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120 lines
4.1 KiB
120 lines
4.1 KiB
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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/dist_op.h"
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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class DistOp : 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", "Dist");
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OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "Dist");
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OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Dist");
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ctx->SetOutputDim("Out", {1});
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}
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};
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class DistOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "The input Tensor of Dist Op.");
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AddInput("Y", "The Right-hand-side input Tensor of Dist Op.");
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AddOutput("Out",
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"The output of Dist Op, "
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"which is the p-norm of (X - Y)");
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AddAttr<float>("p", "the norm to be computed.").SetDefault(2.0f);
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AddComment(R"DOC(
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Dist Operator.
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Given two tensors X and Y, compute Lp-norm of (X-Y). It is not a norm in a strict sense,
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only as a measure of distance. The shapes of X and Y must be broadcastable. Where, Z = X - Y,
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When p = 0, defining $0^0 = 0$, the zero-norm of Z is simply the number of non-zero elements of z.
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$$
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||Z||_{0} = \lim_{p \rightarrow 0} \sum_{i=1}^{m} |z_i|^p
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$$
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When p = inf, the inf-norm of Z is the maximum element of Z.
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$$
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||Z||_\infty=\max_i |z_i|
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$$
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When p = -inf, the negative-inf-norm of Z is the minimum element of Z.
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$$
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||Z||_{-\infty}=\min_i |z_i|
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$$
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Otherwise, the p-norm of Z follows the formula,
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$$
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||Z||_{p} = (\sum_{i=i}^{m} |z_i|^p)^{1/p}
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$$
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)DOC");
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}
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};
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class DistOpGrad : 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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auto x_dims = ctx->GetInputDim("X");
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auto y_dims = ctx->GetInputDim("Y");
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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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if (ctx->HasOutput(framework::GradVarName("Y"))) {
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ctx->SetOutputDim(framework::GradVarName("Y"), y_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 DistGradOpMaker : 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(this->ForwardOpType() + "_grad");
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op->SetInput("X", this->Input("X"));
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op->SetInput("Y", this->Input("Y"));
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op->SetInput("Out", this->Output("Out"));
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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->SetOutput(framework::GradVarName("Y"), this->InputGrad("Y"));
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op->SetAttrMap(this->Attrs());
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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_OPERATOR(dist, ops::DistOp, ops::DistOpMaker,
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ops::DistGradOpMaker<paddle::framework::OpDesc>,
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ops::DistGradOpMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(dist_grad, ops::DistOpGrad);
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REGISTER_OP_CPU_KERNEL(
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dist, ops::DistKernel<paddle::platform::CPUDeviceContext, float>,
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ops::DistKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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dist_grad, ops::DistGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::DistGradKernel<paddle::platform::CPUDeviceContext, double>)
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