sandbox.cuda.dnn – cuDNN

cuDNN is an NVIDIA library with functionality used by deep neural network. It provides optimized versions of some operations like the convolution. cuDNN is not currently installed with CUDA 6.5. You must download and install it yourself.

To install it, decompress the downloaded file and make the *.h and *.so* files available to the compilation environment. There are at least three possible ways of doing so:

  • The easiest is to include them in your CUDA installation. Copy the *.h files to CUDA_ROOT/include and the *.so* files to CUDA_ROOT/lib64 (by default, CUDA_ROOT is /usr/local/cuda on Linux).

  • Alternatively, on Linux, you can set the environment variables LD_LIBRARY_PATH, LIBRARY_PATH and CPATH to the directory extracted from the download. If needed, separate multiple directories with : as in the PATH environment variable.

    example:

    export LD_LIBRARY_PATH=/home/user/path_to_CUDNN_folder/lib64:$LD_LIBRARY_PATH
export CPATH=/home/user/path_to_CUDNN_folder/include:$CPATH
export LIBRARY_PATH=/home/user/path_to_CUDNN_folder/lib64:$LD_LIBRARY_PATH

  • And as a third way, also on Linux, you can copy the *.h files to /usr/include and the *.so* files to /lib64.

By default, Theano will detect if it can use cuDNN. If so, it will use it. If not, Theano optimizations will not introduce cuDNN ops. So Theano will still work if the user did not introduce them manually.

To get an error if Theano can not use cuDNN, use this Theano flag: optimizer_including=cudnn.

Note

CuDNN v3 has now been released. CuDNN v2 remains supported but CuDNN v3 is faster and offers many more options. We recommend that everybody update to v3.

Note

Starting in CuDNN v3, multiple convolution implementations are offered and it is possible to use heuristics to automatically choose a convolution implementation well suited to the parameters of the convolution.

The Theano flag dnn.conv.algo_fwd allows to specify the CuDNN convolution implementation that Theano should use for forward convolutions. Possible values include :

  • small (default) : use a convolution implementation with small memory usage
  • none : use a slower implementation with minimal memory usage
  • large : use a sometimes faster implementation with large memory usage
  • fft : use the Fast Fourrier Transform implementation of convolution (very high memory usage)
  • guess_once : the first time a convolution is executed, the implementation to use is chosen according to CuDNN’s heuristics and reused for every subsequent execution of the convolution.
  • guess_on_shape_change : like guess_once but a new convolution implementation selected every time the shapes of the inputs and kernels don’t match the shapes from the last execution.
  • time_once : the first time a convolution is executed, every convolution implementation offered by CuDNN is executed and timed. The fastest is reused for every subsequent execution of the convolution.
  • time_on_shape_change : like time_once but a new convolution implementation selected every time the shapes of the inputs and kernels don’t match the shapes from the last execution.

The Theano flag dnn.conv.algo_bwd allows to specify the CuDNN convolution implementation that Theano should use for gradient convolutions. Possible values include :

  • none (default) : use the default non-deterministic convolution implementation
  • deterministic : use a slower but deterministic implementation
  • fft : use the Fast Fourrier Transform implementation of convolution (very high memory usage)
  • guess_once : the first time a convolution is executed, the implementation to use is chosen according to CuDNN’s heuristics and reused for every subsequent execution of the convolution.
  • guess_on_shape_change : like guess_once but a new convolution implementation selected every time the shapes of the inputs and kernels don’t match the shapes from the last execution.
  • time_once : the first time a convolution is executed, every convolution implementation offered by CuDNN is executed and timed. The fastest is reused for every subsequent execution of the convolution.
  • time_on_shape_change : like time_once but a new convolution implementation selected every time the shapes of the inputs and kernels don’t match the shapes from the last execution.

guess_* and time_* flag values take into account the amount of available memory when selecting an implementation. This means that slower implementations might be selected if not enough memory is available for the faster implementations.

Note

Normally you should not call GPU Ops directly, but the CPU interface currently does not allow all options supported by cuDNN ops. So it is possible that you will need to call them manually.

Note

The documentation of CUDNN tells that, for the 2 following operations, the reproducibility is not guaranteed with the default implementation: cudnnConvolutionBackwardFilter and cudnnConvolutionBackwardData. Those correspond to the gradient wrt the weights and the gradient wrt the input of the convolution. They are also used sometimes in the forward pass, when they give a speed up.

The Theano flag dnn.conv.algo_bwd can be use to force the use of a slower but deterministic convolution implementation.

Note

There is a problem we do not understand yet when cudnn paths are used with symbolic links. So avoid using that.

Note

cudnn.so* must be readable and executable by everybody. cudnn.h must be readable by everybody.

Functions

theano.sandbox.cuda.dnn.dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1), conv_mode='conv', direction_hint=None, workmem=None, algo=None, precision=None)

GPU convolution using cuDNN from NVIDIA.

The memory layout to use is ‘bc01’, that is ‘batch’, ‘channel’, ‘first dim’, ‘second dim’ in that order.

Parameters:
  • img – Images to do the convolution over.
  • kerns – Convolution filters.
  • border_mode – One of ‘valid’, ‘full’; additionally, the padding size can be directly specified by an integer or a pair of integers (as a tuple), specifying the amount of zero padding added to _both_ the top and bottom (first entry) and left and right (second entry) sides of the image.
  • subsample – Perform subsampling of the output (default: (1, 1)).
  • conv_mode – Perform convolution (kernels flipped) or cross-correlation. One of ‘conv’, ‘cross’ (default: ‘conv’).
  • direction_hint – Used by graph optimizers to change algorithm choice. By default, GpuDnnConv will be used to carry out the convolution. If border_mode is ‘valid’, subsample is (1,1) and direction_hint is ‘bprop weights’, it will use GpuDnnConvGradW. If border_mode is ‘full’, subsample is (1,1) and direction_hint is ‘bprop inputs’, it will use GpuDnnConvGradI. This parameter is used internally by graph optimizers and may be removed at any time without a deprecation period. You have been warned.
  • workmem – deprecated, use parameter algo instead.
  • algo ({‘none’, ‘small’, ‘large’, ‘fft’, ‘guess_once’, ‘guess_on_shape_change’, ‘time_once’, ‘time_on_shape_change’}) – Convolution implementation to use. Some of its values may require certain versions of CuDNN to be installed. Default is the value of config.dnn.conv.algo_fwd.
  • precision ({‘as_input’, ‘float16’, ‘float32’, ‘float64’}) – Description of the dtype in which the computation of the convolution should be done. Possible values are ‘as_input’, ‘float16’, ‘float32’ and ‘float64’. Default is the value of config.dnn.conv.precision.
theano.sandbox.cuda.dnn.dnn_pool(img, ws, stride=(1, 1), mode='max', pad=(0, 0))

GPU pooling using cuDNN from NVIDIA.

The memory layout to use is ‘bc01’, that is ‘batch’, ‘channel’, ‘first dim’, ‘second dim’ in that order.

Parameters:
  • img – Images to do the pooling over.
  • ws – Subsampling window size.
  • stride – Subsampling stride (default: (1, 1)).
  • mode ({‘max’, ‘average_inc_pad’, ‘average_exc_pad}) –
  • pad – (pad_h, pad_w) padding information. pad_h is the number of zero-valued pixels added to each of the top and bottom borders. pad_w is the number of zero-valued pixels added to each of the left and right borders.
  • warning (.) – capability of 3.0 or higer. This means that older GPU will not work with this Op.

Notes

This Op implements the ignore_border=True of max_pool_2d.

Convolution Ops

class theano.sandbox.cuda.dnn.GpuDnnConvDesc(border_mode, subsample=(1, 1), conv_mode='conv', precision='float32')

This Op builds a convolution descriptor for use in the other convolution operations.

See the doc of dnn_conv() for a description of the parameters.

class theano.sandbox.cuda.dnn.GpuDnnConv(workmem=None, inplace=False, algo=None)

The forward convolution.

Parameters:
  • image –
  • kernel –
  • descr – The convolution descriptor.
  • workmem – deprecated, use parameter algo instead.
  • algo –
    [‘none’, ‘small’, ‘large’, ‘fft’, ‘fft_tiling’, ‘guess_once’,
    ‘guess_on_shape_change’, ‘time_once’, ‘time_on_shape_change’]

    Default is the value of config.dnn.conv.algo_fwd.
static get_out_shape(ishape, kshape, border_mode, subsample)

This function computes the output shape for a convolution with the specified parameters. ishape and kshape can be symbolic or scalar.

class theano.sandbox.cuda.dnn.GpuDnnConv3d(workmem=None, inplace=False, algo=None)

The forward convolution.

Parameters:
  • image –
  • kernel –
  • descr – the convolution descriptor
  • workmem – deprecated, use parameter algo instead.
  • algo – [‘none’, ‘guess_once’, ‘guess_on_shape_change’, ‘time_once’, ‘time_on_shape_change’] Default is the value of config.dnn.conv.algo_fwd.
static get_out_shape(ishape, kshape, border_mode, subsample)

This function computes the output shape for a convolution with the specified parameters. ishape and kshape can be symbolic or scalar.

class theano.sandbox.cuda.dnn.GpuDnnConvGradW(inplace=False, workmem=None, algo=None)

The convolution gradient with respect to the weights.

Parameters:
  • image –
  • kernel –
  • descr – The convolution descriptor.
  • workmem – deprecated, use parameter algo instead.
  • algo ({‘none’, ‘deterministic’, ‘fft’, ‘small’, ‘guess_once’, ‘guess_on_shape_change’, ‘time_once’, ‘time_on_shape_change’}) – Default is the value of config.dnn.conv.algo_bwd_filter.
class theano.sandbox.cuda.dnn.GpuDnnConv3dGradW(inplace=False, workmem=None, algo=None)

The convolution gradient with respect to the weights.

Parameters:
  • image –
  • kernel –
  • descr – the convolution descriptor
  • workmem – deprecated, use parameter algo instead.
  • algo – [‘none’, ‘guess_once’, ‘guess_on_shape_change’, ‘time_once’, ‘time_on_shape_change’] Default is the value of config.dnn.conv.algo_bwd_filter.
class theano.sandbox.cuda.dnn.GpuDnnConvGradI(inplace=False, workmem=None, algo=None)

The convolution gradient with respect to the inputs.

Parameters:
  • image –
  • kernel –
  • descr – The convolution descriptor.
  • workmem – deprecated, use parameter algo instead.
  • algo –
    [‘none’, ‘deterministic’, ‘fft’, ‘fft_tiling’, ‘guess_once’,
    ‘guess_on_shape_change’, ‘time_once’, ‘time_on_shape_change’]

    Default is the value of config.dnn.conv.algo_bwd_data.
class theano.sandbox.cuda.dnn.GpuDnnConv3dGradI(inplace=False, workmem=None, algo=None)

The convolution gradient with respect to the inputs.

Parameters:
  • image –
  • kernel –
  • descr – the convolution descriptor
  • workmem – deprecated, use parameter algo instead.
  • algo –
    [‘none’, ‘guess_once’, ‘guess_on_shape_change’,
    ‘time_once’, ‘time_on_shape_change’]

    Default is the value of config.dnn.conv.algo_bwd_data.

Pooling Ops

class theano.sandbox.cuda.dnn.GpuDnnPoolDesc(ws=(1, 1), stride=(1, 1), mode='max', pad=(0, 0))

This Op builds a pooling descriptor for use in the other pooling operations.

Parameters:
  • ws – Windows size.
  • stride – (dx, dy).
  • mode ({‘max’, ‘average_inc_pad’, ‘average_exc_pad’}) – The old deprecated name ‘average’ correspond to ‘average_inc_pad’.
  • pad – (pad_h, pad_w) padding information. pad_h is the number of zero-valued pixels added to each of the top and bottom borders. pad_w is the number of zero-valued pixels added to each of the left and right borders.
class theano.sandbox.cuda.dnn.GpuDnnPool

Pooling.

Parameters:
  • img – The image 4d or 5d tensor.
  • desc – The pooling descriptor.
class theano.sandbox.cuda.dnn.GpuDnnPoolGrad

The pooling gradient.

Parameters:
  • inp – The input of the pooling.
  • out – The output of the pooling in the forward.
  • inp_grad – Same size as out, but is the corresponding gradient information.
  • desc – The pooling descriptor.

Softmax Ops

class theano.sandbox.cuda.dnn.GpuDnnSoftmax(tensor_format, algo, mode)

Op for the cuDNN Softmax.

Parameters:
  • tensor_format – Always set to ‘bc01’.
  • algo – ‘fast’ or ‘accurate’ indicating whether computations should be optimized for speed or accuracy respectively.
  • mode – ‘instance’ or ‘channel’ indicating whether the softmax should be computed per image across ‘c01’ or per spatial location ‘01’ per image across ‘c’.
class theano.sandbox.cuda.dnn.GpuDnnSoftmaxGrad(tensor_format, algo, mode)

Op for the cuDNN SoftmaxGrad.

Parameters:
  • tensor_format – Always set to ‘bc01’.
  • algo – ‘fast’ or ‘accurate’ indicating whether computations should be optimized for speed or accuracy respectively.
  • mode – ‘instance’ or ‘channel’ indicating whether the softmax should be computed per image across ‘c01’ or per spatial location ‘01’ per image across ‘c’.