/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-composable-kernel/checkouts/develop/include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl.hpp Source File

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-composable-kernel/checkouts/develop/include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl.hpp Source File#

Composable Kernel: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-composable-kernel/checkouts/develop/include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl.hpp Source File
Go to the documentation of this file.
 #pragma once
 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
  
 #pragma once
  
 #include <iostream>
 #include <sstream>
  
 #include "ck/utility/common_header.hpp"
 #include "ck/utility/env.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_grouped_gemm.hpp"
 #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
 #include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
 #include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_cshuffle.hpp"
 #include "ck/host_utility/device_prop.hpp"
 #include "ck/host_utility/kernel_launch.hpp"
  
 namespace ck {
 namespace tensor_operation {
 namespace device {
  
 template <typename GridwiseGemm,
           typename GemmDesc,
           typename AElementwiseOperation,
           typename BElementwiseOperation,
           typename CDEElementwiseOperation,
           bool HasMainKBlockLoop>
 __global__ void
 #if CK_USE_LAUNCH_BOUNDS
 __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
 #endif
     kernel_grouped_gemm_xdl(const void CK_CONSTANT_ADDRESS_SPACE* gemm_descs_const,
                             const index_t group_count,
                             const AElementwiseOperation a_element_op,
                             const BElementwiseOperation b_element_op,
                             const CDEElementwiseOperation c_element_op)
 {
 #if defined(__gfx9__)
     __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
  
     const index_t block_id = get_block_1d_id();
  
     const auto gemm_desc_ptr =
         reinterpret_cast<const GemmDesc*>(cast_pointer_to_generic_address_space(gemm_descs_const));
  
     index_t left     = 0;
     index_t right    = group_count;
     index_t group_id = index_t((left + right) / 2);
     while((!(block_id >= gemm_desc_ptr[group_id].BlockStart_ &&
              block_id < gemm_desc_ptr[group_id].BlockEnd_)) &&
           left <= right)
     {
         if(block_id < gemm_desc_ptr[group_id].BlockStart_)
         {
             right = group_id;
         }
         else
         {
             left = group_id;
         }
         group_id = index_t((left + right) / 2);
     }
  
     GridwiseGemm::template Run<HasMainKBlockLoop, InMemoryDataOperationEnum::Set>(
         gemm_desc_ptr[group_id].a_ptr_,
         gemm_desc_ptr[group_id].b_ptr_,
         gemm_desc_ptr[group_id].ds_ptr_,
         gemm_desc_ptr[group_id].e_ptr_,
         p_shared,
         a_element_op,
         b_element_op,
         c_element_op,
         gemm_desc_ptr[group_id].a_grid_desc_ak0_m_ak1_,
         gemm_desc_ptr[group_id].b_grid_desc_bk0_n_bk1_,
         gemm_desc_ptr[group_id].ds_grid_desc_mblock_mperblock_nblock_nperblock_,
         gemm_desc_ptr[group_id].e_grid_desc_mblock_mperblock_nblock_nperblock_,
         gemm_desc_ptr[group_id].block_2_etile_map_);
 #else
     ignore = gemm_descs_const;
     ignore = group_count;
     ignore = a_element_op;
     ignore = b_element_op;
     ignore = c_element_op;
 #endif
 }
  
 template <typename ALayout,
           typename BLayout,
           typename DsLayout,
           typename ELayout,
           typename ADataType,
           typename BDataType,
           typename AccDataType,
           typename CShuffleDataType,
           typename DsDataType,
           typename EDataType,
           typename AElementwiseOperation,
           typename BElementwiseOperation,
           typename CDEElementwiseOperation,
           GemmSpecialization GemmSpec,
           ck::index_t NumPrefetch,
           ck::index_t BlockSize,
           ck::index_t MPerBlock,
           ck::index_t NPerBlock,
           ck::index_t KPerBlock,
           ck::index_t AK1,
           ck::index_t BK1,
           ck::index_t MPerXDL,
           ck::index_t NPerXDL,
           ck::index_t MXdlPerWave,
           ck::index_t NXdlPerWave,
           typename ABlockTransferThreadClusterLengths_K0_M_K1,
           typename ABlockTransferThreadClusterArrangeOrder,
           typename ABlockTransferSrcAccessOrder,
           ck::index_t ABlockTransferSrcVectorDim,
           ck::index_t ABlockTransferSrcScalarPerVector,
           ck::index_t ABlockTransferDstScalarPerVector_K1,
           bool ABlockLdsExtraM,
           typename BBlockTransferThreadClusterLengths_K0_N_K1,
           typename BBlockTransferThreadClusterArrangeOrder,
           typename BBlockTransferSrcAccessOrder,
           ck::index_t BBlockTransferSrcVectorDim,
           ck::index_t BBlockTransferSrcScalarPerVector,
           ck::index_t BBlockTransferDstScalarPerVector_K1,
           bool BBlockLdsExtraN,
           index_t CShuffleMXdlPerWavePerShuffle,
           index_t CShuffleNXdlPerWavePerShuffle,
           typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
           index_t CDEBlockTransferScalarPerVector_NPerBlock,
           LoopScheduler LoopSched = make_default_loop_scheduler()>
 struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
                                                         BLayout,
                                                         DsLayout,
                                                         ELayout,
                                                         ADataType,
                                                         BDataType,
                                                         DsDataType,
                                                         EDataType,
                                                         AElementwiseOperation,
                                                         BElementwiseOperation,
                                                         CDEElementwiseOperation>
 {
     using DeviceOp = DeviceGroupedGemm_Xdl;
  
     static constexpr index_t NumDTensor = DsDataType::Size();
  
     static constexpr auto I0 = Number<0>{};
     static constexpr auto I1 = Number<1>{};
     static constexpr auto I2 = Number<2>{};
  
     static constexpr auto matrix_padder =
         MatrixPadder<GemmSpec, index_t, index_t, index_t>{MPerBlock, NPerBlock, KPerBlock};
  
     static auto MakeAGridDescriptor_M_K(index_t MRaw, index_t KRaw, index_t StrideA)
     {
         const auto a_grid_desc_mraw_kraw = [&]() {
             if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
             {
                 return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
                                                     make_tuple(StrideA, I1));
             }
             else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
             {
                 return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
                                                     make_tuple(I1, StrideA));
             }
         }();
  
         return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
     }
  
     static auto MakeBGridDescriptor_N_K(index_t KRaw, index_t NRaw, index_t StrideB)
     {
         const auto b_grid_desc_nraw_kraw = [&]() {
             if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
             {
                 return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
                                                     make_tuple(I1, StrideB));
             }
             else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
             {
                 return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
                                                     make_tuple(StrideB, I1));
             }
         }();
  
         return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
     }
  
     template <typename ELay>
     static auto MakeEGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideE)
     {
         const auto e_grid_desc_mraw_nraw = [&]() {
             if constexpr(is_same<tensor_layout::gemm::RowMajor, ELay>::value)
             {
                 return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
                                                     make_tuple(StrideE, I1));
             }
             else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ELay>::value)
             {
                 return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
                                                     make_tuple(I1, StrideE));
             }
         }();
  
         return matrix_padder.PadCDescriptor_M_N(e_grid_desc_mraw_nraw);
     }
  
     static auto MakeDsGridDescriptor_M_N(const std::array<index_t, NumDTensor>& MRaws,
                                          const std::array<index_t, NumDTensor>& NRaws,
                                          const std::array<index_t, NumDTensor>& DsStride)
     {
         return generate_tuple(
             [&](auto i) {
                 using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
  
                 return DeviceOp::MakeEGridDescriptor_M_N<DLayout>(MRaws[i], NRaws[i], DsStride[i]);
             },
             Number<NumDTensor>{});
     }
  
     using AGridDesc_M_K  = decltype(MakeAGridDescriptor_M_K(1, 1, 1));
     using BGridDesc_N_K  = decltype(MakeBGridDescriptor_N_K(1, 1, 1));
     using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({}, {}, {}))>;
     using EGridDesc_M_N  = decltype(MakeEGridDescriptor_M_N<ELayout>(1, 1, 1));
  
     using ComputeDataType = ADataType;
  
     // GridwiseGemm
     using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle<
         ADataType, // TODO: distinguish A/B datatype
         BDataType,
         ComputeDataType,
         AccDataType,
         CShuffleDataType,
         DsDataType,
         EDataType,
         AElementwiseOperation,
         BElementwiseOperation,
         CDEElementwiseOperation,
         NumPrefetch, // NumGemmKPrefetchStage
         BlockSize,
         MPerBlock,
         NPerBlock,
         KPerBlock,
         AK1,
         BK1,
         MPerXDL,
         NPerXDL,
         MXdlPerWave,
         NXdlPerWave,
         ABlockTransferThreadClusterLengths_K0_M_K1,
         ABlockTransferThreadClusterArrangeOrder,
         ABlockTransferSrcAccessOrder,
         ABlockTransferSrcVectorDim,
         ABlockTransferSrcScalarPerVector,
         ABlockTransferDstScalarPerVector_K1,
         false, // AThreadTransferSrcResetCoordinateAfterRun,
         ABlockLdsExtraM,
         BBlockTransferThreadClusterLengths_K0_N_K1,
         BBlockTransferThreadClusterArrangeOrder,
         BBlockTransferSrcAccessOrder,
         BBlockTransferSrcVectorDim,
         BBlockTransferSrcScalarPerVector,
         BBlockTransferDstScalarPerVector_K1,
         false, // BThreadTransferSrcResetCoordinateAfterRun,
         BBlockLdsExtraN,
         CShuffleMXdlPerWavePerShuffle,
         CShuffleNXdlPerWavePerShuffle,
         CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
         CDEBlockTransferScalarPerVector_NPerBlock,
         LoopSched>;
  
     using AGridDesc_AK0_M_AK1 =
         remove_cvref_t<decltype(GridwiseGemm::MakeDefaultAGridDescriptor_AK0_M_AK1(
             AGridDesc_M_K{}))>;
     using BGridDesc_BK0_N_BK1 =
         remove_cvref_t<decltype(GridwiseGemm::MakeDefaultBGridDescriptor_BK0_N_BK1(
             BGridDesc_N_K{}))>;
     using DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<
         decltype(GridwiseGemm::MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
             DsGridDesc_M_N{}))>;
     using EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
         remove_cvref_t<decltype(GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
             EGridDesc_M_N{}))>;
  
     struct GroupedGemmBlock2ETileMap
     {
         using Block2ETileMap =
             remove_cvref_t<decltype(GridwiseGemm::MakeDefaultBlock2ETileMap(EGridDesc_M_N{}))>;
  
         GroupedGemmBlock2ETileMap()
         {
             block_2_etile_map_ = GridwiseGemm::MakeDefaultBlock2ETileMap(EGridDesc_M_N{});
             BlockStart_        = -1;
         }
  
         GroupedGemmBlock2ETileMap(const EGridDesc_M_N& e_grid_desc_m_n, ck::index_t BlockStart)
         {
             block_2_etile_map_ = GridwiseGemm::MakeDefaultBlock2ETileMap(e_grid_desc_m_n);
             BlockStart_        = BlockStart;
         }
  
         template <typename TopIdx>
         __host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
         {
             return block_2_etile_map_.CalculateBottomIndex(
                 make_multi_index(idx_top[I0] - BlockStart_));
         }
  
         // it's actually E-Tile
         template <typename CTileIdx, typename CTileDim>
         __host__ __device__ bool ValidCTileIndex(const CTileIdx& c_tile_idx,
                                                  const CTileDim& c_tile_dim) const
         {
             return block_2_etile_map_.ValidCTileIndex(c_tile_idx, c_tile_dim);
         }
  
         __host__ bool CheckValidity(const EGridDesc_M_N& e_grid_desc_m_n) const
         {
             return block_2_etile_map_.CheckValidity(e_grid_desc_m_n);
         }
  
         Block2ETileMap block_2_etile_map_;
         ck::index_t BlockStart_;
     };
  
     struct GemmBiasTransKernelArg
     {
         // pointers
         const ADataType* a_ptr_;
         const BDataType* b_ptr_;
         typename GridwiseGemm::DsGridPointer ds_ptr_;
         EDataType* e_ptr_;
  
         // tensor descriptors for problem definiton
         AGridDesc_M_K a_grid_desc_m_k_;
         BGridDesc_N_K b_grid_desc_n_k_;
         DsGridDesc_M_N ds_grid_desc_m_n_;
         EGridDesc_M_N e_grid_desc_m_n_;
  
         // tensor descriptors for block/thread-wise copy
         AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
         BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
         DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
             ds_grid_desc_mblock_mperblock_nblock_nperblock_;
         EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock e_grid_desc_mblock_mperblock_nblock_nperblock_;
  
         // block-to-e-tile map
         GroupedGemmBlock2ETileMap block_2_etile_map_;
         ck::index_t BlockStart_, BlockEnd_;
     };
  
     // Argument
     struct Argument : public BaseArgument
     {
         Argument(std::vector<const void*>& p_As,
                  std::vector<const void*>& p_Bs,
                  std::vector<std::array<const void*, NumDTensor>>& p_Ds,
                  std::vector<void*>& p_Es,
                  std::vector<GemmDesc>& gemm_descs,
                  AElementwiseOperation a_element_op,
                  BElementwiseOperation b_element_op,
                  CDEElementwiseOperation c_element_op)
             : a_element_op_{a_element_op}, b_element_op_{b_element_op}, c_element_op_{c_element_op}
         {
             grid_size_ = 0;
  
             group_count_ = ck::type_convert<ck::index_t>(gemm_descs.size());
  
             if(!(group_count_ == ck::type_convert<ck::index_t>(p_As.size()) &&
                  group_count_ == ck::type_convert<ck::index_t>(p_Bs.size()) &&
                  group_count_ == ck::type_convert<ck::index_t>(p_Es.size())))
             {
                 throw std::runtime_error("wrong! group_count_ != p_As/b/c.size");
             }
  
             gemm_desc_kernel_arg_.reserve(group_count_);
  
             skipped_group_count_ = 0;
  
             for(std::size_t i = 0; i < gemm_descs.size(); i++)
             {
                 const index_t M = gemm_descs[i].M_;
                 const index_t N = gemm_descs[i].N_;
                 const index_t K = gemm_descs[i].K_;
  
                 a_mtx_mraw_kraw_.emplace_back(M, K);
                 b_mtx_nraw_kraw_.emplace_back(N, K);
  
                 if(M == 0)
                 {
                     skipped_group_count_++;
                     continue;
                 }
  
                 const index_t StrideA = gemm_descs[i].stride_A_;
                 const index_t StrideB = gemm_descs[i].stride_B_;
                 const index_t StrideC = gemm_descs[i].stride_C_;
  
                 // pointer
                 typename GridwiseGemm::DsGridPointer p_ds_grid{};
  
                 static_for<0, NumDTensor, 1>{}([&](auto j) {
                     using DDataType = remove_cvref_t<tuple_element_t<j.value, DsDataType>>;
  
                     p_ds_grid(j) = static_cast<const DDataType*>(p_Ds[i][j]);
                 });
  
                 // tensor descriptors for problem definiton
                 const auto a_grid_desc_m_k = DeviceOp::MakeAGridDescriptor_M_K(M, K, StrideA);
                 const auto b_grid_desc_n_k = DeviceOp::MakeBGridDescriptor_N_K(K, N, StrideB);
  
                 DsGridDesc_M_N ds_grid_desc_m_n;
  
                 static_for<0, NumDTensor, 1>{}([&](auto j) {
                     using DLayout = remove_cvref_t<tuple_element_t<j.value, DsLayout>>;
  
                     ds_grid_desc_m_n(j) = DeviceOp::MakeEGridDescriptor_M_N<DLayout>(
                         M, N, gemm_descs[i].stride_Ds_[j]);
                 });
  
                 const auto e_grid_desc_m_n =
                     DeviceOp::MakeEGridDescriptor_M_N<ELayout>(M, N, StrideC);
  
                 // tensor descriptors for block/thread-wise copy
                 const auto a_grid_desc_ak0_m_ak1 =
                     GridwiseGemm::MakeDefaultAGridDescriptor_AK0_M_AK1(a_grid_desc_m_k);
  
                 const auto b_grid_desc_bk0_n_bk1 =
                     GridwiseGemm::MakeDefaultBGridDescriptor_BK0_N_BK1(b_grid_desc_n_k);
  
                 const index_t grid_size_grp =
                     GroupedGemmBlock2ETileMap(e_grid_desc_m_n, 0)
                         .block_2_etile_map_.CalculateGridSize(e_grid_desc_m_n);
  
                 const index_t BlockStart = grid_size_;
                 const index_t BlockEnd   = grid_size_ + grid_size_grp;
  
                 grid_size_ += grid_size_grp;
  
                 // block-to-e-tile map
                 const auto block_2_etile_map =
                     GroupedGemmBlock2ETileMap(e_grid_desc_m_n, BlockStart);
  
                 if(GridwiseGemm::CheckValidity(a_grid_desc_m_k,
                                                b_grid_desc_n_k,
                                                ds_grid_desc_m_n,
                                                e_grid_desc_m_n,
                                                block_2_etile_map))
                 {
                     // tensor descriptors for block/thread-wise copy
                     DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
                         ds_grid_desc_mblock_mperblock_nblock_nperblock;
  
                     static_for<0, NumDTensor, 1>{}([&](auto j) {
                         ds_grid_desc_mblock_mperblock_nblock_nperblock(j) =
                             GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
                                 ds_grid_desc_m_n[j]);
                     });
  
                     const auto e_grid_desc_mblock_mperblock_nblock_nperblock =
                         GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
                             e_grid_desc_m_n);
  
                     gemm_desc_kernel_arg_.push_back(
                         GemmBiasTransKernelArg{static_cast<const ADataType*>(p_As[i]),
                                                static_cast<const BDataType*>(p_Bs[i]),
                                                p_ds_grid,
                                                static_cast<EDataType*>(p_Es[i]),
                                                a_grid_desc_m_k,
                                                b_grid_desc_n_k,
                                                ds_grid_desc_m_n,
                                                e_grid_desc_m_n,
                                                a_grid_desc_ak0_m_ak1,
                                                b_grid_desc_bk0_n_bk1,
                                                ds_grid_desc_mblock_mperblock_nblock_nperblock,
                                                e_grid_desc_mblock_mperblock_nblock_nperblock,
                                                block_2_etile_map,
                                                BlockStart,
                                                BlockEnd});
                 }
             }
         }
  
         //  private:
         index_t group_count_;
         index_t skipped_group_count_;
  
         AElementwiseOperation a_element_op_;
         BElementwiseOperation b_element_op_;
         CDEElementwiseOperation c_element_op_;
  
         std::vector<GemmBiasTransKernelArg> gemm_desc_kernel_arg_;
         std::vector<Tuple<index_t, index_t>> a_mtx_mraw_kraw_;
         std::vector<Tuple<index_t, index_t>> b_mtx_nraw_kraw_;
  
         index_t grid_size_;
         void* gemm_kernel_host_args_;
     };
  
     // Invoker
     struct Invoker : public BaseInvoker
     {
         using Argument = DeviceOp::Argument;
  
         float Run(const Argument& arg,
                   const StreamConfig& stream_config = StreamConfig{},
                   hipStream_t cpy_stream            = nullptr,
                   hipEvent_t cpy_event              = nullptr)
         {
             bool has_main_k_block_loop = true;
  
             for(std::size_t i = 0; i < arg.gemm_desc_kernel_arg_.size(); i++)
             {
                 if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
                 {
                     std::cout << "group: " << i << " arg.a_grid_desc_ak0_m_ak1_{"
                               << arg.gemm_desc_kernel_arg_[i].a_grid_desc_ak0_m_ak1_.GetLength(I0)
                               << ", "
                               << arg.gemm_desc_kernel_arg_[i].a_grid_desc_ak0_m_ak1_.GetLength(I1)
                               << ", "
                               << arg.gemm_desc_kernel_arg_[i].a_grid_desc_ak0_m_ak1_.GetLength(I2)
                               << "}";
  
                     std::cout << ", arg.b_grid_desc_bk0_n_bk1_{"
                               << arg.gemm_desc_kernel_arg_[i].b_grid_desc_bk0_n_bk1_.GetLength(I0)
                               << ", "
                               << arg.gemm_desc_kernel_arg_[i].b_grid_desc_bk0_n_bk1_.GetLength(I1)
                               << ", "
                               << arg.gemm_desc_kernel_arg_[i].b_grid_desc_bk0_n_bk1_.GetLength(I2)
                               << "}";
  
                     std::cout << ", arg.e_grid_desc_m_n_{ "
                               << arg.gemm_desc_kernel_arg_[i].e_grid_desc_m_n_.GetLength(I0) << ", "
                               << arg.gemm_desc_kernel_arg_[i].e_grid_desc_m_n_.GetLength(I1) << "}"
                               << std::endl;
                 }
  
                 if(!GridwiseGemm::CheckValidity(arg.gemm_desc_kernel_arg_[i].a_grid_desc_m_k_,
                                                 arg.gemm_desc_kernel_arg_[i].b_grid_desc_n_k_,
                                                 arg.gemm_desc_kernel_arg_[i].ds_grid_desc_m_n_,
                                                 arg.gemm_desc_kernel_arg_[i].e_grid_desc_m_n_,
                                                 arg.gemm_desc_kernel_arg_[i].block_2_etile_map_))
                 {
                     throw std::runtime_error(
                         "wrong! GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3 has invalid setting");
                 }
  
                 const auto K = arg.gemm_desc_kernel_arg_[i].a_grid_desc_ak0_m_ak1_.GetLength(I0) *
                                arg.gemm_desc_kernel_arg_[i].a_grid_desc_ak0_m_ak1_.GetLength(I2);
  
                 if(GridwiseGemm::CalculateHasMainKBlockLoop(K) != has_main_k_block_loop)
                 {
                     throw std::runtime_error("wrong! not all gemm has_main_k_block_loop");
                 }
             }
  
             // If the user provides copy stream and copy event, we assume that they're also
             // responsible for providing allocated host memory (eg. pinned) which
             // would be used to copy kernel arguments to the device.
             if(cpy_stream && cpy_event)
             {
                 if(arg.gemm_kernel_host_args_ == nullptr)
                 {
                     std::ostringstream err;
                     err << "No memory has been allocated for gemm kernel host args "
                         << "when providing the copy stream and copy event! In " << __FILE__ << ":"
                         << __LINE__ << ", in function: " << __func__;
                     throw std::runtime_error(err.str());
                 }
                 hipGetErrorString(hipMemcpyAsync(arg.p_workspace_,
                                                  arg.gemm_kernel_host_args_,
                                                  arg.group_count_ * sizeof(GemmBiasTransKernelArg),
                                                  hipMemcpyHostToDevice,
                                                  cpy_stream));
                 hipGetErrorString(hipEventRecord(cpy_event, cpy_stream));
                 hipGetErrorString(hipEventSynchronize(cpy_event));
             }
             else // In this case CK owns memory allocated on host.
             {
                 hipGetErrorString(hipMemcpyAsync(arg.p_workspace_,
                                                  arg.gemm_desc_kernel_arg_.data(),
                                                  arg.gemm_desc_kernel_arg_.size() *
                                                      sizeof(GemmBiasTransKernelArg),
                                                  hipMemcpyHostToDevice,
                                                  stream_config.stream_id_));
             }
  
             float ave_time = 0;
  
             auto launch_kernel = [&](auto has_main_k_block_loop_) {
                 const auto kernel = kernel_grouped_gemm_xdl<GridwiseGemm,
                                                             GemmBiasTransKernelArg,
                                                             AElementwiseOperation,
                                                             BElementwiseOperation,
                                                             CDEElementwiseOperation,
                                                             has_main_k_block_loop_>;
  
                 return launch_and_time_kernel(
                     stream_config,
                     kernel,
                     dim3(arg.grid_size_),
                     dim3(BlockSize),
                     0,
                     cast_pointer_to_constant_address_space(arg.p_workspace_),
                     arg.gemm_desc_kernel_arg_.size(),
                     arg.a_element_op_,
                     arg.b_element_op_,
                     arg.c_element_op_);
             };
  
             if(has_main_k_block_loop)
             {
                 ave_time = launch_kernel(integral_constant<bool, true>{});
             }
             else
             {
                 ave_time = launch_kernel(integral_constant<bool, false>{});
             }
  
             return ave_time;
         }
  
         // polymorphic
         float Run(const BaseArgument* p_arg,
                   const StreamConfig& stream_config = StreamConfig{}) override
         {
             return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
         }
     };
  
     static bool IsSupportedArgument(const Argument& arg)
     {
         if(!ck::is_xdl_supported())
         {
             return false;
         }
  
         if((ck::type_convert<ck::index_t>(arg.gemm_desc_kernel_arg_.size()) +
             arg.skipped_group_count_) != arg.group_count_)
         {
             return false;
         }
  
         bool supported = true;
  
         // If we use padding we do not support vector loads for dimensions not divisible by vector
         // load size.
         if constexpr(GemmSpec != GemmSpecialization::Default)
         {
             // [A|B]BlockTransferSrcVectorDim value define dimension in the block {K0,M,K1} layout,
             // thus we have to adapt it to the {M,K} or {N,K} layout.
             const auto a_raw_vector_dim = ABlockTransferSrcVectorDim != 1 ? 1 : 0;
             const auto b_raw_vector_dim = BBlockTransferSrcVectorDim != 1 ? 1 : 0;
  
             for(index_t i = 0; i < arg.group_count_; ++i)
             {
                 const auto a_vector_dim = arg.a_mtx_mraw_kraw_[i].At(Number<a_raw_vector_dim>{});
                 const auto b_vector_dim = arg.b_mtx_nraw_kraw_[i].At(Number<b_raw_vector_dim>{});
  
                 supported = supported & (a_vector_dim % ABlockTransferSrcScalarPerVector == 0);
                 supported = supported & (b_vector_dim % BBlockTransferSrcScalarPerVector == 0);
             }
         }
  
         return supported;
     }
  
     // polymorphic
     bool IsSupportedArgument(const BaseArgument* p_arg) override
     {
         return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
     }
  
     static auto MakeArgument(std::vector<const void*>& p_As,
                              std::vector<const void*>& p_Bs,
                              std::vector<std::array<const void*, NumDTensor>>& p_Ds,
                              std::vector<void*>& p_Es,
                              std::vector<GemmDesc> gemm_descs,
                              AElementwiseOperation a_element_op,
                              BElementwiseOperation b_element_op,
                              CDEElementwiseOperation c_element_op)
     {
         return Argument{
             p_As, p_Bs, p_Ds, p_Es, gemm_descs, a_element_op, b_element_op, c_element_op};
     }
  
     static auto MakeInvoker() { return Invoker{}; }
  
     // polymorphic
     std::unique_ptr<BaseArgument>
     MakeArgumentPointer(std::vector<const void*>& p_As,
                         std::vector<const void*>& p_Bs,
                         std::vector<std::array<const void*, NumDTensor>>& p_Ds,
                         std::vector<void*>& p_Es,
                         std::vector<GemmDesc>& gemm_descs,
                         AElementwiseOperation a_element_op,
                         BElementwiseOperation b_element_op,
                         CDEElementwiseOperation c_element_op) override
     {
         return std::make_unique<Argument>(
             p_As, p_Bs, p_Ds, p_Es, gemm_descs, a_element_op, b_element_op, c_element_op);
     }
  
     // polymorphic
     std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
     {
         return std::make_unique<Invoker>(Invoker{});
     }
  
     // polymorphic
     std::string GetTypeString() const override
     {
         auto str = std::stringstream();
  
         // clang-format off
         str << "DeviceGroupedGemm_Xdl"
             << "<"
             << BlockSize << ", "
             << MPerBlock << ", "
             << NPerBlock << ", "
             << KPerBlock << ", "
             << AK1 << ", "
             << BK1 << ", "
             << MPerXDL << ", "
             << NPerXDL << ", "
             << MXdlPerWave << ", "
             << NXdlPerWave << ", "
             << ABlockTransferSrcScalarPerVector << ", "
             << BBlockTransferSrcScalarPerVector << ", "
             << CShuffleMXdlPerWavePerShuffle << ", "
             << CShuffleNXdlPerWavePerShuffle << ", "
             << getGemmSpecializationString(GemmSpec)
             << ">";
         // clang-format on
  
         return str.str();
     }
  
     size_t GetWorkSpaceSize(const BaseArgument* p_arg) const override
     {
         auto p_arg_ = dynamic_cast<const Argument*>(p_arg);
         if(p_arg_)
         {
             return p_arg_->group_count_ * sizeof(GemmBiasTransKernelArg);
         }
         else
             throw std::runtime_error("The argument pointer is not an object of "
                                      "DeviceGroupedGemmMultipleDXdlCShuffle::Argument structure!");
     }
  
     size_t GetDeviceKernelArgSize(const BaseArgument* p_arg) const override
     {
         return GetWorkSpaceSize(p_arg);
     }
  
     void SetDeviceKernelArgs(BaseArgument* p_arg, void* p_dev_kernel_args) const override
     {
         return this->SetWorkSpacePointer(p_arg, p_dev_kernel_args);
     }
  
     size_t GetHostKernelArgSize(const BaseArgument* p_arg) const { return GetWorkSpaceSize(p_arg); }
  
     //----------------------------------------------------------------------------------------------
     void SetHostKernelArgsPointer(BaseArgument* p_arg, void* p_host_kernel_args) const
     {
         Argument* pArg_ = dynamic_cast<Argument*>(p_arg);
         if(!pArg_)
         {
             throw std::runtime_error("Failed to cast argument pointer!");
         }
  
         pArg_->gemm_kernel_host_args_ = p_host_kernel_args;
         std::copy(pArg_->gemm_desc_kernel_arg_.begin(),
                   pArg_->gemm_desc_kernel_arg_.end(),
                   static_cast<GemmBiasTransKernelArg*>(pArg_->gemm_kernel_host_args_));
     }
 };
  
 } // namespace device
 } // namespace tensor_operation
 } // namespace ck