[V1] Change return type on get_multimodal_embeddings() (#19446)

Signed-off-by: Russell Bryant <rbryant@redhat.com>

vllm/model_executor/models/aria.py

@@ -601,11 +601,11 @@ class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
         multimodal_embeddings = self._process_image_input(image_input)
         return multimodal_embeddings
 

vllm/model_executor/models/aya_vision.py

@@ -406,11 +406,11 @@ class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
 
         return self._process_image_input(image_input, **kwargs)
 

vllm/model_executor/models/blip2.py

@@ -627,11 +627,11 @@ class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
         vision_embeddings = self._process_image_input(image_input)
         return vision_embeddings
 

vllm/model_executor/models/chameleon.py

@@ -987,11 +987,11 @@ class ChameleonForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_language_model(self) -> torch.nn.Module:
         return self.model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
         assert self.model.vqmodel is not None
         image_tokens = self.model.get_image_tokens(image_input["data"].to(
             self.config.torch_dtype))

vllm/model_executor/models/deepseek_vl2.py

@@ -586,11 +586,11 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
         vision_embeddings = self._process_image_input(image_input)
         return vision_embeddings
 

vllm/model_executor/models/florence2.py

@@ -1032,11 +1032,11 @@ class Florence2ForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
         vision_embeddings = self._process_image_input(image_input)
         return vision_embeddings
 

vllm/model_executor/models/fuyu.py

@@ -324,11 +324,11 @@ class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
 
         return self._process_image_input(image_input)
 

vllm/model_executor/models/gemma3_mm.py

@@ -568,11 +568,11 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
 
         return self._process_image_input(image_input)
 

vllm/model_executor/models/glm4v.py

@@ -593,11 +593,11 @@ class GLM4VForCausalLM(ChatGLMBaseModel, SupportsLoRA, SupportsPP,
     def get_language_model(self) -> torch.nn.Module:
         return self.transformer
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
 
         vision_embeddings = self._process_image_input(image_input)
         return vision_embeddings

vllm/model_executor/models/granite_speech.py

@@ -706,10 +706,11 @@ class GraniteSpeechForConditionalGeneration(
     def get_multimodal_embeddings(
         self,
         **kwargs: object,
-    ) -> Optional[MultiModalEmbeddings]:
+    ) -> MultiModalEmbeddings:
         """Compute the audio embeddings if audio inputs are present."""
         audio_input = self._parse_and_validate_audio_input(**kwargs)
         if audio_input is None:
+            return []
             return None
         audio_features = self._process_audio_input(audio_input)
         return audio_features

vllm/model_executor/models/idefics3.py

@@ -706,11 +706,11 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_language_model(self) -> torch.nn.Module:
         return self.model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
 
         return self._process_image_input(image_input)
 

vllm/model_executor/models/interfaces.py

@@ -44,8 +44,8 @@ class SupportsMultiModal(Protocol):
         MRO of your model class.
     """
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         """
         Returns multimodal embeddings generated from multimodal kwargs 
         to be merged with text embeddings.

vllm/model_executor/models/internvl.py

@@ -1304,11 +1304,12 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
 
         modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
         if not modalities:
+            return []
             return None
 
         # The result multimodal_embeddings is tuple of tensors, with each

vllm/model_executor/models/llava.py

@@ -659,11 +659,11 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
 
         return self._process_image_input(image_input)
 

vllm/model_executor/models/llava_next.py

@@ -478,11 +478,11 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
         vision_embeddings = self._process_image_input(image_input)
         return vision_embeddings
 
@@ -492,7 +492,7 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
         multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
     ) -> torch.Tensor:
 
-        if multimodal_embeddings is None:
+        if not multimodal_embeddings:
             return self.language_model.get_input_embeddings(input_ids)
 
         inputs_embeds = embed_multimodal(

vllm/model_executor/models/llava_next_video.py

@@ -401,11 +401,11 @@ class LlavaNextVideoForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         video_input = self._parse_and_validate_video_input(**kwargs)
         if video_input is None:
-            return None
+            return []
         vision_embeddings = self._process_video_pixels(video_input)
         return vision_embeddings
 

vllm/model_executor/models/llava_onevision.py

@@ -839,11 +839,12 @@ class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         mm_input_by_modality = self._parse_and_validate_multimodal_inputs(
             **kwargs)
         if not mm_input_by_modality:
+            return []
             return None
 
         # The result multimodal_embeddings is tuple of tensors, with each

vllm/model_executor/models/minicpmv.py

@@ -878,11 +878,11 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
     def get_language_model(self) -> torch.nn.Module:
         return self.llm
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
         if not modalities:
-            return None
+            return []
 
         return self._process_multimodal_inputs(modalities)
 

vllm/model_executor/models/minimax_vl_01.py

@@ -318,11 +318,11 @@ class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal,
 
         raise AssertionError("This line should be unreachable.")
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
 
         return self._process_image_input(image_input)
 

vllm/model_executor/models/mistral3.py

@@ -495,11 +495,11 @@ class Mistral3ForConditionalGeneration(nn.Module, SupportsLoRA,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
 
         vision_embeddings = self._process_image_input(image_input)
 

vllm/model_executor/models/mllama4.py

@@ -794,11 +794,10 @@ class Llama4ForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(self,
-                                  **kwargs) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self, **kwargs) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
 
         return self._process_image_input(image_input)
 

vllm/model_executor/models/molmo.py

@@ -1473,11 +1473,11 @@ 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]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
 
         return self._process_image_input(image_input)
 

vllm/model_executor/models/ovis.py

@@ -499,11 +499,11 @@ class Ovis(nn.Module, SupportsMultiModal, SupportsPP):
 
         return tuple(vision_embeddings)
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
 
         image_features = self._process_image_input(image_input)
 

vllm/model_executor/models/paligemma.py

@@ -338,11 +338,11 @@ class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
         vision_embeddings = self._process_image_input(image_input)
         # https://github.com/huggingface/transformers/blob/main/src/transformers/models/paligemma/modeling_paligemma.py#L294 # noqa
         vision_embeddings = vision_embeddings * (self.config.hidden_size**-0.5)

vllm/model_executor/models/phi3v.py

@@ -655,11 +655,11 @@ class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
         vision_embeddings = self._process_image_input(image_input)
         return vision_embeddings
 
@@ -669,7 +669,7 @@ class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP,
         multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
     ) -> torch.Tensor:
         inputs_embeds = self.embed_tokens(input_ids)
-        if multimodal_embeddings is not None:
+        if multimodal_embeddings:
             inputs_embeds = merge_multimodal_embeddings(
                 input_ids, inputs_embeds, multimodal_embeddings,
                 self.image_token_id)

vllm/model_executor/models/phi4mm.py

@@ -1112,11 +1112,12 @@ class Phi4MMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
                                            image_attention_mask)
         return image_embeds
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
 
         modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
         if not modalities:
+            return []
             return None
 
         # The result multimodal_embeddings is tuple of tensors, with each

vllm/model_executor/models/pixtral.py

@@ -409,11 +409,11 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
 
         return self._process_image_input(image_input)
 

vllm/model_executor/models/qwen2_5_omni_thinker.py

@@ -772,13 +772,13 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
 
         mm_input_by_modality = self._parse_and_validate_multimodal_inputs(
             **kwargs)
         if not mm_input_by_modality:
-            return None
+            return []
 
         # The result multimodal_embeddings is tuple of tensors, with each
         # tensor correspoending to a multimodal data item (image or video).

vllm/model_executor/models/qwen2_5_vl.py

@@ -1016,13 +1016,13 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
 
         mm_input_by_modality = self._parse_and_validate_multimodal_inputs(
             **kwargs)
         if not mm_input_by_modality:
-            return None
+            return []
 
         # The result multimodal_embeddings is tuple of tensors, with each
         # tensor correspoending to a multimodal data item (image or video).

vllm/model_executor/models/qwen2_audio.py

@@ -350,11 +350,11 @@ class Qwen2AudioForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         audio_input = self._parse_and_validate_audio_input(**kwargs)
         if audio_input is None:
-            return None
+            return []
         masked_audio_features = self._process_audio_input(audio_input)
         return masked_audio_features
 

vllm/model_executor/models/qwen2_vl.py

@@ -1257,11 +1257,12 @@ class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
 
         modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
         if not modalities:
+            return []
             return None
 
         # The result multimodal_embeddings is tuple of tensors, with each

vllm/model_executor/models/qwen_vl.py

@@ -738,11 +738,11 @@ class QwenVLForConditionalGeneration(QWenBaseModel, SupportsPP, SupportsLoRA,
     def get_language_model(self) -> torch.nn.Module:
         return self.transformer
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
 
         vision_embeddings = self._process_image_input(image_input)
         return vision_embeddings

vllm/model_executor/models/skyworkr1v.py

@@ -869,11 +869,11 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
 
         return self._process_image_input(image_input)
 

vllm/model_executor/models/tarsier.py

@@ -585,11 +585,11 @@ class TarsierForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
-            return None
+            return []
         return self._process_image_input(image_input)
 
     def get_input_embeddings(

vllm/model_executor/models/ultravox.py

@@ -546,11 +546,11 @@ class UltravoxModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
     def get_language_model(self) -> torch.nn.Module:
         return self.language_model
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         audio_input = self._parse_and_validate_audio_input(**kwargs)
         if audio_input is None:
-            return None
+            return []
         audio_embeddings = self._process_audio_input(audio_input)
         return audio_embeddings
 

vllm/model_executor/models/whisper.py

@@ -687,8 +687,8 @@ class WhisperForConditionalGeneration(nn.Module, SupportsTranscription,
     def get_language_model(self) -> torch.nn.Module:
         return self.model.decoder
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
         # TODO: This method does not obey the interface for SupportsMultiModal.
         # Refactor this once encoder/decoder support is implemented in V1.
         audio_input = self._parse_and_validate_audio_input(**kwargs)

vllm/v1/worker/utils.py

@@ -4,11 +4,12 @@ from typing import Optional
 
 import torch
 
+from vllm.model_executor.models.interfaces import MultiModalEmbeddings
 from vllm.v1.kv_cache_interface import KVCacheGroupSpec
 
 
 def sanity_check_mm_encoder_outputs(
-    mm_embeddings: object,
+    mm_embeddings: MultiModalEmbeddings,
     expected_num_items: int,
 ) -> None:
     """