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118 lines
4.3 KiB
118 lines
4.3 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/elementwise_mul_op.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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class ElementWiseMulOp : 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(const framework::InferShapeContext &ctx) const override {
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
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"Input(X) of ElementWiseMulOp should not be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
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"Input(Y) of ElementWiseMulOp should not be null.");
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PADDLE_ENFORCE_NOT_NULL(
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ctx.OutputVar("Out"),
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"Output(Out) of ElementWiseMulOp should not be null.");
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auto x_dim = ctx.Input<Tensor>("X")->dims();
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auto y_dim = ctx.Input<Tensor>("Y")->dims();
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PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
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"Rank of first input must >= rank of second input.")
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ctx.Output<framework::LoDTensor>("Out")->Resize(x_dim);
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}
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};
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class ElementWiseMulOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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ElementWiseMulOpMaker(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", "The first input of elementwise mul op");
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AddInput("Y", "The second input of elementwise mul op");
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AddAttr<int>("axis",
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R"DOC(
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When shape(Y) does not equal shape(X),Y will be broadcasted
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to match the shape of X and axis should be dimension index Y in X
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)DOC")
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.SetDefault(-1)
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.EqualGreaterThan(-1);
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AddOutput("Out", "The output of elementwise mul op");
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AddComment(R"DOC(
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Limited elementwise multiple operator.The equation is: Out = X ⊙ Y.
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1. The shape of Y should be same with X or
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2. Y's shape is a subset of X.
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Y will be broadcasted to match the shape of X and axis should be dimension index Y in X.
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example:
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shape(X) = (2, 3, 4, 5), shape(Y) = (,)
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shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
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shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
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shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
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shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
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)DOC");
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}
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};
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class ElementWiseMulOpGrad : 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(const framework::InferShapeContext &ctx) const override {
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
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"Input(Out@GRAD) should not be null");
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auto x_dims = ctx.Input<Tensor>("X")->dims();
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auto y_dims = ctx.Input<Tensor>("Y")->dims();
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auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
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auto *x_grad =
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ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
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auto *y_grad =
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ctx.Output<framework::LoDTensor>(framework::GradVarName("Y"));
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PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
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"Rank of first input must >= rank of second input.")
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if (x_grad) {
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x_grad->Resize(x_dims);
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}
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if (y_grad) {
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y_grad->Resize(y_dims);
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}
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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(elementwise_mul, ops::ElementWiseMulOp, ops::ElementWiseMulOpMaker,
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elementwise_mul_grad, ops::ElementWiseMulOpGrad);
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REGISTER_OP_CPU_KERNEL(
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elementwise_mul,
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ops::ElementWiseMulKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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elementwise_mul_grad,
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ops::ElementWiseMulGradKernel<paddle::platform::CPUPlace, float>);
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