ConvProjection.h

/* Copyright (c) 2016 Baidu, Inc. 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

#include "Projection.h"
#include "paddle/math/MathUtils.h"

namespace paddle {

/**
 * @brief Convolution projection do the same calculation with CudnnConvLayer.
 */
class ConvProjection : public Projection {
public:
  /**
   * Constructor.
   */
  ConvProjection(const ProjectionConfig& config,
                 ParameterPtr parameter,
                 bool useGpu);

  ~ConvProjection();

  virtual void forward();
  virtual void backward(const UpdateCallback& callback);

protected:
  void getConvParams();
  void initCudnn();

  void reshapeTensorDesc(int batchSize);
  void reshape(int batchSize);

  size_t calOutputSize() {
    imageH_ = in_->getFrameHeight();
    imageW_ = in_->getFrameWidth();
    if (imageH_ == 0) imageH_ = configImgH_;
    if (imageW_ == 0) imageW_ = configImgW_;
    outputH_ = outputSize(imageH_,
                          filterH_,
                          paddingH_,
                          strideH_,
                          /* caffeMode */ true);
    outputW_ = outputSize(imageW_,
                          filterW_,
                          paddingW_,
                          strideW_,
                          /* caffeMode */ true);

    const_cast<Argument*>(out_)->setFrameHeight(outputH_);
    const_cast<Argument*>(out_)->setFrameWidth(outputW_);

    inputOffset_ = (channels_ / groups_) * imageH_ * imageW_;
    outputOffset_ = (numFilters_ / groups_) * outputH_ * outputW_;
    return outputH_ * outputW_ * numFilters_;
  }

  static void* getSpaceBytes(size_t size);

  /// imageH_ and imageW_ is calculated from the input layer.
  int imageH_, imageW_;
  /// configImgH_ and configImgW_ is obtained from config.
  int configImgH_, configImgW_;
  int outputH_, outputW_;
  int channels_, numFilters_;
  int paddingH_, paddingW_;
  int strideH_, strideW_;
  int filterH_, filterW_;
  /// One group offset of input data.
  int inputOffset_;
  /// One group offset of output data.
  int outputOffset_;
  /// One group offset of weight.
  int weightOffset_;
  int groups_;

  /// Cudnn tensor descriptor for input.
  hl_tensor_descriptor inputDesc_;
  /// Cudnn tensor descriptor for output.
  hl_tensor_descriptor outputDesc_;
  /// Cudnn tensor descriptor for filter.
  hl_filter_descriptor filterDesc_;
  /// Cudnn tensor descriptor for a convolution operation.
  hl_convolution_descriptor convDesc_;

  /// Record the algorithm for forward convolution, which is obtained by cudnn
  /// api to search the best suited algorithm.
  int fwdAlgo_;
  /// Record the algorithm for computing convolution gradient with respect to
  /// filter coefficients.
  int bwdFilterAlgo_;
  /// Record the algorithm for computing convolution gradient with respect to
  /// the output.
  int bwdDataAlgo_;
  /// Amount of GPU memory needed as workspace to be able to execute a
  /// forward convolution with the specified algo.
  size_t fwdLimitBytes_;
  /// Amount of GPU memory needed as workspace to be able to execute a
  /// backwardFilter with the specified algo.
  size_t bwdDataLimitBytes_;
  /// Amount of GPU memory needed as workspace to be able to execute a
  /// backwardData with the specified algo.
  size_t bwdFilterLimitBytes_;
  /// Size of total work space.
  size_t workSpaceInBytes_;

  /// Whether to call cuDNN api to choose conv algorithm.
  bool isSelectAlgo_;
  /// batchNum is used to record batch size. If the batch size is changed,
  /// the selection algorithm will be called.
  int batchNum_;
  bool bias_;

  std::unique_ptr<Weight> weight_;
  static ThreadLocalD<std::vector<MemoryHandle*>> convMem_;
};

}  // namespace paddle