pytorch/caffe2/operators/lstm_unit_op.h

#ifndef CAFFE2_OPERATORS_LSTM_UNIT_OP_H_
#define CAFFE2_OPERATORS_LSTM_UNIT_OP_H_

#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/perfkernels/lstm_unit_cpu.h"
#include "caffe2/utils/conversions.h"

namespace caffe2 {
namespace detail {
template <typename T, typename Context>
inline void LSTMUnit(
    const int N,
    const int D,
    const int t,
    const T* H_prev,
    const T* C_prev,
    const T* X,
    const int32_t* seqLengths,
    const bool drop_states,
    T* C,
    T* H,
    const float forget_bias,
    Context* /*context*/) {
  LstmUnitCpu<T>(
      N, D, t, H_prev, C_prev, X, seqLengths, drop_states, C, H, forget_bias);
}

template <typename T, typename Context>
inline void LSTMUnitGradient(
    int N,
    int D,
    int t,
    const T* C_prev,
    const T* X,
    const int32_t* seqLengths,
    const T* C,
    const T* H,
    const T* C_diff,
    const T* H_diff,
    bool drop_states,
    T* H_prev_diff,
    T* C_prev_diff,
    T* X_diff,
    const float forget_bias,
    Context* /*context*/) {
  LstmUnitGradientCpu<T>(
      N,
      D,
      t,
      C_prev,
      X,
      seqLengths,
      C,
      H,
      C_diff,
      H_diff,
      drop_states,
      H_prev_diff,
      C_prev_diff,
      X_diff,
      forget_bias);
}

} // namespace detail

template <typename Context>
class LSTMUnitOp : public Operator<Context> {
 public:
  explicit LSTMUnitOp(const OperatorDef& operator_def, Workspace* ws)
      : Operator<Context>(operator_def, ws),
        forget_bias_(static_cast<float>(
            this->template GetSingleArgument<float>("forget_bias", 0.0))),
        sequence_lengths_(
            this->template GetSingleArgument<bool>("sequence_lengths", true)),
        drop_states_(
            this->template GetSingleArgument<bool>("drop_states", false)) {}
  USE_OPERATOR_CONTEXT_FUNCTIONS;
  using Operator<Context>::Operator;

  template <typename T>
  bool DoRunWithType() {
    // handle potentially-missing sequence lengths input
    const size_t TIMESTEP = SEQ_LENGTHS + (sequence_lengths_ ? 1 : 0);

    // Extract N
    const auto N = Input(CELL_T_M_1).size(1);

    // Gates: 1xNxG
    const auto G = Input(GATES).size(2);
    const auto D = Input(CELL_T_M_1).size(2);

    CAFFE_ENFORCE_EQ(4 * D, G);
    const auto* H_prev = Input(HIDDEN_T_M_1).template data<T>();
    const auto* C_prev = Input(CELL_T_M_1).template data<T>();
    const auto* X = Input(GATES).template data<T>();

    const int32_t* seqLengths = nullptr;
    if (sequence_lengths_) {
      CAFFE_ENFORCE_EQ(Input(SEQ_LENGTHS).numel(), N);
      seqLengths = Input(SEQ_LENGTHS).template data<int32_t>();
    }

    const auto t = static_cast<OperatorBase*>(this)
                       ->Input<Tensor>(TIMESTEP, CPU)
                       .template data<int32_t>()[0];
    Output(CELL_T)->ResizeLike(Input(CELL_T_M_1));
    auto* C = Output(CELL_T)->template mutable_data<T>();
    Output(HIDDEN_T)->ResizeLike(Input(CELL_T_M_1));
    auto* H = Output(HIDDEN_T)->template mutable_data<T>();
    detail::LSTMUnit<T, Context>(
        N,
        D,
        t,
        H_prev,
        C_prev,
        X,
        seqLengths,
        drop_states_,
        C,
        H,
        forget_bias_,
        &context_);
    return true;
  }

  bool RunOnDevice() override {
    return DoRunWithType<float>();
  }

 protected:
  INPUT_TAGS(HIDDEN_T_M_1, CELL_T_M_1, GATES, SEQ_LENGTHS);
  // additional input tags are determined dynamically based on whether
  // sequence_lengths is present.
  OUTPUT_TAGS(HIDDEN_T, CELL_T);

  float forget_bias_;
  bool sequence_lengths_;

 private:
  bool drop_states_;
};

template <typename Context>
class LSTMUnitGradientOp : public Operator<Context> {
 public:
  template <class... Args>
  explicit LSTMUnitGradientOp(Args&&... args)
      : Operator<Context>(std::forward<Args>(args)...),
        forget_bias_(static_cast<float>(
            this->template GetSingleArgument<float>("forget_bias", 0.0))),
        sequence_lengths_(
            this->template GetSingleArgument<bool>("sequence_lengths", true)),
        drop_states_(
            this->template GetSingleArgument<bool>("drop_states", false)) {}
  USE_OPERATOR_CONTEXT_FUNCTIONS;

  template <typename T>
  bool DoRunWithType() {
    // handle potentially-missing sequence lengths input
    const size_t inputOffset = SEQ_LENGTHS + (sequence_lengths_ ? 1 : 0);
    const size_t TIMESTEP = inputOffset;
    const size_t HIDDEN_T = inputOffset + 1;
    const size_t CELL_T = inputOffset + 2;
    const size_t HIDDEN_T_GRAD = inputOffset + 3;
    const size_t CELL_T_GRAD = inputOffset + 4;

    // Extract N
    const auto N = Input(CELL_T_M_1).size(1);

    // Gates: 1xNxG
    const auto G = Input(GATES).size(2);
    const auto D = Input(CELL_T_M_1).size(2);

    CAFFE_ENFORCE_EQ(4 * D, G);
    const auto* C_prev = Input(CELL_T_M_1).template data<T>();
    const auto* X = Input(GATES).template data<T>();
    const auto t = static_cast<OperatorBase*>(this)
                       ->Input<Tensor>(TIMESTEP, CPU)
                       .template data<int32_t>()[0];
    const auto* C = Input(CELL_T).template data<T>();
    const auto* H = Input(HIDDEN_T).template data<T>();
    const auto* C_diff = Input(CELL_T_GRAD).template data<T>();
    const auto* H_diff = Input(HIDDEN_T_GRAD).template data<T>();

    const int32_t* seqLengths = nullptr;
    if (sequence_lengths_) {
      CAFFE_ENFORCE_EQ(Input(SEQ_LENGTHS).numel(), N);
      seqLengths = Input(SEQ_LENGTHS).template data<int32_t>();
    }

    Output(HIDDEN_T_M_1_GRAD)->ResizeLike(Input(HIDDEN_T_M_1));
    auto* H_prev_diff = Output(HIDDEN_T_M_1_GRAD)->template mutable_data<T>();
    Output(CELL_T_M_1_GRAD)->ResizeLike(Input(CELL_T_M_1));
    auto* C_prev_diff = Output(CELL_T_M_1_GRAD)->template mutable_data<T>();
    Output(GATES_GRAD)->ResizeLike(Input(GATES));
    auto* X_diff = Output(GATES_GRAD)->template mutable_data<T>();

    detail::LSTMUnitGradient<T, Context>(
        N,
        D,
        t,
        C_prev,
        X,
        seqLengths,
        C,
        H,
        C_diff,
        H_diff,
        drop_states_,
        H_prev_diff,
        C_prev_diff,
        X_diff,
        forget_bias_,
        &context_);
    return true;
  }

  bool RunOnDevice() override {
    return DoRunWithType<float>();
  }

 protected:
  INPUT_TAGS(HIDDEN_T_M_1, CELL_T_M_1, GATES, SEQ_LENGTHS);
  // additional input tags are determined dynamically based on whether
  // sequence_lengths is present.
  OUTPUT_TAGS(HIDDEN_T_M_1_GRAD, CELL_T_M_1_GRAD, GATES_GRAD);

  float forget_bias_;
  bool sequence_lengths_;

 private:
  bool drop_states_;
};
} // namespace caffe2

#endif // CAFFE2_OPERATORS_LSTM_UNIT_OP_H_