dropout_kernel.cc 3.4 KB
Newer Older
H
hong 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
// Copyright (c) 2022 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/dropout_kernel.h"
#include "paddle/fluid/framework/generator.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"

namespace phi {

template <typename T, typename Context>
void DropoutRawKernel(const Context& dev_ctx,
                      const DenseTensor& x,
26
                      const paddle::optional<DenseTensor>& seed_tensor,
H
hong 已提交
27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104
                      float p,
                      bool is_test,
                      const std::string& mode,
                      int seed,
                      bool fix_seed,
                      DenseTensor* out,
                      DenseTensor* mask) {
  auto* y = out;
  const auto* x_data = x.data<T>();
  auto* y_data = y->mutable_data<T>(dev_ctx.GetPlace());
  float dropout_prob = p;

  auto& dropout_implementation = mode;
  bool upscale_in_train = (dropout_implementation == "upscale_in_train");
  if (!is_test) {
    auto* mask_data = mask->mutable_data<uint8_t>(dev_ctx.GetPlace());
    size_t size = phi::product(mask->dims());

    // Special case when dropout_prob is 1.0
    if (dropout_prob == 1.0f) {
      std::memset(y_data, 0, size * sizeof(*y_data));        // NOLINT
      std::memset(mask_data, 0, size * sizeof(*mask_data));  // NOLINT
      return;
    }
    // std::minstd_rand engine;
    // NOTE: fixed seed should only be used in unittest or for debug.
    // Guarantee to use random seed in training.
    int seed_data = 0;
    if (seed_tensor.get_ptr() != nullptr) {
      seed_data = *(seed_tensor->data<int>());
    } else {
      seed_data = fix_seed ? seed : 0;
    }
    auto engine = paddle::framework::GetCPURandomEngine(seed_data);

    std::uniform_real_distribution<float> dist(0, 1);

    for (size_t i = 0; i < size; ++i) {
      if (dist(*engine) < dropout_prob) {
        mask_data[i] = 0;
        y_data[i] = 0;
      } else {
        mask_data[i] = 1;
        if (upscale_in_train) {
          y_data[i] = x_data[i] / static_cast<T>(1.0f - dropout_prob);
        } else {
          y_data[i] = x_data[i];
        }
      }
    }
  } else {
    if (upscale_in_train) {
      const auto* X_data = x.data<T>();
      auto* Y_data = y->mutable_data<T>(dev_ctx.GetPlace());
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
      for (int i = 0; i < x.numel(); i++) {
        Y_data[i] = X_data[i];
      }
    } else {
      auto X = EigenMatrix<T>::Reshape(x, 1);
      auto Y = EigenMatrix<T>::Reshape(*y, 1);
      auto& place = *dev_ctx.eigen_device();
      Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
    }
  }
}

}  // namespace phi

PD_REGISTER_KERNEL(dropout,
                   CPU,
                   ALL_LAYOUT,
                   phi::DropoutRawKernel,
                   float,
                   double,
                   phi::dtype::bfloat16) {}