wehub-resource-sync
98 lines
3.3 KiB
C++
98 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/dropout_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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namespace phi {
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template <typename T, typename Context>
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void DropoutNdGradKernel(const Context& dev_ctx,
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const DenseTensor& mask,
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const DenseTensor& out_grad,
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const Scalar& p,
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bool is_test,
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const std::string& mode,
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const std::vector<int>& axis,
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DenseTensor* x_grad) {
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auto* grad_x = x_grad;
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auto* grad_y = &out_grad;
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dev_ctx.template Alloc<T>(grad_x);
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auto dX = EigenVector<T>::Flatten(*grad_x);
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auto dY = EigenVector<T>::Flatten(*grad_y);
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float prob = p.to<float>();
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auto& place = *dev_ctx.eigen_device();
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auto& dropout_implementation = mode;
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if (is_test == true) {
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if (dropout_implementation == "upscale_in_train") {
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dX.device(place) = static_cast<T>(1) * dY;
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} else {
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dX.device(place) = dY * static_cast<T>(1.0f - prob);
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}
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} else {
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std::vector<int64_t> out_dims = vectorize(out_grad.dims());
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auto M = EigenVector<uint8_t>::Flatten(mask);
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if (dropout_implementation == "upscale_in_train") {
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if (prob == 1.0f) {
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dX.device(place) = static_cast<T>(0) * dY;
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} else {
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if (axis.empty()) {
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dX.device(place) = dY * M.cast<T>() / static_cast<T>(1.0f - prob);
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} else {
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dX.device(place) = dY * M.broadcast(out_dims).cast<T>() /
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static_cast<T>(1.0f - prob);
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}
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}
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} else {
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if (axis.empty()) {
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dX.device(place) = dY * M.cast<T>();
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} else {
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dX.device(place) = dY * M.broadcast(out_dims).cast<T>();
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}
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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 DropoutGradRawKernel(const Context& dev_ctx,
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const DenseTensor& mask,
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const DenseTensor& out_grad,
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const Scalar& p,
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bool is_test,
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const std::string& mode,
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DenseTensor* x_grad) {
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DropoutNdGradKernel<T, Context>(
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dev_ctx, mask, out_grad, p.to<float>(), is_test, mode, {}, x_grad);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(dropout_grad,
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CPU,
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ALL_LAYOUT,
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phi::DropoutGradRawKernel,
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float,
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double,
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phi::float16,
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phi::bfloat16) {}
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PD_REGISTER_KERNEL(
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dropout_nd_grad, CPU, ALL_LAYOUT, phi::DropoutNdGradKernel, float, double) {
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}
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