[Bugfix] Standardize custom HF Processor init (#37289)

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

vllm/model_executor/models/deepseek_ocr.py

@@ -196,8 +196,10 @@ class DeepseekOCRProcessingInfo(BaseProcessingInfo):
             crop_mode=CROP_MODE,
             strategy="v1",
         )
+
         return self.ctx.get_hf_processor(
-            DeepseekOCRProcessor, **{**kwargs, **v1_processor_config}
+            DeepseekOCRProcessor,
+            **{**v1_processor_config, **kwargs},
         )
 
     def get_supported_mm_limits(self) -> Mapping[str, int | None]:

vllm/model_executor/models/deepseek_ocr2.py

@@ -76,8 +76,10 @@ class DeepseekOCR2ProcessingInfo(BaseProcessingInfo):
             crop_mode=CROP_MODE,
             strategy="v2",
         )
+
         return self.ctx.get_hf_processor(
-            DeepseekOCRProcessor, **{**kwargs, **v2_processor_config}
+            DeepseekOCRProcessor,
+            **{**v2_processor_config, **kwargs},
         )
 
     def get_supported_mm_limits(self) -> Mapping[str, int | None]:

vllm/model_executor/models/glm4v.py

@@ -47,7 +47,10 @@ from vllm.multimodal.processing import (
 )
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs.chatglm import ChatGLMConfig
-from vllm.transformers_utils.processors.glm4v import GLM4VProcessor
+from vllm.transformers_utils.processors.glm4v import (
+    GLM4VImageProcessorFast,
+    GLM4VProcessor,
+)
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .chatglm import ChatGLMBaseModel, ChatGLMModel, GLMTransformer
@@ -387,15 +390,20 @@ class GLM4VProcessingInfo(BaseProcessingInfo):
     def get_hf_config(self):
         return self.ctx.get_hf_config(ChatGLMConfig)
 
-    def get_hf_processor(self, **kwargs: object) -> GLM4VProcessor:
+    def get_image_processor(self, **kwargs):
         config = self.get_hf_config()
         vision_config = config.vision_config
+
         image_size = vision_config["image_size"]
+        kwargs.setdefault("size", {"width": image_size, "height": image_size})
+
+        return GLM4VImageProcessorFast(**kwargs)
 
+    def get_hf_processor(self, **kwargs: object) -> GLM4VProcessor:
         return self.ctx.init_processor(
             GLM4VProcessor,
             tokenizer=self.get_tokenizer(),
-            **{**kwargs, "image_size": image_size},
+            image_processor=self.get_image_processor(**kwargs),
         )
 
     def get_supported_mm_limits(self) -> Mapping[str, int | None]:

vllm/model_executor/models/qwen_vl.py

@@ -44,7 +44,10 @@ from vllm.multimodal.processing import (
     PromptUpdateDetails,
 )
 from vllm.sequence import IntermediateTensors
-from vllm.transformers_utils.processors.qwen_vl import QwenVLProcessor
+from vllm.transformers_utils.processors.qwen_vl import (
+    QwenVLImageProcessorFast,
+    QwenVLProcessor,
+)
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .interfaces import (
@@ -432,15 +435,20 @@ class QwenVLModel(QWenModel):
 
 
 class QwenVLProcessingInfo(BaseProcessingInfo):
-    def get_hf_processor(self, **kwargs: object) -> QwenVLProcessor:
+    def get_image_processor(self, **kwargs):
         config = self.get_hf_config()
         vision_config = config.visual
+
         image_size = vision_config["image_size"]
+        kwargs.setdefault("size", {"width": image_size, "height": image_size})
+
+        return QwenVLImageProcessorFast(**kwargs)
 
+    def get_hf_processor(self, **kwargs: object) -> QwenVLProcessor:
         return self.ctx.init_processor(
             QwenVLProcessor,
             tokenizer=self.get_tokenizer(),
-            **{**kwargs, "image_size": image_size},
+            image_processor=self.get_image_processor(**kwargs),
         )
 
     def get_supported_mm_limits(self) -> Mapping[str, int | None]:

vllm/tokenizers/qwen_vl.py

@@ -61,6 +61,10 @@ def get_qwen_vl_tokenizer(tokenizer: HfTokenizer) -> HfTokenizer:
 
 
 class QwenVLTokenizer(TokenizerLike):
+    image_start_tag: str
+    image_end_tag: str
+    image_pad_tag: str
+
     @classmethod
     def from_pretrained(cls, *args, **kwargs) -> HfTokenizer:
         tokenizer = AutoTokenizer.from_pretrained(*args, **kwargs)

vllm/transformers_utils/processors/glm4v.py

@@ -29,13 +29,8 @@ class GLM4VProcessor(ProcessorMixin):
 
     def __init__(
         self,
+        image_processor: GLM4VImageProcessorFast,
         tokenizer: PreTrainedTokenizer,
-        image_size: int,
-        image_processor: GLM4VImageProcessorFast | None = None,
     ) -> None:
-        self.tokenizer = tokenizer
-        if image_processor is None:
-            image_processor = GLM4VImageProcessorFast(
-                size={"width": image_size, "height": image_size}
-            )
         self.image_processor = image_processor
+        self.tokenizer = tokenizer

vllm/transformers_utils/processors/qwen_vl.py

@@ -31,25 +31,12 @@ class QwenVLProcessor(ProcessorMixin):
 
     def __init__(
         self,
+        image_processor: QwenVLImageProcessorFast,
         tokenizer: QwenVLTokenizer,
-        image_size: int,
-        image_processor: QwenVLImageProcessorFast | None = None,
     ) -> None:
-        self.tokenizer = tokenizer
-        if image_processor is None:
-            image_processor = QwenVLImageProcessorFast(
-                size={"width": image_size, "height": image_size}
-            )
         self.image_processor = image_processor
+        self.tokenizer = tokenizer
 
-    @property
-    def image_start_tag(self) -> str:
-        return self.tokenizer.image_start_tag  # type: ignore[attr-defined]
-
-    @property
-    def image_end_tag(self) -> str:
-        return self.tokenizer.image_end_tag  # type: ignore[attr-defined]
-
-    @property
-    def image_pad_tag(self) -> str:
-        return self.tokenizer.image_pad_tag  # type: ignore[attr-defined]
+        self.image_start_tag = tokenizer.image_start_tag
+        self.image_end_tag = tokenizer.image_end_tag
+        self.image_pad_tag = tokenizer.image_pad_tag