[V1] Support VLMs with fine-grained scheduling (#9871)

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
Co-authored-by: Roger Wang <ywang@roblox.com>

vllm/model_executor/models/gpt2.py

@@ -216,9 +216,11 @@ class GPT2Model(nn.Module):
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
         intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor],
     ) -> Union[torch.Tensor, IntermediateTensors]:
         if get_pp_group().is_first_rank:
-            inputs_embeds = self.wte(input_ids)
+            if inputs_embeds is None:
+                inputs_embeds = self.wte(input_ids)
             position_embeds = self.wpe(position_ids)
             hidden_states = inputs_embeds + position_embeds
         else:
@@ -263,6 +265,9 @@ class GPT2LMHeadModel(nn.Module, SupportsPP):
         self.make_empty_intermediate_tensors = (
             self.transformer.make_empty_intermediate_tensors)
 
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.transformer.wte(input_ids)
+
     def forward(
         self,
         input_ids: torch.Tensor,
@@ -270,9 +275,11 @@ class GPT2LMHeadModel(nn.Module, SupportsPP):
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
         intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, IntermediateTensors]:
         hidden_states = self.transformer(input_ids, positions, kv_caches,
-                                         attn_metadata, intermediate_tensors)
+                                         attn_metadata, intermediate_tensors,
+                                         inputs_embeds)
         return hidden_states
 
     def compute_logits(