[V1] Update interface for idefics3

vllm/model_executor/models/idefics3.py

@@ -39,6 +39,7 @@
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
 from vllm.multimodal.image import cached_get_image_processor
+from vllm.multimodal.inputs import NestedTensors
 from vllm.sequence import IntermediateTensors, SequenceData
 from vllm.transformers_utils.processor import cached_get_processor
 from vllm.utils import is_list_of
@@ -597,33 +598,21 @@ def _process_image_input(self, image_input: ImageInputs) -> torch.Tensor:
         image_features = self._process_image_pixels(image_input)
         return self.connector(image_features)
 
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        return self.text_model.get_input_embeddings(input_ids)
+
     def forward(
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
         intermediate_tensors: Optional[IntermediateTensors] = None,
-        **kwargs: object,
+        inputs_embeds: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, IntermediateTensors]:
-        if intermediate_tensors is not None:
-            input_ids = None
-            inputs_embeds = None
-        else:
-            # always pass the input via `inputs_embeds`
-            # to make sure the computation graph is consistent
-            image_input = self._parse_and_validate_image_input(**kwargs)
-
-            if image_input is not None:
-                vision_embeddings = self._process_image_input(image_input)
-                inputs_embeds = self.text_model.get_input_embeddings(input_ids)
-
-                inputs_embeds = merge_multimodal_embeddings(
-                    input_ids, inputs_embeds, vision_embeddings,
-                    self.image_token_id)
-            else:
-                inputs_embeds = self.text_model.get_input_embeddings(input_ids)
-            input_ids = None
 
         hidden_states = self.text_model(
             input_ids,
@@ -718,23 +707,53 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.logits_processor = LogitsProcessor(config.text_config.vocab_size)
         self.sampler = Sampler()
 
+    def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
+        image_input = self.model._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+        vision_embeddings = self.model._process_image_input(image_input)
+        return vision_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[NestedTensors] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                self.config.image_token_id)
+        return inputs_embeds
+
     def forward(
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
         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]:
-        hidden_states = self.model(
-            input_ids,
-            positions,
-            kv_caches,
-            attn_metadata,
-            intermediate_tensors,
-            **kwargs,
-        )
+        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.
+        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.model.text_model(input_ids,
+                                              positions,
+                                              kv_caches,
+                                              attn_metadata,
+                                              intermediate_tensors,
+                                              inputs_embeds=inputs_embeds)
+
         return hidden_states
 
     def compute_logits(self, hidden_states: torch.Tensor,