[Bugfix] Fix check_interleaved_audio_video false positive for batched non-interleaved requests (#35487)

Signed-off-by: linyueqian <linyueqian@outlook.com>
Co-authored-by: Roger Wang <hey@rogerw.io>

tests/models/multimodal/processing/test_qwen2_5_omni_embed.py

@@ -116,6 +116,32 @@ class TestCheckInterleavedAudioVideo:
             is_video, is_audio, is_video.sum().item(), is_audio.sum().item()
         )
 
+    def test_batched_non_interleaved_no_false_positive(self):
+        """
+        Regression test for https://github.com/vllm-project/vllm/issues/35394.
+
+        5 identical non-interleaved mixed-modality requests batched together:
+        each has [audio][image][video] in separate blocks with text between them.
+        Across the batch, audio from request N falls between video blocks of
+        request N and request N+1, causing the global ranges to overlap.
+        check_interleaved_audio_video must return False (not a false positive).
+        """
+        # Build one request: [text][audio*5][text][image*4][text][video*6][text]
+        single_ids, _ = make_token_seq(5, 4, 6)
+        # Batch 5 identical requests (separated by text tokens to simulate padding)
+        sep = torch.tensor([TEXT_TOKEN_ID] * 3)
+        batched_ids = torch.cat([single_ids, sep] * 5)
+        is_multimodal = (
+            (batched_ids == AUDIO_TOKEN_ID)
+            | (batched_ids == IMAGE_TOKEN_ID)
+            | (batched_ids == VIDEO_TOKEN_ID)
+        )
+        is_video = is_multimodal & (batched_ids == VIDEO_TOKEN_ID)
+        is_audio = is_multimodal & (batched_ids == AUDIO_TOKEN_ID)
+        assert not check_interleaved_audio_video(
+            is_video, is_audio, is_video.sum().item(), is_audio.sum().item()
+        ), "Batched non-interleaved requests should not be detected as interleaved"
+
 
 # ---------------------------------------------------------------------------
 # Tests for embed_input_ids via a minimal mock

vllm/model_executor/models/qwen2_5_omni_thinker.py

@@ -122,8 +122,17 @@ def check_interleaved_audio_video(
     """
     Check if video and audio positions are interleaved in the multimodal region.
 
-    Returns:
-        True if video and audio tokens are interleaved, False otherwise.
+    Returns True only for the use_audio_in_video=True case, where video and
+    audio tokens alternate within a single contiguous region with no gaps.
+
+    A simple range-overlap check produces false positives when multiple
+    non-interleaved requests are batched together: audio tokens from request N
+    fall between video tokens from request N and request N+1, making the
+    global ranges overlap even though each individual request is non-interleaved.
+
+    To distinguish true interleaving from this batching artefact we require
+    that every position in the combined [first_VA, last_VA] range is occupied
+    by either a video or an audio token (no text/image gaps).
     """
     if num_video == 0 or num_audio == 0:
         return False
@@ -131,10 +140,22 @@ def check_interleaved_audio_video(
     video_pos = is_video.nonzero(as_tuple=True)[0]
     audio_pos = is_audio.nonzero(as_tuple=True)[0]
 
-    return (
+    # Quick range-overlap pre-check (necessary but not sufficient).
+    if not (
         video_pos[0].item() < audio_pos[-1].item()
         and audio_pos[0].item() < video_pos[-1].item()
-    )
+    ):
+        return False
+
+    # Density check: for true use_audio_in_video interleaving every position
+    # in the combined span is a video or audio token.  Batched non-interleaved
+    # requests have text/image tokens between the per-request V and A blocks.
+    # combined_start/end encompass all V/A tokens, so num_video + num_audio
+    # equals the number of V/A tokens in range; compare directly to span size.
+    combined_start = min(video_pos[0].item(), audio_pos[0].item())
+    combined_end = max(video_pos[-1].item(), audio_pos[-1].item())
+    total_in_range = combined_end - combined_start + 1
+    return (num_video + num_audio) == total_in_range
 
 
 def merge_interleaved_embeddings(