Count Class — pytorch Architecture

Source Code

aten/src/ATen/native/transformers/cuda/mem_eff_attention/epilogue/epilogue_rescale_output.h lines 62–190

template <
    typename ElementOutput_, ///< Data type used to store tensors
    typename ElementSource_, //< Data type for source (usually matches
                             //`ElementOutput`)
    int Count, ///< Number of elements computed per operation.
               ///< Usually it is 128/sizeof_bits<ElementOutput_>,
               ///< but we use 64 or 32 sometimes when there are not enough data
               ///< to store
    typename ElementAccumulator_, ///< Accumulator data type
    typename ElementCompute_, ///< Data type used to compute linear combination
    bool isFirst,
    bool isLast,
    typename FragmentAlphaBeta_,
    FloatRoundStyle Round = FloatRoundStyle::round_to_nearest>
class MemoryEfficientAttentionNormalize {
 public:
  using ElementOutput = ElementOutput_;
  using ElementSource = ElementSource_;
  using ElementAccumulator = ElementAccumulator_;
  using ElementCompute = ElementCompute_;

  static int const kCount = Count;

  using FragmentOutput = Array<ElementOutput, kCount>;
  using FragmentSource = Array<ElementSource, kCount>;
  using FragmentAccumulator = Array<ElementAccumulator, kCount>;
  using ComputeFragment = Array<ElementCompute, kCount>;
  using FragmentAlphaBeta = FragmentAlphaBeta_;

  static FloatRoundStyle const kRound = Round;

 private:
  //
  // Data members
  //

  FragmentAlphaBeta const& s_prime_;
  FragmentAlphaBeta const& m_prime_;

 public:
  /// Constructs the function object, possibly loading from pointers in host
  /// memory
  CUTLASS_HOST_DEVICE
  MemoryEfficientAttentionNormalize(
      FragmentAlphaBeta const& s_prime,
      FragmentAlphaBeta const& m_prime)
      : s_prime_(s_prime), m_prime_(m_prime) {}

  /// Returns true if source is needed
  CUTLASS_HOST_DEVICE
  bool is_source_needed() const {
    return !isFirst;
  }

  /// Functionally required for serial reduction in the epilogue
  CUTLASS_HOST_DEVICE
  void set_k_partition(int k_partition, int k_partition_count) {}

  /// Computes linear scaling: D = alpha * accumulator + beta * source
  CUTLASS_HOST_DEVICE
  FragmentOutput operator()(
      int row,
      FragmentAccumulator const& accumulator,
      FragmentSource const& source) const {
    assert(!isFirst);

    // Convert source to internal compute numeric type
    NumericArrayConverter<ElementCompute, ElementSource, kCount, Round>
        source_converter;
    NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round>
        accumulator_converter;

    // Convert to destination numeric type
    NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round>
        destination_converter;

    ComputeFragment converted_source = source_converter(source);
    ComputeFragment converted_accumulator = accumulator_converter(accumulator);

    // Perform binary operations
    ComputeFragment intermediate;

    multiplies<ComputeFragment> mul_add_source;
    multiply_add<ComputeFragment> mul_add_accumulator;

    // Row sums for full masked out rows are 0, we set them to 1
    // In order to avoid NaNs in the output and instead sem them to 0.
    ElementCompute denom = s_prime_[row] == 0 ? 1 : s_prime_[row];
    ElementCompute alpha = isLast ? (1 / denom) : 1;
    ElementCompute beta = alpha * m_prime_[row];

    intermediate = mul_add_source(beta, converted_source); // X =  beta * C

    intermediate = mul_add_accumulator(
        alpha, converted_accumulator, intermediate); // D = alpha * Accum + X

    return destination_converter(intermediate);
  }

  /// Computes linear scaling: D = alpha * accumulator
  CUTLASS_HOST_DEVICE
  FragmentOutput operator()(int row, FragmentAccumulator const& accumulator)
      const {
    assert(isFirst);

    // Convert source to internal compute numeric type
    NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round>
        accumulator_converter;

    // Convert to destination numeric type
    NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round>
        destination_converter;

    ComputeFragment converted_accumulator = accumulator_converter(accumulator);

    ComputeFragment intermediate;
    multiplies<ComputeFragment> mul_accumulator;

    // Row sums for full masked out rows are 0, we set them to 1
    // In order to avoid NaNs in the output and instead sem them to 0.
    ElementCompute denom = s_prime_[row] == 0 ? 1 : s_prime_[row];
    ElementCompute alpha = isLast ? (1 / denom) : 1;

    intermediate = mul_accumulator(
        alpha, converted_accumulator); // X =  alpha * C + uniform

    return destination_converter(intermediate);
  }
};