Refactor GPU model runner to streamline multimodal configuration handling

- Removed the unused import of supports_mm_encoder_only from interfaces.
- Updated the dummy run check to utilize the multimodal configuration directly, enhancing clarity and future extensibility.

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

vllm_omni/worker/gpu_model_runner.py

@@ -8,7 +8,7 @@ from vllm.distributed.parallel_state import get_pp_group
 from vllm.forward_context import set_forward_context
 from vllm.logger import init_logger
 from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
-from vllm.model_executor.models.interfaces import supports_mm_encoder_only, supports_mrope
+from vllm.model_executor.models.interfaces import supports_mrope
 from vllm.model_executor.models.interfaces_base import VllmModelForPooling
 from vllm.sampling_params import SamplingType
 from vllm.utils.import_utils import LazyLoader
@@ -447,7 +447,8 @@ class OmniGPUModelRunner(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([])