ << ", in function: " << __func__ << std::endl;
                 }
                 return false;
             }
         }
         else
         {
             if(karg.K % BBlockTransferSrcScalarPerVector != 0)
             {
                 if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
                 {
                     std::cout << "Arg K (" << karg.K
                               << ") value is not a multiple of BBlockTransferSrcScalarPerVector ("
                               << BBlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"
                               << __LINE__ << ", in function: " << __func__ << std::endl;
                 }
                 return false;
             }
         }
  
         if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
         {
             if(karg.N % CBlockTransferScalarPerVector_NWaveNPerXDL != 0)
             {
                 if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
                 {
                     std::cout << "Arg N (" << karg.N
                               << ") value is not a multiple of "
                                  "CBlockTransferScalarPerVector_NWaveNPerXDL ("
                               << CBlockTransferScalarPerVector_NWaveNPerXDL << " )! " << __FILE__
                               << ":" << __LINE__ << ", in function: " << __func__ << std::endl;
                 }
                 return false;
             }
         }
         else
         {
             if(karg.M % CBlockTransferScalarPerVector_NWaveNPerXDL != 0)
             {
                 if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
                 {
                     std::cout << "Arg M (" << karg.M
                               << ") value is not a multiple of "
                                  "CBlockTransferScalarPerVector_NWaveNPerXDL ("
                               << CBlockTransferScalarPerVector_NWaveNPerXDL << " )! " << __FILE__
                               << ":" << __LINE__ << ", in function: " << __func__ << std::endl;
                 }
                 return false;
             }
         }
  
         const auto num_k_loop = karg.K0Padded / K0PerBlock;
         if(!GridwiseGemmPipe::IsSupported(num_k_loop))
         {
             if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
             {
                 std::cout << "The number of k loops (" << num_k_loop
                           << ") value is not supported by GridwiseGemm Pipeline."
                           << " K0Padded: " << karg.K0Padded << ", K0PerBlock: " << K0PerBlock << " "
                           << __FILE__ << ":" << __LINE__ << ", in function: " << __func__
                           << std::endl;
             }
             return false;
         }
  
         return true;
     }
  
     __host__ __device__ static auto GetKPad(index_t K, index_t KBatch)
     {
         const index_t K0Padded =
             math::integer_divide_ceil(K, K1 * K0PerBlock * KBatch) * K0PerBlock;
         const index_t KPad = KBatch * K0Padded * K1;
         return KPad;
     }
  
     __host__ __device__ static constexpr bool CalculateHasMainK0BlockLoop(index_t K0Padded)
     {
         const index_t num_loop = K0Padded / K0PerBlock;
         return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
     }
  
     template <typename CGridDesc>
     __host__ __device__ static constexpr auto
     MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(const CGridDesc& 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
     template <typename CGridDesc>
     __host__ __device__ static constexpr auto MakeCBlockClusterAdaptor(
         const CGridDesc& c_m_n_grid_desc, index_t /* M01 */, index_t /* N01 */, index_t KBatch)
     {
         return BlockToCTileMap_KSplit_M00_N0_M01Adapt<MPerBlock, NPerBlock, CGridDesc>(
             c_m_n_grid_desc, 8, 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>{}));
     }
  
     // return block_id to C matrix tile idx (m0, n0, k_split) mapping
     __host__ __device__ static constexpr auto MakeDefaultBlock2CTileMap()
     {
         return BlockToCTileMap_3DGrid_KSplit<MPerBlock, NPerBlock>();
     }
  
     using CGridDesc_M_N         = remove_cvref_t<decltype(MakeCGridDescriptor_M_N(1, 1, 1))>;
     using DefaultBlock2CTileMap = remove_cvref_t<decltype(MakeDefaultBlock2CTileMap())>;
  
     template <bool HasMainKBlockLoop,
               InMemoryDataOperationEnum CGlobalMemoryDataOperation,
               typename Block2CTileMap>
     __device__ static void Run(const Argument& karg,
                                void* __restrict__ p_shared_block,
                                const Block2CTileMap& block_2_ctile_map,
                                const AElementwiseOperation a_element_op = AElementwiseOperation{},
                                const BElementwiseOperation b_element_op = BElementwiseOperation{},
                                const CElementwiseOperation c_element_op = CElementwiseOperation{})
     {
         const FloatA* p_a_grid           = karg.p_a_grid;
         const FloatB* p_b_grid           = karg.p_b_grid;
         FloatC* p_c_grid                 = karg.p_c_grid;
         const auto a_b_k0_m_k1_grid_desc = MakeAGridDescriptor_KBatch_K0_M_K1(
             karg.M, karg.MPadded, karg.K, karg.StrideA, karg.k_batch, karg.K0Padded, karg.KPadded);
         const auto b_b_k0_n_k1_grid_desc = MakeBGridDescriptor_KBatch_K0_N_K1(
             karg.K, karg.NPadded, karg.N, karg.StrideB, karg.k_batch, karg.K0Padded, karg.KPadded);
         const auto c_grid_desc_m_n = MakeCGridDescriptor_M_N(karg.M, karg.N, karg.StrideC);
  
         const auto c_grid_desc_mblock_mperblock_nblock_nperblock =
             MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(c_grid_desc_m_n);
  
         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());
  
         // divide block work by [KBatch, M, 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;
         }
  
         const index_t block_m_id = __builtin_amdgcn_readfirstlane(block_work_idx[I1]);
         const index_t block_n_id = __builtin_amdgcn_readfirstlane(block_work_idx[I2]);
         const index_t k_batch_id = __builtin_amdgcn_readfirstlane(block_work_idx[I0]);
  
         // 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_m_id * MPerBlock);
  
         const index_t n_block_data_idx_on_grid =
             __builtin_amdgcn_readfirstlane(block_n_id * 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 = [&]() {
             if constexpr(ABlockLdsExtraM)
             {
                 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);
             }
         }();
  
         constexpr auto a_b_k0_m_k1_block_desc = [&]() {
             if constexpr(ABlockLdsExtraM)
             {
                 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);
             }
         }();
         // B matrix in LDS memory, dst of blockwise copy
         constexpr auto b_k0_n_k1_block_desc = [&]() {
             if constexpr(BBlockLdsExtraN)
             {
                 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);
             }
         }();
  
         constexpr auto b_b_k0_n_k1_block_desc = [&]() {
             if constexpr(BBlockLdsExtraN)
             {
                 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);
             }
         }();
         // 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,
                                                 FloatA,
                                                 LDSTypeA,
                                                 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,
                                                 FloatB,
                                                 LDSTypeB,
                                                 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
  
         auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
             BlockSize,
             LDSTypeA,
             LDSTypeB,
             FloatAcc,
             decltype(a_k0_m_k1_block_desc),
             decltype(b_k0_n_k1_block_desc),
             MPerXDL,
             NPerXDL,
             MRepeat,
             NRepeat,
             K1,
             LoopSched,
             ComputeTypeA,
             ComputeTypeB>();
  
         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);
  
         auto p_a_block = reinterpret_cast<LDSTypeA*>(p_shared_block);
         auto p_b_block = reinterpret_cast<LDSTypeB*>(p_a_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 num_k_block_main_loop = __builtin_amdgcn_readfirstlane(
             (a_b_k0_m_k1_grid_desc.GetLength(I1) * a_b_k0_m_k1_grid_desc.GetLength(I3)) /
             (K0PerBlock * K1));
  
         const auto gridwise_gemm_pipeline = GridwiseGemmPipe{};
  
         gridwise_gemm_pipeline.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,
                                                                num_k_block_main_loop);
  
         // 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();
  
             auto c_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
                 static_cast<FloatC*>(p_shared_block),
                 c_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
  
             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_m_id, 0, block_n_id, 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);
                 }
             });
         }
     }
  
     static std::string GetTypeString()
     {
         auto str = std::stringstream();
  
         // clang-format off
         str << "GemmXdlSplitKCShuffle_"
             << getGemmSpecializationString(GemmSpec) << "_"
             << std::string(ALayout::name)[0]
             << std::string(BLayout::name)[0]
             << std::string(CLayout::name)[0]
             << "_"
             << "B" << BlockSize << "_"
             << "Vec" << ABlockTransferSrcScalarPerVector << "x"
             << BBlockTransferSrcScalarPerVector << "x"
             << CBlockTransferScalarPerVector_NWaveNPerXDL << "_"
             << MPerBlock << "x"
             << NPerBlock << "x"
             << K0PerBlock << "x"
             << K1 ;
         // clang-format on
  
         return str.str();
     }
 };
  
 } // namespace ck