add qwen3

vllm-v0.6.2/vllm/v1/engine/mm_input_mapper.py

@@ -0,0 +1,39 @@
+from typing import Any, Dict, List, Optional
+
+from vllm.config import ModelConfig
+from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalDataDict,
+                             MultiModalKwargs, MultiModalRegistry)
+
+
+class MMInputMapper:
+
+    def __init__(
+        self,
+        model_config: ModelConfig,
+        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
+    ):
+        self.mm_registry = mm_registry
+        self.multi_modal_input_mapper = mm_registry.create_input_mapper(
+            model_config)
+        self.mm_registry.init_mm_limits_per_prompt(model_config)
+
+    def process_inputs(
+        self,
+        mm_data: MultiModalDataDict,
+        mm_processor_kwargs: Optional[Dict[str, Any]],
+    ) -> List[MultiModalKwargs]:
+        image_inputs = mm_data["image"]
+        if not isinstance(image_inputs, list):
+            image_inputs = [image_inputs]
+
+        # Process each image input separately so that later we can schedule
+        # them in a fine-grained manner.
+        mm_inputs: List[MultiModalKwargs] = []
+        num_images = len(image_inputs)
+        for i in range(num_images):
+            mm_input = self.multi_modal_input_mapper(
+                {"image": [image_inputs[i]]},
+                mm_processor_kwargs=mm_processor_kwargs,
+            )
+            mm_inputs.append(mm_input)
+        return mm_inputs