wehub-resource-sync
119 lines
4.2 KiB
C++
119 lines
4.2 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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#include "paddle/phi/kernels/adagrad_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/funcs/selected_rows_functor.h"
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#include "paddle/phi/kernels/impl/adagrad_kernel_impl.h"
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namespace phi {
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namespace {
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size_t FindPos(const std::vector<int64_t>& rows, int64_t value) {
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return std::find(rows.begin(), rows.end(), value) - rows.begin();
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}
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} // namespace
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template <typename T>
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struct DenseAdagradFunctor<CPUContext, T> {
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void operator()(const CPUContext& dev_ctx,
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const DenseTensor& param_t,
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const DenseTensor& grad_t,
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const DenseTensor& moment_t,
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const DenseTensor& learning_rate,
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const optional<DenseTensor>& master_param,
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float epsilon_t,
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bool multi_precision,
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DenseTensor* param_out_tensor,
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DenseTensor* moment_out_tensor,
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DenseTensor* master_param_outs) {
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dev_ctx.template Alloc<T>(param_out_tensor);
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dev_ctx.template Alloc<T>(moment_out_tensor);
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T epsilon = static_cast<T>(epsilon_t);
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auto param = EigenVector<T>::Flatten(param_t);
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auto grad = EigenVector<T>::Flatten(grad_t);
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auto moment = EigenVector<T>::Flatten(moment_t);
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auto param_out = EigenVector<T>::Flatten(*param_out_tensor);
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auto moment_out = EigenVector<T>::Flatten(*moment_out_tensor);
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auto place = *dev_ctx.eigen_device();
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moment_out.device(place) = moment + grad * grad;
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Eigen::DSizes<int, 1> m_dsize(static_cast<int>(moment_out_tensor->numel()));
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auto* lr = learning_rate.data<T>();
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param_out.device(place) =
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param - lr[0] * grad / (moment_out.sqrt() + epsilon);
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}
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};
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template <typename T>
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struct SparseAdagradFunctor<CPUContext, T> {
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void operator()(const CPUContext& dev_ctx,
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const SelectedRows& grad,
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const DenseTensor& learning_rate,
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T epsilon,
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DenseTensor* moment,
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DenseTensor* param) {
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// 1. g_m.rows = set(g.rows)
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auto grad_width = grad.value().dims()[1];
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funcs::scatter::MergeAdd<CPUContext, T> merge_func;
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auto grad_merge = merge_func(dev_ctx, grad);
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auto& merge_rows = grad_merge.rows();
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auto* grad_merge_data = grad_merge.mutable_value()->template data<T>();
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// 2. m += g_m * g_m
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auto grad_square = SquareSelectedRows<CPUContext, T>(dev_ctx, grad_merge);
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funcs::SelectedRowsAddToTensor<CPUContext, T> functor;
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functor(dev_ctx, grad_square, moment);
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// 3. update parameter
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auto* lr = learning_rate.data<T>();
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auto* param_data = param->data<T>();
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auto* moment_data = moment->data<T>();
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for (size_t i = 0; i < merge_rows.size(); i++) {
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for (int64_t j = 0; j < grad_width; j++) {
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param_data[merge_rows[i] * grad_width + j] -=
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lr[0] * grad_merge_data[i * grad_width + j] /
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(std::sqrt(moment_data[merge_rows[i] * grad_width + j]) + epsilon);
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}
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}
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}
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};
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template struct SparseAdagradFunctor<CPUContext, float>;
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template struct SparseAdagradFunctor<CPUContext, double>;
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template struct DenseAdagradFunctor<CPUContext, float>;
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template struct DenseAdagradFunctor<CPUContext, double>;
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} // namespace phi
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PD_REGISTER_KERNEL(
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adagrad, CPU, ALL_LAYOUT, phi::AdagradDenseKernel, float, double) {}
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PD_REGISTER_KERNEL(adagrad_dense_param_sparse_grad,
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CPU,
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ALL_LAYOUT,
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phi::AdagradSparseKernel,
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float,
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double) {}
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