add qwen3

vllm-v0.6.2/vllm/multimodal/image.py

@@ -0,0 +1,86 @@
+from functools import lru_cache
+from typing import TYPE_CHECKING, Any, Dict, Optional
+
+import torch
+from PIL import Image
+
+from vllm.inputs.registry import InputContext
+from vllm.logger import init_logger
+from vllm.transformers_utils.processor import get_image_processor
+from vllm.utils import is_list_of
+
+from .base import MultiModalPlugin
+from .inputs import ImageItem, MultiModalData, MultiModalKwargs
+
+if TYPE_CHECKING:
+    from vllm.config import ModelConfig
+
+logger = init_logger(__name__)
+
+cached_get_image_processor = lru_cache(get_image_processor)
+
+
+class ImagePlugin(MultiModalPlugin):
+    """Plugin for image data."""
+
+    def get_data_key(self) -> str:
+        return "image"
+
+    def _get_hf_image_processor(
+        self,
+        model_config: "ModelConfig",
+        mm_processor_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        if mm_processor_kwargs is None:
+            mm_processor_kwargs = {}
+        return cached_get_image_processor(
+            model_config.model,
+            trust_remote_code=model_config.trust_remote_code,
+            **mm_processor_kwargs)
+
+    def _default_input_mapper(
+        self,
+        ctx: InputContext,
+        data: MultiModalData[ImageItem],
+        **mm_processor_kwargs,
+    ) -> MultiModalKwargs:
+        model_config = ctx.model_config
+
+        # PIL image
+        if isinstance(data, Image.Image) or is_list_of(data, Image.Image):
+            image_processor = self._get_hf_image_processor(
+                model_config,
+                mm_processor_kwargs,
+            )
+
+            if image_processor is None:
+                raise RuntimeError("No HuggingFace processor is available "
+                                   "to process the image object")
+            try:
+                # NOTE: It may make sense to forward the mm_processor_kwargs
+                # here too. For now, to keep it simple, we only allow it be
+                # used for the initialization call though, just in case the
+                # signatures of the preprocessor initializer don't match
+                # preprocess()
+                batch_data = image_processor \
+                    .preprocess(data, return_tensors="pt") \
+                    .data
+            except Exception:
+                logger.error(
+                    "Failed to process image (%s) with the default mapper. "
+                    "This is most likely an edge-case with this model's image "
+                    "processor in transformers (type: %s), and not vLLM.",
+                    data,
+                    type(image_processor).__name__)
+                raise
+
+            return MultiModalKwargs(batch_data)
+
+        # Image embedding
+        elif isinstance(data, torch.Tensor) or is_list_of(data, torch.Tensor):
+            return MultiModalKwargs({"image_embeds": data})
+
+        raise TypeError(f"Invalid image type: {type(data)}")
+
+    def _default_max_multimodal_tokens(self, ctx: InputContext) -> int:
+        return 3000