wehub-resource-sync
102 lines
3.3 KiB
C++
102 lines
3.3 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/p_norm_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/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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inline void GetDims(
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const DDim& dim, int axis, int* pre, int* n, int* post, bool asvector) {
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*pre = 1;
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*post = 1;
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*n = static_cast<int>(dim[axis]);
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if (asvector) {
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*n = static_cast<int>(product(dim));
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} else {
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for (int i = 0; i < axis; ++i) {
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(*pre) *= static_cast<int>(dim[i]);
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}
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for (int i = axis + 1; i < dim.size(); ++i) {
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(*post) *= static_cast<int>(dim[i]);
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}
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}
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}
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template <typename T, typename Context>
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void PNormGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out,
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const DenseTensor& out_grad,
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double porder,
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int axis,
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float epsilon,
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bool keepdim UNUSED,
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bool asvector,
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DenseTensor* x_grad) {
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auto* in_x = &x;
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auto* in_norm = &out;
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auto* in_norm_dy = &out_grad;
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auto* out_dx = x_grad;
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dev_ctx.template Alloc<T>(out_dx);
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if (out_dx->numel() == 0) {
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return;
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}
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T eps = static_cast<T>(epsilon);
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auto xdim = in_x->dims();
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if (axis < 0) axis = xdim.size() + axis;
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int pre, n, post;
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GetDims(xdim, axis, &pre, &n, &post, asvector);
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Eigen::DSizes<int64_t, 3> shape(pre, n, post);
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Eigen::DSizes<int64_t, 3> rshape(pre, static_cast<int64_t>(1), post);
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auto* place = dev_ctx.eigen_device();
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auto x_e = EigenVector<T>::Flatten(*in_x);
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auto dx_e = EigenVector<T>::Flatten(*out_dx);
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auto norm_e = EigenVector<T>::Flatten(*in_norm);
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auto norm_dy_e = EigenVector<T>::Flatten(*in_norm_dy);
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auto xr = x_e.reshape(shape);
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auto dx = dx_e.reshape(shape);
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auto norm = norm_e.reshape(rshape);
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auto norm_dy = norm_dy_e.reshape(rshape);
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Eigen::DSizes<int, 1> rdim(1);
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Eigen::DSizes<int, 3> bcast(1, n, 1);
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if (porder == 0) {
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funcs::SetConstant<Context, T> set_zero;
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set_zero(dev_ctx, out_dx, static_cast<T>(0));
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} else if (porder == INFINITY || porder == -INFINITY) {
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dx.device(*place) = (xr.abs() == norm.broadcast(bcast)).template cast<T>() *
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xr.sign() * norm_dy.broadcast(bcast);
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} else {
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dx.device(*place) =
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(xr.abs()).pow(porder - 1.0) /
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((norm.broadcast(bcast)).pow(porder - 1.0) + xr.constant(eps));
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dx.device(*place) = dx * norm_dy.broadcast(bcast) * xr.sign();
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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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p_norm_grad, CPU, ALL_LAYOUT, phi::PNormGradKernel, float, double) {}
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