wehub-resource-sync
73 lines
2.8 KiB
C++
73 lines
2.8 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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/phi/kernels/adamax_kernel.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
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namespace phi {
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template <typename T, typename Context>
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void AdamaxKernel(const Context& dev_ctx,
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const DenseTensor& param,
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const DenseTensor& grad,
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const DenseTensor& learning_rate,
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const DenseTensor& moment,
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const DenseTensor& inf_norm,
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const DenseTensor& beta1_pow,
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const optional<DenseTensor>& master_param UNUSED,
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float beta1,
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float beta2,
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float epsilon,
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bool multi_precision UNUSED,
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DenseTensor* param_out,
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DenseTensor* moment_out,
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DenseTensor* inf_norm_out,
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DenseTensor* master_param_outs UNUSED) {
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dev_ctx.template Alloc<T>(param_out);
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dev_ctx.template Alloc<T>(moment_out);
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dev_ctx.template Alloc<T>(inf_norm_out);
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T beta1_ = static_cast<T>(beta1);
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T beta2_ = static_cast<T>(beta2);
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T epsilon_ = static_cast<T>(epsilon);
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auto eigen_param = EigenVector<T>::Flatten(param);
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auto eigen_grad = EigenVector<T>::Flatten(grad);
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auto eigen_moment = EigenVector<T>::Flatten(moment);
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auto eigen_inf_norm = EigenVector<T>::Flatten(inf_norm);
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auto eigen_lr = EigenVector<T>::Flatten(learning_rate);
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auto eigen_beta1_pow = EigenVector<T>::Flatten(beta1_pow);
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auto eigen_param_out = EigenVector<T>::Flatten(*param_out);
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auto eigen_moment_out = EigenVector<T>::Flatten(*moment_out);
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auto eigen_inf_norm_out = EigenVector<T>::Flatten(*inf_norm_out);
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auto& place = *dev_ctx.eigen_device();
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eigen_moment_out.device(place) =
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beta1_ * eigen_moment + (static_cast<T>(1) - beta1_) * eigen_grad;
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eigen_inf_norm_out.device(place) =
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eigen_grad.abs().cwiseMax((beta2_ * eigen_inf_norm) + epsilon_);
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auto lr_t = eigen_lr / (static_cast<T>(1) - eigen_beta1_pow);
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Eigen::DSizes<int, 1> m_dsize(moment_out->numel());
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eigen_param_out.device(place) =
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eigen_param -
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lr_t.broadcast(m_dsize) * (eigen_moment_out / eigen_inf_norm_out);
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}
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} // namespace phi
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