cudnn_helper.h 6.2 KB
Newer Older
D
dangqingqing 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

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. */

#pragma once

Y
Pass CI  
Yu Yang 已提交
17
#include <vector>
D
dangqingqing 已提交
18 19 20 21 22 23 24 25 26 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
#include "paddle/platform/dynload/cudnn.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/macros.h"

namespace paddle {
namespace platform {

enum class DataLayout {
  kNHWC,
  kNCHW,
  kNCHW_VECT_C,
};

enum class PoolingMode {
  kMaximum,
  kAverage,
};

template <typename T>
class CudnnDataType;

template <>
class CudnnDataType<float> {
 public:
  static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
};

template <>
class CudnnDataType<double> {
 public:
  static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
};

inline cudnnTensorFormat_t GetCudnnTensorFormat(const DataLayout& order) {
  switch (order) {
    case DataLayout::kNHWC:
      return CUDNN_TENSOR_NHWC;
    case DataLayout::kNCHW:
      return CUDNN_TENSOR_NCHW;
    default:
      PADDLE_THROW("Unknown cudnn equivalent for order");
  }
  return CUDNN_TENSOR_NCHW;
}

class ScopedTensorDescriptor {
 public:
  ScopedTensorDescriptor() {
    PADDLE_ENFORCE(dynload::cudnnCreateTensorDescriptor(&desc_));
  }
  ~ScopedTensorDescriptor() {
    PADDLE_ENFORCE(dynload::cudnnDestroyTensorDescriptor(desc_));
  }

  inline cudnnTensorDescriptor_t descriptor(const cudnnTensorFormat_t format,
                                            const cudnnDataType_t type,
                                            const std::vector<int>& dims) {
    // the format is not used now, but it maybe useful feature
    std::vector<int> strides(dims.size());
    strides[dims.size() - 1] = 1;
78 79
    for (int i = dims.size() - 2; i >= 0; i--) {
      strides[i] = dims[i + 1] * strides[i + 1];
D
dangqingqing 已提交
80
    }
81 82
    PADDLE_ENFORCE(dynload::cudnnSetTensorNdDescriptor(
        desc_, type, dims.size(), dims.data(), strides.data()));
D
dangqingqing 已提交
83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110
    return desc_;
  }

  template <typename T>
  inline cudnnTensorDescriptor_t descriptor(const DataLayout& order,
                                            const std::vector<int>& dims) {
    return descriptor(GetCudnnTensorFormat(order), CudnnDataType<T>::type,
                      dims);
  }

 private:
  cudnnTensorDescriptor_t desc_;
  DISABLE_COPY_AND_ASSIGN(ScopedTensorDescriptor);
};

class ScopedFilterDescriptor {
 public:
  ScopedFilterDescriptor() {
    PADDLE_ENFORCE(dynload::cudnnCreateFilterDescriptor(&desc_));
  }
  ~ScopedFilterDescriptor() {
    PADDLE_ENFORCE(dynload::cudnnDestroyFilterDescriptor(desc_));
  }

  inline cudnnFilterDescriptor_t descriptor(const cudnnTensorFormat_t format,
                                            const cudnnDataType_t type,
                                            const std::vector<int>& kernel) {
    // filter layout: output input spatial_dim_y spatial_dim_x
111 112
    PADDLE_ENFORCE(dynload::cudnnSetFilterNdDescriptor(
        desc_, type, format, kernel.size(), kernel.data()));
D
dangqingqing 已提交
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141
    return desc_;
  }

  template <typename T>
  inline cudnnFilterDescriptor_t descriptor(const DataLayout& order,
                                            const std::vector<int>& kernel) {
    return descriptor(GetCudnnTensorFormat(order), CudnnDataType<T>::type,
                      kernel);
  }

 private:
  cudnnFilterDescriptor_t desc_;
  DISABLE_COPY_AND_ASSIGN(ScopedFilterDescriptor);
};

class ScopedConvolutionDescriptor {
 public:
  ScopedConvolutionDescriptor() {
    PADDLE_ENFORCE(dynload::cudnnCreateConvolutionDescriptor(&desc_));
  }
  ~ScopedConvolutionDescriptor() {
    PADDLE_ENFORCE(dynload::cudnnDestroyConvolutionDescriptor(desc_));
  }

  inline cudnnConvolutionDescriptor_t descriptor(
      cudnnDataType_t type, const std::vector<int>& pads,
      const std::vector<int>& strides, const std::vector<int>& dilations) {
    PADDLE_ENFORCE_EQ(pads.size(), strides.size());
    PADDLE_ENFORCE_EQ(pads.size(), dilations.size());
142 143 144 145 146 147 148 149 150 151 152 153

#if CUDNN_VERSION < 6000
    // cudnn v5 does not support dilation conv, the argument is called upscale
    // instead of dilations and it is must be one.
    for (size_t i = 0; i < dilations.size(); ++i) {
      PADDLE_ENFORCE_EQ(
          dilations[i], 1,
          "Dilations conv is not supported in this cuDNN version");
    }
#endif

    PADDLE_ENFORCE(dynload::cudnnSetConvolutionNdDescriptor(
D
dangqingqing 已提交
154 155
        desc_, pads.size(), pads.data(), strides.data(), dilations.data(),
        CUDNN_CROSS_CORRELATION, type));
156
    return desc_;
D
dangqingqing 已提交
157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185
  }

  template <typename T>
  inline cudnnConvolutionDescriptor_t descriptor(
      const std::vector<int>& pads, const std::vector<int>& strides,
      const std::vector<int>& dilations) {
    return descriptor(CudnnDataType<T>::type, pads, strides, dilations);
  }

 private:
  cudnnConvolutionDescriptor_t desc_;
  DISABLE_COPY_AND_ASSIGN(ScopedConvolutionDescriptor);
};

class ScopedPoolingDescriptor {
 public:
  ScopedPoolingDescriptor() {
    PADDLE_ENFORCE(dynload::cudnnCreatePoolingDescriptor(&desc_));
  }
  ~ScopedPoolingDescriptor() {
    PADDLE_ENFORCE(dynload::cudnnDestroyPoolingDescriptor(desc_));
  }

  inline cudnnPoolingDescriptor_t descriptor(const PoolingMode& mode,
                                             const std::vector<int>& kernel,
                                             const std::vector<int>& pads,
                                             const std::vector<int>& strides) {
    PADDLE_ENFORCE_EQ(kernel.size(), pads.size());
    PADDLE_ENFORCE_EQ(kernel.size(), strides.size());
186
    PADDLE_ENFORCE(dynload::cudnnSetPoolingNdDescriptor(
D
dangqingqing 已提交
187 188 189 190 191
        desc_, (mode == PoolingMode::kMaximum
                    ? CUDNN_POOLING_MAX
                    : CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING),
        CUDNN_PROPAGATE_NAN,  // Always propagate nans.
        kernel.size(), kernel.data(), pads.data(), strides.data()));
192
    return desc_;
D
dangqingqing 已提交
193 194 195 196 197 198 199 200 201
  }

 private:
  cudnnPoolingDescriptor_t desc_;
  DISABLE_COPY_AND_ASSIGN(ScopedPoolingDescriptor);
};

}  // namespace platform
}  // namespace paddle