kernl VS torchdynamo

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?

For 1), what is the easiest way to speed up inference (assume only PyTorch and primarily GPU but also some CPU)? I have been using ONNX and Torchscript but there is a bit of a learning curve and sometimes it can be tricky to get the model to actually work. Is there anything else worth trying? I am enthused by things like TorchDynamo (although I have not tested it extensively) due to its apparent ease of use. I also saw the post yesterday about Kernl using (OpenAI) Triton kernels to speed up transformer models which also looks interesting. Are things like SageMaker Neo or NeuralMagic worth trying? My only reservation with some of these is they still seem to be pretty model/architecture specific. I am a little reluctant to put much time into these unless I know others have had some success first.