wehub-resource-sync
127 lines
4.1 KiB
C++
127 lines
4.1 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/prelu_grad_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/full_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void PReluGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& alpha,
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const DenseTensor& out_grad,
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const std::string& data_format,
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const std::string& mode,
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DenseTensor* x_grad,
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DenseTensor* alpha_grad) {
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if (x_grad->numel() == 0) {
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dev_ctx.template Alloc<T>(x_grad);
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if (alpha_grad) {
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Full<T, Context>(dev_ctx, alpha_grad->dims(), 0, alpha_grad);
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}
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return;
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}
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const T* alpha_ptr = alpha.data<T>();
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const T* x_ptr = x.data<T>();
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const T* out_grad_ptr = out_grad.data<T>();
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int numel = static_cast<int>(x.numel());
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auto dim = x.dims();
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int index = 0;
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int i = 0;
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if (x_grad) {
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T* x_grad_ptr = dev_ctx.template Alloc<T>(x_grad);
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if (mode == "channel") {
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if (data_format == "NCHW") {
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int temp = 1;
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for (int j = 2; j < dim.size(); j++) {
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temp *= static_cast<int>(dim[j]);
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}
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for (i = 0; i < numel; i++) {
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index = static_cast<int>((i / temp) % dim[1]);
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x_grad_ptr[i] = x_ptr[i] > 0 ? out_grad_ptr[i]
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: alpha_ptr[index] * out_grad_ptr[i];
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}
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} else {
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for (i = 0; i < numel; i++) {
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index = static_cast<int>(i % dim[dim.size() - 1]);
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x_grad_ptr[i] = x_ptr[i] > 0 ? out_grad_ptr[i]
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: alpha_ptr[index] * out_grad_ptr[i];
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}
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}
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} else if (mode == "element") {
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int temp = 1;
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for (int j = 1; j < dim.size(); j++) {
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temp *= static_cast<int>(dim[j]);
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}
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for (i = 0; i < numel; i++) {
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index = i % temp;
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x_grad_ptr[i] =
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x_ptr[i] > 0 ? out_grad_ptr[i] : alpha_ptr[index] * out_grad_ptr[i];
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}
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} else {
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for (i = 0; i < numel; i++) {
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x_grad_ptr[i] =
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x_ptr[i] > 0 ? out_grad_ptr[i] : alpha_ptr[0] * out_grad_ptr[i];
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}
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}
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}
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index = 0;
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if (alpha_grad) {
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T* alpha_grad_ptr = dev_ctx.template Alloc<T>(alpha_grad);
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memset(alpha_grad_ptr, 0, sizeof(T) * alpha_grad->numel());
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if (mode == "channel") {
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if (data_format == "NCHW") {
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int temp = 1;
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for (int j = 2; j < dim.size(); j++) {
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temp *= static_cast<int>(dim[j]);
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}
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for (i = 0; i < numel; i++) {
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index = static_cast<int>((i / temp) % dim[1]);
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alpha_grad_ptr[index] +=
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x_ptr[i] > 0 ? 0 : x_ptr[i] * out_grad_ptr[i];
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}
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} else {
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for (i = 0; i < numel; i++) {
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index = static_cast<int>(i % dim[dim.size() - 1]);
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alpha_grad_ptr[index] +=
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x_ptr[i] > 0 ? 0 : x_ptr[i] * out_grad_ptr[i];
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}
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}
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} else if (mode == "element") {
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int temp = 1;
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for (int j = 1; j < dim.size(); j++) {
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temp *= static_cast<int>(dim[j]);
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}
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for (i = 0; i < numel; i++) {
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index = i % temp;
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alpha_grad_ptr[index] += x_ptr[i] > 0 ? 0 : x_ptr[i] * out_grad_ptr[i];
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}
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} else {
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for (i = 0; i < numel; i++) {
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alpha_grad_ptr[0] += x_ptr[i] > 0 ? 0 : x_ptr[i] * out_grad_ptr[i];
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}
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}
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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prelu_grad, CPU, ALL_LAYOUT, phi::PReluGradKernel, float, double) {}
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