wehub-resource-sync
130 lines
4.7 KiB
C++
130 lines
4.7 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/adagrad_kernel.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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template <typename Context, typename T>
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struct SparseAdagradFunctor {
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void operator()(const Context& 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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};
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template <typename Context, typename T>
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struct DenseAdagradFunctor {
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void operator()(const Context& 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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};
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template <typename Context, typename T>
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SelectedRows SquareSelectedRows(const Context& dev_ctx,
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const SelectedRows& input) {
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SelectedRows out;
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out.set_rows(input.rows());
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out.set_height(input.height());
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out.mutable_value()->Resize(input.value().dims());
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dev_ctx.template Alloc<T>(out.mutable_value());
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auto e_out = EigenVector<T>::Flatten(*(out.mutable_value()));
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auto e_in = EigenVector<T>::Flatten(input.value());
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e_out.device(*dev_ctx.eigen_device()) = e_in.square();
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return out;
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}
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template <typename T, typename Context>
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void AdagradDenseKernel(const Context& 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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DenseAdagradFunctor<Context, T> functor;
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functor(dev_ctx,
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param_t,
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grad_t,
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moment_t,
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learning_rate,
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master_param,
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epsilon_t,
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multi_precision,
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param_out_tensor,
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moment_out_tensor,
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master_param_outs);
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}
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template <typename T, typename Context>
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void AdagradSparseKernel(const Context& dev_ctx,
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const DenseTensor& param_t,
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const SelectedRows& 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 UNUSED,
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float epsilon_t,
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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* master_param_outs UNUSED) {
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auto* param_out_tensor = param_out;
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auto* moment_out_tensor = moment_out;
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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_tensor = ¶m_t;
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PADDLE_ENFORCE_EQ(param_tensor->IsSharedBufferWith(*param_out_tensor),
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true,
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common::errors::InvalidArgument(
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"the input tensor not equal with output tensor"));
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auto* moment_tensor = &moment_t;
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PADDLE_ENFORCE_EQ(moment_tensor->IsSharedBufferWith(*moment_out_tensor),
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true,
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common::errors::InvalidArgument(
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"the input moment not equal with output moment"));
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SparseAdagradFunctor<Context, T> functor;
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functor(dev_ctx,
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grad_t,
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learning_rate,
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epsilon,
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moment_out_tensor,
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param_out_tensor);
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}
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} // namespace phi
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