未验证 提交 1a26f5a5 编写于 作者: A Abhinav Arora 提交者: GitHub

Adding the Sign Op for L1 Weight Decay Regularization (#5138)

上级 12d862b5
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You 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/operators/sign_op.h"
namespace paddle {
namespace operators {
class SignOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SignOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SignOp should not be null.");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Out");
}
};
template <typename AttrType>
class SignOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SignOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) Input tensor of sign operator.");
AddOutput("Out", "(Tensor) Output tensor of sign operator.");
AddComment(R"DOC(Sign operator
The equation is: Out = X.sign()
)DOC");
}
};
class SignGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *grad_op = new framework::OpDescBind();
grad_op->SetType("scale");
grad_op->SetInput("X", OutputGrad("Out"));
grad_op->SetOutput("Out", InputGrad("X"));
grad_op->SetAttr("scale", 0.0f);
return std::unique_ptr<framework::OpDescBind>(grad_op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(sign, ops::SignOp, ops::SignOpMaker<float>,
ops::SignGradMaker);
REGISTER_OP_CPU_KERNEL(sign,
ops::SignKernel<paddle::platform::CPUPlace, float>);
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You 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/operators/sign_op.h"
REGISTER_OP_GPU_KERNEL(
sign, paddle::operators::SignKernel<paddle::platform::GPUPlace, float>);
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You 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. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename Place, typename T>
class SignKernel : public framework::OpKernel<T> {
public:
virtual void Compute(const framework::ExecutionContext& context) const {
auto* out = context.Output<framework::Tensor>("Out");
auto* in = context.Input<framework::Tensor>("X");
out->mutable_data<T>(in->place());
auto eigen_out = framework::EigenVector<T>::Flatten(*out);
auto eigen_in = framework::EigenVector<T>::Flatten(*in);
auto& place = context.GetEigenDevice<Place>();
eigen_out.device(place) = eigen_in.sign();
}
};
} // namespace operators
} // namespace paddle
import unittest
import numpy as np
from op_test import OpTest
class TestSignOp(OpTest):
def setUp(self):
self.op_type = "sign"
self.inputs = {
'X': np.random.uniform(-10, 10, (10, 10)).astype("float32")
}
self.outputs = {'Out': np.sign(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
if __name__ == "__main__":
unittest.main()
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