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103 lines
4.0 KiB
103 lines
4.0 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_pool_op.h"
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namespace paddle {
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namespace operators {
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class SequencePoolOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContextBase* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of SequenceAvgPoolOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of SequenceAvgPoolOp should not be null.");
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ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
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}
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};
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class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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SequencePoolOpMaker(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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"A float LoDTensor, the variable-length input of SequencePoolOp");
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AddOutput(
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"Out",
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"A float LoDTensor, the variable-length output of SequencePoolOp.");
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AddAttr<int>(
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"strategy",
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"(int, default AVERAGE) the pooling strategy of SequencePoolOp.")
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.SetDefault(AVERAGE)
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.InEnum({AVERAGE, SUM, SQRT, MAX, LAST, FIRST});
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AddComment(R"DOC(
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SequencePoolOp pools features of all time-steps of each instance.
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For a mini-batch of 3 variable lengths sentences, containing 2, 3, and 2 time-steps:
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Assume X is a [7,M,N] float LoDTensor, and X->lod()[0] = [0, 2, 5, 7].
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Besides, for the sake of simplicity, we assume M=1 and N=1,
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and the value of X = [[1, 3], [2, 4, 6], [5, 1]].
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Thus, Out is a [3,1,1] float LoDTensor, but Out->lod() is nullptr.
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And for different strategy, the value of Out is as follows:
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- AVERAGE: [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
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- SUM: [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
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- SQRT: [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
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6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
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- MAX: [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
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- LAST: [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
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- FIRST: [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
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)DOC");
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}
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};
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class SequencePoolGradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContextBase* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Gradient of Out should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("X"), "The input X should not be null.");
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auto og_dims = ctx->GetInputDim(framework::GradVarName("Out"));
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auto x_dims = ctx->GetInputDim("X");
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PADDLE_ENFORCE_EQ(og_dims.size(), x_dims.size(),
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"The rank of output grad must equal to Input(X).");
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for (int64_t i = 1; i < og_dims.size(); ++i) {
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PADDLE_ENFORCE_EQ(og_dims[i], x_dims[i], "The dimension mismatch.");
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}
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ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
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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_pool, ops::SequencePoolOp, ops::SequencePoolOpMaker,
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sequence_pool_grad, ops::SequencePoolGradOp);
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REGISTER_OP_CPU_KERNEL(
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sequence_pool, ops::SequencePoolKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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sequence_pool_grad,
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ops::SequencePoolGradKernel<paddle::platform::CPUPlace, float>);
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