[Core][Frontend] Support Passing Processor Kwargs

[alex-jw-brooks]

In support of #7861 - It's split into two PRs to make it easier to review

This PR adds support for passing processor_kwargs at initialization time to override values, e.g., in the image processor config and adds a pattern that we can use to easily implement such overrides, especially in the case of multimodal models. More specifically, it adds a well-defined way to pass valid processor_kwargs as expanded keyword-only arguments (which should have default values) to:

• The input mapper (e.g,. default mapper wrapping the image processor from HF, or a custom mapper implemented in the model class)
• The input processor
• Max token calculations
• Dummy data for memory profiling

This is important since the provided overrides may greatly change the number of image tokens per multimodal instance.

It also forwards some args to the video processor, since those weren't being pushed through yet.

Some Implementation Considerations

I've tried to implement this to be easily extensible for potential inference-time configuration and intuitive to use coming from transformers. More specifically, I was aiming for:

• avoid writing any configs on disk (since that's the main reason we need this anyway)
• avoid mutating any configs in-memory
• Make no assumptions about config structure are made; having a user specify where the value of something like num_crops comes from is a bad experience, and implementing a per-model mapping of allowed config overrides is also fragile since things could be moved around a bit for models that are written to try to generically support fine-tuned models with different LLMs, etc
• Assume that most overrides are going to be added gradually added; especially for the mapper, since it is a VLLM model implementation detail of whether or not it's using a custom mapper or falling back to something like the HF image processor. If a processor_kwarg is not supported, it will warn that it'll be unused and drop it from the processor kwargs, instead of throwing some nasty unexpected keyword argument deep in the model class
• Keep compatibility with the existing interfaces for max_token_counts, mapper, input processor, etc
• Should be easy for us to check if something has already been added for a model

Examples

I've opened a second PR on top of this PR which makes num_crops at init time for phi3v models which illustrates the usage here; see this commit for what adding a processor_kwarg looks like, as well as example tests for each place it's used in phi3v.

You can also try running those examples on this to see what happens if it's unsupported. Since it leverages the default mapper, whose kwargs can't easily be inspected since it gets created through the automodel, there will be some mismatches that eventually crash since the processor kwargs will be used to initialize the auto model, but you'll at least get a bunch of things like:

The following intended overrides are not keyword-only args and and will be dropped: {'num_crops'}

out in the warmup. In situations where the class has its own mapper, and a bad kwarg was used, this would be ignored like it is everywhere else (same case as the sad not found kwargs tests here)

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[DarkLight1337]

Hmm, originally I was thinking of dynamic kwargs via multi-modal inputs, e.g.

llm.generate({"multi_modal_data": {"image": {"data": image, "options": image_kwargs}}})

but this works too!

I'll take a closer look later today.

[DarkLight1337]

Suggested change

	processor_kwargs=None,
	processor_kwargs: Optional[Dict[str, Any]] = None,

[DarkLight1337]

Suggested change

	def register_input_processor(self, processor: InputProcessor) -> Callable:
	def register_input_processor(self, processor: InputProcessor):

I intentionally didn't specify the return type here so that the type variables are automatically inferred without having to spell them all out.

[DarkLight1337]

If you do want to explicitly specify it, it should return Callable[[N], N] here.

[DarkLight1337]

Suggested change

	processor: Callable, **processor_kwargs) -> LLMInputs:
	processor: InputProcessor, **processor_kwargs: Any) -> LLMInputs:

[DarkLight1337]

Suggested change

	def create_input_processor(self, model_config: "ModelConfig") -> Callable:
	def create_input_processor(self, model_config: "ModelConfig"):

[DarkLight1337]

Suggested change

	model_config: "ModelConfig") -> Callable:
	model_config: "ModelConfig"):

[DarkLight1337]

If you do want to explicitly specify it, it should return InputProcessor here.

[alex-jw-brooks]

Sounds good, thanks @DarkLight1337! 🙂 I wanted to open this one first since it's useful by itself, and to make sure the overall approach looks reasonable and had good test coverage since it's touching a few different things.

I'm happy to add that in a follow-up PR though - I did implement this with that in mind as next steps, and it won't require changes to individual models to support it if additional init time processor kwargs are exposed in the meantime! 🤞