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106 lines
3.9 KiB
106 lines
3.9 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/operators/sequence_softmax_op.h"
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namespace paddle {
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namespace operators {
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class SequenceSoftmaxOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of SequenceSoftmaxOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of SequenceSoftmaxOp should not be null.");
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ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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};
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class SequenceSoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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SequenceSoftmaxOpMaker(framework::OpProto* proto,
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framework::OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X",
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"(LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension "
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"of length 1.");
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AddOutput("Out",
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"(LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension "
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"of length 1.");
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AddComment(R"DOC(
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Sequence Softmax Operator.
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SequenceSoftmaxOp computes the softmax activation among all time-steps for each
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sequence. The dimension of each time-step should be 1. Thus, the shape of
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input Tensor can be either [N, 1] or [N], where N is the sum of the length
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of all sequences.
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The algorithm works as follows:
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for i-th sequence in a mini-batch:
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$$Out(X[lod[i]:lod[i+1]], :) =
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\frac{\exp(X[lod[i]:lod[i+1], :])}
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{\sum(\exp(X[lod[i]:lod[i+1], :]))}$$
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For example, for a mini-batch of 3 sequences with variable-length,
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each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7],
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then softmax will be computed among X[0:2, :], X[2:5, :], X[5:7, :]
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and N turns out to be 7.
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)DOC");
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}
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};
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class SequenceSoftmaxGradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("Out"),
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"Input(Out) of SequenceSoftmaxGradOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasInput(framework::GradVarName("Out")),
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"Input(Out@GRAD) of SequenceSoftmaxGradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of SequenceSoftmaxOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
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"Output(X@GRAD) of SequenceSoftmaxOp should not be null.");
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PADDLE_ENFORCE_EQ(
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ctx->GetInputDim("Out"),
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ctx->GetInputDim(framework::GradVarName("Out")),
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"Input(Out) and Input(Out@GRAD) of SequenceSoftmaxGradOp should be of "
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"the same shape.");
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP(sequence_softmax, ops::SequenceSoftmaxOp,
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ops::SequenceSoftmaxOpMaker, sequence_softmax_grad,
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ops::SequenceSoftmaxGradOp);
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REGISTER_OP_CPU_KERNEL(
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sequence_softmax,
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ops::SequenceSoftmaxKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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sequence_softmax_grad,
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ops::SequenceSoftmaxGradKernel<paddle::platform::CPUPlace, float>);
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