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big_dog_2k OP t1_iua8wgp wrote

Thanks. I was aware of this and had some difficultly in the past. Evaluation criteria now compares precision loss across model outputs as well as the performance (accuracy or equivalent) measured on the full system. What methods have you found to mitigate this? I would love to know!


pommedeterresautee t1_iuacchj wrote

To mitigate precision issues:

  • on ONNX related engines, we built a tool to check the output of each node and tag those that won't behave well in fp16 or bf16. Described here:
  • on Kernl, we "just" understand what happens as the code is simple (and we wrote it). We choose to not do terrible things to make the inference faster, basically no approx in our kernels, and accumulation is in fp32 (basically it's even better than vanilla mixed precision, and still much faster). IMO that's the most robust approach...

big_dog_2k OP t1_iuaexff wrote

Thank you! I think I will try kernl today as well. If I understand correctly, only Ampere generation cards are supported? Also, does it work on any huggingface model or are there still exceptions?


pommedeterresautee t1_iuaodj2 wrote

Yes for Ampere.

For HF models, the Kernels will work for most of them out of the box but you need to have search replace patterns for your specific architecture. That's why we do not have our own implementations of X and Y.

Check for an example.


big_dog_2k OP t1_iuaw55q wrote

Great. I might try this out as I like the direction this is going plus it seems like Pytorch is heading in a similar way. I'll let you know if I have questions or I will raise them on github. I appreciate all the information!