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 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
  
 #pragma once
  
 #include <iostream>
 #include <sstream>
 #include <typeinfo>
  
 #include "ck/utility/common_header.hpp"
 #include "ck/tensor_description/tensor_descriptor.hpp"
 #include "ck/tensor_description/tensor_descriptor_helper.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 #include "ck/tensor_operation/gpu/device/device_gemm_v2.hpp"
 #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
 #include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v3.hpp"
 #include "ck/host_utility/device_prop.hpp"
 #include "ck/host_utility/kernel_launch.hpp"
 #include "ck/host_utility/flush_cache.hpp"
  
 #include "ck/utility/reduction_enums.hpp"
 #include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
 #include "ck/tensor_operation/gpu/device/impl/device_reduce_threadwise_multi_d.hpp"
  
 namespace ck {
 namespace tensor_operation {
 namespace device {
  
 template <typename ALayout,
           typename BLayout,
           typename DsLayout,
           typename CLayout,
           typename ADataType,
           typename BDataType,
           typename DsDataType,
           typename CDataType,
           typename GemmAccDataType,
           typename CShuffleDataType,
           typename AElementwiseOperation,
           typename BElementwiseOperation,
           typename CElementwiseOperation,
           GemmSpecialization GemmSpec,
           index_t BlockSize,
           index_t MPerBlock,
           index_t NPerBlock,
           index_t KPerBlock,
           index_t AK1,
           index_t BK1,
           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 ABlockLdsExtraM,
           typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
           typename BBlockTransferThreadClusterArrangeOrder,
           typename BBlockTransferSrcAccessOrder,
           index_t BBlockTransferSrcVectorDim,
           index_t BBlockTransferSrcScalarPerVector,
           index_t BBlockTransferDstScalarPerVector_BK1,
           bool BBlockLdsExtraN,
           index_t CShuffleMXdlPerWavePerShuffle,
           index_t CShuffleNXdlPerWavePerShuffle,
           typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
           index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
           BlockGemmPipelineScheduler BlkGemmPipeSched = BlockGemmPipelineScheduler::Intrawave,
           BlockGemmPipelineVersion BlkGemmPipelineVer = BlockGemmPipelineVersion::v1,
           typename ReduceDataType                     = CDataType,
           typename ComputeTypeA                       = CDataType,
           typename ComputeTypeB                       = ComputeTypeA>
 struct DeviceGemm_Xdl_CShuffleV3R1 : public DeviceGemmV2R1<ALayout,
                                                            BLayout,
                                                            DsLayout,
                                                            CLayout,
                                                            ADataType,
                                                            BDataType,
                                                            DsDataType,
                                                            CDataType,
                                                            AElementwiseOperation,
                                                            BElementwiseOperation,
                                                            CElementwiseOperation>
 {
     GET_NXDL_PER_WAVE_IMPL
     static constexpr auto NXdlPerWave64 = GetNXdlPerWave<true>();
     static constexpr auto NXdlPerWave32 = GetNXdlPerWave<false>();
  
     static constexpr index_t NumDTensor = DsDataType::Size();
  
     using PassThrough = ck::tensor_operation::element_wise::PassThrough;
  
     // GridwiseGemm
     template <index_t NXdlPerWave_>
     using GridwiseGemmBase = GridwiseGemm_xdl_cshuffle_v3<
         ALayout,
         BLayout,
         CLayout,
         ADataType,
         BDataType,
         GemmAccDataType,
         CShuffleDataType,
         ReduceDataType,
         AElementwiseOperation,
         BElementwiseOperation,
         PassThrough,
         GemmSpec,
         BlockSize,
         MPerBlock,
         NPerBlock,
         KPerBlock,
         AK1,
         BK1,
         MPerXDL,
         NPerXDL,
         MXdlPerWave,
         NXdlPerWave_,
         ABlockTransferThreadClusterLengths_AK0_M_AK1,
         ABlockTransferThreadClusterArrangeOrder,
         ABlockTransferSrcAccessOrder,
         ABlockTransferSrcVectorDim,
         ABlockTransferSrcScalarPerVector,
         ABlockTransferDstScalarPerVector_AK1,
         false,
         ABlockLdsExtraM,
         BBlockTransferThreadClusterLengths_BK0_N_BK1,
         BBlockTransferThreadClusterArrangeOrder,
         BBlockTransferSrcAccessOrder,
         BBlockTransferSrcVectorDim,
         BBlockTransferSrcScalarPerVector,
         BBlockTransferDstScalarPerVector_BK1,
         false,
         BBlockLdsExtraN,
         CShuffleMXdlPerWavePerShuffle,
         CShuffleNXdlPerWavePerShuffle,
         CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
         CShuffleBlockTransferScalarPerVector_NPerBlock,
         BlkGemmPipeSched,
         BlkGemmPipelineVer,
         ComputeTypeA,
         ComputeTypeB>;
     using GridwiseGemm64 = GridwiseGemmBase<math::max(NXdlPerWave64, 1)>;
     using GridwiseGemm32 = GridwiseGemmBase<NXdlPerWave32>;
  
     struct Argument : public GridwiseGemm64::Argument
     {
         Argument(const ADataType* p_a_grid_,
                  const BDataType* p_b_grid_,
                  const std::array<const void*, NumDTensor> p_ds_,
                  CDataType* p_c_grid_,
                  index_t M_,
                  index_t N_,
                  index_t K_,
                  index_t StrideA_,
                  index_t StrideB_,
                  std::array<ck::index_t, NumDTensor> StrideDs_,
                  index_t StrideC_,
                  index_t k_batch_)
             : GridwiseGemm64::Argument(p_a_grid_,
                                        p_b_grid_,
                                        reinterpret_cast<ReduceDataType*>(p_c_grid_),
                                        M_,
                                        N_,
                                        K_,
                                        StrideA_,
                                        StrideB_,
                                        StrideC_,
                                        k_batch_,
                                        true),
               p_ds(p_ds_),
               StrideDs(StrideDs_)
         {
         }
  
         const std::array<const void*, NumDTensor> p_ds;
         std::array<ck::index_t, NumDTensor> StrideDs;
     };
  
     using ReduceAdd               = ck::reduce::Add;
     using OutElementwiseOperation = CElementwiseOperation;
  
     static constexpr auto DsVectorLengthSequence = generate_sequence_v2(
         [](auto i) {
             using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
             if constexpr(std::is_same<CLayout, DLayout>::value)
                 return Number<CShuffleBlockTransferScalarPerVector_NPerBlock>{};
             else
                 return Number<1>{};
         },
         Number<NumDTensor>{});
  
     using DeviceReduceInstance = DeviceReduceThreadWiseMultiD<
         ReduceDataType,  // InDataType,
         DsDataType,      // DsDatatype
         GemmAccDataType, // AccDataType,
         CDataType,       // OutDataType,
         3,               // Rank
         1,               // NumReduceDim
         ReduceAdd,
         PassThrough,
         OutElementwiseOperation,
         256,                                            // BlockSize_,
         CShuffleBlockTransferScalarPerVector_NPerBlock, // MThreadSliceSize_,
         1,                                              // KThreadSliceSize_,
         0,                                              // InSrcVectorDim_,
         CShuffleBlockTransferScalarPerVector_NPerBlock, // InSrcVectorSize_,
         CShuffleBlockTransferScalarPerVector_NPerBlock, // OutDstVectorSize_
         decltype(DsVectorLengthSequence)>;
  
     // Invoker
     struct Invoker : public BaseInvoker
     {
         float RunReduce(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
         {
             static constexpr index_t NumInDim  = 3;
             static constexpr index_t NumOutDim = 2;
  
             std::array<ck::index_t, NumInDim> in_lengths   = {arg.KBatch, arg.M, arg.N};
             std::array<ck::index_t, NumOutDim> out_lengths = {arg.M, arg.N};
  
             std::array<ck::index_t, NumInDim> in_strides;
             std::array<ck::index_t, NumOutDim> out_strides;
             if constexpr(std::is_same<CLayout, ck::tensor_layout::gemm::RowMajor>::value)
             {
                 in_strides  = {arg.M * arg.N, arg.N, 1};
                 out_strides = {arg.N, 1};
             }
             else
             {
                 in_strides  = {arg.M * arg.N, 1, arg.M};
                 out_strides = {1, arg.M};
             }
  
             std::array<int, 1> reduce_dims{0};
  
             std::array<std::array<index_t, NumOutDim>, NumDTensor> DsLengths;
             std::array<std::array<index_t, NumOutDim>, NumDTensor> DsStrides;
  
             static_for<0, NumDTensor, 1>{}([&](auto i) {
                 DsLengths[i] = out_lengths;
  
                 using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
                 if constexpr(std::is_same<DLayout, ck::tensor_layout::gemm::RowMajor>::value)
                 {
                     DsStrides[i] = {arg.StrideDs[i], 1};
                 }
                 else
                 {
                     DsStrides[i] = {1, arg.StrideDs[i]};
                 }
             });
  
             auto reduce = DeviceReduceInstance{};
  
             auto argument_ptr = reduce.MakeArgumentPointer(in_lengths,
                                                            in_strides,
                                                            DsLengths,
                                                            DsStrides,
                                                            out_lengths,
                                                            out_strides,
                                                            reduce_dims,
                                                            arg.p_workspace_,
                                                            arg.p_ds,
                                                            arg.p_c_grid,
                                                            PassThrough{},
                                                            OutElementwiseOperation{});
  
             auto invoker_ptr = reduce.MakeInvokerPointer();
  
             float ave_time = 0;
  
             if(reduce.IsSupportedArgument(argument_ptr.get()))
             {
                 ave_time = invoker_ptr->Run(argument_ptr.get(), stream_config);
             }
             else
             {
                 throw std::runtime_error(
                     "The runtime parameters seems not supported by the device instance, exiting!");
             }
  
             return ave_time;
         }
  
         template <typename GridwiseGemm>
         float RunImp(const Argument& arg_, const StreamConfig& stream_config = StreamConfig{})
         {
             auto arg = *reinterpret_cast<const typename GridwiseGemm::Argument*>(&arg_);
  
             if(!(!(arg.IsReduceAdd() || NumDTensor > 0) &&
                  std::is_same<CDataType, ReduceDataType>::value))
             {
                 if(arg.p_workspace_ == nullptr)
                 {
                     throw std::runtime_error("using reduce , but empty workspace!");
                 }
  
                 arg.p_c_grid = reinterpret_cast<ReduceDataType*>(arg.p_workspace_);
             }
  
             if(stream_config.log_level_ > 0)
             {
                 arg.Print();
             }
  
             if(!GridwiseGemm::CheckValidity(arg))
             {
                 throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
             }
  
             index_t gdx, gdy, gdz;
             std::tie(gdx, gdy, gdz) = GridwiseGemm::CalculateGridSize(arg.M, arg.N, arg.KBatch);
  
             float ave_time = 0;
  
             index_t k_grain = arg.KBatch * KPerBlock;
             index_t K_split = (arg.K + k_grain - 1) / k_grain * KPerBlock;
  
             const bool has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K_split);
  
             const auto Run = [&](const auto& kernel) {
                 if(stream_config.flush_cache)
                 {
                     ck::utility::RotatingMemWrapper<typename GridwiseGemm::Argument> rotating_mem(
                         arg,
                         stream_config.rotating_count,
                         arg.M * arg.K * sizeof(ADataType),
                         arg.K * arg.N * sizeof(BDataType));
                     rotating_mem.Print();
  
                     auto run_flush_cache = [&]() {
                         // flush icache
                         ck::utility::flush_icache();
                         // rotating mem
                         rotating_mem.Next();
                     };
  
                     ave_time = ck::utility::launch_and_time_kernel_with_preprocess<false>(
                         stream_config,
                         run_flush_cache,
                         kernel,
                         dim3(gdx, gdy, gdz),
                         dim3(BlockSize),
                         0,
                         arg);
                 }
                 else
                 {
                     ave_time = launch_and_time_kernel(
                         stream_config, kernel, dim3(gdx, gdy, gdz), dim3(BlockSize), 0, arg);
                 }
             };
  
             constexpr index_t minimum_occupancy =
                 BlkGemmPipeSched == BlockGemmPipelineScheduler::Intrawave ? 1 : 2;
  
             if(has_main_k_block_loop)
             {
                 // Tail number always full
                 if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v1 ||
                              BlkGemmPipelineVer == BlockGemmPipelineVersion::v3)
                 {
  
                     const auto kernel = kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
                                                                     true,
                                                                     InMemoryDataOperationEnum::Set,
                                                                     minimum_occupancy>;
                     Run(kernel);
                 }
                 // Tail number could be One to Seven
                 else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v2)
                 {
                     if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::One)
                     {
                         const auto kernel =
                             kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
                                                         true,
                                                         InMemoryDataOperationEnum::Set,
                                                         minimum_occupancy,
                                                         TailNumber::One>;
                         Run(kernel);
                     }
                     else if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Full)
                     {
                         const auto kernel =
                             kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
                                                         true,
                                                         InMemoryDataOperationEnum::Set,
                                                         minimum_occupancy,
                                                         TailNumber::Full>;
                         Run(kernel);
                     }
  
                     if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 2)
                     {
                         if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Two)
                         {
                             const auto kernel =
                                 kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
                                                             true,
                                                             InMemoryDataOperationEnum::Set,
                                                             minimum_occupancy,
                                                             TailNumber::Two>;
                             Run(kernel);
                         }
                     }
  
                     if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 3)
                     {
                         if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Three)
                         {
                             const auto kernel =
                                 kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
                                                             true,
                                                             InMemoryDataOperationEnum::Set,
                                                             minimum_occupancy,
                                                             TailNumber::Three>;
                             Run(kernel);
                         }
                     }
  
                     if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 4)
                     {
                         if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Four)
                         {
                             const auto kernel =
                                 kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
                                                             true,
                                                             InMemoryDataOperationEnum::Set,
                                                             minimum_occupancy,
                                                             TailNumber::Four>;
                             Run(kernel);
                         }
                     }
  
                     if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 5)
                     {
                         if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Five)
                         {
                             const auto kernel =
                                 kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
                                                             true,
                                                             InMemoryDataOperationEnum::Set,
                                                             minimum_occupancy,
                                                             TailNumber::Five>;
                             Run(kernel);
                         }
                     }
  
                     if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 6)
                     {
                         if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Six)
                         {
                             const auto kernel =
                                 kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
                                                             true,
                                                             InMemoryDataOperationEnum::Set,
                                                             minimum_occupancy,
                                                             TailNumber::Six>;
                             Run(kernel);
                         }
                     }
  
                     if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 7)
                     {
                         if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Seven)
                         {
                             const auto kernel =
                                 kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
                                                             true,
                                                             InMemoryDataOperationEnum::Set,
                                                             minimum_occupancy,
                                                             TailNumber::Seven>;
                             Run(kernel);
                         }
                     }
                 }
                 // Tail number could be Odd or Even
                 else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v4)
                 {
  
                     if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
                     {
                         const auto kernel =
                             kernel_gemm_xdl_cshuffle_v3_2lds<GridwiseGemm,
                                                              true,
                                                              InMemoryDataOperationEnum::Set,
                                                              minimum_occupancy,
                                                              TailNumber::Odd>;
                         Run(kernel);
                     }
                     else
                     {
                         const auto kernel =
                             kernel_gemm_xdl_cshuffle_v3_2lds<GridwiseGemm,
                                                              true,
                                                              InMemoryDataOperationEnum::Set,
                                                              minimum_occupancy,
                                                              TailNumber::Even>;
                         Run(kernel);
                     }
                 }
                 else
                 {
                     if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
                     {
                         const auto kernel =
                             kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
                                                         true,
                                                         InMemoryDataOperationEnum::Set,
                                                         minimum_occupancy,
                                                         TailNumber::Odd>;
                         Run(kernel);
                     }
                     else
                     {
                         const auto kernel =
                             kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
                                                         true,
                                                         InMemoryDataOperationEnum::Set,
                                                         minimum_occupancy,
                                                         TailNumber::Even>;
                         Run(kernel);
                     }
                 }
             }
             else
             {
                 // Tail number always 1
                 if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v1)
                 {
  
                     const auto kernel = kernel_gemm_xdl_cshuffle_v3<GridwiseGemm,
                                                                     false,
                                                                     InMemoryDataOperationEnum::Set,
                                                                     minimum_occupancy>;
                     Run(kernel);
                 }
             }
  
             if(!(!(arg.IsReduceAdd() || NumDTensor > 0) &&
                  std::is_same<CDataType, ReduceDataType>::value))
             {
                 // reduce c data
                 ave_time += RunReduce(arg_, stream_config);
             }
             return ave_time;
         }
  
         INVOKER_RUN_IMPL
  
         // polymorphic
         float Run(const BaseArgument* p_arg,
                   const StreamConfig& stream_config = StreamConfig{}) override
         {
             return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
         }
     };
  
     static constexpr bool IsValidCompilationParameter()
     {
         // TODO: properly implement this check
         return true;
     }
  
     static bool IsSupportedArgument(const Argument& arg)
     {
         if(!ck::is_xdl_wmma_supported<ComputeTypeA, ComputeTypeB, MPerXDL, NPerXDL>())
         {
             return false;
         }
         if((arg.K % AK1 != 0 || arg.K % BK1 != 0) && !(GemmSpec == GemmSpecialization::MKPadding ||
                                                        GemmSpec == GemmSpecialization::NKPadding ||
                                                        GemmSpec == GemmSpecialization::MNKPadding ||
                                                        GemmSpec == GemmSpecialization::KPadding))
         {
             return false;
         }
  
         if(get_warp_size() == 64)
         {
             if constexpr(NXdlPerWave64 > 0)
             {
                 return GridwiseGemm64::CheckValidity(arg);
             }
         }
         else
         {
             if constexpr(NXdlPerWave32 > 0)
             {
                 return GridwiseGemm32::CheckValidity(
                     reinterpret_cast<const typename GridwiseGemm32::Argument&>(arg));
             }
         }
         return false;
     }
  
     // polymorphic
     bool IsSupportedArgument(const BaseArgument* p_arg) override
     {
         return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
     }
  
     static auto MakeArgument(const ADataType* p_a,
                              const BDataType* p_b,
                              const std::array<const void*, NumDTensor> p_ds,
                              CDataType* p_c,
                              index_t M,
                              index_t N,
                              index_t K,
                              index_t StrideA,
                              index_t StrideB,
                              std::array<ck::index_t, NumDTensor> StrideDs,
                              index_t StrideC,
                              index_t KBatch,
                              AElementwiseOperation,
                              BElementwiseOperation,
                              CElementwiseOperation)
     {
         return Argument{p_a, p_b, p_ds, p_c, M, N, K, StrideA, StrideB, StrideDs, StrideC, KBatch};
     }
  
     static auto MakeInvoker() { return Invoker{}; }
  
     // polymorphic
     std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
                                                       const void* p_b,
                                                       std::array<const void*, NumDTensor> p_ds,
                                                       void* p_c,
                                                       index_t M,
                                                       index_t N,
                                                       index_t K,
                                                       index_t StrideA,
                                                       index_t StrideB,
                                                       std::array<ck::index_t, NumDTensor> StrideDs,
                                                       index_t StrideC,
                                                       index_t KBatch,
                                                       AElementwiseOperation,
                                                       BElementwiseOperation,
                                                       CElementwiseOperation) override
     {
         return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
                                           static_cast<const BDataType*>(p_b),
                                           p_ds,
                                           static_cast<CDataType*>(p_c),
                                           M,
                                           N,
                                           K,
                                           StrideA,
                                           StrideB,
                                           StrideDs,
                                           StrideC,
                                           KBatch);
     }
  
     // polymorphic
     std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
     {
         return std::make_unique<Invoker>(Invoker{});
     }
  
     // polymorphic
     std::string GetTypeString() const override
     {
         auto str = std::stringstream();
  
         std::map<BlockGemmPipelineScheduler, std::string> BlkGemmPipelineSchedulerToString{
             {BlockGemmPipelineScheduler::Intrawave, "Intrawave"},
             {BlockGemmPipelineScheduler::Interwave, "Interwave"}};
  
         std::map<BlockGemmPipelineVersion, std::string> BlkGemmPipelineVersionToString{
             {BlockGemmPipelineVersion::v1, "v1"},
             {BlockGemmPipelineVersion::v2, "v2"},
             {BlockGemmPipelineVersion::v3, "v3"},
             {BlockGemmPipelineVersion::v4, "v4"},
             {BlockGemmPipelineVersion::v5, "v5"}};
  
         // clang-format off
         str << "DeviceGemmXdlUniversalReduce"
             << "<"
             << getGemmSpecializationString(GemmSpec) << ", "
             << std::string(ALayout::name)[0]
             << std::string(BLayout::name)[0]
             << std::string(CLayout::name)[0]
             << ">"
             << " BlkSize: "
             << BlockSize << ", "
             << "BlkTile: "
             << MPerBlock<<"x"<<NPerBlock<<"x"<<KPerBlock << ", "
             << "WaveTile: "
             << MPerXDL<<"x"<<NPerXDL << ", "
             << "WaveMap: "
             << MXdlPerWave<<"x" << NXdlPerWave<<", "
             << "VmemReadVec: "
             << ABlockTransferSrcScalarPerVector<<"x"<<BBlockTransferSrcScalarPerVector<<", "
             << "BlkGemmPipelineScheduler: "
             << BlkGemmPipelineSchedulerToString[BlkGemmPipeSched] << ", "
             << "BlkGemmPipelineVersion: "
             << BlkGemmPipelineVersionToString[BlkGemmPipelineVer] << ", "
             << "BlkGemmPipelinePrefetchStages: "
             << GridwiseGemm64::BlockwiseGemmPipe::PrefetchStages;
         // clang-format on
  
         return str.str();
     }
  
     size_t GetWorkSpaceSize(const BaseArgument* p_arg) const override
     {
         auto arg = *dynamic_cast<const Argument*>(p_arg);
  
         if(!(!(arg.IsReduceAdd() || NumDTensor > 0) &&
              std::is_same<CDataType, ReduceDataType>::value))
         {
             std::cout << "using workspace" << std::endl;
             return arg.M * arg.N * arg.KBatch * sizeof(ReduceDataType);
         }
  
         return 0;
     }
 };
  
 } // namespace device
 } // namespace tensor_operation
 } // namespace ck