CacheOpA Class — pytorch Architecture

Source Code

aten/src/ATen/native/transformers/cuda/mem_eff_attention/gemm/custom_mma_multistage.h lines 60–244

template <
    /// Size of the Gemm problem - concept: gemm::GemmShape<>
    typename Shape_,
    /// Iterates over tiles of A operand in global memory
    //  (concept: ReadableTileIterator | ForwardTileIterator |
    //  MaskedTileIterator)
    typename IteratorA_,
    /// Iterates over tiles of A operand in shared memory
    /// (concept: WriteableTileIterator | RandomAccessTileIterator)
    typename SmemIteratorA_,
    /// Cache operation for operand A
    cutlass::arch::CacheOperation::Kind CacheOpA,
    /// Iterates over tiles of B operand in global memory
    //  (concept: ReadableTileIterator | ForwardTileIterator |
    //  MaskedTileIterator)
    typename IteratorB_,
    /// Iterates over tiles of B operand in shared memory
    /// (concept: WriteableTileIterator | RandomAccessTileIterator)
    typename SmemIteratorB_,
    /// Cache operation for operand B
    cutlass::arch::CacheOperation::Kind CacheOpB,
    /// Data type of accumulator matrix
    typename ElementC_,
    /// Data type of accumulator matrix
    typename LayoutC_,
    /// Policy describing tuning details (concept: MmaPolicy)
    typename Policy_,
    /// Number of stages,
    int Stages,
    /// Use zfill or predicate for out-of-bound cp.async
    SharedMemoryClearOption SharedMemoryClear = SharedMemoryClearOption::kNone,
    /// Upper boundon the K dimension
    int kMaxK = cutlass::platform::numeric_limits<int>::max(),
    /// Used for partial specialization
    typename Enable = bool>
class CustomMmaMultistage : public CustomMmaBase<Shape_, Policy_, Stages> {
 public:
  ///< Base class
  using Base = CustomMmaBase<Shape_, Policy_, Stages>;
  ///< Size of the Gemm problem - concept: gemm::GemmShape<>
  using Shape = Shape_;
  ///< Iterates over tiles of A operand in global memory
  using IteratorA = IteratorA_;
  ///< Iterates over tiles of B operand in global memory
  using IteratorB = IteratorB_;
  ///< Data type of accumulator matrix
  using ElementC = ElementC_;
  ///< Layout of accumulator matrix
  using LayoutC = LayoutC_;
  ///< Policy describing tuning details
  using Policy = Policy_;

  using SmemIteratorA = SmemIteratorA_;
  using SmemIteratorB = SmemIteratorB_;

  static cutlass::arch::CacheOperation::Kind const kCacheOpA = CacheOpA;
  static cutlass::arch::CacheOperation::Kind const kCacheOpB = CacheOpB;

  //
  // Dependent types
  //

  /// Fragment of accumulator tile
  using FragmentC = typename Policy::Operator::FragmentC;

  /// Warp-level Mma
  using Operator = typename Policy::Operator;

  /// Minimum architecture is Sm80 to support cp.async
  using ArchTag = arch::Sm80;

  /// Complex transform on A operand
  static ComplexTransform const kTransformA = Operator::kTransformA;

  /// Complex transform on B operand
  static ComplexTransform const kTransformB = Operator::kTransformB;

  /// Internal structure exposed for introspection.
  struct Detail {
    static_assert(
        Base::kWarpGemmIterations > 1,
        "The pipelined structure requires at least two warp-level "
        "GEMM operations.");

    /// Number of cp.async instructions to load one stage of operand A
    static int const AsyncCopyIterationsPerStageA =
        IteratorA::ThreadMap::Iterations::kCount;

    /// Number of cp.async instructions to load one stage of operand B
    static int const AsyncCopyIterationsPerStageB =
        IteratorB::ThreadMap::Iterations::kCount;

    /// Number of stages
    static int const kStages = Stages;

    /// Number of cp.async instructions to load on group of operand A
    static int const kAccessesPerGroupA =
        (AsyncCopyIterationsPerStageA + Base::kWarpGemmIterations - 1) /
        Base::kWarpGemmIterations;

    /// Number of cp.async instructions to load on group of operand B
    static int const kAccessesPerGroupB =
        (AsyncCopyIterationsPerStageB + Base::kWarpGemmIterations - 1) /
        Base::kWarpGemmIterations;
  };

  static bool const kSmemContainsEntireMat = kMaxK <= Shape::kK * Stages;
  static constexpr int kNumStagesConcurrentLoad =
      kSmemContainsEntireMat ? Stages : Stages - 1;

 private:
  using WarpLoadedFragmentA = typename Operator::FragmentA;
  using WarpLoadedFragmentB = typename Operator::FragmentB;
  using WarpTransformedFragmentA = typename Operator::TransformedFragmentA;
  using WarpTransformedFragmentB = typename Operator::TransformedFragmentB;

 private:
  //
  // Data members
  //

  /// Iterator to write threadblock-scoped tile of A operand to shared memory
  SmemIteratorA smem_iterator_A_;

  /// Iterator to write threadblock-scoped tile of B operand to shared memory
  SmemIteratorB smem_iterator_B_;

  bool prologue_done_;

  // Set to `True` to ensure the accumulator will be zero outside the GEMM
  // footprint
  bool zero_outside_bounds_;

 public:
  /// Construct from tensor references
  CUTLASS_DEVICE
  CustomMmaMultistage(
      ///< Shared storage needed for internal use by threadblock-scoped GEMM
      typename Base::SharedStorageA& shared_storageA,
      typename Base::SharedStorageB& shared_storageB,
      ///< ID within the threadblock
      int thread_idx,
      ///< ID of warp
      int warp_idx,
      ///< ID of each thread within a warp
      int lane_idx)
      : Base(shared_storageA, shared_storageB, thread_idx, warp_idx, lane_idx),
        smem_iterator_A_(shared_storageA.ref(), thread_idx),
        smem_iterator_B_(shared_storageB.ref(), thread_idx),
        prologue_done_(false),
        zero_outside_bounds_(false) {
    // Compute warp location within threadblock tile by mapping the warp_id to
    // three coordinates:
    //   _m: the warp's position within the threadblock along the M dimension
    //   _n: the warp's position within the threadblock along the N dimension
    //   _k: the warp's position within the threadblock along the K dimension

    int warp_idx_mn = warp_idx % (Base::WarpCount::kM * Base::WarpCount::kN);
    int warp_idx_k = warp_idx / (Base::WarpCount::kM * Base::WarpCount::kN);

    int warp_idx_m = warp_idx_mn % Base::WarpCount::kM;
    int warp_idx_n = warp_idx_mn / Base::WarpCount::kM;

    // Add per-warp offsets in units of warp-level tiles
    this->warp_tile_iterator_A_.add_tile_offset(
        {warp_idx_m, Base::kWarpGemmIterations * warp_idx_k});
    this->warp_tile_iterator_B_.add_tile_offset(
        {Base::kWarpGemmIterations * warp_idx_k, warp_idx_n});
  }
  CUTLASS_DEVICE
  CustomMmaMultistage(
      ///< Shared storage needed for internal use by threadblock-scoped GEMM
      typename Base::SharedStorage& st,
      ///< ID within the threadblock
      int thread_idx,
      ///< ID of warp
      int warp_idx,
      ///< ID of each thread within a warp
      int lane_idx)
      : CustomMmaMultistage(
            st.operand_A,
            st.operand_B,
            thread_idx,
            warp_idx,
            lane_idx) {}