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125 lines
4.2 KiB
125 lines
4.2 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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#include "paddle/fluid/operators/clip_op.h"
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#include <memory>
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namespace paddle {
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namespace operators {
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class ClipOp : 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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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "clip");
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OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "clip");
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auto x_dims = ctx->GetInputDim("X");
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ctx->SetOutputDim("Out", x_dims);
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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};
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template <typename AttrType>
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class ClipOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"Tensor, the input of clip op, data type should be float32 or "
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"float64.");
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AddInput("Min",
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"Tensor, the lower bound, data type should be float32 "
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"or float64.")
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.AsDispensable();
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AddInput("Max",
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"Tensor, the upper bound, data type should be float32 "
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"or float64.")
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.AsDispensable();
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AddOutput(
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"Out",
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"Tensor, the clipped tensor, with the same shape and data type as "
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"input(x)");
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AddAttr<AttrType>("min", "float number, the minimum value to clip by.");
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AddAttr<AttrType>("max", "float number, the maximum value to clip by.");
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AddComment(R"DOC(
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Clip Operator.
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The clip operator limits the value of given input within an interval [min, max],
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just as the following equation,
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$$
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Out = \MIN(\MAX(x, min), max)
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$$
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)DOC");
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}
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};
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class ClipOpGrad : 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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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "clip_grad");
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OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
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"Out@GRAD", "clip_grad");
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auto x_dims = ctx->GetInputDim("X");
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if (ctx->HasOutput(framework::GradVarName("X"))) {
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ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
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}
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}
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};
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template <typename T>
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class ClipGradOpMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> op) const override {
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op->SetType("clip_grad");
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op->SetInput("X", this->Input("X"));
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if (this->HasInput("Min")) {
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op->SetInput("Min", this->Input("Min"));
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}
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if (this->HasInput("Max")) {
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op->SetInput("Max", this->Input("Max"));
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}
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op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
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op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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op->SetAttrMap(this->Attrs());
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}
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};
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DECLARE_INPLACE_OP_INFERER(ClipInplaceInferer, {"X", "Out"});
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DECLARE_INPLACE_OP_INFERER(ClipGradInplaceInferer,
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{framework::GradVarName("Out"),
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framework::GradVarName("X")});
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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_OPERATOR(clip, ops::ClipOp, ops::ClipOpMaker<float>,
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ops::ClipGradOpMaker<paddle::framework::OpDesc>,
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ops::ClipGradOpMaker<paddle::imperative::OpBase>,
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ops::ClipInplaceInferer);
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REGISTER_OPERATOR(clip_grad, ops::ClipOpGrad, ops::ClipGradInplaceInferer);
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REGISTER_OP_CPU_KERNEL(
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clip, ops::ClipKernel<paddle::platform::CPUDeviceContext, float>,
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ops::ClipKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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clip_grad, ops::ClipGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::ClipGradKernel<paddle::platform::CPUDeviceContext, double>);
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