[Model] Add Support for Multimodal Granite Models

[alex-jw-brooks]

This PR adds the main architectural change needed to support upcoming multimodal granite models from IBM, which are a variant of llava-next. To this end, we need to allow passing a list of feature_sample_layers, which are indices in the visual encoder whose hidden states will be concatenated and used as the visual encoder output as an alternative to the last layer. This change should not affect any existing llava next models, and is made in all of the supported visual encoders for Llava-based models in vLLM (clip/siglip/pixtral) to keep things as generic as possible.

These models have not been released quite yet, and we have not yet ported the change to transformers, which is why there are no new tests added. However, I have run my own benchmarks to ensure the results in vLLM are identical in quality to our own implementation, and verified that the feature_sample_layers can be passed through each supported visual encoder type as a sanity check. Once the models are out and supported in transformers, I can add it to the corresponding test here!

FYI @njhill

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[DarkLight1337]

This looks a bit overcomplicated, and can be easily confused with num_hidden_layers. I prefer having a flag to simply return all of the hidden states (similar to HF) up to num_hidden_layers, and extract them after calling forward method.

[DarkLight1337]

The reason why we set num_hidden_layers at initialization time is to avoid loading the unused layers. However, there is no such advantage to passing feature_sample_layers, so it would be better to pass it dynamically when calling forward on the vision encoder.

[alex-jw-brooks]

Sounds good, thanks @DarkLight1337! I moved most of the vision feature layer handling out to inference time, added the flag for getting all of the hidden states + selecting from them after, and fixed the initialization behavior so that we only load up to the deepest layer needed if there are multiple feature layers also

[DarkLight1337]

Seems like an unnecessary formatting change.

[alex-jw-brooks]

Yup! I put it back the way it was, accidentally deleted some trailing commas + auto formatted in some places 😄

[DarkLight1337]

Otherwise LGTM

[DarkLight1337]

cc @ywang96 in case you're planning to refactor related code in the vision encoders.