wehub-resource-sync
83 lines
2.6 KiB
C++
83 lines
2.6 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/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/kernels/full_kernel.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 KLDivLossForward {
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bool log_target = false;
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HOSTDEVICE KLDivLossForward(bool logTarget) : log_target(logTarget) {}
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HOSTDEVICE T operator()(const T& target, const T& input) const {
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if (log_target) {
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return std::exp(target) * (target - input);
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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 target * (std::log(target) - input);
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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 KLDivLossKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& label,
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const std::string& reduction,
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bool log_target,
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DenseTensor* out) {
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if (x.numel() == 0) {
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Full<T, Context>(dev_ctx, out->dims(), NAN, out);
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return;
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}
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auto& place = *(dev_ctx.eigen_device());
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auto* input = &x;
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auto* target = &label;
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auto* loss = out;
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const int64_t n = input->dims()[0];
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dev_ctx.template Alloc<T>(loss);
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auto input_t = EigenVector<T>::Flatten(*input);
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auto target_t = EigenVector<T>::Flatten(*target);
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auto loss_t = EigenVector<T>::Flatten(*loss);
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auto output = target_t.binaryExpr(input_t, KLDivLossForward<T>(log_target));
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if ("none" == reduction) {
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loss_t.device(place) = output;
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} else if ("batchmean" == reduction) {
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auto output_sum = output.sum();
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if (n > 0) {
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loss_t.device(place) = output_sum / output_sum.constant(n);
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} else {
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loss_t.device(place) = output_sum;
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}
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} else if ("mean" == reduction) {
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loss_t.device(place) = output.mean();
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} else if ("sum" == reduction) {
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loss_t.device(place) = output.sum();
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}
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}
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} // namespace phi
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