[V1] Fix multimodal profiling for Molmo (#11325)

Signed-off-by: ywang96 <ywang@example.com>
Co-authored-by: ywang96 <ywang@example.com>

vllm/model_executor/models/molmo.py

@@ -928,7 +928,11 @@ def image_input_mapper_for_molmo(
     data: object,
 ):
     if isinstance(data, list):
+        assert len(data) == 1, "Molmo supports only one image per prompt."
         data = data[0]
+
+    # Remove unused dummy PIL image
+    data.pop('raw_mm_data', None)
     return MultiModalKwargs(data)
 
 
@@ -974,6 +978,7 @@ def dummy_data_for_molmo(ctx: InputContext, seq_len: int,
     dummy_imgdata = {
         "images": out["images"],
         "image_input_idx": out["image_input_idx"],
+        "raw_mm_data": dummy_image,
     }
     if "image_masks" in out:
         dummy_imgdata["image_masks"] = out["image_masks"]

vllm/v1/engine/mm_input_mapper.py

@@ -151,17 +151,31 @@ class MMHasher:
     def __init__(self):
         pass
 
-    def hash_mm_data(
+    def hash_dummy_mm_data(
             self,
             mm_data: Optional[MultiModalDataDict]) -> Optional[List[str]]:
+        """Hash user-defined dummy multimodal data used for profiling."""
+
         if mm_data is None:
             return None
 
         image_inputs = mm_data['image']
 
+        # This is a temporary workaround for models (e.g, Molmo) that
+        # process multimodal data in the input processor (therefore
+        # image_inputs is MultiModalKwargs instead of raw input format).
+        # `raw_mm_data` with the original input format is expected
+        # in this case.
+        if isinstance(image_inputs, dict):
+            assert "raw_mm_data" in image_inputs and isinstance(
+                image_inputs["raw_mm_data"], PIL.Image.Image)
+            image_inputs = image_inputs.pop("raw_mm_data")
+
         return self.hash_images(image_inputs)
 
-    def hash_prompt(self, prompt: PromptType) -> Optional[List[str]]:
+    def hash_prompt_mm_data(self, prompt: PromptType) -> Optional[List[str]]:
+        """Hash multimodal data in the user input prompt if they exist."""
+
         if "multi_modal_data" not in prompt:
             return None
 
@@ -171,6 +185,7 @@ class MMHasher:
         return self.hash_images(image_inputs)
 
     def hash_images(self, image_inputs) -> Optional[List[str]]:
+        """Hash PIL image objects to strings."""
         if not isinstance(image_inputs, list):
             image_inputs = [image_inputs]
         assert len(image_inputs) > 0

vllm/v1/engine/processor.py

@@ -79,7 +79,7 @@ class Processor:
         # Compute MM hashes (if enabled)
         mm_hashes = None
         if self.use_hash:
-            mm_hashes = self.mm_hasher.hash_prompt(prompt)
+            mm_hashes = self.mm_hasher.hash_prompt_mm_data(prompt)
 
         # Process inputs.
         preprocessed_inputs = self.input_preprocessor.preprocess(

vllm/v1/worker/gpu_model_runner.py

@@ -638,7 +638,7 @@ class GPUModelRunner:
             # Compute MM hashes (if enabled)
             mm_hashes = None
             if self.use_hash:
-                mm_hashes = self.mm_hasher.hash_mm_data(dummy_mm_data)
+                mm_hashes = self.mm_hasher.hash_dummy_mm_data(dummy_mm_data)
 
             dummy_mm_kwargs = self.mm_input_mapper_client.process_inputs(
                 mm_data=dummy_mm_data,