wehub-resource-sync
87 lines
2.7 KiB
C++
87 lines
2.7 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 <string>
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#include "paddle/common/hostdevice.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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namespace phi {
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using Array1 = Eigen::DSizes<int64_t, 1>;
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template <typename T>
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struct KLDivLossBackward {
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bool log_target = false;
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HOSTDEVICE KLDivLossBackward(bool logTarget) : log_target(logTarget) {}
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HOSTDEVICE T operator()(const T& target, const T& grad) const {
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if (log_target) {
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return static_cast<T>(-1.) * std::exp(target) * grad;
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} else {
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if (target <= 0) {
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return 0;
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} else {
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return static_cast<T>(-1.) * target * grad;
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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 KLDivLossGradKernel(const Context& dev_ctx,
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const DenseTensor& x UNUSED,
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const DenseTensor& label,
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const DenseTensor& d_out,
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const std::string& reduction,
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bool log_target,
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DenseTensor* d_x) {
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if (d_x->numel() == 0) {
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dev_ctx.template Alloc<T>(d_x);
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return;
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}
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auto& place = *dev_ctx.eigen_device();
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auto* target = &label;
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auto* input_grad = d_x;
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auto* loss_grad = &d_out;
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// TODO(large-tensor): downstream functors may still use int; guard until
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// upgraded.
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int64_t n = input_grad->dims()[0];
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const int64_t numel = input_grad->numel();
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const int64_t expand = numel / loss_grad->numel();
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dev_ctx.template Alloc<T>(input_grad);
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auto target_t = EigenVector<T>::Flatten(*target);
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auto input_grad_t = EigenVector<T>::Flatten(*input_grad);
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auto loss_grad_t = EigenVector<T>::Flatten(*loss_grad);
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auto loss_grad_expand = loss_grad_t.broadcast(Array1(expand));
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auto grad_t = loss_grad_expand;
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input_grad_t.device(place) =
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target_t.binaryExpr(grad_t, KLDivLossBackward<T>(log_target));
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if ("mean" == reduction) {
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input_grad_t.device(place) = input_grad_t / static_cast<T>(numel);
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} else if ("batchmean" == reduction) {
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input_grad_t.device(place) = input_grad_t / static_cast<T>(n);
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}
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}
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} // namespace phi
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