[Bugfix][VLM] Fix mixed-modality inference backward compatibility for V0 (#12313)

Signed-off-by: Roger Wang <ywang@roblox.com>

vllm/model_executor/models/llava_onevision.py

@@ -816,7 +816,7 @@ class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
         return image_feature
 
     def get_multimodal_embeddings(
-            self, **kwargs) -> Optional[List[Tuple[NestedTensors, str]]]:
+            self, **kwargs) -> Optional[tuple[torch.Tensor, ...]]:
         modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
         if not modalities:
             return None
@@ -842,8 +842,7 @@ class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_input_embeddings(
         self,
         input_ids: torch.Tensor,
-        multimodal_embeddings: Optional[List[Tuple[NestedTensors,
-                                                   str]]] = None,
+        multimodal_embeddings: Optional[tuple[torch.Tensor, ...]] = None,
     ) -> torch.Tensor:
         inputs_embeds = self.language_model.get_input_embeddings(input_ids)
         if multimodal_embeddings is not None:
@@ -852,6 +851,34 @@ class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
                 [self.config.image_token_index, self.config.video_token_index])
         return inputs_embeds
 
+    def get_input_embeddings_v0(
+        self,
+        input_ids: torch.Tensor,
+        image_input: Optional[NestedTensors] = None,
+        video_input: Optional[NestedTensors] = None,
+    ) -> torch.Tensor:
+
+        inputs_embeds = self.get_input_embeddings(input_ids)
+        if image_input is not None:
+            image_embeds = self._process_image_input(image_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                image_embeds,
+                placeholder_token_id=self.config.image_token_index,
+            )
+
+        if video_input is not None:
+            video_embeds = self._process_video_pixels(video_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                video_embeds,
+                placeholder_token_id=self.config.video_token_index,
+            )
+
+        return inputs_embeds
+
     def forward(
         self,
         input_ids: torch.Tensor,
@@ -871,12 +898,20 @@ class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
         if intermediate_tensors is not None:
             inputs_embeds = None
 
-        # NOTE: In v1, inputs_embeds is always generated at model runner, this
-        # condition is for v0 compatibility.
+        # NOTE: In v1, inputs_embeds is always generated at model runner from
+        # `get_multimodal_embeddings` and `get_input_embeddings`, this
+        # condition is only for v0 compatibility.
         elif inputs_embeds is None:
-            multimodal_embeddings = self.get_multimodal_embeddings(**kwargs)
-            inputs_embeds = self.get_input_embeddings(input_ids,
-                                                      multimodal_embeddings)
+            image_input = self._parse_and_validate_image_input(**kwargs)
+            video_input = self._parse_and_validate_video_input(**kwargs)
+
+            if image_input is None and video_input is None:
+                inputs_embeds = None
+            else:
+                inputs_embeds = self.get_input_embeddings_v0(
+                    input_ids,
+                    image_input=image_input,
+                    video_input=video_input)
                 input_ids = None
 
         hidden_states = self.language_model.model(input_ids,

vllm/model_executor/models/qwen2_vl.py

@@ -55,7 +55,7 @@ from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import (ImageItem, ModalityData,
                                     MultiModalFieldConfig, MultiModalKwargs,
-                                    NestedTensors, VideoItem)
+                                    VideoItem)
 from vllm.multimodal.parse import (ImageSize, ModalityDataItems,
                                    MultiModalDataItems, MultiModalDataParser)
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
@@ -1233,7 +1233,7 @@ class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
         return modalities
 
     def get_multimodal_embeddings(
-            self, **kwargs) -> Optional[List[Tuple[NestedTensors, str]]]:
+            self, **kwargs) -> Optional[tuple[torch.Tensor, ...]]:
 
         modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
         if not modalities:
@@ -1260,8 +1260,7 @@ class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_input_embeddings(
         self,
         input_ids: torch.Tensor,
-        multimodal_embeddings: Optional[List[Tuple[NestedTensors,
-                                                   str]]] = None,
+        multimodal_embeddings: Optional[tuple[torch.Tensor, ...]] = None,
     ) -> torch.Tensor:
         inputs_embeds = self.language_model.get_input_embeddings(input_ids)
         if multimodal_embeddings is not None:
@@ -1270,6 +1269,33 @@ class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
                 [self.config.image_token_id, self.config.video_token_id])
         return inputs_embeds
 
+    def get_input_embeddings_v0(
+        self,
+        input_ids: torch.Tensor,
+        image_input: Optional[tuple[torch.Tensor, ...]] = None,
+        video_input: Optional[tuple[torch.Tensor, ...]] = None,
+    ) -> torch.Tensor:
+
+        inputs_embeds = self.get_input_embeddings(input_ids)
+        if image_input is not None:
+            image_embeds = self._process_image_input(image_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                image_embeds,
+                placeholder_token_id=self.config.image_token_id,
+            )
+
+        if video_input is not None:
+            video_embeds = self._process_video_input(video_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                video_embeds,
+                placeholder_token_id=self.config.video_token_id,
+            )
+        return inputs_embeds
+
     def forward(
         self,
         input_ids: torch.Tensor,
@@ -1303,21 +1329,24 @@ class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
         if intermediate_tensors is not None:
             inputs_embeds = None
 
-        # NOTE: In v1, inputs_embeds is always generated at model runner, this
-        # condition is for v0 compatibility.
+        # NOTE: In v1, inputs_embeds is always generated at model runner from
+        # `get_multimodal_embeddings` and `get_input_embeddings`, this
+        # condition is only for v0 compatibility.
         elif inputs_embeds is None:
-            multimodal_embeddings = self.get_multimodal_embeddings(**kwargs)
+            image_input = self._parse_and_validate_image_input(**kwargs)
+            video_input = self._parse_and_validate_video_input(**kwargs)
 
-            # We need to check for usage of mrope here in case there is
-            # multimodal data.
-            # TODO (ywang96): move this to model runner in V1.
-            if multimodal_embeddings is not None and uses_mrope(self.config):
+            if image_input is None and video_input is None:
+                inputs_embeds = None
+            else:
+                if uses_mrope(self.config):
                     assert positions.ndim == 2 and positions.size(0) == 3, (
                         "multimodal section rotary embedding requires "
                         f"(3, seq_len) positions, but got {positions.size()}")
-
-            inputs_embeds = self.get_input_embeddings(input_ids,
-                                                      multimodal_embeddings)
+                inputs_embeds = self.get_input_embeddings_v0(
+                    input_ids,
+                    image_input=image_input,
+                    video_input=video_input)
                 input_ids = None
 
         hidden_states = self.language_model.model(