/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-composable-kernel/checkouts/develop/include/ck_tile/ops/flatmm/kernel/mixed_prec_flatmm_kernel.hpp Source File

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-composable-kernel/checkouts/develop/include/ck_tile/ops/flatmm/kernel/mixed_prec_flatmm_kernel.hpp Source File#

Composable Kernel: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-composable-kernel/checkouts/develop/include/ck_tile/ops/flatmm/kernel/mixed_prec_flatmm_kernel.hpp Source File
Go to the documentation of this file.
 // Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
 // SPDX-License-Identifier: MIT
  
 #pragma once
  
 #include <iostream>
 #include <string>
  
 #include "ck_tile/core.hpp"
 #include "ck_tile/ops/common.hpp"
  
 #include "ck_tile/ops/flatmm/kernel/flatmm_kernel.hpp"
  
 namespace ck_tile {
  
 template <typename TilePartitioner_, typename FlatmmPipeline_, typename EpiloguePipeline_>
 struct F16xMXF4FlatmmKernel : FlatmmKernel<TilePartitioner_, FlatmmPipeline_, EpiloguePipeline_>
 {
     using Underlying = FlatmmKernel<TilePartitioner_, FlatmmPipeline_, EpiloguePipeline_>;
  
     using TilePartitioner = remove_cvref_t<TilePartitioner_>;
     using FlatmmPipeline  = remove_cvref_t<FlatmmPipeline_>;
     using BlockGemmShape =
         remove_cvref_t<typename FlatmmPipeline::BlockGemmShape>; // TileFlatmmShape
     using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
     using ALayout          = remove_cvref_t<typename FlatmmPipeline::ALayout>;
     using BLayout          = remove_cvref_t<typename FlatmmPipeline::BLayout>;
     using ELayout          = remove_cvref_t<typename FlatmmPipeline::CLayout>;
     using DsLayout         = remove_cvref_t<typename EpiloguePipeline::DsLayout>;
     using DsDataType       = remove_cvref_t<typename EpiloguePipeline::DsDataType>;
     static constexpr index_t KernelBlockSize  = FlatmmPipeline::BlockSize;
     static constexpr bool UsePersistentKernel = FlatmmPipeline::UsePersistentKernel;
  
     using ADataType = remove_cvref_t<typename FlatmmPipeline::ADataType>;
     using BDataType = remove_cvref_t<typename FlatmmPipeline::BDataType>;
     // Below type is actually accumulation data type - the output of block GEMM.
     using EDataType = remove_cvref_t<typename EpiloguePipeline::ODataType>;
  
     static constexpr int QuantPackedSize = numeric_traits<BDataType>::PackedSize;
     static constexpr int N_Pack          = 2;
  
     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 I3 = number<3>();
     static constexpr auto I4 = number<4>();
  
     static_assert(DsLayout::size() == DsDataType::size(),
                   "The size of DsLayout and DsDataType should be the same");
     // using KernelArgs = FlatmmKernelArgs<DsLayout::size()>;
  
     [[nodiscard]] CK_TILE_HOST static const std::string GetName()
     {
         // clang-format off
         return concat('_', "mixed_prec_gemm", gemm_prec_str<ADataType, BDataType>, FlatmmPipeline::GetName());
         // clang-format on
     }
  
     template <class ScaleM, class ScaleN>
     CK_TILE_HOST static constexpr auto
     GridSize(const FlatmmKernelArgs<ScaleM, ScaleN, DsDataType::size()>& kargs)
     {
         if constexpr(UsePersistentKernel)
         {
             hipDeviceProp_t prop;
             int deviceId = 0; // default device
  
             constexpr int block_size = F16xMXF4FlatmmKernel::BlockSize().x;
             int dync_smem_size       = 0;
             int maxActiveBlocksPerCU = 0;
  
             [[maybe_unused]] auto e = hipGetDeviceProperties(&prop, deviceId);
  
             e = hipOccupancyMaxActiveBlocksPerMultiprocessor(
                 &maxActiveBlocksPerCU,
                 reinterpret_cast<void*>(
                     kentry<1,
                            F16xMXF4FlatmmKernel,
                            FlatmmKernelArgs<ScaleM, ScaleN, DsDataType::size()>>),
                 block_size,
                 dync_smem_size);
  
             const int persistent_block_size = prop.multiProcessorCount * maxActiveBlocksPerCU;
             const int total_work_tile_cnt   = TilePartitioner::GridSize(kargs.M, kargs.N);
  
             // std::cout << "maxActiveBlocksPerCU: " << maxActiveBlocksPerCU
             //           << ", persistent_block_size: " << persistent_block_size
             //           << ", total_work_tile_cnt: " << total_work_tile_cnt << std::endl;
  
             assert(kargs.k_batch == 1);
             return dim3(min(persistent_block_size, total_work_tile_cnt), 1, kargs.k_batch);
         }
         else
         {
             return dim3(TilePartitioner::GridSize(kargs.M, kargs.N), 1, kargs.k_batch);
         }
     }
  
     using SplitKBatchOffset = typename Underlying::SplitKBatchOffset;
  
     template <typename KernelArgs>
     CK_TILE_DEVICE static auto MakeABlockWindow(const ADataType* a_ptr,
                                                 const KernelArgs& kargs,
                                                 const index_t k_size,
                                                 const index_t block_idx_m)
     {
         // Step 1: Create tensor view
         const auto& a_tensor_view = [&]() {
             if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
             {
                 return make_naive_tensor_view<address_space_enum::global>(
                     a_ptr,
                     make_tuple(kargs.M, k_size),
                     make_tuple(kargs.stride_A, 1),
                     number<FlatmmPipeline::GetVectorSizeA()>{},
                     number<1>{});
             }
             else
             {
                 return make_naive_tensor_view<address_space_enum::global>(
                     a_ptr,
                     make_tuple(k_size, kargs.M),
                     make_tuple(kargs.stride_A, 1),
                     number<FlatmmPipeline::GetVectorSizeA()>{},
                     number<1>{});
             }
         }();
  
         // Step 2: Create padded view
         const auto& a_pad_view = [&]() {
             if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
             {
                 return pad_tensor_view(a_tensor_view,
                                        make_tuple(number<TilePartitioner::MPerBlock>{},
                                                   number<TilePartitioner::KPerBlock>{}),
                                        sequence<false, FlatmmPipeline::kPadK>{});
             }
             else
             {
                 return pad_tensor_view(a_tensor_view,
                                        make_tuple(number<TilePartitioner::KPerBlock>{},
                                                   number<TilePartitioner::MPerBlock>{}),
                                        sequence<false, FlatmmPipeline::kPadM>{});
             }
         }();
  
         // Step 3: Create tile window
         if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
         {
             return make_tile_window(a_pad_view,
                                     make_tuple(number<TilePartitioner::MPerBlock>{},
                                                number<TilePartitioner::KPerBlock>{}),
                                     {block_idx_m, 0});
         }
         else
         {
             return make_tile_window(a_pad_view,
                                     make_tuple(number<TilePartitioner::KPerBlock>{},
                                                number<TilePartitioner::MPerBlock>{}),
                                     {0, block_idx_m});
         }
     }
  
     template <typename KernelArgs>
     CK_TILE_DEVICE static auto MakeBFlatBlockWindow(const BDataType* b_flat_ptr,
                                                     const KernelArgs& kargs,
                                                     const index_t block_idx_n)
     {
         // Step 1: Create tensor view
         index_t kFlatK = kargs.K * BlockGemmShape::WarpTile::at(I1);
         index_t kFlatN = kargs.N * kargs.K / kFlatK;
  
         const auto& b_flat_tensor_view = make_naive_tensor_view<address_space_enum::global>(
             b_flat_ptr,
             make_tuple(kFlatN, kFlatK),
             make_tuple(kFlatK, 1),
             number<FlatmmPipeline::GetVectorSizeB()>{},
             number<1>{});
  
         // Step 2: No padding needed for b_flat
         // Step 3: Create tile window
         return make_tile_window(
             b_flat_tensor_view,
             make_tuple(number<FlatmmPipeline::flatNPerWarp>{},
                        number<FlatmmPipeline::flatKPerWarp>{}),
             {static_cast<int>(block_idx_n / BlockGemmShape::WarpTile::at(I1)), 0});
     }
  
     template <typename KernelArgs>
     CK_TILE_DEVICE static auto MakeDBlockWindows(const std::array<const void*, NumDTensor>& ds_ptr,
                                                  const KernelArgs& kargs,
                                                  const index_t block_idx_m,
                                                  const index_t block_idx_n)
     {
         // Step 1: Create tensor views
         const auto& ds_tensor_view = generate_tuple(
             [&](auto i) {
                 using DiLayout   = remove_cvref_t<std::tuple_element_t<i.value, DsLayout>>;
                 using DDataType_ = remove_cvref_t<std::tuple_element_t<i.value, DsDataType>>;
                 if constexpr(std::is_same_v<DiLayout, tensor_layout::gemm::RowMajor>)
                 {
                     return make_naive_tensor_view<address_space_enum::global>(
                         static_cast<const DDataType_*>(ds_ptr[i]),
                         make_tuple(kargs.M, kargs.N),
                         make_tuple(kargs.stride_Ds[i], 1),
                         number<EpiloguePipeline::GetVectorSizeD(i)>{},
                         number<1>{});
                 }
                 else
                 {
                     return make_naive_tensor_view<address_space_enum::global>(
                         static_cast<const DDataType_*>(ds_ptr[i]),
                         make_tuple(kargs.N, kargs.M),
                         make_tuple(kargs.stride_Ds[i], 1),
                         number<EpiloguePipeline::GetVectorSizeD(i)>{},
                         number<1>{});
                 }
             },
             number<NumDTensor>{});
  
         // Step 2: Create padded views
         const auto& ds_pad_view = generate_tuple(
             [&](auto i) {
                 using DiLayout = remove_cvref_t<std::tuple_element_t<i.value, DsLayout>>;
                 if constexpr(std::is_same_v<DiLayout, tensor_layout::gemm::RowMajor>)
                 {
                     return pad_tensor_view(ds_tensor_view[i],
                                            make_tuple(number<TilePartitioner::MPerBlock>{},
                                                       number<TilePartitioner::NPerBlock>{}),
                                            sequence<false, FlatmmPipeline::kPadN>{});
                 }
                 else
                 {
                     return pad_tensor_view(ds_tensor_view[i],
                                            make_tuple(number<TilePartitioner::NPerBlock>{},
                                                       number<TilePartitioner::MPerBlock>{}),
                                            sequence<false, FlatmmPipeline::kPadM>{});
                 }
             },
             number<NumDTensor>{});
  
         // Step 3: Create tile windows
         return generate_tuple(
             [&](auto i) {
                 using DiLayout = remove_cvref_t<std::tuple_element_t<i.value, DsLayout>>;
                 if constexpr(std::is_same_v<DiLayout, tensor_layout::gemm::RowMajor>)
                 {
                     return make_tile_window(ds_pad_view[i],
                                             make_tuple(number<TilePartitioner::MPerBlock>{},
                                                        number<TilePartitioner::NPerBlock>{}),
                                             {block_idx_m, block_idx_n});
                 }
                 else
                 {
                     return make_tile_window(ds_pad_view[i],
                                             make_tuple(number<TilePartitioner::NPerBlock>{},
                                                        number<TilePartitioner::MPerBlock>{}),
                                             {block_idx_n, block_idx_m});
                 }
             },
             number<NumDTensor>{});
     }
  
     template <memory_operation_enum DstInMemOp = memory_operation_enum::set, typename KernelArgs>
     CK_TILE_DEVICE static auto MakeEBlockWindow(EDataType* e_ptr,
                                                 const KernelArgs& kargs,
                                                 const index_t block_idx_m,
                                                 const index_t block_idx_n)
     {
         // Step 1: Create tensor view
         const auto& e_tensor_view = [&]() {
             if constexpr(std::is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
             {
                 return make_naive_tensor_view<address_space_enum::global, DstInMemOp>(
                     e_ptr,
                     make_tuple(kargs.M, kargs.N),
                     make_tuple(kargs.stride_E, 1),
                     number<EpiloguePipeline::GetVectorSizeC()>{},
                     number<1>{});
             }
             else
             {
                 return make_naive_tensor_view<address_space_enum::global, DstInMemOp>(
                     e_ptr,
                     make_tuple(kargs.N, kargs.M),
                     make_tuple(kargs.stride_E, 1),
                     number<1>{},
                     number<1>{});
             }
         }();
  
         // Step 2: Create padded view
         const auto& e_pad_view = [&]() {
             if constexpr(std::is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
             {
                 return pad_tensor_view(e_tensor_view,
                                        make_tuple(number<TilePartitioner::MPerBlock>{},
                                                   number<TilePartitioner::NPerBlock>{}),
                                        sequence<false, FlatmmPipeline::kPadN>{});
             }
             else
             {
                 return pad_tensor_view(e_tensor_view,
                                        make_tuple(number<TilePartitioner::MPerBlock>{},
                                                   number<TilePartitioner::NPerBlock>{}),
                                        sequence<FlatmmPipeline::kPadM, false>{});
             }
         }();
  
         // Step 3: Create tile window
         return make_tile_window(
             e_pad_view,
             make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
             {block_idx_m, block_idx_n});
     }
  
     template <typename KernelArgs>
     CK_TILE_DEVICE static auto MakeScaleBBlockWindow(const KernelArgs& kargs,
                                                      const index_t block_idx_n)
     {
         auto scale_n = kargs.scale_n_ptr;
  
         // Step 1: Create tensor view
         index_t FlatScaleK =
             (kargs.K / decltype(scale_n)::GranularityK) * N_Pack * BlockGemmShape::WarpTile::at(I1);
         index_t FlatScaleN = kargs.N / N_Pack / BlockGemmShape::WarpTile::at(I1);
  
         const auto scale_b_flat_view = make_naive_tensor_view<address_space_enum::global>(
             reinterpret_cast<const e8m0_t*>(scale_n.ptr),
             make_tuple(FlatScaleN, FlatScaleK),
             make_tuple(FlatScaleK, 1),
             number<8>{},
             number<1>{});
  
         // Step 2: Create tile window
         return make_tile_window(
             scale_b_flat_view,
             make_tuple(number<FlatmmPipeline::flatNPerWarp>{},
                        number<FlatmmPipeline::flatKPerWarp * N_Pack * 4 / 32>{}),
             {block_idx_n / BlockGemmShape::WarpTile::at(I1) / N_Pack, 0});
     }
  
     template <class ScaleM, class ScaleN, bool UseDefaultScheduler = true>
     CK_TILE_DEVICE static void
     RunFlatmm(const ADataType* a_ptr,
               const BDataType* b_flat_ptr,
               const std::array<const void*, NumDTensor>& ds_ptr,
               EDataType* e_ptr,
               void* smem_ptr_ping,
               void* smem_ptr_pong,
               const FlatmmKernelArgs<ScaleM, ScaleN, DsDataType::size()>& kargs,
               const SplitKBatchOffset& splitk_batch_offset,
               const index_t block_idx_m,
               const index_t block_idx_n)
     {
         // Create block windows using specialized methods
         const auto& a_block_window =
             MakeABlockWindow(a_ptr, kargs, splitk_batch_offset.splitted_k, block_idx_m);
         const auto& b_flat_block_window = MakeBFlatBlockWindow(b_flat_ptr, kargs, block_idx_n);
         const auto& ds_block_window    = MakeDBlockWindows(ds_ptr, kargs, block_idx_m, block_idx_n);
         const auto& scale_block_window = MakeScaleBBlockWindow(kargs, block_idx_n);
  
         const index_t num_loop = TilePartitioner::GetLoopNum(splitk_batch_offset.splitted_k);
  
         static_assert(ScaleM::GranularityK == ScaleN::GranularityK // have the same granK
                           || ScaleM::GranularityMN == -1           // or ScaleA is disable
                           || ScaleN::GranularityMN == -1,          // or ScaleB is disable
                       "ScaleM and ScaleN should have the same GranularityK");
         constexpr bool DoEpiScale =
             (ScaleM::GranularityMN != -1 && ScaleM::GranularityK == 0) || // per token
             (ScaleN::GranularityMN != -1 && ScaleN::GranularityK == 0);   // per channel
  
         // Run GEMM cooperatively by whole workgroup.
         auto a_block_window_with_distr =
             ck_tile::make_tile_window(a_block_window.get_bottom_tensor_view(),
                                       a_block_window.get_window_lengths(),
                                       a_block_window.get_window_origin(),
                                       FlatmmPipeline::GetADramTileDistribution());
         const auto& c_block_tile = FlatmmPipeline{}(a_block_window_with_distr,
                                                     b_flat_block_window,
                                                     scale_block_window,
                                                     num_loop,
                                                     smem_ptr_ping,
                                                     smem_ptr_pong);
  
         // Run Epilogue Pipeline with k_batch dispatching
         if constexpr(DoEpiScale)
         {
             if(kargs.k_batch == 1)
             {
                 auto e_block_window = MakeEBlockWindow<memory_operation_enum::set>(
                     e_ptr, kargs, block_idx_m, block_idx_n);
                 EpiloguePipeline{}(e_block_window,
                                    c_block_tile,
                                    ds_block_window,
                                    smem_ptr_ping,
                                    kargs.scale_m_ptr + block_idx_m,
                                    kargs.scale_n_ptr + block_idx_n);
             }
             else
             {
                 auto e_block_window = MakeEBlockWindow<memory_operation_enum::atomic_add>(
                     e_ptr, kargs, block_idx_m, block_idx_n);
                 EpiloguePipeline{}(e_block_window,
                                    c_block_tile,
                                    ds_block_window,
                                    smem_ptr_ping,
                                    kargs.scale_m_ptr + block_idx_m,
                                    kargs.scale_n_ptr + block_idx_n);
             }
         }
         else if(UseDefaultScheduler || (get_warp_id() == 0))
         {
             if(kargs.k_batch == 1)
             {
                 auto e_block_window = MakeEBlockWindow<memory_operation_enum::set>(
                     e_ptr, kargs, block_idx_m, block_idx_n);
                 EpiloguePipeline{}(e_block_window, c_block_tile, ds_block_window, smem_ptr_ping);
             }
             else
             {
                 auto e_block_window = MakeEBlockWindow<memory_operation_enum::atomic_add>(
                     e_ptr, kargs, block_idx_m, block_idx_n);
                 EpiloguePipeline{}(e_block_window, c_block_tile, ds_block_window, smem_ptr_ping);
             }
         }
     }
  
     template <class ScaleM, class ScaleN>
     CK_TILE_DEVICE void operator()(FlatmmKernelArgs<ScaleM, ScaleN, DsDataType::size()> kargs,
                                    int partition_idx = blockIdx.x) const
     {
         int total_work_tile_cnt = TilePartitioner::GridSize(kargs.M, kargs.N);
  
         do
         {
             const auto [iM, iN] =
                 TilePartitioner{kargs.M, kargs.N}.GetOutputTileIndex(partition_idx);
             const index_t i_m = __builtin_amdgcn_readfirstlane(iM * TilePartitioner::MPerBlock);
             const index_t i_n = __builtin_amdgcn_readfirstlane(iN * TilePartitioner::NPerBlock);
  
             const SplitKBatchOffset splitk_batch_offset(kargs);
             // options
             const ADataType* a_ptr =
                 static_cast<const ADataType*>(kargs.a_ptr) + splitk_batch_offset.a_k_split_offset;
             const BDataType* b_flat_ptr = static_cast<const BDataType*>(kargs.b_ptr) +
                                           splitk_batch_offset.b_k_split_offset / QuantPackedSize;
             EDataType* e_ptr = static_cast<EDataType*>(kargs.e_ptr);
  
             // allocate LDS
             __shared__ char smem_ptr_ping[Underlying::GetSmemPingSize()];
             __shared__ char smem_ptr_pong[Underlying::GetSmemPongSize()];
  
             if constexpr(!(EpiloguePipeline::GetVectorSizeC() % 2 != 0 &&
                            is_any_of<EDataType, fp16_t, bf16_t>::value))
             {
                 constexpr auto scheduler_type = (FlatmmPipeline::NumWaveGroups == 1);
                 RunFlatmm<ScaleM, ScaleN, scheduler_type>(a_ptr,
                                                           b_flat_ptr,
                                                           kargs.ds_ptr,
                                                           e_ptr,
                                                           smem_ptr_ping,
                                                           smem_ptr_pong,
                                                           kargs,
                                                           splitk_batch_offset,
                                                           i_m,
                                                           i_n);
             }
             partition_idx += gridDim.x;
         } while(UsePersistentKernel && partition_idx < total_work_tile_cnt);
     }
 };
  
 } // namespace ck_tile