kernl VS openai-whisper-cpu

I periodically check kernl.ai to see whether the documentation and tutorial sections have been expanded. My advice is put some real effort and focus in to examples and tutorials. It is key for an optimization/acceleration library. 10x-ing the users of a library like this is much more likely to come from spending 10 out of every 100 developer hours writing tutorials, as opposed to spending those 8 or 9 of those tutorial-writing hours on developing new features which only a small minority understand how to apply.

FlashAttention + quantization has to the best of knowledge not yet been explored, but I think it would a great engineering direction. I would not expect to see this any time soon natively in PyTorch's BetterTransformer though. /u/pommedeterresautee & folks at ELS-RD made an awesome work releasing kernl where custom implementations (through OpenAI Triton) could maybe easily live.

Check https://github.com/ELS-RD/kernl/blob/main/src/kernl/optimizer/linear.py for an example.

https://github.com/ELS-RD/kernl/issues/141 > Would it be possible to use kernl to speed up Stable Diffusion?

I feel the same.

For example some stats from Whisper [0] (audio transcoding) show the following for the medium model (see other models in the link):

---

GPU medium fp32 Linear 1.7s

CPU medium fp32 nn.Linear 60.7

CPU medium qint8 (quant) nn.Linear 23.1

---

So the same model runs 35.7 times faster on GPU, and compared to an CPU-optimized model still 13.6.

I was expecting around an order or magnitude of improvement. Then again, I do not know if in the case of this article the entire model was in the GPU, or just a fraction of it (22 layers), which might explain the result.

[0] https://github.com/MiscellaneousStuff/openai-whisper-cpu

According to https://github.com/MiscellaneousStuff/openai-whisper-cpu the medium model needs 1.7 seconds to transcribe 30 seconds of audio when run on a GPU.

There is a very simple method built-in to PyTorch which can give you over 3x speed improvement for the large model, which you could also combine with the method proposed in this post. https://github.com/MiscellaneousStuff/openai-whisper-cpu

For CPU inference, model quantization is a very easy to apply method with great average speedups which is already built-in to PyTorch. For example, I applied dynamic quantization to the OpenAI Whisper model (speech recognition) across a range of model sizes (ranging from tiny which had 39M params to large which had 1.5B params). Refer to the below table for performance increases:

GitHub