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70 lines
2.7 KiB
70 lines
2.7 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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template <typename Place, typename T>
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class AdadeltaOpKernel : 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_tensor = ctx.Output<framework::Tensor>("ParamOut");
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auto avg_squared_grad_out_tensor =
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ctx.Output<framework::Tensor>("AvgSquaredGradOut");
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auto avg_squared_update_out_tensor =
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ctx.Output<framework::Tensor>("AvgSquaredUpdateOut");
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param_out_tensor->mutable_data<T>(ctx.GetPlace());
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avg_squared_grad_out_tensor->mutable_data<T>(ctx.GetPlace());
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avg_squared_update_out_tensor->mutable_data<T>(ctx.GetPlace());
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T rho = static_cast<T>(ctx.Attr<float>("rho"));
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T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
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auto param = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("Param"));
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auto grad = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("Grad"));
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// Squared gradient accumulator
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auto avg_squared_grad = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("AvgSquaredGrad"));
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// Squared updates accumulator
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auto avg_squared_update = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("AvgSquaredUpdate"));
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auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
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auto avg_squared_grad_out =
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framework::EigenVector<T>::Flatten(*avg_squared_grad_out_tensor);
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auto avg_squared_update_out =
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framework::EigenVector<T>::Flatten(*avg_squared_update_out_tensor);
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auto place = ctx.GetEigenDevice<Place>();
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avg_squared_grad_out.device(place) =
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rho * avg_squared_grad + (1 - rho) * grad.square();
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auto update =
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-((avg_squared_update + epsilon) / (avg_squared_grad_out + epsilon))
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.sqrt() *
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grad;
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avg_squared_update_out.device(place) =
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rho * avg_squared_update + (1 - rho) * update.square();
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param_out.device(place) = param + update;
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}
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};
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} // namespace operators
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} // namespace paddle
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