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CUDNN

Julia wrapper for the NVIDIA cuDNN GPU deep learning library

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CUDNN

Overview

This is a Julia wrapper for the NVIDIA cuDNN GPU accelerated deep learning library which provides convolution, pooling, and various activation functions. The Julia implementation consists of a low level interface and a high level interface.

The low level interface wraps each function from libcudnn.so in a Julia function in libcudnn.jl and each data type from cudnn.h in a Julia datatype in types.jl. These were generated semi-automatically using Clang. Documentation about the low level functions and types can be found in the cuDNN Library User Guide.

The high level interface is defined in CUDNN.jl. I kept the original names from the C library and provided more convenient type signatures, return values, and keyword arguments with reasonable defaults. I will mostly describe the high level interface below. All low level arguments from the C library are supported by the high level interface using keyword arguments, however only the most useful ones are documented below. Please see CUDNN.jl for the complete interface.

Types

All CUDNN operations act on CudaArray's which are provided by CUDArt. Currently only Float32 and Float64 are supported for element types, and only 4-D CudaArray's (useful for 2-D images) are supported by the majority of CUDNN functions. 5-D CudaArray operations (to process 3-D point clouds) and Float16 type support came with CUDNN v3 but have not been tested in CUDNN.jl.

The default order of tensor dimensions in the C library documentation is NCHW, with W being the fastest changing dimension. These stand for number of images (N), channels (C), height (H) and width (W) for image applications. C is row-major whereas Julia is column major. So the default size of CudaArray tensors in CUDNN.jl are (W,H,C,N) with W being fastest changing dimension. Similarly the default order of CudaArray filter dimensions in Julia are (W,H,C,K) standing for width, height, number of input feature maps, and number of output feature maps respectively.

Convolution

cudnnConvolutionForward(src, filter, [dest]) This function computes and returns dest, the convolution of src with filter under default settings (no padding, stride=1). For more convolution options please see ConvolutionDescriptor in CUDNN.jl and the C library documentation. For 2-D images if src has size (W,H,C,N) and filter has size (X,Y,C,K), the output dest will have size (W-X+1,H-Y+1,K,N) . If dest is not specified it will be allocated. The base conv2 function has been overloaded to handle 4-D CudaArray's using cudnnConvolutionForward with padding size one less than filter size.

For the following, assume y=x*w+b where x is the forward input to a convolution layer, y is the output, w is a filter, b is the bias vector, * denotes convolution, and + denotes broadcast addition. J is the loss function and dJ/dy is the gradient of the loss function with respect to y.

cudnnConvolutionBackwardFilter(src, diff, grad) Given src=x and diff=dJ/dy, this function computes and returns grad=dJ/dw.

cudnnConvolutionBackwardData(filter, diff, grad) Given filter=w and diff=dJ/dy, this function computes and returns grad=dJ/dx.

Bias

cudnnAddTensor(bias, src) adds the values in the bias tensor to the src tensor. The dimensions n,w,h of the bias tensor must be 1 and the dimension c of the two tensors must match. There are other modes of operation specified by the mode keyword argument documented in the C library reference. The default mode is compatible with cudnnConvolutionBackwardBias.

cudnnConvolutionBackwardBias(src, [dest]) Given src=dJ/dy this function computes and returns dest=dJ/db. It is assumed that there is a single scalar bias for each channel, i.e. the same number is added to every pixel of every image for that channel after the convolution. Thus dJ/db is simply the sum of dJ/dy across each channel, i.e. dest=sum(src,(1,2,4)). For 2-D images if src has size (W,H,C,N), dest will have size (1,1,C,1). If dest is not specified it will be allocated.

Pooling

Pooling operations are defined by keyword arguments window, padding, stride, and mode. The first three can be specified as integers (in which case each dimension will have the same parameter) or tuples (if different parameters for different dimensions is required). window specifies the size of the pooling area. padding and stride are parameters indicating the amount of padding to use around the input (0 by default) and the stride for the pooling operation (same as window by default). There are three pooling modes specified by the mode keyword argument: CUDNN_POOLING_MAX, CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING, CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING. The first one takes the maximum of each pooling area, the last two take the average. They differ on whether or not they include the zero padded entries in the averages. CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING implementation was buggy last I checked, so use it at your own risk.

cudnnPoolingForward(src, dest) Performs the pooling operation specified by pd on src, writes the result to dest and returns dest. The C and N dimensions of src and dest should match. If a src dimension (other than C,N) is x, and the corresponding pooling area dimension is d, padding is p, stride is s, then the corresponding dest dimension should be y=1+ceil((x+2p-d)/s).

cudnnPoolingBackward(src, srcDiff, dest, destDiff) If x=dest is the forward input to the pooling operation, y=src is the forward output, and dJ/dy=srcDiff is the loss gradient, this function computes and returns dJ/dx=destDiff.

Activation Functions

cudnnActivationForward(src, [dest]) applies a neural network activation function (relu by default) to src and writes the result to dest. dest is optional and the operation is performed in-place on src if dest is not specified. The type of activation function can be specified using the mode keyword argument. Currently supported modes are CUDNN_ACTIVATION_RELU, CUDNN_ACTIVATION_SIGMOID, and CUDNN_ACTIVATION_TANH.

cudnnActivationBackward(src, srcDiff, dest, [destDiff]) computes the loss gradient of the input to the activation function from the gradient of the output of the activation function. If y=f(x) where f is the forward activation function and J is loss, the arguments would be src=y, srcDiff=dJ/dy, dest=x, and destDiff=dJ/dx. destDiff is optional, srcDiff will be overwritten if destDiff is not specified. The default activation function is relu but others can be specified using the mode keyword argument similar to cudnnActivationForward.

cudnnSoftmaxForward(src, [dest]) treats the entries in src as unnormalized log probabilities and produces normalized probabilities in dest. The src and dest tensors have the same dimensionality. If dest is not specified, src is written in-place. The optional keyword argument mode specifies over which entries the normalization is performed. Given a src tensor with dimensions (W,H,C,N) mode=CUDNN_SOFTMAX_MODE_INSTANCE (default) normalizes per image (N) across the dimensions C,H,W; i.e. computes dest=exp(src)./sum(exp(src),(1,2,3)) after which sum(dest[:,:,:,n])==1.0 for all n. If mode=CUDNN_SOFTMAX_MODE_CHANNEL, the normalization is performed per spatial location (H,W) per image (N) across the dimension C, i.e. dest=exp(src)./sum(exp(src), 3) after which sum(dest[w,h,:,n])==1.0 for all w,h,n. In the typical use case where size(src) is (1,1,C,N), giving unnormalized probabilities for N instances and C classes both modes would compute the same answer.

cudnnSoftmaxBackward(src, srcDiff, [destDiff]) Let us assume x was the input (unnormalized log probabilities) to SoftmaxForward and y was the output (normalized log probabilities). SoftmaxBackward takes src=y, srcDiff=dJ/dy, and computes destDiff=dJ/dx. The softmax loss function is J=-log(y1) where y1 is the probability the model assigns to the correct answer (here assumed to be 1). Some calculus shows dJ/dy1 = -1/y1 and dJ/dyi = 1/y1 for i!=1. Some more calculus shows dJ/dx1=y1-1 and dJ/dxi=yi for i!=1. Unfortunately cudnn gives us twice these answers and I don't know where the factor of 2 comes from. I recommend dividing dJ/dyi with 2 for srcDiff to get the correct derivatives. You should also divide all dJ/dyi by the number of instances (N) so that the step size is not effected by the batch size.

Other Tensor Functions

cudnnTransformTensor(alpha, src, beta, dest) computes alpha * src + beta * dest and places the result in dest. Both beta and dest are optional and are set to 0 and ones(src) respectively if not specified. Buggy in CUDNN v3.

cudnnSetTensor(src, value) sets each element of the src tensor to value. fill!(src, value) is defined to call this function.

cudnnScaleTensor(src, alpha) scales each element of the src tensor with alpha. scale!(src, alpha) is defined to call this function.

First Commit

04/28/2015

Last Touched

about 2 months ago

Commits

100 commits

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