wehub-resource-sync
99 lines
3.5 KiB
Plaintext
99 lines
3.5 KiB
Plaintext
// 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 <vector>
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/common/amp_type_traits.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/elementwise_base.h"
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namespace phi {
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template <typename T>
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struct LabelSmoothFunctor {
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using MT = typename MPTypeTrait<T>::Type;
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MT epsilon;
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MT label_dim;
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__forceinline__ LabelSmoothFunctor(float epsilon_data, int label_dim_data) {
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epsilon = static_cast<MT>(epsilon_data);
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label_dim = static_cast<MT>(label_dim_data);
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}
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__device__ __forceinline__ T operator()(const T x) const {
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return static_cast<T>(static_cast<MT>(static_cast<MT>(1) - epsilon) *
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static_cast<MT>(x) +
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static_cast<MT>(epsilon / label_dim));
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}
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};
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template <typename T>
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__global__ void LabelSmoothRunDistKernel(const int64_t N,
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const float epsilon,
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const int dist_numel,
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const T* src,
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const T* dist_data,
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T* dst) {
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using MT = typename MPTypeTrait<T>::Type;
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CUDA_KERNEL_LOOP_TYPE(idx, N, int64_t) {
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int64_t dist_idx = idx % dist_numel;
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dst[idx] = static_cast<T>((static_cast<MT>(1) - static_cast<MT>(epsilon)) *
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static_cast<MT>(src[idx]) +
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static_cast<MT>(epsilon) *
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static_cast<MT>(dist_data[dist_idx]));
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}
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}
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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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auto size_prob = label.numel();
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const T* in_data = label.data<T>();
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T* out_data = dev_ctx.template Alloc<T>(out);
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if (prior_dist.get_ptr()) {
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int threads = 512;
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int grid = (size_prob + threads - 1) / threads;
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auto stream = dev_ctx.stream();
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const auto* dist_t = prior_dist.get_ptr();
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auto dist_numel = dist_t->numel();
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const T* dist_data = dist_t->data<T>();
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LabelSmoothRunDistKernel<T><<<grid, threads, 0, stream>>>(
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size_prob, epsilon, dist_numel, in_data, dist_data, out_data);
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} else {
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std::vector<const DenseTensor*> ins = {&label};
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std::vector<DenseTensor*> outs = {out};
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auto functor = LabelSmoothFunctor<T>(epsilon, label_dim);
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funcs::ElementwiseKernel<T>(dev_ctx, ins, &outs, functor);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(label_smooth,
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GPU,
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ALL_LAYOUT,
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phi::LabelSmoothKernel,
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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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