[V1] Refactor model executable interface for multimodal models (#10570)

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

vllm/model_executor/models/llava_next.py

@@ -19,6 +19,7 @@ from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import NestedTensors
 from vllm.sequence import IntermediateTensors, PoolerOutput
 from vllm.utils import is_list_of
 
@@ -565,6 +566,30 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
             for i, patch_features_batch in enumerate(patch_embeddings)
         ]
 
+    def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+        vision_embeddings = self._process_image_input(image_input)
+        return vision_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[NestedTensors] = None,
+    ) -> torch.Tensor:
+
+        if multimodal_embeddings is None:
+            return self.language_model.get_input_embeddings(input_ids)
+
+        inputs_embeds = embed_multimodal(
+            input_ids,
+            self.config.image_token_index,
+            self.language_model.model.get_input_embeddings,
+            multimodal_embeddings,
+        )
+        return inputs_embeds
+
     def forward(
         self,
         input_ids: torch.Tensor,
@@ -572,6 +597,7 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
         intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
         **kwargs: object,
     ) -> Union[torch.Tensor, IntermediateTensors]:
         """Run forward pass for LlaVA-NeXT.
@@ -620,24 +646,14 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
         """
         if intermediate_tensors is not None:
             inputs_embeds = None
-        else:
-            image_input = self._parse_and_validate_image_input(**kwargs)
 
-            if image_input is not None:
-                inputs_embeds = embed_multimodal(
-                    input_ids,
-                    self.config.image_token_index,
-                    self.language_model.model.get_input_embeddings,
-                    lambda _: self._process_image_input(image_input),
-                )
-            else:
-                inputs_embeds = self.language_model.model.get_input_embeddings(
-                    input_ids)
-
-        # always pass the input via `inputs_embeds`
-        # to make sure the computation graph is consistent
-        # for `torch.compile` integration
-        input_ids = None
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
 
         hidden_states = self.language_model.model(input_ids,
                                                   positions,
@@ -645,7 +661,6 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
                                                   attn_metadata,
                                                   intermediate_tensors,
                                                   inputs_embeds=inputs_embeds)
-
         return hidden_states
 
     def compute_logits(