Add compile_fn parameter for Trainer

[mieshkiwrk]

Add support for compile_fn for Trainer for example to compile model after applying strategy

Example usage: needed to compile after applying DDP strategy to get pre/post forward also compiled

Fixes #20242

---

📚 Documentation preview 📚: https://pytorch-lightning--20269.org.readthedocs.build/en/20269/

[mieshkiwrk]

Both benchmarks checks failed due to timeout

[mieshkiwrk]

bump

[codecov]

Codecov Report

Attention: Patch coverage is 75.00000% with 1 line in your changes missing coverage. Please review.

Project coverage is 81%. Comparing base (5be58f6) to head (4648ea2).
Report is 1 commits behind head on master.

❗ There is a different number of reports uploaded between BASE (5be58f6) and HEAD (4648ea2). Click for more details.

HEAD has 553 uploads less than BASE

Flag	BASE (5be58f6)	HEAD (4648ea2)
cpu	147	21
lightning	106	16
pytest	87	2
python3.9	43	6
python3.10	42	6
lightning_fabric	25	0
gpu	4	2
python3.11	42	6
python3.12	20	3
pytorch2.1	38	12
pytest-full	64	21
pytorch2.3	9	3
pytorch_lightning	20	7
pytorch2.2	9	3
pytorch2.4	8	3

Additional details and impacted files

@@            Coverage Diff            @@
##           master   #20269     +/-   ##
=========================================
- Coverage      89%      81%     -8%     
=========================================
  Files         267      264      -3     
  Lines       23084    23032     -52     
=========================================
- Hits        20585    18620   -1965     
- Misses       2499     4412   +1913     

[lantiga]

Thank you @mieshkiwrk.

The way we recommend users to use torch.compile with lightning is to call torch.compile on the model and then pass it to the trainer.

import torch
import lightning as L

model = MyLightningModule()

model = torch.compile(model)

trainer = L.Trainer()
trainer.fit(model)

This PR would add an additional entrypoint and there's probably a simpler way to go about it (for users).

We should replicate what Fabric does here: https://github.com/Lightning-AI/pytorch-lightning/blob/master/src/lightning/fabric/wrappers.py#L421

where we capture the arguments passed to torch.compile so we can re-apply it when using strategies, just like we do in Fabric but in the Trainer:

#19280

Would you like to take a stab at it?

[mieshkiwrk]

Let me try, looks like I see what's needed to be done

[mieshkiwrk]

@lantiga, something like this would be fine? Wanted to make sure about re-using _unwrap_compiled and _to_compiled from fabric or should these be copied to lightning.pytorch wrappers?
If this approach would be okay I'll add comments, verify sanity checks and some unit test

[lantiga]

hey, thanks for updating the PR
the approach looks good! I don’t think we need reapply_compile, we will want this whenever we have an optimized model in input (the call is largely free, until we run the first forward)
I’d reuse Fabric’s wrapper, unless we need to tweak them in which case I’d duplicate them