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add cuDNN 8 support

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This commit is contained in:
YashasSamaga 2020-06-30 21:51:23 +05:30
parent 206c843f36
commit 62a63021c7
3 changed files with 91 additions and 16 deletions
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4
modules/dnn/CMakeLists.txt
View File

@ -21,14 +21,10 @@ if(OPENCV_DNN_OPENCL AND HAVE_OPENCL)
add_definitions(-DCV_OCL4DNN=1)
endif()
if(NOT DEFINED OPENCV_DNN_CUDA AND HAVE_CUDNN AND CUDNN_VERSION VERSION_LESS 8.0)
message(STATUS "DNN: CUDNN 8.0 is not supported yes. Details: https://github.com/opencv/opencv/issues/17496")
endif()
ocv_option(OPENCV_DNN_CUDA "Build with CUDA support"
HAVE_CUDA
AND HAVE_CUBLAS
AND HAVE_CUDNN
AND CUDNN_VERSION VERSION_LESS 8.0
)
if(OPENCV_DNN_CUDA AND HAVE_CUDA AND HAVE_CUBLAS AND HAVE_CUDNN)

56
modules/dnn/src/cuda4dnn/csl/cudnn/convolution.hpp
View File

@ -225,6 +225,15 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace cu
);
}
CUDA4DNN_CHECK_CUDNN(cudnnSetConvolutionGroupCount(descriptor, group_count));
#if CUDNN_MAJOR >= 8
/* cuDNN 7 and below use FMA math by default. cuDNN 8 includes TF32 Tensor Ops
* in the default setting. TF32 convolutions have lower precision than FP32.
* Hence, we set the math type to CUDNN_FMA_MATH to reproduce old behavior.
*/
CUDA4DNN_CHECK_CUDNN(cudnnSetConvolutionMathType(descriptor, CUDNN_FMA_MATH));
#endif
if (std::is_same<T, half>::value)
CUDA4DNN_CHECK_CUDNN(cudnnSetConvolutionMathType(descriptor, CUDNN_TENSOR_OP_MATH));
} catch (...) {
@ -254,15 +263,49 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace cu
*/
ConvolutionAlgorithm(
const Handle& handle,
const ConvolutionDescriptor<T>& conv,
const FilterDescriptor<T>& filter,
const TensorDescriptor<T>& input,
const TensorDescriptor<T>& output)
const ConvolutionDescriptor<T>& convDesc,
const FilterDescriptor<T>& filterDesc,
const TensorDescriptor<T>& inputDesc,
const TensorDescriptor<T>& outputDesc)
{
#if CUDNN_MAJOR >= 8
int requestedAlgoCount = 0, returnedAlgoCount = 0;
CUDA4DNN_CHECK_CUDNN(cudnnGetConvolutionForwardAlgorithmMaxCount(handle.get(), &requestedAlgoCount));
std::vector<cudnnConvolutionFwdAlgoPerf_t> results(requestedAlgoCount);
CUDA4DNN_CHECK_CUDNN(
cudnnGetConvolutionForwardAlgorithm_v7(
handle.get(),
inputDesc.get(), filterDesc.get(), convDesc.get(), outputDesc.get(),
requestedAlgoCount,
&returnedAlgoCount,
&results[0]
)
);
size_t free_memory, total_memory;
CUDA4DNN_CHECK_CUDA(cudaMemGetInfo(&free_memory, &total_memory));
bool found_conv_algorithm = false;
for (int i = 0; i < returnedAlgoCount; i++)
{
if (results[i].status == CUDNN_STATUS_SUCCESS &&
results[i].algo != CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED &&
results[i].memory < free_memory)
{
found_conv_algorithm = true;
algo = results[i].algo;
workspace_size = results[i].memory;
break;
}
}
if (!found_conv_algorithm)
CV_Error (cv::Error::GpuApiCallError, "cuDNN did not return a suitable algorithm for convolution.");
#else
CUDA4DNN_CHECK_CUDNN(
cudnnGetConvolutionForwardAlgorithm(
handle.get(),
input.get(), filter.get(), conv.get(), output.get(),
inputDesc.get(), filterDesc.get(), convDesc.get(), outputDesc.get(),
CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
0, /* no memory limit */
&algo
@ -272,10 +315,11 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace cu
CUDA4DNN_CHECK_CUDNN(
cudnnGetConvolutionForwardWorkspaceSize(
handle.get(),
input.get(), filter.get(), conv.get(), output.get(),
inputDesc.get(), filterDesc.get(), convDesc.get(), outputDesc.get(),
algo, &workspace_size
)
);
#endif
}
ConvolutionAlgorithm& operator=(const ConvolutionAlgorithm&) = default;

47
modules/dnn/src/cuda4dnn/csl/cudnn/transpose_convolution.hpp
View File

@ -30,15 +30,49 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace cu
TransposeConvolutionAlgorithm(
const Handle& handle,
const ConvolutionDescriptor<T>& conv,
const FilterDescriptor<T>& filter,
const TensorDescriptor<T>& input,
const TensorDescriptor<T>& output)
const ConvolutionDescriptor<T>& convDesc,
const FilterDescriptor<T>& filterDesc,
const TensorDescriptor<T>& inputDesc,
const TensorDescriptor<T>& outputDesc)
{
#if CUDNN_MAJOR >= 8
int requestedAlgoCount = 0, returnedAlgoCount = 0;
CUDA4DNN_CHECK_CUDNN(cudnnGetConvolutionBackwardDataAlgorithmMaxCount(handle.get(), &requestedAlgoCount));
std::vector<cudnnConvolutionBwdDataAlgoPerf_t> results(requestedAlgoCount);
CUDA4DNN_CHECK_CUDNN(
cudnnGetConvolutionBackwardDataAlgorithm_v7(
handle.get(),
filterDesc.get(), inputDesc.get(), convDesc.get(), outputDesc.get(),
requestedAlgoCount,
&returnedAlgoCount,
&results[0]
)
);
size_t free_memory, total_memory;
CUDA4DNN_CHECK_CUDA(cudaMemGetInfo(&free_memory, &total_memory));
bool found_conv_algorithm = false;
for (int i = 0; i < returnedAlgoCount; i++)
{
if (results[i].status == CUDNN_STATUS_SUCCESS &&
results[i].algo != CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED &&
results[i].memory < free_memory)
{
found_conv_algorithm = true;
dalgo = results[i].algo;
workspace_size = results[i].memory;
break;
}
}
if (!found_conv_algorithm)
CV_Error (cv::Error::GpuApiCallError, "cuDNN did not return a suitable algorithm for transpose convolution.");
#else
CUDA4DNN_CHECK_CUDNN(
cudnnGetConvolutionBackwardDataAlgorithm(
handle.get(),
filter.get(), input.get(), conv.get(), output.get(),
filterDesc.get(), inputDesc.get(), convDesc.get(), outputDesc.get(),
CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST,
0, /* no memory limit */
&dalgo
@ -48,10 +82,11 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace cu
CUDA4DNN_CHECK_CUDNN(
cudnnGetConvolutionBackwardDataWorkspaceSize(
handle.get(),
filter.get(), input.get(), conv.get(), output.get(),
filterDesc.get(), inputDesc.get(), convDesc.get(), outputDesc.get(),
dalgo, &workspace_size
)
);
#endif
}
TransposeConvolutionAlgorithm& operator=(const TransposeConvolutionAlgorithm&) = default;