wehub-resource-sync
77 lines
2.5 KiB
C++
77 lines
2.5 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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#pragma once
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#include "paddle/phi/common/amp_type_traits.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/huber_loss_grad_kernel.h"
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namespace phi {
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template <typename T>
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struct HuberLossBackward {
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HOSTDEVICE HuberLossBackward(const T& delta, T sign)
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: sign(sign), delta(delta) {}
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HOSTDEVICE T operator()(const T& val) const {
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T abs_val = abs(val);
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if (abs_val <= delta) {
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return sign * val;
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} else {
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if (val > static_cast<T>(0)) {
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return sign * delta;
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} else {
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return static_cast<T>(-1) * sign * delta;
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}
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}
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}
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T sign;
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T delta;
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};
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template <typename T, typename Context>
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void HuberLossGradKernel(const Context& dev_ctx,
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const DenseTensor& residual,
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const DenseTensor& out_grad,
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float delta,
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DenseTensor* input_grad,
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DenseTensor* label_grad) {
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T delta_ = static_cast<T>(delta);
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auto& place = *dev_ctx.eigen_device();
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auto eigen_residual = EigenVector<T>::Flatten(residual);
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auto eigen_out_grad = EigenVector<T>::Flatten(out_grad);
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if (input_grad) {
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dev_ctx.template Alloc<T>(input_grad);
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auto eigen_input_grad = EigenVector<T>::Flatten(*input_grad);
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eigen_input_grad.device(place) = eigen_residual.unaryExpr(
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HuberLossBackward<T>(delta_, static_cast<T>(-1.0)));
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eigen_input_grad.device(place) = eigen_out_grad * eigen_input_grad;
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}
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if (label_grad) {
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dev_ctx.template Alloc<T>(label_grad);
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auto eigen_label_grad = EigenVector<T>::Flatten(*label_grad);
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eigen_label_grad.device(place) = eigen_residual.unaryExpr(
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HuberLossBackward<T>(delta_, static_cast<T>(1.0)));
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eigen_label_grad.device(place) = eigen_out_grad * eigen_label_grad;
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}
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}
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} // namespace phi
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