[Bugfix][Gemma 4] Don't read fft_length when profiling unified audio (#56721)

Signed-off-by: Abhinav Verma <verma.abhinav275@gmail.com>
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>

tests/models/multimodal/processing/test_gemma4_unified.py

@@ -6,9 +6,13 @@ from collections.abc import Mapping
 import pytest
 import torch
 from PIL import Image as PILImage
+from transformers.models.gemma4 import Gemma4AudioFeatureExtractor
 
 from vllm.exceptions import VLLMValidationError
-from vllm.model_executor.models.gemma4_mm import Gemma4ImagePixelInputs
+from vllm.model_executor.models.gemma4_mm import (
+    Gemma4ImagePixelInputs,
+    _dummy_audio_num_samples,
+)
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import MultiModalFieldConfig
 
@@ -204,3 +208,41 @@ def test_limit_mm_per_prompt(
             mm_items=processor.info.parse_mm_data(mm_data),
             hf_processor_mm_kwargs={},
         )
+
+
+def test_dummy_audio_length_falls_back_for_the_unified_extractor():
+    """The unified audio feature extractor does not define ``fft_length``.
+
+    ``Gemma4DummyInputsBuilder.get_dummy_mm_data`` read that attribute
+    unconditionally, so memory profiling raised ``AttributeError`` for every
+    unified Gemma 4 deployment in which audio is the modality with the largest
+    per-item token count -- which is the case as soon as ``audio_seq_length``
+    is raised above the video budget.
+    """
+
+    class _UnifiedFeatureExtractor:
+        sampling_rate = 16000
+
+    class _UnifiedProcessor:
+        feature_extractor = _UnifiedFeatureExtractor()
+        audio_seq_length = 3000
+        audio_ms_per_token = 40
+
+    # 3000 tokens * 40 ms = 120 s at 16 kHz
+    assert _dummy_audio_num_samples(_UnifiedProcessor()) == 120 * 16000
+
+
+def test_dummy_audio_length_still_uses_fft_length_for_the_tower_extractor():
+    """The mel/tower extractor keeps its existing behaviour."""
+
+    class _TowerFeatureExtractor(Gemma4AudioFeatureExtractor):
+        def __init__(self):  # noqa: D107 - bypass the real feature-extractor init
+            self.fft_length = 512
+            self.sampling_rate = 16000
+
+    class _TowerProcessor:
+        feature_extractor = _TowerFeatureExtractor()
+        audio_seq_length = 750
+        audio_ms_per_token = 40
+
+    assert _dummy_audio_num_samples(_TowerProcessor()) == 512

vllm/model_executor/models/gemma4_mm.py

@@ -24,6 +24,7 @@ from PIL import Image as PILImage
 from torch import nn
 from transformers import AutoModel, BatchFeature
 from transformers.models.gemma4 import (
+    Gemma4AudioFeatureExtractor,
     Gemma4Config,
     Gemma4Processor,
     Gemma4VisionConfig,
@@ -456,6 +457,28 @@ class Gemma4ProcessingInfo(BaseProcessingInfo):
 # ---------------------------------------------------------------------------
 
 
+def _dummy_audio_num_samples(processor: Any) -> int:
+    """Length in samples of a worst-case dummy audio item.
+
+    ``fft_length`` is set in ``Gemma4AudioFeatureExtractor.__init__`` and is not
+    defined by ``Gemma4UnifiedAudioFeatureExtractor``, which the encoder-free
+    Gemma 4 variant uses. Reading it unconditionally therefore raises
+    ``AttributeError`` during memory profiling on that variant, which is reached
+    whenever audio is the modality with the largest per-item token count.
+
+    For any other extractor, fall back to the audio budget the processor itself
+    declares -- the same pair of values used to warn about over-long audio
+    elsewhere in this file.
+    """
+    feature_extractor = processor.feature_extractor
+    if isinstance(feature_extractor, Gemma4AudioFeatureExtractor):
+        return feature_extractor.fft_length
+
+    sampling_rate = feature_extractor.sampling_rate
+    max_duration_s = processor.audio_seq_length * processor.audio_ms_per_token / 1000.0
+    return int(max_duration_s * sampling_rate)
+
+
 class Gemma4DummyInputsBuilder(BaseDummyInputsBuilder[Gemma4ProcessingInfo]):
     def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
         num_images = mm_counts.get("image", 0)
@@ -508,7 +531,7 @@ class Gemma4DummyInputsBuilder(BaseDummyInputsBuilder[Gemma4ProcessingInfo]):
         }
 
         if num_audios > 0:
-            audio_len = processor.feature_extractor.fft_length
+            audio_len = _dummy_audio_num_samples(processor)
             data["audio"] = self._get_dummy_audios(
                 length=audio_len,
                 num_audios=num_audios,