Refactor multimodal processing imports and update configuration handling in Qwen3OmniMoeThinker

- Changed the import path for multimodal processing to improve module organization.
- Removed the unused multimodal configuration parameter from the Qwen3OmniMoeThinker initialization, streamlining the class definition.

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

vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_thinker.py

@@ -82,7 +82,7 @@ from vllm.model_executor.models.vision import (
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import MultiModalFeatureSpec, MultiModalKwargsItems
 from vllm.multimodal.parse import AudioProcessorItems, MultiModalDataItems
-from vllm.multimodal.processing import (
+from vllm.multimodal.processing.processor import (
     MultiModalPromptUpdates,
     PlaceholderFeaturesInfo,
     PromptReplacement,
@@ -674,7 +674,6 @@ class Qwen3OmniMoeThinkerForConditionalGeneration(
             norm_eps=getattr(thinker_config.text_config, "rms_norm_eps", 1e-6),
             quant_config=quant_config,
             prefix=maybe_prefix(prefix, "visual"),
-            multimodal_config=multimodal_config,
         )
         self.quant_config = quant_config
 

vllm_omni/worker/gpu_generation_model_runner.py

@@ -20,7 +20,6 @@ from vllm.forward_context import set_forward_context
 from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
     RoutedExpertsCapturer,
 )
-from vllm.model_executor.models.interfaces import supports_mm_encoder_only
 from vllm.utils.math_utils import cdiv
 from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
 from vllm.v1.outputs import AsyncModelRunnerOutput, make_empty_encoder_model_runner_output
@@ -435,7 +434,8 @@ class GPUGenerationModelRunner(OmniGPUModelRunner):
             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([])