[VLM] Reorganize profiling/processing-related code (#11812)

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

tests/models/decoder_only/vision_language/processing/test_phi3v.py

@@ -1,21 +1,13 @@
 """Tests for phi3v's multimodal preprocessing kwargs."""
 import pytest
-from transformers import AutoTokenizer
 
-from vllm.inputs import InputProcessingContext
-from vllm.model_executor.models.phi3v import _IMAGE_TOKEN_ID
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.utils import cached_get_tokenizer
 
 from .....conftest import _ImageAssets
 from ....utils import build_model_context
 
 
-# Wrap lazy imports to avoid initializing CUDA during test collection
-@pytest.fixture()
-def processor_for_phi3v():
-    from vllm.model_executor.models.phi3v import Phi3VMultiModalProcessor
-    return Phi3VMultiModalProcessor
-
-
 @pytest.mark.parametrize("model_id", ["microsoft/Phi-3.5-vision-instruct"])
 # yapf: disable
 @pytest.mark.parametrize(
@@ -29,7 +21,6 @@ def processor_for_phi3v():
 # yapf: enable
 @pytest.mark.parametrize("num_imgs", [1, 2])
 def test_processor_override(
-    processor_for_phi3v,
     image_assets: _ImageAssets,
     model_id: str,
     mm_processor_kwargs: dict[str, int],
@@ -37,21 +28,26 @@ def test_processor_override(
     num_imgs: int,
 ):
     """Ensure input_processor_for_phi3v handles num_crops properly."""
+    # Avoid initializing CUDA early
+    from vllm.model_executor.models.phi3v import _IMAGE_TOKEN_ID
+
     ctx = build_model_context(
         model_name=model_id,
         tokenizer_name=model_id,
         trust_remote_code=True,
         limit_mm_per_prompt={"image": num_imgs},
     )
-    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
-    ctx = InputProcessingContext(ctx.model_config, tokenizer)
+    tokenizer = cached_get_tokenizer(ctx.model_config.tokenizer)
+    processor = MULTIMODAL_REGISTRY.create_processor(
+        ctx.model_config,
+        tokenizer=tokenizer,
+    )
 
     # Build the image str / prompt based on the number of images we pass
     img_str = "".join([f"<|image_{idx}|>\n" for idx in range(1, num_imgs + 1)])
     prompt = f"<|user|>\n{img_str}<|end|>\n<|assistant|>\n"
     mm_data = {"image": [image_assets[0].pil_image] * num_imgs}
 
-    processor = processor_for_phi3v(ctx)
     processed_inputs = processor.apply(prompt, mm_data, mm_processor_kwargs)
 
     # Ensure we have the right number of placeholders per num_crops size