This is a release candidate for oneDNN v2.4. Please provide feedback and submit defect reports via Github issues.
Performance Optimizations
- Improved primitive cache performance for Intel Graphics products.
- Intel Architecture Processors
- Improved performance for future Intel Xeon Scalable processor (code name Sapphire Rapids). The functionality is disabled by default and should be enabled via CPU dispatcher control.
- Improved binary primitive performance for cases when one of the tensors is broadcasted.
- Improved reorder primitive performance for memory formats with padding and/or zero points.
- Intel Graphics Products
- Introduced initial optimizations for future Intel Arc graphics (code name Alchemist and DG2).
- AArch64-based Processors
- Improved inner product and eltwise primitives performance with ACL.
- Introduced support for sum and for indirect and Winograd convolution implementations with ACL.
- NVIDIA Graphics
- Improved convolution performance with eltwise post-op.
Functionality
- Introduced PReLU post-op support in convolution and matmul.
- Extended maximum allowed post-ops chain for compute primitives (convolution, deconvolution, inner product, and matmul) to 32.
- Introduced support for zero points in sum post-op for convolution and matmul. The functionality is implemented only for CPUs.
- Extended binary primitive with support for mixed data types for input tensors. The functionality is implemented only for CPUs.
- Extended sum post-op for convolution and matmul primitives with support for mixed data types. The functionality is implemented only for CPUs.
- Added USM support for OpenCL GPU runtime.
Usability
- Added compile time options to manage the set of supported primitives and workload types. See DNNL_ENABLE_WORKLOAD and DNNL_ENABLE_PRIMITIVE in build options for more details. This feature allows to reduce binary footprint of the library for specialized applications.
- Reduced overall library size by trimming down use of templates, OpenCL headers, and TBB headers. The configurations that benefitted the most are CPU only configuration with TBB threading and GPU only configuration. Note, that binary footprint depends on the compiler used to build the library and build options.
- Introduced floating point math mode API. The API allows the library to use bfloat16 or float16 hardware acceleration in fp32 operations. Currently this mode is not supported in the implementation.
- Added a build option DNNL_LIBRARY_NAME to change the library name and CMake target. This feature helps projects that use multiple oneDNN configurations.
Breaking Changes
- Updated minimal supported ACL version from 21.08 (was 21.05).
Deprecated functionality
- Intel MKL-DNN compatibility API is deprecated and will be removed in the next update. See Transition from Intel MKL-DNN to oneDNN page for instructions on moving to new API.
Thanks to the Contributors
This release contains contributions from the project core team as well as
Aleksandr Nikolaev @alenik01, Arthur Mitrano @aaraujom, Diana Bite @diaena, Jing Xu @jingxu10, Kentaro Kawakami @kawakami-k, Kevin Putnam @intelkevinputnam, MITSUNARI Shigeo @herumi, Nathan John Sircombe @nSircombe, Nicolas Chauvet (kwizart) @kwizart, Peter Caday @petercad. We would also like to thank everyone who asked questions and reported issues.