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53 lines
1.9 KiB
53 lines
1.9 KiB
7 years ago
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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 "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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#include "paddle/platform/transform.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
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template <typename Place, typename T>
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class ClipByNormKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto max_norm = context.Attr<T>("max_norm");
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auto* input = context.Input<Tensor>("X");
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auto* output = context.Output<Tensor>("Out");
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output->mutable_data<T>(context.GetPlace());
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auto x = EigenVector<T>::Flatten(*input);
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auto out = EigenVector<T>::Flatten(*output);
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auto x_norm = x.square().sum().sqrt();
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auto place = context.GetEigenDevice<Place>();
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auto temp = (x_norm <= max_norm).template cast<T>().eval();
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auto scaling = temp + (static_cast<T>(1) - temp) * max_norm / x_norm;
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Eigen::array<int, 1> one_dim{{1}};
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Eigen::DSizes<int, 1> m_dsize(input->numel());
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out.device(place) = x * scaling.reshape(one_dim).broadcast(m_dsize);
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}
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};
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} // namespace operators
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} // namespace paddle
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