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68 lines
2.5 KiB
68 lines
2.5 KiB
/* 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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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 DeviceContext, typename T>
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class RmspropOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* param_out = ctx.Output<Tensor>("ParamOut");
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auto* moment_out = ctx.Output<Tensor>("MomentOut");
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auto* mean_square_out = ctx.Output<Tensor>("MeanSquareOut");
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auto grad = ctx.Input<Tensor>("Grad");
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param_out->mutable_data<T>(ctx.GetPlace());
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moment_out->mutable_data<T>(ctx.GetPlace());
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mean_square_out->mutable_data<T>(ctx.GetPlace());
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float epsilon = ctx.Attr<float>("epsilon");
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float rho = ctx.Attr<float>("decay");
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float momentum = ctx.Attr<float>("momentum");
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auto p = EigenVector<T>::Flatten(*ctx.Input<Tensor>("Param"));
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auto ms = EigenVector<T>::Flatten(*ctx.Input<Tensor>("MeanSquare"));
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auto lr = EigenVector<T>::Flatten(*ctx.Input<Tensor>("LearningRate"));
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auto g = EigenVector<T>::Flatten(*grad);
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auto mom = EigenVector<T>::Flatten(*ctx.Input<Tensor>("Moment"));
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auto p_out = EigenVector<T>::Flatten(*param_out);
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auto mom_out = EigenVector<T>::Flatten(*moment_out);
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auto ms_out = EigenVector<T>::Flatten(*mean_square_out);
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auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
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Eigen::DSizes<int, 1> grad_dsize(grad->numel());
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ms_out.device(place) = rho * ms + (1 - rho) * g * g;
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mom_out.device(place) =
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momentum * mom +
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lr.broadcast(grad_dsize) * g / (ms_out + epsilon).sqrt();
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p_out.device(place) = p - mom_out;
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}
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};
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} // namespace operators
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} // namespace paddle
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