/*
All modification made by Intel Corporation: © 2016 Intel Corporation

All contributions by the University of California:
Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
All rights reserved.

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Copyright (c) 2014, 2015, the respective contributors
All rights reserved.
For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md


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modification, are permitted provided that the following conditions are met:

    * Redistributions of source code must retain the above copyright notice,
      this list of conditions and the following disclaimer.
    * Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.
    * Neither the name of Intel Corporation nor the names of its contributors
      may be used to endorse or promote products derived from this software
      without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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*/

#ifndef CAFFE_CUDNN_CONV_LAYER_HPP_
#define CAFFE_CUDNN_CONV_LAYER_HPP_

#include <vector>

#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"

#include "caffe/layers/conv_layer.hpp"

namespace caffe {

#ifdef USE_CUDNN
/*
 * @brief cuDNN implementation of ConvolutionLayer.
 *        Fallback to ConvolutionLayer for CPU mode.
 *
 * cuDNN accelerates convolution through forward kernels for filtering and bias
 * plus backward kernels for the gradient w.r.t. the filters, biases, and
 * inputs. Caffe + cuDNN further speeds up the computation through forward
 * parallelism across groups and backward parallelism across gradients.
 *
 * The CUDNN engine does not have memory overhead for matrix buffers. For many
 * input and filter regimes the CUDNN engine is faster than the CAFFE engine,
 * but for fully-convolutional models and large inputs the CAFFE engine can be
 * faster as long as it fits in memory.
*/
template <typename Dtype>
class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
 public:
  explicit CuDNNConvolutionLayer(const LayerParameter& param)
      : ConvolutionLayer<Dtype>(param), handles_setup_(false) {}
  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual ~CuDNNConvolutionLayer();

 protected:
  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);

  bool handles_setup_;
  cudnnHandle_t* handle_;
  cudaStream_t*  stream_;

  // algorithms for forward and backwards convolutions
  cudnnConvolutionFwdAlgo_t *fwd_algo_;
  cudnnConvolutionBwdFilterAlgo_t *bwd_filter_algo_;
  cudnnConvolutionBwdDataAlgo_t *bwd_data_algo_;

  vector<cudnnTensorDescriptor_t> bottom_descs_, top_descs_;
  cudnnTensorDescriptor_t    bias_desc_;
  cudnnFilterDescriptor_t      filter_desc_;
  vector<cudnnConvolutionDescriptor_t> conv_descs_;
  int bottom_offset_, top_offset_, bias_offset_;

  size_t *workspace_fwd_sizes_;
  size_t *workspace_bwd_data_sizes_;
  size_t *workspace_bwd_filter_sizes_;
  size_t workspaceSizeInBytes;  // size of underlying storage
  void *workspaceData;  // underlying storage
  void **workspace;  // aliases into workspaceData
};
#endif

}  // namespace caffe

#endif  // CAFFE_CUDNN_CONV_LAYER_HPP_
