cuDNN R2

include/caffe/common_layers.hpp

@@ -386,8 +386,8 @@ class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
 
  bool handles_setup_;
  cudnnHandle_t handle_;
- cudnnTensor4dDescriptor_t bottom_desc_;
- cudnnTensor4dDescriptor_t top_desc_;
+ cudnnTensorDescriptor_t bottom_desc_;
+ cudnnTensorDescriptor_t top_desc_;
 };
 #endif
 

include/caffe/neuron_layers.hpp

@@ -433,8 +433,8 @@ class CuDNNReLULayer : public ReLULayer<Dtype> {
 
  bool handles_setup_;
  cudnnHandle_t handle_;
- cudnnTensor4dDescriptor_t bottom_desc_;
- cudnnTensor4dDescriptor_t top_desc_;
+ cudnnTensorDescriptor_t bottom_desc_;
+ cudnnTensorDescriptor_t top_desc_;
 };
 #endif
 
@@ -516,8 +516,8 @@ class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
 
  bool handles_setup_;
  cudnnHandle_t handle_;
- cudnnTensor4dDescriptor_t bottom_desc_;
- cudnnTensor4dDescriptor_t top_desc_;
+ cudnnTensorDescriptor_t bottom_desc_;
+ cudnnTensorDescriptor_t top_desc_;
 };
 #endif
 
@@ -601,8 +601,8 @@ class CuDNNTanHLayer : public TanHLayer<Dtype> {
 
  bool handles_setup_;
  cudnnHandle_t handle_;
- cudnnTensor4dDescriptor_t bottom_desc_;
- cudnnTensor4dDescriptor_t top_desc_;
+ cudnnTensorDescriptor_t bottom_desc_;
+ cudnnTensorDescriptor_t top_desc_;
 };
 #endif
 

include/caffe/util/cudnn.hpp

@@ -50,41 +50,45 @@ template <typename Dtype> class dataType;
 template<> class dataType<float> {
  public:
  static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
+ static float oneval, zeroval;
+ static const void *one, *zero;
 };
 template<> class dataType<double> {
  public:
  static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
+ static double oneval, zeroval;
+ static const void *one, *zero;
 };
 
 template <typename Dtype>
-inline void createTensor4dDesc(cudnnTensor4dDescriptor_t* desc) {
- CUDNN_CHECK(cudnnCreateTensor4dDescriptor(desc));
+inline void createTensor4dDesc(cudnnTensorDescriptor_t* desc) {
+ CUDNN_CHECK(cudnnCreateTensorDescriptor(desc));
 }
 
 template <typename Dtype>
-inline void setTensor4dDesc(cudnnTensor4dDescriptor_t* desc,
+inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
  int n, int c, int h, int w,
  int stride_n, int stride_c, int stride_h, int stride_w) {
  CUDNN_CHECK(cudnnSetTensor4dDescriptorEx(*desc, dataType<Dtype>::type,
- n, c, h, w, stride_n, stride_c, stride_h, stride_w));
+  n, c, h, w, stride_n, stride_c, stride_h, stride_w));
 }
 
 template <typename Dtype>
-inline void setTensor4dDesc(cudnnTensor4dDescriptor_t* desc,
+inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
  int n, int c, int h, int w) {
  const int stride_w = 1;
  const int stride_h = w * stride_w;
  const int stride_c = h * stride_h;
  const int stride_n = c * stride_c;
  setTensor4dDesc<Dtype>(desc, n, c, h, w,
- stride_n, stride_c, stride_h, stride_w);
+  stride_n, stride_c, stride_h, stride_w);
 }
 
 template <typename Dtype>
 inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
  int n, int c, int h, int w) {
  CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
- CUDNN_CHECK(cudnnSetFilterDescriptor(*desc, dataType<Dtype>::type,
+ CUDNN_CHECK(cudnnSetFilter4dDescriptor(*desc, dataType<Dtype>::type,
  n, c, h, w));
 }
 
@@ -95,29 +99,29 @@ inline void createConvolutionDesc(cudnnConvolutionDescriptor_t* conv) {
 
 template <typename Dtype>
 inline void setConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
- cudnnTensor4dDescriptor_t bottom, cudnnFilterDescriptor_t filter,
+ cudnnTensorDescriptor_t bottom, cudnnFilterDescriptor_t filter,
  int pad_h, int pad_w, int stride_h, int stride_w) {
- CUDNN_CHECK(cudnnSetConvolutionDescriptor(*conv, bottom, filter,
+ CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
  pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
 }
 
 template <typename Dtype>
-inline void createPoolingDesc(cudnnPoolingDescriptor_t* conv,
+inline void createPoolingDesc(cudnnPoolingDescriptor_t* pool_desc,
  PoolingParameter_PoolMethod poolmethod, cudnnPoolingMode_t* mode,
- int h, int w, int stride_h, int stride_w) {
+ int h, int w, int pad_h, int pad_w, int stride_h, int stride_w) {
  switch (poolmethod) {
  case PoolingParameter_PoolMethod_MAX:
  *mode = CUDNN_POOLING_MAX;
  break;
  case PoolingParameter_PoolMethod_AVE:
- *mode = CUDNN_POOLING_AVERAGE;
+ *mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
  break;
  default:
  LOG(FATAL) << "Unknown pooling method.";
  }
- CUDNN_CHECK(cudnnCreatePoolingDescriptor(conv));
- CUDNN_CHECK(cudnnSetPoolingDescriptor(*conv, *mode, h, w,
- stride_h, stride_w));
+ CUDNN_CHECK(cudnnCreatePoolingDescriptor(pool_desc));
+ CUDNN_CHECK(cudnnSetPooling2dDescriptor(*pool_desc, *mode, h, w,
+ pad_h, pad_w, stride_h, stride_w));
 }
 
 } // namespace cudnn

include/caffe/vision_layers.hpp

@@ -246,11 +246,13 @@ class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
  bool handles_setup_;
  cudnnHandle_t* handle_;
  cudaStream_t* stream_;
- vector<cudnnTensor4dDescriptor_t> bottom_descs_, top_descs_;
- cudnnTensor4dDescriptor_t bias_desc_;
+ 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_, weight_offset_, bias_offset_;
+ size_t workspaceSizeInBytes;
+ void *workspace;
 };
 #endif
 
@@ -445,7 +447,7 @@ class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
 
  bool handles_setup_;
  cudnnHandle_t handle_;
- cudnnTensor4dDescriptor_t bottom_desc_, top_desc_;
+ cudnnTensorDescriptor_t bottom_desc_, top_desc_;
  cudnnPoolingDescriptor_t pooling_desc_;
  cudnnPoolingMode_t mode_;
 };

src/caffe/layers/cudnn_conv_layer.cpp

@@ -43,10 +43,10 @@ void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
 
  // Create tensor descriptor(s) for data and corresponding convolution(s).
  for (int i = 0; i < bottom.size(); i++) {
- cudnnTensor4dDescriptor_t bottom_desc;
+ cudnnTensorDescriptor_t bottom_desc;
  cudnn::createTensor4dDesc<Dtype>(&bottom_desc);
  bottom_descs_.push_back(bottom_desc);
- cudnnTensor4dDescriptor_t top_desc;
+ cudnnTensorDescriptor_t top_desc;
  cudnn::createTensor4dDesc<Dtype>(&top_desc);
  top_descs_.push_back(top_desc);
  cudnnConvolutionDescriptor_t conv_desc;
@@ -104,12 +104,12 @@ CuDNNConvolutionLayer<Dtype>::~CuDNNConvolutionLayer() {
  if (!handles_setup_) { return; }
 
  for (int i = 0; i < bottom_descs_.size(); i++) {
- cudnnDestroyTensor4dDescriptor(bottom_descs_[i]);
- cudnnDestroyTensor4dDescriptor(top_descs_[i]);
+ cudnnDestroyTensorDescriptor(bottom_descs_[i]);
+ cudnnDestroyTensorDescriptor(top_descs_[i]);
  cudnnDestroyConvolutionDescriptor(conv_descs_[i]);
  }
  if (this->bias_term_) {
- cudnnDestroyTensor4dDescriptor(bias_desc_);
+ cudnnDestroyTensorDescriptor(bias_desc_);
  }
  cudnnDestroyFilterDescriptor(filter_desc_);
 

src/caffe/layers/cudnn_conv_layer.cu

@@ -21,21 +21,57 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
 
  // Forward through cuDNN in parallel over groups.
  for (int g = 0; g < this->group_; g++) {
+ cudnnConvolutionFwdAlgo_t algo;
+
+ // pick the convolution algorithm
+ // TODO(shelhamer) this should be done during reshape
+ // TODO(shelhamer) the choice of automatic or manual algorithm picking
+ // should be exposed in proto
+ CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(handle_[g],
+ bottom_descs_[i],
+ filter_desc_,
+ conv_descs_[i],
+ top_descs_[i],
+ CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
+ 0, // memoryLimitInBytes,
+ &algo));
+
+ // get minimum size of the workspace needed for the desired algorithm
+ size_t workspaceSizeInBytes_temp = 0;
+
+ CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(handle_[g],
+ bottom_descs_[i],
+ filter_desc_,
+ conv_descs_[i],
+ top_descs_[i],
+ algo,
+ &workspaceSizeInBytes));
+
+ if (workspaceSizeInBytes_temp > workspaceSizeInBytes) {
+ workspaceSizeInBytes = workspaceSizeInBytes_temp;
+ // free the existing workspace and allocate a new (larger) one
+ cudaFree(this->workspace);
+ cudaMalloc(&(this->workspace), workspaceSizeInBytes);
+ }
+
  // Filters.
  CUDNN_CHECK(cudnnConvolutionForward(handle_[g],
- bottom_descs_[i], bottom_data + bottom_offset_ * g,
- filter_desc_, weight + weight_offset_ * g,
- conv_descs_[i],
- top_descs_[i], top_data + top_offset_ * g,
- CUDNN_RESULT_NO_ACCUMULATE));
+ cudnn::dataType<Dtype>::one,
+ bottom_descs_[i], bottom_data + bottom_offset_ * g,
+ filter_desc_, weight + weight_offset_ * g,
+ conv_descs_[i],
+ algo, workspace, workspaceSizeInBytes,
+ cudnn::dataType<Dtype>::zero,
+ top_descs_[i], top_data + top_offset_ * g));
 
  // Bias.
  if (this->bias_term_) {
  const Dtype* bias_data = this->blobs_[1]->gpu_data();
- Dtype alpha = 1.;
- CUDNN_CHECK(cudnnAddTensor4d(handle_[g], CUDNN_ADD_SAME_C, &alpha,
- bias_desc_, bias_data + bias_offset_ * g,
- top_descs_[i], top_data + top_offset_ * g));
+ CUDNN_CHECK(cudnnAddTensor(handle_[g], CUDNN_ADD_SAME_C,
+ cudnn::dataType<Dtype>::one,
+ bias_desc_, bias_data + bias_offset_ * g,
+ cudnn::dataType<Dtype>::one,
+ top_descs_[i], top_data + top_offset_ * g));
  }
  }
 
@@ -68,20 +104,22 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
  // Gradient w.r.t. bias.
  if (this->bias_term_ && this->param_propagate_down_[1]) {
  CUDNN_CHECK(cudnnConvolutionBackwardBias(handle_[0*this->group_ + g],
- top_descs_[i], top_diff + top_offset_ * g,
- bias_desc_, bias_diff + bias_offset_ * g,
- CUDNN_RESULT_ACCUMULATE));
+ cudnn::dataType<Dtype>::one,
+ top_descs_[i], top_diff + top_offset_ * g,
+ cudnn::dataType<Dtype>::one,
+ bias_desc_, bias_diff + bias_offset_ * g));
  }
 
  // Gradient w.r.t. weights.
  if (this->param_propagate_down_[0]) {
  const Dtype* bottom_data = bottom[i]->gpu_data();
  CUDNN_CHECK(cudnnConvolutionBackwardFilter(handle_[1*this->group_ + g],
- bottom_descs_[i], bottom_data + bottom_offset_ * g,
- top_descs_[i], top_diff + top_offset_ * g,
- conv_descs_[i],
- filter_desc_, weight_diff + weight_offset_ * g,
- CUDNN_RESULT_ACCUMULATE));
+ cudnn::dataType<Dtype>::one,
+ bottom_descs_[i], bottom_data + bottom_offset_ * g,
+ top_descs_[i], top_diff + top_offset_ * g,
+ conv_descs_[i],
+ cudnn::dataType<Dtype>::one,
+ filter_desc_, weight_diff + weight_offset_ * g));
  }
 
  // Gradient w.r.t. bottom data.
@@ -91,11 +129,12 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
  }
  Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
  CUDNN_CHECK(cudnnConvolutionBackwardData(handle_[2*this->group_ + g],
- filter_desc_, weight + weight_offset_ * g,
- top_descs_[i], top_diff + top_offset_ * g,
- conv_descs_[i],
- bottom_descs_[i], bottom_diff + bottom_offset_ * g,
- CUDNN_RESULT_NO_ACCUMULATE));
+ cudnn::dataType<Dtype>::one,
+ filter_desc_, weight + weight_offset_ * g,
+ top_descs_[i], top_diff + top_offset_ * g,
+ conv_descs_[i],
+ cudnn::dataType<Dtype>::zero,
+ bottom_descs_[i], bottom_diff + bottom_offset_ * g));
  }
  }
 

src/caffe/layers/cudnn_pooling_layer.cpp

@@ -13,15 +13,13 @@ template <typename Dtype>
 void CuDNNPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
  const vector<Blob<Dtype>*>& top) {
  PoolingLayer<Dtype>::LayerSetUp(bottom, top);
- // Sanity check: CUDNN currently only supports pad == 0.
- CHECK_EQ(this->pad_h_, 0);
- CHECK_EQ(this->pad_w_, 0);
  CUDNN_CHECK(cudnnCreate(&handle_));
  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
  cudnn::createTensor4dDesc<Dtype>(&top_desc_);
  cudnn::createPoolingDesc<Dtype>(&pooling_desc_,
  this->layer_param_.pooling_param().pool(), &mode_,
- this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_);
+ this->kernel_h_, this->kernel_w_, this->pad_h_, this->pad_w_,
+ this->stride_h_, this->stride_w_);
  handles_setup_ = true;
 }
 
@@ -40,8 +38,8 @@ CuDNNPoolingLayer<Dtype>::~CuDNNPoolingLayer() {
  // Check that handles have been setup before destroying.
  if (!handles_setup_) { return; }
 
- cudnnDestroyTensor4dDescriptor(bottom_desc_);
- cudnnDestroyTensor4dDescriptor(top_desc_);
+ cudnnDestroyTensorDescriptor(bottom_desc_);
+ cudnnDestroyTensorDescriptor(top_desc_);
  cudnnDestroyPoolingDescriptor(pooling_desc_);
  cudnnDestroy(handle_);
 }

src/caffe/layers/cudnn_pooling_layer.cu

@@ -15,7 +15,10 @@ void CuDNNPoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
  const Dtype* bottom_data = bottom[0]->gpu_data();
  Dtype* top_data = top[0]->mutable_gpu_data();
  CUDNN_CHECK(cudnnPoolingForward(handle_, pooling_desc_,
- bottom_desc_, bottom_data, top_desc_, top_data));
+ cudnn::dataType<Dtype>::one,
+ bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ top_desc_, top_data));
 }
 
 template <typename Dtype>
@@ -29,8 +32,11 @@ void CuDNNPoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
  const Dtype* bottom_data = bottom[0]->gpu_data();
  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
  CUDNN_CHECK(cudnnPoolingBackward(handle_, pooling_desc_,
- top_desc_, top_data, top_desc_, top_diff,
- bottom_desc_, bottom_data, bottom_desc_, bottom_diff));
+ cudnn::dataType<Dtype>::one,
+ top_desc_, top_data, top_desc_, top_diff,
+ bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ bottom_desc_, bottom_diff));
 }
 
 INSTANTIATE_LAYER_GPU_FUNCS(CuDNNPoolingLayer);

src/caffe/layers/cudnn_relu_layer.cpp

@@ -35,8 +35,8 @@ CuDNNReLULayer<Dtype>::~CuDNNReLULayer() {
  // Check that handles have been setup before destroying.
  if (!handles_setup_) { return; }
 
- cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
- cudnnDestroyTensor4dDescriptor(this->top_desc_);
+ cudnnDestroyTensorDescriptor(this->bottom_desc_);
+ cudnnDestroyTensorDescriptor(this->top_desc_);
  cudnnDestroy(this->handle_);
 }