sequence_softmax_op.cc

/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#include "paddle/operators/sequence_softmax_op.h"

namespace paddle {
namespace operators {

class SequenceSoftmaxOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("X"),
                   "Input(X) of SequenceSoftmaxOp should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("Out"),
                   "Output(Out) of SequenceSoftmaxOp should not be null.");
    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
    ctx->ShareLoD("X", /*->*/ "Out");
  }
};

class SequenceSoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  SequenceSoftmaxOpMaker(framework::OpProto* proto,
                         framework::OpAttrChecker* op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    AddInput("X",
             "(LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension "
             "of length 1.");
    AddOutput("Out",
              "(LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension "
              "of length 1.");
    AddComment(R"DOC(
Sequence Softmax Operator.

SequenceSoftmaxOp computes the softmax activation among all time-steps for each
sequence. The dimension of each time-step should be 1. Thus, the shape of
input Tensor can be either [N, 1] or [N], where N is the sum of the length
of all sequences.

The algorithm works as follows:
    for i-th sequence in a mini-batch:
        $$Out(X[lod[i]:lod[i+1]], :) =
            \frac{\exp(X[lod[i]:lod[i+1], :])}
            {\sum(\exp(X[lod[i]:lod[i+1], :]))}$$

For example, for a mini-batch of 3 sequences with variable-length,
each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7],
then softmax will be computed among X[0:2, :], X[2:5, :], X[5:7, :]
and N turns out to be 7.

)DOC");
  }
};

class SequenceSoftmaxGradOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("Out"),
                   "Input(Out) of SequenceSoftmaxGradOp should not be null.");
    PADDLE_ENFORCE(
        ctx->HasInput(framework::GradVarName("Out")),
        "Input(Out@GRAD) of SequenceSoftmaxGradOp should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("X"),
                   "Input(X) of SequenceSoftmaxOp should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
                   "Output(X@GRAD) of SequenceSoftmaxOp should not be null.");

    PADDLE_ENFORCE_EQ(
        ctx->GetInputDim("Out"),
        ctx->GetInputDim(framework::GradVarName("Out")),
        "Input(Out) and Input(Out@GRAD) of SequenceSoftmaxGradOp should be of "
        "the same shape.");

    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OP(sequence_softmax, ops::SequenceSoftmaxOp,
            ops::SequenceSoftmaxOpMaker, sequence_softmax_grad,
            ops::SequenceSoftmaxGradOp);
REGISTER_OP_CPU_KERNEL(
    sequence_softmax,
    ops::SequenceSoftmaxKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(
    sequence_softmax_grad,
    ops::SequenceSoftmaxGradKernel<paddle::platform::CPUDeviceContext, float>);