What makes this announcement different for us is we’ve already benchmarked some of the most popular open source PyTorch models and gotten substantial speedups ranging from 30% to 2x https://github.com/pytorch/torchdynamo/issues/681.
There are no tricks here, we’ve pip installed popular libraries like https://github.com/huggingface/transformers, https://github.com/huggingface/accelerate and https://github.com/rwightman/pytorch-image-models and then ran torch.compile() on them and that’s it.
It’s rare to get both performance and convenience, but this is why the core team finds PyTorch 2.0 so exciting. The Hugging Face team is also excited, in their words:
Ross Wightman the primary maintainer of TIMM: “PT 2.0 works out of the box with majority of timm models for inference and train workloads and no code changes”