Sync from v0.13

tests/models/multimodal/processing/test_phi4mm.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for phi4mm's multimodal preprocessing kwargs."""
+
+import pytest
+
+from vllm.multimodal import MULTIMODAL_REGISTRY
+
+from ....conftest import ImageTestAssets
+from ...utils import build_model_context
+
+
+@pytest.mark.parametrize("model_id", ["microsoft/Phi-4-multimodal-instruct"])
+@pytest.mark.parametrize(
+    ("mm_processor_kwargs", "expected_toks_per_img"),
+    [
+        ({"dynamic_hd": 4}, 1329),
+        ({"dynamic_hd": 16}, 4433),
+        # the default num_crops of phi-4-multimodal is 36
+        ({}, 9585),
+    ],
+)
+@pytest.mark.parametrize("num_imgs", [1, 2])
+@pytest.mark.parametrize("kwargs_on_init", [True, False])
+def test_processor_override(
+    image_assets: ImageTestAssets,
+    model_id: str,
+    mm_processor_kwargs: dict[str, int],
+    expected_toks_per_img: int,
+    num_imgs: int,
+    kwargs_on_init: bool,
+):
+    """Ensure Phi4MMMultiModalProcessor handles dynamic_hd properly."""
+    # Avoid initializing CUDA early
+    from vllm.model_executor.models.phi4mm import _IMAGE_PLACEHOLDER_TOKEN_ID
+
+    ctx = build_model_context(
+        model_id,
+        mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
+        limit_mm_per_prompt={"image": num_imgs},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+    hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
+
+    # 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"
+
+    image_size = ctx.get_hf_config().embd_layer["image_embd_layer"]["crop_size"]
+    dummy_image_size = (image_size * 7, image_size * 7)
+    dummy_image = image_assets[0].pil_image.resize(dummy_image_size)
+    mm_data = {"image": [dummy_image] * num_imgs}
+
+    processed_inputs = processor.apply(prompt, mm_data, hf_processor_mm_kwargs)
+
+    # Ensure we have the right number of placeholders per num_crops size
+    img_tok_count = processed_inputs["prompt_token_ids"].count(
+        _IMAGE_PLACEHOLDER_TOKEN_ID
+    )
+    assert img_tok_count == expected_toks_per_img * num_imgs