caffe2/operators/lstm_unit_op.cc

#include "lstm_unit_op.h"

namespace caffe2 {
REGISTER_CPU_OPERATOR(LSTMUnit, LSTMUnitOp<CPUContext>);
OPERATOR_SCHEMA(LSTMUnit)
    .NumInputs(4, 5)
    .NumOutputs(2)
    .SetDoc(R"DOC(
LSTMUnit computes the activations of a standard LSTM (without peephole
connections), in a sequence-length aware fashion.

Concretely, given the (fused) inputs X (TxNxD), the previous cell
state (NxD), and the sequence lengths (N), computes the LSTM
activations, avoiding computation if the input is invalid (as in, the
value at X{t][n] >= seqLengths[n].

)DOC")
    .Arg("forget_bias", "Bias term to add in while calculating forget gate")
    .Arg(
        "sequence_lengths",
        "When false, the sequence lengths input is left out, "
        "and all following inputs are shifted left by one.");
REGISTER_CPU_OPERATOR(LSTMUnitGradient, LSTMUnitGradientOp<CPUContext>);
OPERATOR_SCHEMA(LSTMUnitGradient)
    .NumInputs(8, 9)
    .NumOutputs(3)
    .Arg(
        "sequence_lengths",
        "When false, the sequence lengths input is left out, "
        "and all following inputs are shifted left by one.");

class GetLSTMUnitGradient : public GradientMakerBase {
  using GradientMakerBase::GradientMakerBase;
  vector<OperatorDef> GetGradientDefs() override {
    if (GetFlagArgument(def_, "sequence_lengths", true)) {
      return SingleGradientDef(
          "LSTMUnitGradient",
          "",
          vector<string>{
              I(0), I(1), I(2), I(3), I(4), O(0), O(1), GO(0), GO(1)},
          vector<string>{GI(0), GI(1), GI(2)});
    } else {
      return SingleGradientDef(
          "LSTMUnitGradient",
          "",
          vector<string>{I(0), I(1), I(2), I(3), O(0), O(1), GO(0), GO(1)},
          vector<string>{GI(0), GI(1), GI(2)});
    }
  }
};
REGISTER_GRADIENT(LSTMUnit, GetLSTMUnitGradient);
}