CNTK v2.4 Release Notes

Highlights of this release

• Moved to CUDA9, cuDNN 7 and Visual Studio 2017.
• Removed Python 3.4 support.
• Added Volta GPU and FP16 support.
• Better ONNX support.
• CPU perf improvement.
• More OPs.

OPs

• top_k operation: in the forward pass it computes the top (largest) k values and corresponding indices along the specified axis. In the backward pass the gradient is scattered to the top k elements (an element not in the top k gets a zero gradient).
• gather operation now supports an axis argument
• squeeze and expand_dims operations for easily removing and adding singleton axes
• zeros_like and ones_like operations. In many situations you can just rely on CNTK correctly broadcasting a simple 0 or 1 but sometimes you need the actual tensor.
• depth_to_space: Rearranges elements in the input tensor from the depth dimension into spatial blocks. Typical use of this operation is for implementing sub-pixel convolution for some image super-resolution models.
• space_to_depth: Rearranges elements in the input tensor from the spatial dimensions to the depth dimension. It is largely the inverse of DepthToSpace.
• sum operation: Create a new Function instance that computes element-wise sum of input tensors.
• softsign operation: Create a new Function instance that computes the element-wise softsign of a input tensor.
• asinh operation: Create a new Function instance that computes the element-wise asinh of a input tensor.
• log_softmax operation: Create a new Function instance that computes the logsoftmax normalized values of a input tensor.
• hard_sigmoid operation: Create a new Function instance that computes the hard_sigmoid normalized values of a input tensor.
• element_and, element_not, element_or, element_xor element-wise logic operations
• reduce_l1 operation: Computes the L1 norm of the input tensor's element along the provided axes.
• reduce_l2 operation: Computes the L2 norm of the input tensor's element along the provided axes..
• reduce_sum_square operation: Computes the sum square of the input tensor's element along the provided axes.
• image_scaler operation: Alteration of image by scaling its individual values.

ONNX

• There have been several improvements to ONNX support in CNTK.
• Updates
  • Updated ONNX Reshape op to handle InferredDimension.
  • Adding producer_name and producer_version fields to ONNX models.
  • Handling the case when neither auto_pad nor pads atrribute is specified in ONNX Conv op.
• Bug fixes
  • Fixed bug in ONNX Pooling op serialization
  • Bug fix to create ONNX InputVariable with only one batch axis.
  • Bug fixes and updates to implementation of ONNX Transpose op to match updated spec.
  • Bug fixes and updates to implementation of ONNX Conv, ConvTranspose, and Pooling ops to match updated spec.

Operators

• Group convolution
  • Fixed bug in group convolution. Output of CNTK Convolution op will change for groups > 1. More optimized implementation of group convolution is expected in the next release.
  • Better error reporting for group convolution in Convolution layer.

Halide Binary Convolution

• The CNTK build can now use optional Halide libraries to build Cntk.BinaryConvolution.so/dll library that can be used with the netopt module. The library contains optimized binary convolution operators that perform better than the python based binarized convolution operators. To enable Halide in the build, please download Halide release and set HALIDE_PATH environment varibale before starting a build. In Linux, you can use ./configure --with-halide[=directory] to enable it. For more information on how to use this feature, please refer to How_to_use_network_optimization.