/
rrelu_grad_kernel.cc
44 lines (37 loc) · 1.48 KB
/
rrelu_grad_kernel.cc
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
// 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/rrelu_grad_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
template <typename T, typename Context>
void RReluGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& noise,
const DenseTensor& out_grad,
DenseTensor* x_grad) {
const T* n_ptr = noise.data<T>();
const T* x_ptr = x.data<T>();
const T* out_grad_ptr = out_grad.data<T>();
int numel = x.numel();
if (!x_grad) return;
int i = 0;
T* x_grad_ptr = dev_ctx.template Alloc<T>(x_grad);
for (i = 0; i < numel; i++) {
x_grad_ptr[i] = x_ptr[i] > 0 ? out_grad_ptr[i] : n_ptr[i] * out_grad_ptr[i];
}
}
} // namespace phi
PD_REGISTER_KERNEL(
rrelu_grad, CPU, ALL_LAYOUT, phi::RReluGradKernel, float, double) {}