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261 lines
11 KiB
261 lines
11 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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#pragma once
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#include <string>
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#include "paddle/fluid/framework/data_layout.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/operator.h"
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#ifdef PADDLE_WITH_MKLDNN
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#include "paddle/fluid/platform/mkldnn_helper.h"
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#endif
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namespace paddle {
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namespace operators {
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class ElementwiseOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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using Tensor = framework::Tensor;
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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 elementwise op should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Y"),
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"Input(Y) of elementwise op should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of elementwise op should not be null.");
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auto x_dim = ctx->GetInputDim("X");
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auto y_dim = ctx->GetInputDim("Y");
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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->SetOutputDim("Out", x_dim);
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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auto input_data_type =
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framework::ToDataType(ctx.Input<Tensor>("X")->type());
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#ifdef PADDLE_WITH_MKLDNN
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if (platform::CanMKLDNNBeUsed(ctx)) {
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return framework::OpKernelType(input_data_type, ctx.GetPlace(),
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framework::DataLayout::kMKLDNN,
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framework::LibraryType::kMKLDNN);
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}
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#endif
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return framework::OpKernelType(input_data_type, ctx.GetPlace());
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}
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};
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class ElementwiseOpInferVarType : public framework::VarTypeInference {
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public:
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void operator()(const framework::OpDesc& op_desc,
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framework::BlockDesc* block) const override {
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auto x_name = op_desc.Input("X")[0];
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auto out_name = op_desc.Output("Out")[0];
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auto& x = block->FindRecursiveOrCreateVar(x_name);
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auto& out = block->FindRecursiveOrCreateVar(out_name);
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out.SetType(x.GetType());
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}
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};
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class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() final {
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AddInput("X", "(Tensor), The first input tensor of elementwise op.");
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AddInput("Y", "(Tensor), The second input tensor of elementwise op.");
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// AddOutput("SavedShape", "(Tensor), save X, Y shape for grad to save
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// memory.").AsIntermediate();
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AddOutput("Out", "The output of elementwise op.");
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AddAttr<int>("axis",
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"(int, default -1). The start dimension index "
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"for broadcasting Y onto X.")
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.SetDefault(-1)
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.EqualGreaterThan(-1);
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AddAttr<bool>("use_mkldnn", "(bool, default false). Used by MKLDNN.")
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.SetDefault(false);
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AddComment(string::Sprintf(R"DOC(
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Limited Elementwise %s Operator
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The equation is:
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$$%s$$
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- $X$: a tensor of any dimension.
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- $Y$: a tensor whose dimensions must be less than or equal to the dimensions of $X$.
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There are two cases for this operator:
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1. The shape of $Y$ is the same with $X$.
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2. The shape of $Y$ is a continuous subsequence of $X$.
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For case 2:
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1. Broadcast $Y$ to match the shape of $X$, where $axis$ is the start dimension index
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for broadcasting $Y$ onto $X$.
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2. If $axis$ is -1 (default), $axis = rank(X) - rank(Y)$.
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3. The trailing dimensions of size 1 for $Y$ will be ignored for the consideration of
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subsequence, such as shape(Y) = (2, 1) => (2).
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For example:
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.. code-block:: python
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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), with axis=-1(default) or axis=2
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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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shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0
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The inputs $X$ and $Y$ can carry the different LoD information.
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But the output only shares the LoD information with the input $X$.
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)DOC",
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GetName(), GetEquation()));
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SetReuse();
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}
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protected:
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virtual std::string GetName() const = 0;
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virtual std::string GetEquation() const = 0;
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virtual void SetReuse() {}
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};
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class ElementwiseOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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using Tensor = framework::Tensor;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
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PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Input(Out@GRAD) should not be null");
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auto x_dims = ctx->GetInputDim("X");
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auto y_dims = ctx->GetInputDim("Y");
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auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
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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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auto x_grad_name = framework::GradVarName("X");
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auto y_grad_name = framework::GradVarName("Y");
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if (ctx->HasOutput(x_grad_name)) {
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ctx->SetOutputDim(x_grad_name, x_dims);
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}
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if (ctx->HasOutput(y_grad_name)) {
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ctx->SetOutputDim(y_grad_name, y_dims);
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}
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}
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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auto input_data_type = framework::ToDataType(
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ctx.Input<Tensor>(framework::GradVarName("Out"))->type());
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#ifdef PADDLE_WITH_MKLDNN
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if (platform::CanMKLDNNBeUsed(ctx)) {
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return framework::OpKernelType(input_data_type, ctx.GetPlace(),
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framework::DataLayout::kMKLDNN,
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framework::LibraryType::kMKLDNN);
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}
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#endif
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return framework::OpKernelType(input_data_type, ctx.GetPlace());
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}
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};
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// For Add, Sub op, the X, Out is not needed.
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class ElementwiseOpExplicitGrad : public ElementwiseOpGrad {
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public:
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using operators::ElementwiseOpGrad::ElementwiseOpGrad;
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using operators::ElementwiseOpGrad::GetExpectedKernelType;
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using Tensor = framework::Tensor;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Input(Out@GRAD) should not be null");
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auto x_grad_name = framework::GradVarName("X");
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if (ctx->HasOutput(x_grad_name)) {
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auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
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ctx->SetOutputDim(x_grad_name, out_dims);
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}
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auto y_grad_name = framework::GradVarName("Y");
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if (ctx->HasOutput(y_grad_name)) {
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PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
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auto y_dims = ctx->GetInputDim("Y");
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ctx->SetOutputDim(y_grad_name, 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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/*
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*/
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#define REGISTER_ELEMWISE_GRAD_MAKER(kernel_type, op_name) \
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class kernel_type##GradMaker \
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: public paddle::framework::SingleGradOpDescMaker { \
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public: \
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using ::paddle::framework::SingleGradOpDescMaker::SingleGradOpDescMaker; \
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\
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protected: \
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std::unique_ptr<paddle::framework::OpDesc> Apply() const override { \
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auto* op = new paddle::framework::OpDesc(); \
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op->SetType(#kernel_type "_grad"); \
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op->SetInput("Y", Input("Y")); \
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op->SetInput(::paddle::framework::GradVarName("Out"), \
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OutputGrad("Out")); \
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op->SetAttrMap(Attrs()); \
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op->SetOutput(::paddle::framework::GradVarName("X"), InputGrad("X")); \
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op->SetOutput(::paddle::framework::GradVarName("Y"), InputGrad("Y")); \
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return std::unique_ptr<::paddle::framework::OpDesc>(op); \
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} \
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}
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#define REGISTER_ELEMWISE_OP(op_type, op_name, equation) \
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class __ElemwiseOp##op_type##Maker__ \
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: public ::paddle::operators::ElementwiseOpMaker { \
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protected: \
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virtual std::string GetName() const { return op_name; } \
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virtual std::string GetEquation() const { return equation; } \
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}; \
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REGISTER_OPERATOR(op_type, ::paddle::operators::ElementwiseOp, \
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__ElemwiseOp##op_type##Maker__, \
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::paddle::operators::ElementwiseOpInferVarType, \
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::paddle::framework::DefaultGradOpDescMaker<true>); \
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REGISTER_OPERATOR(op_type##_grad, ::paddle::operators::ElementwiseOpGrad)
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#define REGISTER_ELEMWISE_EXPLICIT_OP(op_type, op_name, equation, ...) \
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class __ElemwiseOp##op_type##Maker__ \
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: public ::paddle::operators::ElementwiseOpMaker { \
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protected: \
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virtual std::string GetName() const { return op_name; } \
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virtual std::string GetEquation() const { return equation; } \
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virtual void SetReuse() { Reuse(__VA_ARGS__); } \
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}; \
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REGISTER_OPERATOR(op_type, ::paddle::operators::ElementwiseOp, \
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__ElemwiseOp##op_type##Maker__, \
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::paddle::operators::ElementwiseOpInferVarType, \
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op_type##GradMaker); \
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REGISTER_OPERATOR(op_type##_grad, \
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::paddle::operators::ElementwiseOpExplicitGrad)
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