Similarly let’s try out a TIMM example
import timm
import torch
model = timm.create_model('resnext101_32x8d', pretrained=True, num_classes=2)
opt_model = torch.compile(model, backend="inductor")
opt_model(torch.randn(64,3,7,7))

Our goal with PyTorch was to build a breadth-first compiler that would speed up the vast majority of actual models people run in open source. The Hugging Face Hub ended up being an extremely valuable benchmarking tool for us, ensuring that any optimization we work on actually helps accelerate models people want to run.
So please try out PyTorch 2.0, enjoy the free perf and if you’re not seeing it then please open an issue and we will make sure your model is supported https://github.com/pytorch/torchdynamo/issues
After all, we can’t claim we’re created a breadth-first unless YOUR models actually run faster.