Right now, we don't have specific tests for torch.compile. Instead, we
have a "hack" that allows to run _all_ tests with torch.compile if we
set the environment variable PEFT_DEBUG_WITH_TORCH_COMPILE=1.

This is not very practical because it takes a lot of time to run all
these tests with compilation enabled. Also, currently hundreds of tests
are failing, which makes it impossible to understand more closely what
does or does not work.

This PR removes the aforementioned "hack" and instead replaces it with a
list of explicit torch.compile tests. Currently, these tests cover
training/inference with a bunch of different tuner types, as well as
more advanced features with LoRA (e.g. quantization, multiple adapters,
etc.).

Some of these tests pass and some of them fail. This is documented now,
so that users can quickly look up if their use case would be compatible
with torch.compile. This is very useful to have, because sometimes
torch.compile may appear to work but actually returns the wrong result.
For users, it's not immediately obvious when this happens.

The test suite is not exhaustive, there are many combinations of
features that could be added. However, it should be a good starting
point and can be extended later.

The test suite does _not_ cover whether torch.compile actually
accelerates the code. This may not be the case even if it works
correctly (e.g. because of graph breaks). Testing this would require
bigger models and more data, which is prohibitively slow to test.

---------

Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>