dlprimitives VS oneDNN

I'm working on PyTorch OpenCL backend based on dlprimitives core library. It exists for a while but until now it required building custom pytorch version.

BTW Performance numbers: https://github.com/artyom-beilis/dlprimitives/blob/master/docs/benchmarks/benchmarks-gtx1080.md (I just added below TF2 that is missing in docs)

Full benchmarks can be found there: https://github.com/artyom-beilis/dlprimitives/blob/master/docs/summary.md

If you are talking about non-small matrix multiplication in MKL, is now in opensource as a part of oneDNN. It literally has exactly the same code, as in MKL (you can see this by inspecting constants or doing high-precision benchmarks).

For small matmul there is libxsmm. It may take tremendous efforts make something faster than oneDNN and libxsmm, as jit-based approach of https://github.com/oneapi-src/oneDNN/blob/main/src/gpu/jit/g... is too flexible: if someone finds a better sequence, oneDNN can reuse it without major change of design.

But MKL is not limited to matmul, I understand it...

For completeness, it looks like this question was posted to the oneDNN GitHub repo and the response was to stay tune for updates.

From the release note of oneDNN v2.5:

Intel are trying to support their oneAPI compute framework on Arm and IBM POWER and z/Architecture (s390x) but since they ever released only a single discrete GPU with the Xe architecture it's unclear whether they'll support Xe GPU compute on e.g. ARM https://github.com/oneapi-src/oneDNN