wehub-resource-sync
97 lines
3.3 KiB
C++
97 lines
3.3 KiB
C++
// Copyright (c) 2024 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/impl/lrn_kernel_impl.h"
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#include <memory>
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#include <string>
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#include <vector>
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#include "paddle/phi/backends/onednn/onednn_helper.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/blas/blas.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 T>
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struct LRNGradFunctor<CPUContext, T> {
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void operator()(const CPUContext& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out,
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const DenseTensor& mid,
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DenseTensor* x_g,
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const DenseTensor& out_g,
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int64_t N,
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int64_t C,
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int64_t H,
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int64_t W,
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int n,
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T alpha,
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T beta,
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const DataLayout data_layout) {
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T ratio = -2 * alpha * beta;
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auto x_g_e = EigenVector<T>::Flatten(*x_g);
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x_g_e = x_g_e.constant(0.0);
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auto e_x = EigenTensor<T, 4>::From(x);
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auto e_x_g = EigenTensor<T, 4>::From(*x_g);
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auto e_out = EigenTensor<T, 4>::From(out);
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auto e_out_g = EigenTensor<T, 4>::From(out_g);
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auto e_mid = EigenTensor<T, 4>::From(mid);
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const int start = -(n - 1) / 2;
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const int end = start + n;
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for (int64_t m = 0; m < N; m++) {
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for (int64_t i = 0; i < C; i++) {
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auto offsets = Eigen::array<int64_t, 4>({{m, i, 0, 0}});
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auto extents = Eigen::array<int64_t, 4>({{1, 1, H, W}});
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if (data_layout == DataLayout::NHWC) {
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offsets = Eigen::array<int64_t, 4>({{m, 0, 0, i}});
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extents = Eigen::array<int64_t, 4>({{1, H, W, 1}});
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}
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auto i_x = e_x.slice(offsets, extents);
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auto i_x_g = e_x_g.slice(offsets, extents);
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auto i_out_g = e_out_g.slice(offsets, extents);
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auto i_mid = e_mid.slice(offsets, extents);
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i_x_g = i_mid.pow(-beta) * i_out_g;
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for (int c = start; c < end; c++) {
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int64_t ch = i + c;
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if (ch < 0 || ch >= C) {
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continue;
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}
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if (data_layout != DataLayout::NHWC) {
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offsets = Eigen::array<int64_t, 4>({{m, ch, 0, 0}});
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} else {
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offsets = Eigen::array<int64_t, 4>({{m, 0, 0, ch}});
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}
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auto c_out = e_out.slice(offsets, extents);
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auto c_mid = e_mid.slice(offsets, extents);
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auto c_out_g = e_out_g.slice(offsets, extents);
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i_x_g += ratio * c_out_g * c_out * i_x / c_mid;
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}
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}
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}
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}
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};
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template struct LRNGradFunctor<CPUContext, float>;
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template struct LRNGradFunctor<CPUContext, double>;
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} // namespace phi
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PD_REGISTER_KERNEL(lrn_grad, CPU, ALL_LAYOUT, phi::LRNGradKernel, float) {}
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