[Model] Use context managers for encoder- and LM-only mode (#32605)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/v1/worker/gpu_model_runner.py

@@ -65,7 +65,6 @@ from vllm.model_executor.models.interfaces import (
     SupportsXDRoPE,
     is_mixture_of_experts,
     supports_eagle3,
-    supports_mm_encoder_only,
     supports_mrope,
     supports_multimodal_pruning,
     supports_transcription,
@@ -4271,7 +4270,8 @@ class GPUModelRunner(
             remove_lora: If False, dummy LoRAs are not destroyed after the run
             activate_lora: If False, dummy_run is performed without LoRAs.
         """
-        if supports_mm_encoder_only(self.model):
+        mm_config = self.vllm_config.model_config.multimodal_config
+        if mm_config and mm_config.mm_encoder_only:
             # The current dummy run only covers LM execution, so we can skip it.
             # mm encoder dummy run may need to add in the future.
             return torch.tensor([]), torch.tensor([])
@@ -4558,7 +4558,8 @@ class GPUModelRunner(
         # like `inf` or `nan`.
         # To avoid breaking the sampler, we use a random tensor here instead.
 
-        if supports_mm_encoder_only(self.model):
+        mm_config = self.vllm_config.model_config.multimodal_config
+        if mm_config and mm_config.mm_encoder_only:
             # MM Encoder only model no need to run sampler.
             return torch.tensor([])
 
@@ -4687,7 +4688,8 @@ class GPUModelRunner(
         self,
         hidden_states: torch.Tensor,
     ) -> PoolerOutput:
-        if supports_mm_encoder_only(self.model):
+        mm_config = self.vllm_config.model_config.multimodal_config
+        if mm_config and mm_config.mm_encoder_only:
             # MM Encoder only model not need to run pooler.
             return torch.tensor([])