This PR adds the set_requires_grad method to PEFT models (both PeftModel
and BaseTuner). As the name suggests, this is a method to set the
requires_grad attribute of the specified PEFT adapters.
For more general context, this is mostly relevant when dealing with
multiple adapters. As is, users can already set the active adapter(s)
with set_adapter, which automatically adjust the requires_grad attribute
too, so that only the active adapters will have grads enabled. However,
there can be situations where activity status and requires grad may
differ. Right now, users would need to manually set requires_grad to
deal with that, which is error prone (e.g. forgetting modules_to_save).
This PR closes this gap in the API.
As this functionality is quite general purpose, I added a
set_requires_grad function to functional.py for easier integration.
Note: The set_requires_grad method will raise an error when called with
prompt learning methods like prompt tuning. This is because these
methods don't have a universal base class (BaseTuner and BaseTunerLayer)
that would allow to add this API. Moreover, they only support a single
adapter at a time, hence there is not much need to have this method in
the first place.
A side effect of not supporting prompt learning is that on the
PeftModel, we are free to allow set_requires_grad to accept more than
one adapter, which would normally be difficult, because prompt learning
only allows one adapter.
Deduplicate a lot of redundant code from PEFT method's model.py:
merge_and_unload
unload
delete_adapter
set_adapter
enable_adapter_layers
disable_adapter_layers
_replace_module
_unload_and_optionally_merge
_mark_only_adapters_as_trainable
_check_new_adapter_config
_check_target_module_exists
_prepare_adapter_config
__getattr__
get_peft_config_as_dict (fully deleted)
Related changes:
A new module, functional.py, is introduced, which contains functions
(just reimported from elsewhere) that can be useful for libraries that
want to integrate PEFT. I would suggest that we should treat them as
public API and thus guarantee backwards compatibility.
I also deduplicated almost identical
TRANSFORMERS_MODULES_TO_XXX_TARGET_MODULES_MAPPING constants by copying
them from LoRA and only overriding a few values that differ. Moreover,
some PEFT methods didn't have their own
TRANSFORMERS_MODULES_TO_XXX_TARGET_MODULES_MAPPING but used the one from
LoRA instead. They now each have their own constant, which is a copy
from the one from LoRA.
Benjamin Bossan