cudnn.torch

Torch7 FFI bindings for NVidia CuDNN (R3) kernels!

Modules are API compatible their nn equivalents. Fully unit-tested against nn implementations.

Installation

• Install CuDNN (version R3)
• Have at least Cuda 7.0
• Have libcudnn.so in your library path (Install it from https://developer.nvidia.com/cuDNN )

Modules

-- All inputs have to be 3D or 4D(batch-mode), except ReLU, Tanh and Sigmoid
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.SpatialSoftMax(fastMode [= false])   -- SoftMax across feature-maps (per spatial location)

-- 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)

I have no time to support these, so please don't expect a quick response to filed github issues.

For version CuDNN R1, checkout the branch R1 For version CuDNN R2, checkout the branch R2