wehub-resource-sync
92 lines
3.2 KiB
C++
92 lines
3.2 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/log_softmax_grad_kernel.h"
|
|
|
|
#include "paddle/phi/backends/cpu/cpu_context.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
#include "paddle/phi/kernels/funcs/axis_utils.h"
|
|
#include "paddle/phi/kernels/funcs/eigen/common.h"
|
|
#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
|
|
#include "paddle/phi/kernels/funcs/math_function.h"
|
|
|
|
namespace phi {
|
|
|
|
template <typename T, int MajorType = Eigen::RowMajor>
|
|
using EigenMatrixTemplate = EigenMatrix<T, MajorType>;
|
|
|
|
template <typename Context, typename T>
|
|
struct LogSoftmaxGradFunctor {
|
|
void operator()(const Context& dev_ctx,
|
|
const DenseTensor* Y,
|
|
const DenseTensor* dY,
|
|
DenseTensor* dX,
|
|
const int axis) {
|
|
constexpr int kBatchDim = 0;
|
|
constexpr int kClassDim = 1;
|
|
|
|
const int n = funcs::SizeToAxis(axis, Y->dims());
|
|
const int d = funcs::SizeFromAxis(axis, Y->dims());
|
|
DDim dim_2d{n, d};
|
|
|
|
auto y = EigenMatrixTemplate<T>::From(*Y, dim_2d);
|
|
auto dy = EigenMatrixTemplate<T>::From(*dY, dim_2d);
|
|
auto dx = EigenMatrixTemplate<T>::From(*dX, dim_2d);
|
|
|
|
const int axis_dim = static_cast<int>(Y->dims()[axis]);
|
|
const int batch_size = y.dimension(kBatchDim);
|
|
const int num_classes = y.dimension(kClassDim);
|
|
const int num_remain = num_classes / axis_dim;
|
|
|
|
Eigen::DSizes<int, 1> along_class(kClassDim);
|
|
Eigen::DSizes<int, 3> batch_axis_remain(batch_size, axis_dim, num_remain);
|
|
Eigen::DSizes<int, 2> one_axis(1, axis_dim);
|
|
|
|
dx.device(*dev_ctx.eigen_device()) =
|
|
dy - (y.exp()) * (dy.reshape(batch_axis_remain)
|
|
.sum(along_class)
|
|
.broadcast(one_axis));
|
|
}
|
|
};
|
|
|
|
template <typename T, typename Context>
|
|
void LogSoftmaxGradKernel(const Context& dev_ctx,
|
|
const DenseTensor& out,
|
|
const DenseTensor& out_grad,
|
|
int axis,
|
|
DenseTensor* x_grad) {
|
|
const int rank = out.dims().size();
|
|
const int canonical_axis = funcs::CanonicalAxis(axis, rank);
|
|
|
|
dev_ctx.template Alloc<T>(x_grad);
|
|
// For 0D Tensor
|
|
if (rank == 0) {
|
|
funcs::set_constant(dev_ctx, x_grad, static_cast<T>(0.0));
|
|
return;
|
|
}
|
|
if (out.numel() != 0) {
|
|
LogSoftmaxGradFunctor<Context, T>()(
|
|
dev_ctx, &out, &out_grad, x_grad, canonical_axis);
|
|
}
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(log_softmax_grad,
|
|
CPU,
|
|
ALL_LAYOUT,
|
|
phi::LogSoftmaxGradKernel,
|
|
float,
|
|
double) {}
|