wehub-resource-sync
133 lines
4.3 KiB
C++
133 lines
4.3 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/affine_channel_grad_kernel.h"
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#include <string>
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#include <unordered_map>
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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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namespace phi {
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template <typename T>
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using EigenArrayMap =
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Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
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template <typename T>
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using ConstEigenArrayMap =
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Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
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template <typename T>
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using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
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template <typename T>
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using ConstEigenVectorArrayMap =
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Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
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template <typename T, typename Context>
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void AffineChannelGradKernel(const Context& dev_ctx,
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const DenseTensor& x_in,
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const DenseTensor& scale_in,
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const DenseTensor& bias_in,
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const DenseTensor& out_grad,
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const std::string& data_layout,
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DenseTensor* x_grad,
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DenseTensor* scale_grad,
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DenseTensor* bias_grad) {
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auto* x = &x_in;
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auto* scale = &scale_in;
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auto* dy = &out_grad;
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auto* dx = x_grad;
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auto* dscale = scale_grad;
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auto* dbias = bias_grad;
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const DataLayout layout = StringToDataLayout(data_layout);
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auto dims = x->dims();
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int N = static_cast<int>(dims[0]);
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int C = static_cast<int>(layout == DataLayout::NCHW ? dims[1]
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: dims[dims.size() - 1]);
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int HxW = static_cast<int>(x->numel() / N / C);
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auto* dy_d = dy->data<T>();
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auto* scale_d = scale->data<T>();
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ConstEigenVectorArrayMap<T> scale_e(scale_d, C);
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T* dx_d = dx ? dev_ctx.template Alloc<T>(dx) : nullptr;
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T* dscale_d = dscale ? dev_ctx.template Alloc<T>(dscale) : nullptr;
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T* dbias_d = dbias ? dev_ctx.template Alloc<T>(dbias) : nullptr;
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EigenVectorArrayMap<T> dscale_e(dscale_d, C);
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EigenVectorArrayMap<T> dbias_e(dbias_d, C);
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if (layout == DataLayout::NCHW) {
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// compute dscale and dbias
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int64_t stride = static_cast<int64_t>(C) * HxW;
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auto* original_dy_d = dy_d;
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if (dscale && dbias) {
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auto* x_d = x->data<T>();
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for (int i = 0; i < N; i++) {
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ConstEigenArrayMap<T> x_e(x_d, HxW, C);
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ConstEigenArrayMap<T> dy_e(dy_d, HxW, C);
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if (i == 0) {
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dscale_e = (x_e * dy_e).colwise().sum();
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} else {
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dscale_e += (x_e * dy_e).colwise().sum();
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}
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if (i == 0) {
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dbias_e = dy_e.colwise().sum();
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} else {
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dbias_e += dy_e.colwise().sum();
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}
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x_d += stride;
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dy_d += stride;
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}
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}
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// compute dx
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if (dx) {
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dy_d = original_dy_d;
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for (int i = 0; i < N; i++) {
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ConstEigenArrayMap<T> dy_e(dy_d, HxW, C);
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EigenArrayMap<T> dx_e(dx_d, HxW, C);
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dx_e = dy_e.rowwise() * scale_e.transpose();
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dy_d += stride;
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dx_d += stride;
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}
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}
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} else {
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int64_t num = static_cast<int64_t>(N) * HxW;
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ConstEigenArrayMap<T> dy_e(dy_d, C, num);
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// compute dscale and dbias
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if (dscale && dbias) {
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auto* x_d = x->data<T>();
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ConstEigenArrayMap<T> x_e(x_d, C, num);
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dscale_e = (x_e * dy_e).rowwise().sum();
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dbias_e = dy_e.rowwise().sum();
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}
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// compute dx
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if (dx) {
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EigenArrayMap<T> dx_e(dx_d, C, num);
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dx_e = dy_e.colwise() * scale_e;
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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(affine_channel_grad,
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CPU,
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ALL_LAYOUT,
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phi::AffineChannelGradKernel,
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float,
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double) {}
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