chore: import upstream snapshot with attribution
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// 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/label_smooth_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 LabelSmoothKernel(const Context& dev_ctx,
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const DenseTensor& label,
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const optional<DenseTensor>& prior_dist,
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float epsilon,
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DenseTensor* out) {
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auto label_dim = label.dims()[label.dims().size() - 1];
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dev_ctx.template Alloc<T>(out);
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auto& dev = *dev_ctx.eigen_device();
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if (label_dim != 0) {
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auto eigen_out = EigenVector<T>::Flatten(*out);
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auto eigen_in = EigenVector<T>::Flatten(label);
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if (prior_dist.is_initialized()) {
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auto dist = EigenVector<T>::Flatten(*prior_dist.get_ptr());
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eigen_out.device(dev) =
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static_cast<T>(1 - epsilon) * eigen_in +
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static_cast<T>(epsilon) *
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dist.broadcast(Eigen::DSizes<int, 1>(
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static_cast<int>(label.numel() / label_dim)));
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} else {
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eigen_out.device(dev) =
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static_cast<T>(1 - epsilon) * eigen_in +
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static_cast<T>(epsilon / static_cast<float>(label_dim));
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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(
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label_smooth, CPU, ALL_LAYOUT, phi::LabelSmoothKernel, float, double) {}
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