torch.compile

In PEFT, torch.compile works for some but not all features. The reason why it won’t always work is because PEFT is highly dynamic in certain places (loading and switching between multiple adapters, for instance), which can cause trouble for torch.compile. In other places, torch.compile may work, but won’t be as fast as expected because of graph breaks.

If you don’t see an error, it doesn’t necessarily mean that torch.compile worked correctly. It might give you an output, but the output is incorrect. This guide describes what works with torch.compile and what doesn’t.

Training and inference with torch.compile

These features work with torch.compile. Everything listed below was tested with a causal LM:

• Training with Trainer from 🤗 transformers
• Training with a custom PyTorch loop
• Inference
• Generation

The following adapters were tested successfully:

• AdaLoRA
• BOFT
• IA³
• Layer Norm Tuning
• LoHa
• LoRA
• LoRA + DoRA
• OFT
• VeRA

The following adapters don’t work correctly for training or inference when using torch.compile:

• LoKr
• LoRA targeting embedding layers

Advanced PEFT features with torch.compile

Below are some of the more advanced PEFT features that work. They were all tested with LoRA.

• modules_to_save (i.e. config = LoraConfig(..., modules_to_save=...))
• Merging adapters (one or multiple)
• Merging multiple adapters into one adapter (i.e. calling model.add_weighted_adapter(...))

Generally, we can expect that if a feature works correctly with LoRA and is also supported by other adapter types, it should also work for that adapter type.

The more advanced PEFT features below don’t work in conjunction with torch.compile. Tests were run with LoRA:

• Using PEFT adapters with quantization (bitsandbytes)
• Inference with multiple adapters
• Unloading (i.e. calling model.merge_and_unload())
• Disabling adapters (i.e. using with model.disable_adapter())
• Mixed adapter batches (i.e. calling model(batch, adapter_names=["__base__", "default", "other", ...]))

Test cases

All the use cases listed above are tested inside of peft/tests/test_torch_compile.py. If you want to check in more detail how we tested a certain feature, please go to that file and check the test that corresponds to your use case.