[Bugfix] Fix incorrect qwen2.5-vl attention mask pre-computation (#15200)

Signed-off-by: Isotr0py <2037008807@qq.com>

tests/models/decoder_only/vision_language/test_models.py

@@ -508,6 +508,19 @@ VLM_TEST_SETTINGS = {
             limit_mm_per_prompt={"image": 4},
         )],
     ),
+    # regression test for https://github.com/vllm-project/vllm/issues/15122
+    "qwen2_5_vl-windows-attention": VLMTestInfo(
+        models=["Qwen/Qwen2.5-VL-3B-Instruct"],
+        test_type=VLMTestType.CUSTOM_INPUTS,
+        max_model_len=4096,
+        max_num_seqs=2,
+        auto_cls=AutoModelForVision2Seq,
+        vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
+        custom_test_opts=[CustomTestOptions(
+            inputs=custom_inputs.windows_attention_image_qwen2_5_vl(),
+            limit_mm_per_prompt={"image": 1},
+        )],
+    ),
 }
 # yapf: enable
 

tests/models/decoder_only/vision_language/vlm_utils/custom_inputs.py

@@ -1,7 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 """Custom input builders for edge-cases in different models."""
+from io import BytesIO
 from typing import Callable
 
+import requests
+from PIL import Image
+
 from vllm.multimodal.image import rescale_image_size
 from vllm.multimodal.video import (rescale_video_size, resize_video,
                                    sample_frames_from_video)
@@ -102,3 +106,17 @@ def different_patch_input_cases_internvl():
         build_single_image_inputs(images, formatted_sprompts, wrapped_sf),
         build_multi_image_inputs([images], formatted_mprompts, wrapped_sf),
     ]
+
+
+def windows_attention_image_qwen2_5_vl():
+    # image from regression issue: https://github.com/vllm-project/vllm/issues/15122
+    image_url = "https://aomediacodec.github.io/av1-avif/testFiles/Link-U/hato.jpg"
+    image = Image.open(BytesIO(requests.get(image_url).content))
+
+    question = "Describe the image."
+    img_prompt = "<|vision_start|><|image_pad|><|vision_end|>"
+    prompt = (f"<|im_start|>User\n{img_prompt}{question}<|im_end|>\n"
+              "<|im_start|>assistant\n")
+
+    wrapped_sf = ImageSizeWrapper(type=SizeType.SIZE_FACTOR, data=[0.5])
+    return build_single_image_inputs([image], [prompt], wrapped_sf)