paddlepaddle--paddle/paddle/phi/kernels/funcs/math/prelu.cu

// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "paddle/phi/kernels/funcs/math/prelu.h"

namespace phi {
namespace math {

#define CUDA_NUM_THREADS 1024

inline static int PADDLE_GET_BLOCKS(const int N) {
  return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS;
}

template <typename T>
__global__ void PReluChannelFirstWiseKernel(const T *input,
                                            const T *alpha,
                                            T *output,
                                            size_t channel_num,
                                            size_t plane_size,
                                            size_t numel) {
  CUDA_KERNEL_LOOP(index, numel) {
    size_t temp = index / plane_size;
    size_t channel_index = temp % channel_num;
    T scale = alpha[channel_index];
    T x = input[index];
    T zero = static_cast<T>(0);
    output[index] = (x > zero) ? x : scale * x;
  }
}

template <typename T>
__global__ void PReluChannelLastWiseKernel(const T *input,
                                           const T *alpha,
                                           T *output,
                                           size_t channel_num,
                                           size_t numel) {
  CUDA_KERNEL_LOOP(index, numel) {
    size_t channel_index = index % channel_num;
    T scale = alpha[channel_index];
    T x = input[index];
    T zero = static_cast<T>(0);
    output[index] = (x > zero) ? x : scale * x;
  }
}

template <typename T>
__global__ void PReluElementWiseKernel(const T *input,
                                       const T *alpha,
                                       T *output,
                                       size_t spatial_size,
                                       size_t numel) {
  CUDA_KERNEL_LOOP(index, numel) {
    size_t element_index = index % spatial_size;
    T scale = alpha[element_index];
    T x = input[index];
    T zero = static_cast<T>(0);
    output[index] = (x > zero) ? x : scale * x;
  }
}

template <typename T>
__global__ void PReluScalarKernel(const T *input,
                                  const T *alpha,
                                  T *output,
                                  size_t numel) {
  T scale = alpha[0];
  CUDA_KERNEL_LOOP(index, numel) {
    T x = input[index];
    T zero = static_cast<T>(0);
    output[index] = (x > zero) ? x : scale * x;
  }
}

template <typename T>
void PreluChannelWiseDirectCUDAFunctor<T>::operator()(gpuStream_t stream,
                                                      const T *input,
                                                      const T *alpha,
                                                      T *output,
                                                      size_t batch_size,
                                                      size_t channel,
                                                      bool channel_last,
                                                      size_t numel) {
  if (channel_last) {
    PReluChannelLastWiseKernel<<<PADDLE_GET_BLOCKS(numel),
                                 CUDA_NUM_THREADS,
                                 0,
                                 stream>>>(
        input, alpha, output, channel, numel);
  } else {
    PReluChannelFirstWiseKernel<<<PADDLE_GET_BLOCKS(numel),
                                  CUDA_NUM_THREADS,
                                  0,
                                  stream>>>(
        input, alpha, output, channel, numel / batch_size / channel, numel);
  }
}

template <typename T>
void PreluElementWiseDirectCUDAFunctor<T>::operator()(gpuStream_t stream,
                                                      const T *input,
                                                      const T *alpha,
                                                      T *output,
                                                      size_t batch_size,
                                                      size_t numel) {
  PReluElementWiseKernel<<<PADDLE_GET_BLOCKS(numel),
                           CUDA_NUM_THREADS,
                           0,
                           stream>>>(
      input, alpha, output, numel / batch_size, numel);
}

template <typename T>
void PreluScalarDirectCUDAFunctor<T>::operator()(gpuStream_t stream,
                                                 const T *input,
                                                 const T *alpha,
                                                 T *output,
                                                 size_t numel) {
  PReluScalarKernel<<<PADDLE_GET_BLOCKS(numel), CUDA_NUM_THREADS, 0, stream>>>(
      input, alpha, output, numel);
}

template class PreluChannelWiseDirectCUDAFunctor<float>;
template class PreluChannelWiseDirectCUDAFunctor<phi::float16>;
template class PreluChannelWiseDirectCUDAFunctor<phi::bfloat16>;
template class PreluChannelWiseDirectCUDAFunctor<double>;

template class PreluElementWiseDirectCUDAFunctor<float>;
template class PreluElementWiseDirectCUDAFunctor<phi::float16>;
template class PreluElementWiseDirectCUDAFunctor<phi::bfloat16>;
template class PreluElementWiseDirectCUDAFunctor<double>;

template class PreluScalarDirectCUDAFunctor<float>;
template class PreluScalarDirectCUDAFunctor<phi::float16>;
template class PreluScalarDirectCUDAFunctor<phi::bfloat16>;
template class PreluScalarDirectCUDAFunctor<double>;

}  // namespace math
}  // namespace phi