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68 lines
2.8 KiB
68 lines
2.8 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class AdamaxOpKernel : 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 moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
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auto inf_norm_out_tensor = ctx.Output<framework::Tensor>("InfNormOut");
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param_out_tensor->mutable_data<T>(ctx.GetPlace());
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moment_out_tensor->mutable_data<T>(ctx.GetPlace());
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inf_norm_out_tensor->mutable_data<T>(ctx.GetPlace());
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T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
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T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
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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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auto moment = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("Moment"));
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auto inf_norm = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("InfNorm"));
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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 param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
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auto moment_out = framework::EigenVector<T>::Flatten(*moment_out_tensor);
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auto inf_norm_out =
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framework::EigenVector<T>::Flatten(*inf_norm_out_tensor);
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auto* place = ctx.template device_context<DeviceContext>().eigen_device();
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moment_out.device(*place) = beta1 * moment + (1 - beta1) * grad;
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inf_norm_out.device(*place) =
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grad.abs().cwiseMax((beta2 * inf_norm) + epsilon);
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auto lr_t = lr / (1 - beta1_pow);
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Eigen::DSizes<int, 1> m_dsize(moment_out_tensor->numel());
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param_out.device(*place) =
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param - lr_t.broadcast(m_dsize) * (moment_out / inf_norm_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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