architecture: factor HFCompatible out

[leondz]

HFCompatible was embedded in generators.base, tying slow-to-import HF-specific stuff to base classes. This PR moves HFCompatible to a separate module, with a candidate location in garak.resources.api.huggingface, enabling fast base class loading.

[jmartin-tech]

Move looks reasonable, I am on the fence on using garak.resources.api to represent wrappers for dependencies.

One idea would be to use garak/resources/huggingface/__init__.py to expose the class, this could allow for keeping the class definitions in unique files imported to __init__py for exposure as more Compatible types are identified over time. Just a thought that came to mind, no strong argument to favor this at this time.

This PR also suggests there is another consumer for this mixin in buffs.paraphrase.PegasusT5.

[jmartin-tech]

Just an observation not required for this PR, it looks like this class could benefit from a refactor to use HFCompatible and expose the para_model_name and hf_args as DEFAULT_PARAMS.

Suggested change

	class PegasusT5(Buff):
	class PegasusT5(Buff, HFCompatible):

[jmartin-tech]

This change is incomplete the class needs to be extended to consume the HFCompatible mixin in _load_model():

self.torch_device moves to the standardized self.device and should be detected/populated from hf_args["device"] with a call to self._select_hf_device() in _load_model():

    def __init__(self, config_root=_config) -> None:
        self.max_length = 60
        self.temperature = 1.5
        self.num_return_sequences = 6
        self.num_beams = self.num_return_sequences
        self.tokenizer = None
        self.para_model = None
        super().__init__(config_root=config_root)

   def _load_model(self):
        from transformers import PegasusForConditionalGeneration, PegasusTokenizer

        self.device = self._select_hf_device()
        model_kwargs = self._gather_hf_params(
            hf_constructor=PegasusForConditionalGeneration.from_pretrained
        )  # will defer to device_map if device map was `auto` may not match self.device

        self.para_model = PegasusForConditionalGeneration.from_pretrained(
            self.para_model_name, **model_kwargs
        ).to(self.device)
        self.tokenizer = PegasusTokenizer.from_pretrained(self.para_model_name)

Not an issue for this PR, however I suspect a few more items should likely be promoted to DEFAULT_PARAMS. The max_length, temperature, num_return_sequences, and possibly num_beams if the value does not always have to be equal to num_return_sequences should likely be exposed a configurable. Since Fast looks like it may also have similar items to promote I think that can be deferred.

[leondz]

Agree. Thanks for the details. Will mark as ready for review when out of draft.

[leondz]

Adding the model_kwargs part led to the paraphraser returning all blanks. Did the rest of the integration and added a test to catch this unwanted behaviour.

[leondz]

Still getting blank results when using _gather_hf_params, will take a look in a bit, but if you have suggestions, they're welcome!

[jmartin-tech]

Lets drop the _gather_hf_params() for now as the PegasusForConditionalGeneration.from_pretrained() looks like it is not handling the extra args in the way the current code is expecting, I suspect device vs device_map is also impacting the expectations.