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107 lines
4.2 KiB
107 lines
4.2 KiB
7 years ago
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/* 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_concat_op.h"
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namespace paddle {
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namespace operators {
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class SequenceConcatOp : 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_GT(ctx->Inputs("X").size(), 0UL,
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"Inputs(X) of SequenceConcatOp should not be empty.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of SequenceConcatOp should not be null.");
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const size_t level = static_cast<size_t>(ctx->Attrs().Get<int>("level"));
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const size_t axis = static_cast<size_t>(ctx->Attrs().Get<int>("axis"));
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PADDLE_ENFORCE(level == 0UL || level == 1UL,
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"Sequence Concat Op only support one or two sequence now.");
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auto ins_dims = ctx->GetInputsDim("X");
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framework::DDim out_dims = ins_dims[0];
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const size_t n = ins_dims.size();
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for (size_t i = 1; i < n; i++) {
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out_dims[axis] += ins_dims[i][axis];
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}
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ctx->SetOutputDim("Out", out_dims);
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}
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};
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class SequenceConcatOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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SequenceConcatOpMaker(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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"Multip LodTensors, the variable-length inputs of "
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"SequenceConcatOp")
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.AsDuplicable();
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AddOutput("Out",
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"A float LodTensor, the variable-length output of "
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"SequenceConcatOp.");
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AddAttr<int>("axis",
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"The axis which the inputs will be joined with."
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"If axis is 0, the inputs will be joined with Lod index.")
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.SetDefault(0);
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AddAttr<int>("level",
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"The level which the inputs will be joined with."
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"If level is 0, the inputs will be joined with word."
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"If level is 1, the inputs will be joined with sentence.")
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.SetDefault(0);
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AddComment(R"DOC(
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SequenceConcatOp concat multip LodTensors and only supports one or two levels.
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- Case1:
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axis is 1, level is 1, the Lod of Inputs are the same,
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LoD(x0) = {{0,2,4},{0,1,2,3,4}}; Dims(x0) = (2,3,4)
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LoD(x1) = {{0,2,4},{0,1,2,3,4}}; Dims(x1) = (2,4,4)
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LoD(Out) = {{0,2,4},{01,2,3,4}}; Dims(Out) = (2,7,4)
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- Case2:
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If axis is 0, level is 1, the Lod of inputs are different,
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LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (2,3,4)
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LoD(x1) = {{0,3,5}, {0,1,3,4,5}}; Dims(x1) = (3,3,4)
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LoD(Out) = {{0,5,9}, {0,1,2,4,5,6,7,8,9}}; Dims(Out) = (5,3,4)
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)DOC");
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}
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};
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class SequenceConcatGradOp : 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_GT(ctx->Outputs(framework::GradVarName("X")).size(), 0UL,
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"Gradient of X should not be empty.")
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ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("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_concat, ops::SequenceConcatOp, ops::SequenceConcatOpMaker,
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sequence_concat_grad, ops::SequenceConcatGradOp);
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REGISTER_OP_CPU_KERNEL(
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sequence_concat,
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ops::SequenceConcatOpKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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sequence_concat_grad,
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ops::SequenceConcatGradOpKernel<paddle::platform::CPUPlace, float>);
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