You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
112 lines
3.9 KiB
112 lines
3.9 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
Indicesou may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License. */
|
|
|
|
#include "paddle/fluid/operators/norm_op.h"
|
|
#include <memory>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
class NormOpMaker : public framework::OpProtoAndCheckerMaker {
|
|
public:
|
|
void Make() override {
|
|
AddInput("X", "(Tensor) A tensor of rank >= axis.");
|
|
AddAttr<int>("axis",
|
|
"The axis on which to apply normalization. If axis < 0, "
|
|
"the dimension to normalization is rank(X) + axis. -1 is "
|
|
"the last dimension.");
|
|
AddAttr<float>("epsilon",
|
|
"(float, default 1e-10) The epsilon value is used "
|
|
"to avoid division by zero.")
|
|
.SetDefault(1.0e-10f);
|
|
AddOutput("Norm",
|
|
"(Tensor) A tensor saved the `sqrt(sum(x) + epsion)` will "
|
|
"be used in backward kernel.")
|
|
.AsIntermediate();
|
|
AddOutput("Out", "(Tensor) A tensor of the same shape as X.");
|
|
AddComment(R"DOC(
|
|
|
|
Given a tensor, apply 2-normalization along the provided axis.
|
|
|
|
$$
|
|
y = \frac{x}{ \sqrt{\sum {x^2} + epsion }}
|
|
$$
|
|
|
|
where, $\sum {x^2}$ is calculated along the `axis` dimension.
|
|
|
|
)DOC");
|
|
}
|
|
};
|
|
|
|
class NormOp : public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
void InferShape(framework::InferShapeContext* ctx) const override {
|
|
PADDLE_ENFORCE(ctx->HasInput("X"),
|
|
"Input(X) of NormOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasOutput("Out"),
|
|
"Output(Out) of NormOp should not be null.");
|
|
auto xdim = ctx->GetInputDim("X");
|
|
ctx->SetOutputDim("Out", xdim);
|
|
int axis = ctx->Attrs().Get<int>("axis");
|
|
if (axis < 0) axis = xdim.size() + axis;
|
|
xdim[axis] = 1;
|
|
ctx->SetOutputDim("Norm", xdim);
|
|
}
|
|
};
|
|
|
|
class NormOpGrad : public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
void InferShape(framework::InferShapeContext* ctx) const override {
|
|
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
|
|
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
|
|
"Input(X@GRAD) should not be null.");
|
|
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
|
|
}
|
|
};
|
|
|
|
template <typename T>
|
|
class NormOpGradOpMaker : public framework::SingleGradOpMaker<T> {
|
|
public:
|
|
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
|
|
|
|
protected:
|
|
void Apply(GradOpPtr<T> op) const override {
|
|
op->SetType("norm_grad");
|
|
op->SetAttrMap(this->Attrs());
|
|
op->SetInput("X", this->Input("X"));
|
|
op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
|
|
op->SetInput("Norm", this->Output("Norm"));
|
|
op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|
|
|
|
namespace ops = paddle::operators;
|
|
using CPU = paddle::platform::CPUDeviceContext;
|
|
|
|
REGISTER_OPERATOR(norm, ops::NormOp, ops::NormOpMaker,
|
|
ops::NormOpGradOpMaker<paddle::framework::OpDesc>,
|
|
ops::NormOpGradOpMaker<paddle::imperative::OpBase>);
|
|
REGISTER_OPERATOR(norm_grad, ops::NormOpGrad);
|
|
REGISTER_OP_CPU_KERNEL(norm, ops::NormKernel<CPU, float>,
|
|
ops::NormKernel<CPU, double>);
|
|
REGISTER_OP_CPU_KERNEL(norm_grad, ops::NormGradKernel<CPU, float>,
|
|
ops::NormGradKernel<CPU, double>);
|