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Composable Kernel: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-composable-kernel/checkouts/develop/include/ck_tile/ops/gemm/kernel/streamk_gemm_kernel.hpp Source File
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 // Copyright © Advanced Micro Devices, Inc., or its affiliates.
 // SPDX-License-Identifier:  MIT
  
 #pragma once
  
 #include "ck_tile/ops/gemm/kernel/gemm_kernel.hpp"
 #include "ck_tile/ops/common.hpp"
 #include "ck_tile/host/concat.hpp"
  
 namespace ck_tile {
 namespace reboot {
  
 struct StreamKHostArgs : public ck_tile::UniversalGemmHostArgs<>
 {
     CK_TILE_HOST explicit StreamKHostArgs(const void* a_ptr_,
                                           const void* b_ptr_,
                                           void* c_ptr_,
                                           index_t M_,
                                           index_t N_,
                                           index_t K_,
                                           index_t stride_A_,
                                           index_t stride_B_,
                                           index_t stride_C_,
                                           StreamKReductionStrategy reduction_strategy_)
         : UniversalGemmHostArgs<>({a_ptr_},
                                   {b_ptr_},
                                   {/*ds_ptr*/},
                                   c_ptr_,
                                   /*k_batch_ =*/1,
                                   M_,
                                   N_,
                                   K_,
                                   {stride_A_},
                                   {stride_B_},
                                   {/*stride_Ds_*/},
                                   stride_C_),
           reduction_strategy{reduction_strategy_}
     {
     }
  
     ck_tile::StreamKReductionStrategy reduction_strategy;
 };
  
 //   The main kernel functions are the operator() functions. There is one for Persistent
 //   and one for Non-Persistent data parallel sections of the Stream-K algorithm.
 //
 //   Both the Non-Persistent and Persistent kernels make use of `BaseGemm()` and
 //   `StreamKGemm()`. `BaseGemm()` computes offsets into the A,B,C tensors, then calls
 //   `RunGemm()` which runs the GEMM pipeline and epilogue. `StreamKGemm()` performs the
 //   main Stream-K algorithm. Each iteration of the Stream-K loop calls `BaseGemm()`.
 template <typename TilePartitioner_, typename GemmPipeline_, typename EpiloguePipeline_>
 struct StreamKKernel
 {
     using UniversalGemmKernel =
         UniversalGemmKernel<TilePartitioner_, GemmPipeline_, EpiloguePipeline_>;
  
     static constexpr index_t kBlockSize = UniversalGemmKernel::kBlockSize;
     static constexpr bool PersistentDP  = UniversalGemmKernel::PersistentKernel;
  
     using TilePartitioner  = TilePartitioner_;
     using GemmPipeline     = GemmPipeline_;
     using EpiloguePipeline = EpiloguePipeline_;
  
     static_assert(
         TilePartitioner::PERSISTENT == PersistentDP,
         "Persistent flag from TilePartitioner must match Persistent flag from UniversalGemm.");
  
     using ALayout = typename GemmPipeline::ALayout;
     using BLayout = typename GemmPipeline::BLayout;
     using CLayout = typename GemmPipeline::CLayout;
  
     using ADataType   = typename GemmPipeline::ADataType;
     using BDataType   = typename GemmPipeline::BDataType;
     using CDataType   = typename EpiloguePipeline::ODataType;
     using AccDataType = typename EpiloguePipeline::AccDataType;
  
     template <typename T>
     static constexpr bool is_tuple_v = is_detected<is_tuple, T>::value;
  
     static_assert(!is_tuple_v<ALayout> && !is_tuple_v<ADataType>,
                   "ALayout and ADataType must be scalars.");
  
     static_assert(!is_tuple_v<BLayout> && !is_tuple_v<BDataType>,
                   "BLayout and BDataType must be scalars.");
  
     static_assert(!is_tuple_v<CLayout> && !is_tuple_v<CDataType>,
                   "CLayout and CDataType must be scalars.");
  
     struct StreamKKernelArgs : ck_tile::UniversalGemmKernelArgs<>
     {
         StreamKKernelArgs(const StreamKHostArgs& host_args, index_t grid)
             : UniversalGemmKernelArgs{host_args.as_ptr,
                                       host_args.bs_ptr,
                                       host_args.ds_ptr,
                                       host_args.e_ptr,
                                       host_args.M,
                                       host_args.N,
                                       host_args.K,
                                       host_args.stride_As,
                                       host_args.stride_Bs,
                                       host_args.stride_Ds,
                                       host_args.stride_E,
                                       host_args.k_batch},
               reduction_strategy{host_args.reduction_strategy},
               // The workspace pointer is set to nullptr because we must first
               // instantiate the TilePartitioner to get the necessary size
               workspace_ptr{nullptr},
               tile_partitioner{TilePartitioner{host_args.M, host_args.N, host_args.K, grid}}
  
         {
         }
  
         StreamKReductionStrategy reduction_strategy;
         void* workspace_ptr;
         TilePartitioner tile_partitioner;
     };
  
     using KernelArgs = StreamKKernelArgs;
     using Kernel     = StreamKKernel<TilePartitioner, GemmPipeline, EpiloguePipeline>;
  
     [[nodiscard]] CK_TILE_HOST static const std::string GetName()
     {
         // clang-format off
         using P_ = GemmPipeline;
         using WarpTile = typename P_::BlockGemmShape::WarpTile;
  
         return concat('_', "streamk", gemm_prec_str<ADataType, BDataType>(),
                       concat('x', P_::MPerBlock, P_::NPerBlock, P_::KPerBlock),
                       concat('x', WarpTile::at(number<0>{}), WarpTile::at(number<1>{}), WarpTile::at(number<2>{})),
                       concat('x', P_::GetVectorSizeA(), P_::GetVectorSizeB(), P_::GetVectorSizeC()),
                       concat('x', P_::kPadM, P_::kPadN, P_::kPadK));
         // clang-format on
     }
  
     CK_TILE_HOST static auto GridSize(const TilePartitioner& tile_partitioner) -> dim3
     {
         return tile_partitioner.grid_size();
     }
  
     CK_TILE_HOST static auto MaxOccupancyGridSize(const stream_config& s) -> dim3
     {
         return UniversalGemmKernel::MaxOccupancyGridSize(s);
     }
  
     CK_TILE_HOST static constexpr auto BlockSize() -> dim3
     {
         return UniversalGemmKernel::BlockSize();
     }
  
     CK_TILE_HOST static StreamKKernelArgs MakeKernelArgs(const StreamKHostArgs& host_args,
                                                          int num_cu    = NumCU(),
                                                          int occupancy = Occupancy())
     {
         const index_t grid = num_cu * occupancy;
  
         return StreamKKernelArgs{host_args, grid};
     }
  
     template <bool UseDefaultScheduler = true>
     CK_TILE_DEVICE static void
     RunGemm(const std::array<const ADataType*, UniversalGemmKernel::NumATensor>& as_ptr,
             const std::array<const BDataType*, UniversalGemmKernel::NumBTensor>& bs_ptr,
             const std::array<const void*, UniversalGemmKernel::NumDTensor>& ds_ptr,
             CDataType* c_ptr,
             void* smem_ptr_0,
             const typename UniversalGemmKernel::KernelArgs& kargs,
             const index_t num_loop,
             const index_t block_idx_m,
             const index_t block_idx_n,
             const index_t k_size)
     {
         // Create Gemm tensor views, pad views and tile windows
         const auto& gemm_tensor_views_tuple =
             UniversalGemmKernel::template MakeGemmTensorViews<EpiloguePipeline::MemoryOperation>(
                 as_ptr, bs_ptr, ds_ptr, c_ptr, kargs, k_size);
  
         const auto& gemm_pad_views = UniversalGemmKernel::MakeGemmPadViews(gemm_tensor_views_tuple);
         auto gemm_tile_windows =
             UniversalGemmKernel::MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
  
         // Run GEMM cooperatively by whole workgroup.
         const auto& as_block_window = gemm_tile_windows.at(UniversalGemmKernel::I0);
         const auto& bs_block_window = gemm_tile_windows.at(UniversalGemmKernel::I1);
         const auto& ds_block_window = gemm_tile_windows.at(UniversalGemmKernel::I2);
  
         // Since num_loop can vary per WG and per iteration of the Stream-K while loop, we compute
         // has_hot_loop and tail_num here. This is a similar pattern used by grouped GEMM. In this
         // case, we call the GemmPipeline's operator() function that takes both has_hot_loop and
         // tail_num.
         const bool has_hot_loop   = GemmPipeline::BlockHasHotloop(num_loop);
         const TailNumber tail_num = GemmPipeline::GetBlockLoopTailNum(num_loop);
  
         const auto& c_block_tile = GemmPipeline{}(as_block_window[UniversalGemmKernel::I0],
                                                   bs_block_window[UniversalGemmKernel::I0],
                                                   num_loop,
                                                   has_hot_loop,
                                                   tail_num,
                                                   smem_ptr_0);
  
         if(UseDefaultScheduler || (get_warp_id() == 0))
         {
             // Run Epilogue Pipeline
             auto& c_block_window = gemm_tile_windows.at(UniversalGemmKernel::I3);
  
             EpiloguePipeline{}(c_block_window, c_block_tile, ds_block_window, smem_ptr_0);
         }
     }
  
     CK_TILE_HOST static bool IsSupportedArgument(const StreamKKernelArgs& kargs)
     {
         return UniversalGemmKernel::IsSupportedArgument(kargs);
     }
  
     CK_TILE_HOST static uint32_t GetWorkSpaceSize(const StreamKKernelArgs& kargs)
     {
         return kargs.tile_partitioner.get_workspace_size(sizeof(AccDataType));
     }
  
     CK_TILE_HOST static void SetWorkSpacePointer(StreamKKernelArgs& kargs, void* workspace_ptr)
     {
         kargs.workspace_ptr = workspace_ptr;
     }
  
     CK_TILE_DEVICE void BaseGemm(StreamKKernelArgs& kargs,
                                  index_t tile_idx,
                                  index_t num_loop,
                                  index_t i_k_a,
                                  index_t i_k_b,
                                  index_t k_size,
                                  void* smem_ptr_0) const
     {
         const auto c_macro_tile_idx = kargs.tile_partitioner.get_output_tile_index(tile_idx);
         index_t i_m = c_macro_tile_idx[UniversalGemmKernel::I0] * TilePartitioner::MPerBlock;
         index_t i_n = c_macro_tile_idx[UniversalGemmKernel::I1] * TilePartitioner::NPerBlock;
  
         const ADataType* a_ptr = static_cast<const ADataType*>(kargs.as_ptr[0]) + i_k_a;
         const BDataType* b_ptr = static_cast<const BDataType*>(kargs.bs_ptr[0]) + i_k_b;
         CDataType* c_ptr       = static_cast<CDataType*>(kargs.e_ptr);
  
         // Run the GEMM pipeline and Epilogue.
         RunGemm(
             {a_ptr}, {b_ptr}, {/*ds_ptr*/}, c_ptr, smem_ptr_0, kargs, num_loop, i_m, i_n, k_size);
     }
  
     CK_TILE_DEVICE void SignalStorePartialDone(const StreamKKernelArgs& kargs,
                                                index_t cta_idx) const
     {
         auto sk_flags_ptr = static_cast<uint32_t*>(kargs.workspace_ptr);
         workgroup_barrier sk_flags(sk_flags_ptr);
         sk_flags.wait_set(0, 1, cta_idx);
     }
  
     CK_TILE_DEVICE void WaitStorePartialDone(const StreamKKernelArgs& kargs, index_t cta_idx) const
     {
         auto sk_flags_ptr = static_cast<uint32_t*>(kargs.workspace_ptr);
         workgroup_barrier sk_flags(sk_flags_ptr);
         sk_flags.wait_eq(1, cta_idx);
     }
  
     template <typename OAccTile>
     CK_TILE_DEVICE void AddBlockTile(OAccTile& in_out_block_tile,
                                      const OAccTile& in_block_tile) const
     {
         using BlockType        = remove_cvref_t<decltype(in_out_block_tile)>;
         constexpr auto o_spans = BlockType::get_distributed_spans();
         sweep_tile_span(o_spans[number<0>{}], [&](auto idx0) {
             sweep_tile_span(o_spans[number<1>{}], [&](auto idx1) {
                 constexpr auto idx     = make_tuple(idx0, idx1);
                 in_out_block_tile(idx) = in_out_block_tile[idx] + in_block_tile[idx];
             });
         });
     }
  
     template <typename DataType, typename OAccTileDist>
     CK_TILE_DEVICE auto LoadPartial(const StreamKKernelArgs& kargs,
                                     index_t cta_idx,
                                     const OAccTileDist& c_block_tile_dist) const
     {
         const auto c_block_tile_buffer_size =
             TilePartitioner::MPerBlock * TilePartitioner::NPerBlock * sizeof(DataType);
         void* partial_buffer_ptr = static_cast<char*>(kargs.workspace_ptr) +
                                    kargs.tile_partitioner.get_flags_buffer_size() +
                                    cta_idx * c_block_tile_buffer_size;
  
         const auto& partial_tensor_view = make_naive_tensor_view<address_space_enum::global>(
             static_cast<DataType*>(partial_buffer_ptr),
             make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
             make_tuple(TilePartitioner::NPerBlock, 1),
             number<GemmPipeline::GetVectorSizeC()>{},
             number<1>{});
  
         auto partial_tile_window = make_tile_window(
             partial_tensor_view,
             make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
             {0, 0},
             c_block_tile_dist);
  
         return load_tile(partial_tile_window);
     }
  
     template <typename OAccTile>
     CK_TILE_DEVICE void StorePartial(const StreamKKernelArgs& kargs,
                                      index_t cta_idx,
                                      const OAccTile& c_block_tile) const
     {
         const auto c_block_tile_buffer_size = TilePartitioner::MPerBlock *
                                               TilePartitioner::NPerBlock *
                                               sizeof(typename OAccTile::DataType);
         void* partial_buffer_ptr = static_cast<char*>(kargs.workspace_ptr) +
                                    kargs.tile_partitioner.get_flags_buffer_size() +
                                    cta_idx * c_block_tile_buffer_size;
  
         const auto& partial_tensor_view = make_naive_tensor_view<address_space_enum::global>(
             static_cast<typename OAccTile::DataType*>(partial_buffer_ptr),
             make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
             make_tuple(TilePartitioner::NPerBlock, 1),
             number<GemmPipeline::GetVectorSizeC()>{},
             number<1>{});
  
         auto partial_tile_window = make_tile_window(
             partial_tensor_view,
             make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
             {0, 0});
  
         store_tile(partial_tile_window, c_block_tile);
     }
  
     CK_TILE_DEVICE void
     StreamKGemm(StreamKKernelArgs& kargs, index_t cta_idx, void* smem_ptr_0) const
     {
         index_t iter_start, iter_end;
         kargs.tile_partitioner.get_iter_boundaries(iter_start, iter_end, cta_idx);
  
         while(iter_start < iter_end)
         {
             // Get the 1D tile index in the C tensor that this workgroup will work in for this
             // iteration of the loop.
             index_t tile_idx =
                 amd_wave_read_first_lane(kargs.tile_partitioner.get_tile_index(iter_start));
  
             // Get the start and end boundaries for the current tile.
             index_t tile_iter_start, tile_iter_end;
             kargs.tile_partitioner.get_tile_boundaries(tile_iter_start, tile_iter_end, tile_idx);
  
             // Get the start and end iteration within the current tile for the workgroup.
             index_t local_iter_start = amd_wave_read_first_lane(
                 kargs.tile_partitioner.get_local_iter(iter_start, tile_iter_start));
             index_t local_iter_end =
                 amd_wave_read_first_lane(kargs.tile_partitioner.get_local_iter_end(
                     tile_iter_start, iter_end, tile_iter_end));
  
             // Get the iteration length.
             index_t num_loop_sk = local_iter_end - local_iter_start;
  
             // Determine the total size along the K dimension the workgroup is using in this
             // iteration (used to construct tensor views).
             index_t k_size = num_loop_sk * TilePartitioner::KPerBlock;
  
             // Get the K offsets for the A and B tensors
             auto [i_k_a, i_k_b] = GetKOffsets<ALayout, BLayout>(
                 local_iter_start, kargs.stride_As[0], kargs.stride_Bs[0]);
  
             if constexpr(TilePartitioner::ReductionStrategy == StreamKReductionStrategy::Atomic)
             {
                 BaseGemm(kargs, tile_idx, num_loop_sk, i_k_a, i_k_b, k_size, smem_ptr_0);
             }
             else
             {
                 const auto c_macro_tile_idx =
                     kargs.tile_partitioner.get_output_tile_index(tile_idx);
                 index_t i_m =
                     c_macro_tile_idx[UniversalGemmKernel::I0] * TilePartitioner::MPerBlock;
                 index_t i_n =
                     c_macro_tile_idx[UniversalGemmKernel::I1] * TilePartitioner::NPerBlock;
  
                 const ADataType* a_ptr = static_cast<const ADataType*>(kargs.as_ptr[0]) + i_k_a;
                 const BDataType* b_ptr = static_cast<const BDataType*>(kargs.bs_ptr[0]) + i_k_b;
                 CDataType* c_ptr       = static_cast<CDataType*>(kargs.e_ptr);
  
                 // Create Gemm tensor views, pad views and tile windows
                 const auto& gemm_tensor_views_tuple =
                     UniversalGemmKernel::template MakeGemmTensorViews<
                         EpiloguePipeline::MemoryOperation>(
                         {a_ptr}, {b_ptr}, {/*ds_ptr*/}, c_ptr, kargs, k_size);
  
                 const auto& gemm_pad_views =
                     UniversalGemmKernel::MakeGemmPadViews(gemm_tensor_views_tuple);
                 auto gemm_tile_windows =
                     UniversalGemmKernel::MakeGemmTileWindows(gemm_pad_views, i_m, i_n);
  
                 // Run GEMM cooperatively by whole workgroup.
                 const auto& as_block_window = gemm_tile_windows.at(UniversalGemmKernel::I0);
                 const auto& bs_block_window = gemm_tile_windows.at(UniversalGemmKernel::I1);
                 const auto& ds_block_window = gemm_tile_windows.at(UniversalGemmKernel::I2);
  
                 // Since num_loop can vary per WG and per iteration of the Stream-K while loop,
                 // we compute has_hot_loop and tail_num here. This is a similar pattern used by
                 // grouped GEMM. In this case, we call the GemmPipeline's operator() function
                 // that takes both has_hot_loop and tail_num.
                 const bool has_hot_loop   = GemmPipeline::BlockHasHotloop(num_loop_sk);
                 const TailNumber tail_num = GemmPipeline::GetBlockLoopTailNum(num_loop_sk);
  
                 const auto& c_block_tile = GemmPipeline{}(as_block_window[UniversalGemmKernel::I0],
                                                           bs_block_window[UniversalGemmKernel::I0],
                                                           num_loop_sk,
                                                           has_hot_loop,
                                                           tail_num,
                                                           smem_ptr_0);
  
                 auto tile_started = iter_start == tile_iter_start;
                 auto tile_ended   = iter_end >= tile_iter_end;
                 if(!tile_started)
                 {
                     StorePartial(kargs, cta_idx, c_block_tile);
                     // Ensure device-wide visibility of partial results stored in global memory
                     // before signaling completion. __threadfence() guarantees that all global
                     // memory writes by this thread are visible to other threads on the device.
                     __threadfence(); // send signal when the store is done
                     SignalStorePartialDone(kargs, cta_idx);
                 }
                 else
                 {
                     auto accum_block_tile = c_block_tile;
                     if(!tile_ended)
                     {
                         const index_t iter_per_tile = kargs.tile_partitioner.get_iters_per_tile();
                         const index_t iter_per_cta  = kargs.tile_partitioner.get_iters_per_sk_cta();
                         const index_t extra_iters   = kargs.tile_partitioner.get_extra_iters();
                         int accum_iters             = local_iter_end - local_iter_start;
                         int next_cta                = cta_idx + 1;
  
                         while(accum_iters < iter_per_tile)
                         {
                             WaitStorePartialDone(kargs, next_cta);
  
                             using BlockType = remove_cvref_t<decltype(c_block_tile)>;
                             AddBlockTile(
                                 accum_block_tile,
                                 LoadPartial<typename BlockType::DataType>(
                                     kargs, next_cta, c_block_tile.get_tile_distribution()));
  
                             accum_iters += iter_per_cta + (next_cta < extra_iters);
                             ++next_cta;
                         }
                     }
  
                     auto& c_block_window = gemm_tile_windows.at(UniversalGemmKernel::I3);
                     EpiloguePipeline{}(
                         c_block_window, accum_block_tile, ds_block_window, smem_ptr_0);
                 }
             }
  
             // Prepare for next Stream-K loop iteration.
             iter_start = tile_iter_end;
             block_sync_lds();
         }
     }
  
     template <bool U = PersistentDP>
     CK_TILE_DEVICE typename std::enable_if_t<!U> operator()(StreamKKernelArgs kargs) const
     {
         // Allocate LDS
         __shared__ char smem_ptr_0[UniversalGemmKernel::GetSmemSize()];
  
         index_t block_idx   = ck_tile::get_block_1d_id();
         index_t dp_num_loop = kargs.tile_partitioner.get_iters_per_tile();
         index_t dp_ctas     = kargs.tile_partitioner.get_dp_ctas();
         bool is_dp_ctas     = block_idx < kargs.tile_partitioner.get_dp_ctas();
  
         // Check if at the data parallel section
         if(is_dp_ctas)
         {
             BaseGemm(kargs, block_idx, dp_num_loop, 0, 0, kargs.K, smem_ptr_0);
         }
         else
         {
             // Stream-K
             StreamKGemm(kargs, block_idx - dp_ctas, smem_ptr_0);
         }
     }
  
     template <bool U = PersistentDP>
     CK_TILE_DEVICE typename std::enable_if_t<U> operator()(StreamKKernelArgs kargs) const
     {
         // Allocate LDS
         __shared__ char smem_ptr_0[UniversalGemmKernel::GetSmemSize()];
  
         index_t block_idx   = ck_tile::get_block_1d_id();
         index_t dp_num_loop = kargs.tile_partitioner.get_iters_per_tile();
  
         // Data-parallel section
         for(index_t tile_idx = block_idx; tile_idx < kargs.tile_partitioner.get_dp_tiles();
             tile_idx += kargs.tile_partitioner.get_grid())
         {
             BaseGemm(kargs, tile_idx, dp_num_loop, 0, 0, kargs.K, smem_ptr_0);
         }
  
         // Stream-K section
         StreamKGemm(kargs, block_idx, smem_ptr_0);
     }
  
     private:
     template <typename ALayout, typename BLayout>
     CK_TILE_DEVICE static tuple<index_t, index_t>
     GetKOffsets(index_t iter_offset, index_t stride_a, index_t stride_b)
     {
         index_t stride_offset_a;
         index_t stride_offset_b;
         if constexpr(std::is_same_v<ALayout, ck_tile::tensor_layout::gemm::ColumnMajor>)
         {
             stride_offset_a = stride_a;
         }
         else
         {
             stride_offset_a = 1;
         }
  
         if constexpr(std::is_same_v<BLayout, ck_tile::tensor_layout::gemm::RowMajor>)
         {
             stride_offset_b = stride_b;
         }
         else
         {
             stride_offset_b = 1;
         }
  
         index_t base_offset = iter_offset * TilePartitioner::KPerBlock;
  
         return make_tuple(base_offset * stride_offset_a, base_offset * stride_offset_b);
     }
  
     CK_TILE_HOST static int NumCU()
     {
         hipDeviceProp_t dev_prop;
         hipDevice_t dev;
         hip_check_error(hipGetDevice(&dev));
         hip_check_error(hipGetDeviceProperties(&dev_prop, dev));
         int num_cu = dev_prop.multiProcessorCount;
  
         return num_cu;
     }
  
     CK_TILE_HOST static int Occupancy()
     {
         int occupancy;
  
         // Since occupancy of 1 is valid for stream k, we set min_num_block_per_cu to 1
         constexpr int min_block_per_cu = 1;
         const auto kernel              = kentry<min_block_per_cu, Kernel, KernelArgs>;
  
         hip_check_error(
             hipOccupancyMaxActiveBlocksPerMultiprocessor(&occupancy, kernel, kBlockSize, 0));
  
         return occupancy;
     }
 };
 } // namespace reboot
  
 struct StreamKHostArgs : public ck_tile::UniversalGemmHostArgs<>
 {
     CK_TILE_HOST explicit StreamKHostArgs(const void* a_ptr_,
                                           const void* b_ptr_,
                                           void* c_ptr_,
                                           index_t M_,
                                           index_t N_,
                                           index_t K_,
                                           index_t stride_A_,
                                           index_t stride_B_,
                                           index_t stride_C_,
                                           StreamKReductionStrategy reduction_strategy_,
                                           uint32_t num_sk_blocks_ = 0xffffffff)
         : UniversalGemmHostArgs<>({a_ptr_},
                                   {b_ptr_},
                                   {/*ds_ptr*/},
                                   c_ptr_,
                                   /*k_batch_ =*/1,
                                   M_,
                                   N_,
                                   K_,
                                   {stride_A_},
                                   {stride_B_},
                                   {/*stride_Ds_*/},
                                   stride_C_),
           reduction_strategy{reduction_strategy_},
           num_sk_blocks{num_sk_blocks_}
     {
     }
  
     ck_tile::StreamKReductionStrategy reduction_strategy;
     uint32_t num_sk_blocks;
 };
  
 template <typename TilePartitioner_, typename GemmPipeline_, typename EpiloguePipeline_>
 struct StreamKKernel
 {
     using UniversalGemmKernel =
         UniversalGemmKernel<TilePartitioner_, GemmPipeline_, EpiloguePipeline_>;
  
     static constexpr index_t kBlockSize = UniversalGemmKernel::kBlockSize;
  
     using TilePartitioner  = remove_cvref_t<TilePartitioner_>;
     using GemmPipeline     = remove_cvref_t<GemmPipeline_>;
     using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
  
     using ALayout = remove_cvref_t<typename GemmPipeline::ALayout>;
     using BLayout = remove_cvref_t<typename GemmPipeline::BLayout>;
     using CLayout = remove_cvref_t<typename GemmPipeline::CLayout>;
  
     using ADataType = remove_cvref_t<typename GemmPipeline::ADataType>;
     using BDataType = remove_cvref_t<typename GemmPipeline::BDataType>;
     using CDataType = remove_cvref_t<typename EpiloguePipeline::ODataType>;
  
     static_assert(!is_detected<is_tuple, ALayout>::value &&
                       !is_detected<is_tuple, ADataType>::value,
                   "ALayout and ADataType must be scalars.");
  
     static_assert(!is_detected<is_tuple, BLayout>::value &&
                       !is_detected<is_tuple, BDataType>::value,
                   "BLayout and BDataType must be scalars.");
  
     static_assert(!is_detected<is_tuple, CLayout>::value &&
                       !is_detected<is_tuple, CDataType>::value,
                   "CLayout and CDataType must be scalars.");
  
     struct StreamKKernelArgs : ck_tile::UniversalGemmKernelArgs<>
     {
         StreamKReductionStrategy reduction_strategy;
         uint32_t num_sk_blocks;
         void* workspace_ptr;
         TilePartitioner tile_partitioner;
     };
  
     using KernelArgs = StreamKKernelArgs;
     using Kernel     = StreamKKernel<TilePartitioner, GemmPipeline, EpiloguePipeline>;
  
     [[nodiscard]] CK_TILE_HOST static const std::string GetName()
     {
         // clang-format off
         using P_ = GemmPipeline;
         using WarpTile = typename P_::BlockGemmShape::WarpTile;
  
         return concat('_', "streamk", gemm_prec_str<ADataType, BDataType>(),
                       concat('x', P_::MPerBlock, P_::NPerBlock, P_::KPerBlock),
                       concat('x', WarpTile::at(number<0>{}), WarpTile::at(number<1>{}), WarpTile::at(number<2>{})),
                       concat('x', P_::GetVectorSizeA(), P_::GetVectorSizeB(), P_::GetVectorSizeC()),
                       concat('x', P_::kPadM, P_::kPadN, P_::kPadK));
         // clang-format on
     }
  
     CK_TILE_HOST static auto GridSize(const TilePartitioner& tile_partitioner) -> dim3
     {
         return tile_partitioner.GridSize();
     }
  
     CK_TILE_HOST static auto MaxOccupancyGridSize(const stream_config& s) -> dim3
     {
         return UniversalGemmKernel::MaxOccupancyGridSize(s);
     }
  
     CK_TILE_HOST static constexpr auto BlockSize() -> dim3
     {
         return UniversalGemmKernel::BlockSize();
     }
  
     CK_TILE_HOST static StreamKKernelArgs MakeKernelArgs(const StreamKHostArgs& host_args,
                                                          int num_cu    = NumCU(),
                                                          int occupancy = Occupancy())
     {
         return StreamKKernelArgs{{host_args.as_ptr,
                                   host_args.bs_ptr,
                                   host_args.ds_ptr,
                                   host_args.e_ptr,
                                   host_args.M,
                                   host_args.N,
                                   host_args.K,
                                   host_args.stride_As,
                                   host_args.stride_Bs,
                                   host_args.stride_Ds,
                                   host_args.stride_E,
                                   host_args.k_batch},
                                  host_args.reduction_strategy,
                                  host_args.num_sk_blocks,
                                  // The workspace pointer is set to nullptr because we must first
                                  // instantiate the TilePartitioner to get the necessary size
                                  /*workspace_ptr =*/nullptr,
                                  TilePartitioner{static_cast<uint32_t>(host_args.M),
                                                  static_cast<uint32_t>(host_args.N),
                                                  static_cast<uint32_t>(host_args.K),
                                                  static_cast<uint32_t>(num_cu),
                                                  static_cast<uint32_t>(occupancy),
                                                  host_args.num_sk_blocks}};
     }
  
     template <bool UseDefaultScheduler = true>
     CK_TILE_DEVICE static void
     RunGemm(const std::array<const ADataType*, UniversalGemmKernel::NumATensor>& as_ptr,
             const std::array<const BDataType*, UniversalGemmKernel::NumBTensor>& bs_ptr,
             const std::array<const void*, UniversalGemmKernel::NumDTensor>& ds_ptr,
             CDataType* c_ptr,
             void* smem_ptr_0,
             const typename UniversalGemmKernel::KernelArgs& kargs,
             const index_t num_loop,
             const index_t block_idx_m,
             const index_t block_idx_n,
             const index_t k_size)
     {
         // Create Gemm tensor views, pad views and tile windows
         const auto& gemm_tensor_views_tuple =
             UniversalGemmKernel::template MakeGemmTensorViews<EpiloguePipeline::MemoryOperation>(
                 as_ptr, bs_ptr, ds_ptr, c_ptr, kargs, k_size);
  
         const auto& gemm_pad_views = UniversalGemmKernel::MakeGemmPadViews(gemm_tensor_views_tuple);
         auto gemm_tile_windows =
             UniversalGemmKernel::MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
  
         // Run GEMM cooperatively by whole workgroup.
         const auto& as_block_window = gemm_tile_windows.at(UniversalGemmKernel::I0);
         const auto& bs_block_window = gemm_tile_windows.at(UniversalGemmKernel::I1);
         const auto& ds_block_window = gemm_tile_windows.at(UniversalGemmKernel::I2);
  
         // Since num_loop can vary per WG and per iteration of the Stream-K while loop, we compute
         // has_hot_loop and tail_num here. This is a similar pattern used by grouped GEMM. In this
         // case, we call the GemmPipeline's operator() function that takes both has_hot_loop and
         // tail_num.
         const bool has_hot_loop   = GemmPipeline::BlockHasHotloop(num_loop);
         const TailNumber tail_num = GemmPipeline::GetBlockLoopTailNum(num_loop);
  
         const auto& c_block_tile = GemmPipeline{}(as_block_window[UniversalGemmKernel::I0],
                                                   bs_block_window[UniversalGemmKernel::I0],
                                                   num_loop,
                                                   has_hot_loop,
                                                   tail_num,
                                                   smem_ptr_0);
  
         if(UseDefaultScheduler || (get_warp_id() == 0))
         {
             // Run Epilogue Pipeline
             auto& c_block_window = gemm_tile_windows.at(UniversalGemmKernel::I3);
  
             EpiloguePipeline{}(c_block_window, c_block_tile, ds_block_window, smem_ptr_0);
         }
     }
  
     CK_TILE_HOST static bool IsSupportedArgument(const StreamKKernelArgs& kargs)
     {
         if(kargs.reduction_strategy == StreamKReductionStrategy::Reduction)
         {
             if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
             {
                 CK_TILE_ERROR("CK Tile Stream-K only supports the atomic reduction strategy.");
             }
             return false;
         }
         return UniversalGemmKernel::IsSupportedArgument(kargs);
     }
  
     CK_TILE_HOST static uint32_t GetWorkSpaceSize(const StreamKKernelArgs& kargs)
     {
         // For reduction, we need to determine the amount of device space for acculumation
         // results and semaphores.
         if(kargs.reduction_strategy == ck_tile::StreamKReductionStrategy::Reduction)
         {
             return kargs.tile_partitioner.GetWorkSpaceSize(sizeof(CDataType));
         }
  
         // Otherwise, no additional space is needed since blocks atomically store their results.
         return 0;
     }
  
     CK_TILE_HOST static void SetWorkSpacePointer(StreamKKernelArgs& kargs, void* workspace_ptr)
     {
         kargs.workspace_ptr = workspace_ptr;
     }
  
     CK_TILE_DEVICE void operator()(StreamKKernelArgs kargs) const
     {
         // Allocate LDS
         __shared__ char smem_ptr_0[UniversalGemmKernel::GetSmemSize()];
  
         uint32_t block_idx = ck_tile::get_block_1d_id();
  
         bool is_padding_block =
             amd_wave_read_first_lane(block_idx >= kargs.tile_partitioner.sk_num_blocks &&
                                      block_idx < kargs.tile_partitioner.dp_start_block_idx);
  
         // Padding blocks make it such that the DP blocks are aligned with the number of CUs; they
         // should not partake in the GEMM
         if(is_padding_block)
             return;
  
         // Determine the K offset of the first and final macro tile in the A and B tensors along the
         // K dimension.
         uint32_t iter_start, iter_end;
         kargs.tile_partitioner.GetBlockItr(block_idx, iter_start, iter_end);
  
         // Main Stream-K loop
         while(true)
         {
             // Determine the number of macro tiles in A and B this WG is resposible for in the
             // current C macro tile.
             uint32_t current_iter_length = amd_wave_read_first_lane(
                 kargs.tile_partitioner.GetCurrentIterLength(iter_start, iter_end));
  
             // Determine the 1D tile_idx and the iter_offset for this WG.
             // The tile_idx is the 1D macro tile index in the C tensor.
             // The iter_offset is the starting macro tile index in the K dimension for the WG in the
             // current iteration of the while loop.
             uint32_t tile_idx, iter_offset;
             kargs.tile_partitioner.GetTileIdxWithOffset(iter_start, tile_idx, iter_offset);
  
             // Get the 2D tile index in the C tensor for this WG using the 1D index (i.e. tile_idx)
             auto spatial_idx = kargs.tile_partitioner.GetOutputTileIndex(tile_idx);
  
             // Get the offsets in A, B, C tensors.
             index_t i_m         = static_cast<index_t>(spatial_idx[UniversalGemmKernel::I0] *
                                                TilePartitioner::MPerBlock);
             index_t i_n         = static_cast<index_t>(spatial_idx[UniversalGemmKernel::I1] *
                                                TilePartitioner::NPerBlock);
             auto [i_k_a, i_k_b] = GetKOffsets<ALayout, BLayout>(
                 static_cast<index_t>(iter_offset), kargs.stride_As[0], kargs.stride_Bs[0]);
  
             // Determine the total size along the K dimension the WG is using in this iteration
             // (used to construct tensor views).
             index_t k_size = static_cast<index_t>(current_iter_length * TilePartitioner::KPerBlock);
  
             // Update pointer offsets for A, B, and C.
             const ADataType* a_ptr = static_cast<const ADataType*>(kargs.as_ptr[0]) + i_k_a;
             const BDataType* b_ptr = static_cast<const BDataType*>(kargs.bs_ptr[0]) + i_k_b;
             CDataType* c_ptr       = static_cast<CDataType*>(kargs.e_ptr);
  
             // Run the GEMM pipeline and Epilogue.
             RunGemm({a_ptr},
                     {b_ptr},
                     {/*ds_ptr*/},
                     c_ptr,
                     smem_ptr_0,
                     kargs,
                     current_iter_length,
                     i_m,
                     i_n,
                     k_size);
  
             // Prepare for next Stream-K loop iteration.
             iter_start += current_iter_length;
             if(iter_end <= iter_start)
                 break;
             block_sync_lds();
         }
     }
  
     private:
     template <typename ALayout, typename BLayout>
     CK_TILE_DEVICE static tuple<index_t, index_t>
     GetKOffsets(index_t iter_offset, index_t stride_a, index_t stride_b)
     {
         index_t stride_offset_a;
         index_t stride_offset_b;
         if constexpr(std::is_same_v<ALayout, ck_tile::tensor_layout::gemm::ColumnMajor>)
         {
             stride_offset_a = stride_a;
         }
         else
         {
             stride_offset_a = 1;
         }
  
         if constexpr(std::is_same_v<BLayout, ck_tile::tensor_layout::gemm::RowMajor>)
         {
             stride_offset_b = stride_b;
         }
         else
         {
             stride_offset_b = 1;
         }
  
         index_t base_offset = iter_offset * TilePartitioner::KPerBlock;
  
         return make_tuple(base_offset * stride_offset_a, base_offset * stride_offset_b);
     }
  
     CK_TILE_HOST static int NumCU()
     {
         hipDeviceProp_t dev_prop;
         hipDevice_t dev;
         hip_check_error(hipGetDevice(&dev));
         hip_check_error(hipGetDeviceProperties(&dev_prop, dev));
         int num_cu = dev_prop.multiProcessorCount;
  
         return num_cu;
     }
  
     CK_TILE_HOST static int Occupancy()
     {
         int occupancy;
  
         // Since occupancy of 1 is valid for stream k, we set min_num_block_per_cu to 1
         constexpr int min_block_per_cu = 1;
         const auto kernel              = kentry<min_block_per_cu, Kernel, KernelArgs>;
  
         hip_check_error(
             hipOccupancyMaxActiveBlocksPerMultiprocessor(&occupancy, kernel, kBlockSize, 0));
  
         return occupancy;
     }
 };
  
 } // namespace ck_tile