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Pre-release
Pre-release

@tprimak tprimak released this Dec 11, 2019

This is a preview release for DNNL v2.0. The release is based on DNNL v1.1 and the release notes below include incremental changes.

Binary distribution of this software is available as Intel(R) oneAPI Deep Neural Network Library in Intel(R) oneAPI.

New functionality

  • SYCL API extensions and interoperability with SYCL code
  • Support for Intel DPC++ compiler and runtime

Usability

  • SYCL interoperability examples

Known Limitations

  • Some f32/f16 convolutions with non-square spatial shape of filters may produce incorrect results on GPU.
  • Some bf16 backward convolutions with 3D spatial and negative padding may produce segfault on CPU.
  • Non-Intel GPUs are not supported. The library API allows to create a DNNL engine by index (the order of devices is determined by the SYCL runtime), and there is no check for GPU devices being non-Intel. To have more control, users can create a DNNL engine passing SYCL device and context explicitly.
  • RNN primitive may hang on GPU if the number of recurrent cells is bigger than 40.
  • int8 RNN may produce incorrect results on GPU.
  • Backward propagation of Layer Normalization primitive produces incorrect results.
  • Intel Processor Graphics Gen11 is not supported.
  • When running GPU kernels that take longer than a certain time (it depends on OS and system settings) you may face a situation resulting in apparent hang of the application. Configure driver to disable this timeout and avoid hanging of DPC++ or OpenCL programs, including DNNL examples.

On Linux:

$ sudo bash -c 'echo N > /sys/module/i915/parameters/enable_hangcheck'

On Windows increase TdrDelay and TdrDdiDelay values using registry.

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