118 lines
4.3 KiB
C++
118 lines
4.3 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
#include "paddle/phi/kernels/rmsprop_kernel.h"
|
|
|
|
#include "paddle/phi/backends/cpu/cpu_context.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
#include "paddle/phi/kernels/impl/rmsprop_kernel_impl.h"
|
|
namespace phi {
|
|
template <typename T>
|
|
struct RmsFunctor<T, CPUContext> {
|
|
RmsFunctor(const CPUContext &dev_ctx,
|
|
const DenseTensor ¶m,
|
|
const DenseTensor &mean_square,
|
|
const DenseTensor &grad,
|
|
const DenseTensor &moment,
|
|
const DenseTensor &learning_rate,
|
|
const optional<DenseTensor> &mean_grad_opt,
|
|
const optional<DenseTensor> &master_param UNUSED,
|
|
float epsilon_t,
|
|
float decay_t,
|
|
float momentum_t,
|
|
bool centered,
|
|
bool multi_precision UNUSED,
|
|
DenseTensor *param_out,
|
|
DenseTensor *moment_out,
|
|
DenseTensor *mean_square_out,
|
|
DenseTensor *mean_grad_out,
|
|
DenseTensor *master_param_outs UNUSED) {
|
|
auto epsilon = static_cast<T>(epsilon_t);
|
|
auto rho = static_cast<T>(decay_t);
|
|
auto momentum = static_cast<T>(momentum_t);
|
|
|
|
auto &p_tensor = param;
|
|
auto &ms_tensor = mean_square;
|
|
auto &lr_tensor = learning_rate;
|
|
auto &mom_tensor = moment;
|
|
|
|
PADDLE_ENFORCE_EQ(p_tensor.IsSharedBufferWith(*param_out),
|
|
true,
|
|
common::errors::InvalidArgument(
|
|
"Param and ParamOut must be the same Tensor"));
|
|
PADDLE_ENFORCE_EQ(mom_tensor.IsSharedBufferWith(*moment_out),
|
|
true,
|
|
common::errors::InvalidArgument(
|
|
"Moment and MomentOut must be the same Tensor"));
|
|
PADDLE_ENFORCE_EQ(
|
|
ms_tensor.IsSharedBufferWith(*mean_square_out),
|
|
true,
|
|
common::errors::InvalidArgument(
|
|
"MeanSquare and MeanSquareOut must be the same Tensor"));
|
|
|
|
auto &grad_tensor = grad;
|
|
auto &place = *dev_ctx.eigen_device();
|
|
auto lr_value = lr_tensor.data<T>()[0];
|
|
|
|
auto p = EigenVector<T>::Flatten(p_tensor);
|
|
auto ms = EigenVector<T>::Flatten(ms_tensor);
|
|
auto g = EigenVector<T>::Flatten(grad_tensor);
|
|
auto mom = EigenVector<T>::Flatten(mom_tensor);
|
|
|
|
auto p_out = EigenVector<T>::Flatten(*param_out);
|
|
auto mom_out = EigenVector<T>::Flatten(*moment_out);
|
|
auto ms_out = EigenVector<T>::Flatten(*mean_square_out);
|
|
|
|
ms_out.device(place) = rho * ms + (1 - rho) * g * g;
|
|
if (centered) {
|
|
auto mg_tensor = mean_grad_opt.get_ptr();
|
|
if (mg_tensor) {
|
|
PADDLE_ENFORCE_EQ(
|
|
mg_tensor->Holder(),
|
|
mean_grad_out->Holder(),
|
|
common::errors::InvalidArgument(
|
|
"MeanGrad and MeanGradOut must be the same Tensor"));
|
|
} else {
|
|
PADDLE_ENFORCE_EQ(
|
|
mg_tensor,
|
|
mean_grad_out,
|
|
common::errors::InvalidArgument(
|
|
"MeanGrad and MeanGradOut must be the same Tensor"));
|
|
}
|
|
auto mg = EigenVector<T>::Flatten(*mg_tensor);
|
|
auto mg_out = EigenVector<T>::Flatten(*mean_grad_out);
|
|
|
|
mg_out.device(place) = rho * mg + (1 - rho) * g;
|
|
mom_out.device(place) =
|
|
momentum * mom +
|
|
lr_value * g / (ms_out - mg_out.square() + epsilon).sqrt();
|
|
} else {
|
|
mom_out.device(place) =
|
|
momentum * mom + lr_value * g / (ms_out + epsilon).sqrt();
|
|
}
|
|
p_out.device(place) = p - mom_out;
|
|
}
|
|
};
|
|
|
|
} // namespace phi
|
|
PD_REGISTER_KERNEL(
|
|
rmsprop, CPU, ALL_LAYOUT, phi::RmspropDenseKernel, float, double) {}
|
|
|
|
PD_REGISTER_KERNEL(rmsprop_dense_param_sparse_grad,
|
|
CPU,
|
|
ALL_LAYOUT,
|
|
phi::RmspropSparseKernel,
|
|
float,
|
|
double) {}
|