[CI/Build] Move model-specific multi-modal processing tests (#11934)

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

tests/models/multimodal/processing/test_phi3v.py

@@ -0,0 +1,55 @@
+"""Tests for phi3v's multimodal preprocessing kwargs."""
+import pytest
+
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.utils import cached_get_tokenizer
+
+from ....conftest import _ImageAssets
+from ...utils import build_model_context
+
+
+@pytest.mark.parametrize("model_id", ["microsoft/Phi-3.5-vision-instruct"])
+# yapf: disable
+@pytest.mark.parametrize(
+    ("mm_processor_kwargs", "expected_toks_per_img"),
+    [
+        ({"num_crops": 4}, 757),
+        ({"num_crops": 16}, 1921),
+        # the default num_crops of phi-3.5-vision is 4
+        ({}, 757),
+    ])
+# yapf: enable
+@pytest.mark.parametrize("num_imgs", [1, 2])
+def test_processor_override(
+    image_assets: _ImageAssets,
+    model_id: str,
+    mm_processor_kwargs: dict[str, int],
+    expected_toks_per_img: int,
+    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 = 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}
+
+    processed_inputs = processor.apply(prompt, mm_data, mm_processor_kwargs)
+
+    # Ensure we have the right number of placeholders per num_crops size
+    img_tok_count = processed_inputs["prompt_token_ids"].count(_IMAGE_TOKEN_ID)
+    assert img_tok_count == expected_toks_per_img * num_imgs