/caffe

cuDNN R2 #2038

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4 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
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12 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
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34 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
View
8 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_;
};
View
10 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_);
View
83 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));
}
}
View
10 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_);
}
View
12 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);
View
4 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_);
}
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