Torch7 FFI bindings for NVIDIA cuDNN (R5) kernels!
Modules are API compatible their nn equivalents. Fully unit-tested against nn implementations.
Conversion between nn and cudnn is available through cudnn.convert function.
- Install cuDNN (version R5 EA)
- Have at least CUDA 7.0
- Have
libcudnn.soin your library path (Install it from https://developer.nvidia.com/cuDNN )
-- All inputs have to be 3D or 4D(batch-mode), except ReLU, Tanh, Sigmoid, and BatchNormalization
cudnn.SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, [dW = 1], [dH = 1], [padW = 0], [padH = 0], [groups = 1])
cudnn.SpatialMaxPooling(kW, kH, dW, dH, padW, padH)
cudnn.SpatialAveragePooling(kW, kH, dW, dH, padW, padH)
-- the pointwise functions take an additional optional argument. if inplace=true then they do operations in-place without using any extra memory for themselves
cudnn.ReLU(inplace[=false])
cudnn.Tanh(inplace[=false])
cudnn.Sigmoid(inplace[=false])
-- SoftMax can be run in fast mode or accurate mode. Default is accurate mode.
cudnn.SoftMax(fastMode [= false]) -- SoftMax across each image (just like nn.SoftMax)
cudnn.LogSoftMax() -- LogSoftMax across each image (just like nn.LogSoftMax)
cudnn.SpatialSoftMax(fastMode [= false]) -- SoftMax across feature-maps (per spatial location)
cudnn.SpatialLogSoftMax() -- LogSoftMax across feature-maps (per spatial location)
cudnn.SpatialCrossEntropyCriterion() -- A spatial version of LogSoftMax + ClassNLLCriterion in one shot
-- Volumetric inputs (4D or 5D batched mode)
cudnn.VolumetricConvolution(nInputPlane, nOutputPlane, kT, kW, kH, dT, dW, dH, padT, padW, padH)
cudnn.VolumetricMaxPooling(kT, kW, kH, dT, dW, dH, padT, padW, padH)
cudnn.VolumetricAveragePooling(kT, kW, kH, dT, dW, dH, padT, padW, padH)There are two globally availabe modes useful for tuning performance:
require 'cudnn'
cudnn.benchmark = true -- uses the inbuilt cudnn auto-tuner to find the fastest convolution algorithms.
-- If this is set to false, uses some in-built heuristics that might not always be fastest.by default cudnn.benchmark is set to false.
cudnn.fastest = true -- this is like the :fastest() mode for the Convolution modules,
-- simply picks the fastest convolution algorithm, rather than tuning for workspace sizeby default, cudnn.fastest is set to false.
cudnn.verbose = true -- this prints out some more verbose information useful for debuggingby default, cudnn.verbose is set to false.
Conversion is done by cudnn.convert function which takes a network and backend arguments and goes over
network modules recursively substituting equivalents. No memory copy is done, just metatables are swapped.
net = nn.Sequential()
net:add(nn.SpatialConvolution(3,96,11,11,3,3))
net:add(nn.ReLU())
cudnn.convert(net, cudnn)
print(net)will result in:
nn.Sequential {
[input -> (1) -> (2) -> output]
(1): cudnn.SpatialConvolution(3 -> 96, 11x11, 3,3)
(2): cudnn.ReLU
}
For version CuDNN R1, checkout the branch R1 For version CuDNN R2, checkout the branch R2 For version CuDNN R3, checkout the branch R3 For version CuDNN R3, checkout the branch master
R5 Release Notes:
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent