[Model] Add SupportsMultiModal.get_language_model interface (#16007)

Signed-off-by: NickLucche <nlucches@redhat.com>

docs/source/contributing/model/multimodal.md

@@ -79,6 +79,17 @@ Further update the model as follows:
             return inputs_embeds
     ```
 
+- Implement {meth}`~vllm.model_executor.models.interfaces.SupportsMultiModal.get_language_model` getter to provide stable access to the underlying language model.
+
+    ```python
+    class YourModelForImage2Seq(nn.Module):
+        ...
+
+        def get_language_model(self) -> torch.nn.Module:
+            # Change `language_model` according to your implementation.
+            return self.language_model
+    ```
+
 - Once the above steps are done, update the model class with the {class}`~vllm.model_executor.models.interfaces.SupportsMultiModal` interface.
 
   ```diff

vllm/model_executor/models/aria.py

@@ -605,6 +605,9 @@ class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
 
         return self.multi_modal_projector(image_outputs, image_attn_mask)
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/aya_vision.py

@@ -424,6 +424,9 @@ class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
             num_patches=num_patches,
         )
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/blip2.py

@@ -627,6 +627,9 @@ class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
 
         return self.language_projection(query_output)
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/chameleon.py

@@ -988,6 +988,9 @@ class ChameleonForConditionalGeneration(nn.Module, SupportsMultiModal,
             data=self._validate_pixel_values(pixel_values),
         )
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/deepseek_vl2.py

@@ -604,6 +604,9 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
         return self._pixel_values_to_embedding(
             pixel_values=pixel_values, images_spatial_crop=images_spatial_crop)
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/florence2.py

@@ -1050,6 +1050,9 @@ class Florence2ForConditionalGeneration(nn.Module, SupportsMultiModal,
         pixel_values = image_input["data"]
         return self._encode_image(pixel_values)
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/fuyu.py

@@ -341,6 +341,9 @@ class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
 
         return vision_embeddings_flat.split(patches_per_image, dim=0)
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/gemma3_mm.py

@@ -591,6 +591,9 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
             e.flatten(0, 1) for e in image_embeds.split(num_patches.tolist())
         ]
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/glm4v.py

@@ -596,6 +596,9 @@ class GLM4VForCausalLM(ChatGLMBaseModel, SupportsLoRA, SupportsPP,
 
         return self.transformer.vision(pixel_values)
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.transformer
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/idefics3.py

@@ -710,6 +710,9 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
             e.flatten(0, 1) for e in image_features.split(num_patches.tolist())
         ]
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/interfaces.py

@@ -56,6 +56,18 @@ class SupportsMultiModal(Protocol):
         """
         ...
 
+    def get_language_model(self) -> torch.nn.Module:
+        """
+        Returns the underlying language model used for text generation.
+
+        This is typically the `torch.nn.Module` instance responsible for 
+        processing the merged multimodal embeddings and producing hidden states
+
+        Returns:
+            torch.nn.Module: The core language model component.
+        """
+        ...
+
     # Only for models that support v0 chunked prefill
     # TODO(ywang96): Remove this overload once v0 is deprecated
     @overload

vllm/model_executor/models/internvl.py

@@ -884,6 +884,9 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
         else:
             self.visual_token_mask = None
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/llava.py

@@ -674,6 +674,9 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
         image_embeds = torch.split(image_embeds, feature_sizes)
         return image_embeds
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/llava_next.py

@@ -480,6 +480,9 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
             for i, patch_features_batch in enumerate(patch_embeddings)
         ]
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/llava_next_video.py

@@ -421,6 +421,9 @@ class LlavaNextVideoForConditionalGeneration(nn.Module, SupportsMultiModal,
 
         return [e.flatten(0, 1) for e in embeds]
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         video_input = self._parse_and_validate_video_input(**kwargs)

vllm/model_executor/models/llava_onevision.py

@@ -852,6 +852,9 @@ class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
         image_feature = image_feature.view(batch_frames, -1, dim)
         return image_feature
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         modalities = self._parse_and_validate_multimodal_inputs(**kwargs)

vllm/model_executor/models/minicpmv.py

@@ -892,6 +892,9 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
 
         return multimodal_embeddings
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.llm
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         modalities = self._parse_and_validate_multimodal_inputs(**kwargs)

vllm/model_executor/models/mistral3.py

@@ -514,6 +514,9 @@ class Mistral3ForConditionalGeneration(nn.Module, SupportsMultiModal,
             image_embeds = (image_embeds, )
         return image_embeds
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/mllama.py

@@ -1325,6 +1325,9 @@ class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal,
         cross_attention_states = cross_attention_states_flat
         return cross_attention_states
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_cross_attention_states(
         self,
         image_inputs: MllamaImagePixelInputs,

vllm/model_executor/models/mllama4.py

@@ -742,6 +742,9 @@ class Llama4ForConditionalGeneration(nn.Module, SupportsMultiModal,
             for img in vision_embeddings_flat.split(patches_per_image, dim=0)
         ]
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(self,
                                   **kwargs) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/molmo.py

@@ -1488,6 +1488,9 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA,
             )
         ]
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/paligemma.py

@@ -323,6 +323,9 @@ class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal,
 
         return self.multi_modal_projector(image_features)
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/phi3v.py

@@ -674,6 +674,9 @@ class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP,
 
         return image_embeds
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/phi4mm.py

@@ -1802,3 +1802,6 @@ class Phi4MMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal,
             connector=["audio_projection_for_vision", "audio_projection"],
             tower_model=["vision_encoder", "embed_tokens_extend"],
         )
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.model

vllm/model_executor/models/pixtral.py

@@ -396,6 +396,9 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
         image_embeds = torch.split(image_embeds, feature_sizes)
         return image_embeds
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/qwen2_5_vl.py

@@ -967,6 +967,9 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
                     **kwargs)
         return modalities
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
 

vllm/model_executor/models/qwen2_audio.py

@@ -355,6 +355,9 @@ class Qwen2AudioForConditionalGeneration(nn.Module, SupportsMultiModal,
         return torch.split(masked_audio_features,
                            audio_output_lengths.flatten().tolist())
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         audio_input = self._parse_and_validate_audio_input(**kwargs)

vllm/model_executor/models/qwen2_vl.py

@@ -1276,6 +1276,9 @@ class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
 
         return modalities
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
 

vllm/model_executor/models/qwen_vl.py

@@ -740,6 +740,9 @@ class QwenVLForConditionalGeneration(QWenBaseModel, SupportsPP, SupportsLoRA,
 
         return self.transformer.visual(image_input["data"])
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.transformer
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/skyworkr1v.py

@@ -889,6 +889,9 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
         else:
             self.visual_token_mask = None
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/ultravox.py

@@ -563,6 +563,9 @@ class UltravoxModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
         ]
         return flattened_embeddings.split(embed_lens)
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         audio_input = self._parse_and_validate_audio_input(**kwargs)

vllm/model_executor/models/whisper.py

@@ -692,6 +692,9 @@ class WhisperForConditionalGeneration(nn.Module, SupportsTranscription,
         )
         return decoder_outputs
 
+    def get_language_model(self) -> torch.nn.Module:
+        return self.model.decoder
+
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         # TODO: This method does not obey the interface for SupportsMultiModal.