[Bugfix] Fix Positive Feature Layers in Llava Models

[alex-jw-brooks]

This PR fixes two small issues with positive feature layers in llava models.

• There is an off by one indexing inconsistency compared to transformers when there are multiple feature layers used (e.g., IBM's granite vision models). E.g., if you consider a model with 24 visual encoder blocks:

  • In vLLM, we use only the output of the visual encoder blocks, i.e., [hs0, hs1, ..., hs23]
  • In transformers, the input to the visual encoder is also part of the hidden_states, i.e., [input_embed, hs0, hs1, ..., hs23] - Example in the Clip visual encoder can be found here

• One extra layer is loaded - When the vision feature layer is positive, the layer count is one higher than the corresponding negative index, e.g., given a model with 24 visual encoder blocks, feature layer -1 produces the same output as 23, but this should be 24 since 0 should correspond to the input embedding. This also breaks if you request 24 since it tries to load one more layer than it should

Note that most llava models use one feature layer that's typically negative, so generally don't hit these cases

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[DarkLight1337]

LGTM, thanks for fixing!

[alex-jw-brooks]

This is the cause for the extra block being loaded, e.g., given num_hidden_layers=24, feature_layer_index=-1 produces 24, but feature_layer_index=24 produces 25

[alex-jw-brooks]

Awesome, thanks for the quick review Cyrus! 😄