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74 lines
3.0 KiB
74 lines
3.0 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 AdamOpKernel : 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 moment1_out_tensor = ctx.Output<framework::Tensor>("Moment1Out");
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auto moment2_out_tensor = ctx.Output<framework::Tensor>("Moment2Out");
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param_out_tensor->mutable_data<T>(ctx.GetPlace());
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moment1_out_tensor->mutable_data<T>(ctx.GetPlace());
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moment2_out_tensor->mutable_data<T>(ctx.GetPlace());
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float beta1 = ctx.Attr<float>("beta1");
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float beta2 = ctx.Attr<float>("beta2");
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float epsilon = 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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auto moment1 = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("Moment1"));
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auto moment2 = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("Moment2"));
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auto lr = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("LearningRate"));
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auto beta1_pow = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("Beta1Pow"));
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auto beta2_pow = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("Beta2Pow"));
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auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
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auto moment1_out = framework::EigenVector<T>::Flatten(*moment1_out_tensor);
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auto moment2_out = framework::EigenVector<T>::Flatten(*moment2_out_tensor);
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auto place = ctx.GetEigenDevice<Place>();
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moment1_out.device(place) = beta1 * moment1 + (1 - beta1) * grad;
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moment2_out.device(place) = beta2 * moment2 + (1 - beta2) * grad.square();
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// All of these are tensors of 1 element
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auto lr_t = lr * (1 - beta2_pow).sqrt() / (1 - beta1_pow);
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// Eigen does not support automatic broadcast
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// Get dimensions of moment vector to broadcast lr_t
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Eigen::DSizes<int, 1> m_dsize(moment1_out_tensor->numel());
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param_out.device(place) =
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param -
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lr_t.broadcast(m_dsize) *
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(moment1_out / (moment2_out.sqrt() + epsilon));
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}
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};
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} // namespace operators
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} // namespace paddle
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