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[Fix] Introduce audio channels spec (#31595)

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Signed-off-by: Jeremy Teboul <jeremyte@meta.com>
This commit is contained in:
Jeremy Teboul
2026-01-09 11:34:51 -08:00
committed by GitHub
parent 308feab33f
commit 657e9c0e18
9 changed files with 717 additions and 189 deletions
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189
tests/models/multimodal/processing/test_qwen3_omni.py
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@@ -28,10 +28,9 @@ def test_processor_with_audio_sample_rate(
"""
Test that vLLM's processor generates expected outputs with audio_sample_rate.
This validates the reviewer's request that we test the actual processor
can handle different audio_sample_rate values and generate audio tokens.
This validates that the processor correctly handles audio_sample_rate
passed via hf_processor_mm_kwargs and generates audio tokens.
"""
# Setup: Build model context and processor
ctx = build_model_context(
model_id,
limit_mm_per_prompt={"audio": 1, "image": 0, "video": 0},
@@ -48,18 +47,17 @@ def test_processor_with_audio_sample_rate(
prompt = "<|audio_start|><|audio_pad|><|audio_end|>"
mm_data = {"audio": [(audio_data, audio_sample_rate)]}
# Execute: Apply processor with audio_sample_rate in mm_kwargs
# Apply processor with audio_sample_rate in mm_kwargs
hf_processor_mm_kwargs: dict[str, Any] = {
"audio_sample_rate": audio_sample_rate,
}
processed_inputs = processor.apply(prompt, mm_data, hf_processor_mm_kwargs)
# Assert: Verify audio tokens are generated
# Verify audio tokens are generated
hf_processor = processor.info.get_hf_processor(**hf_processor_mm_kwargs)
audio_token_id = tokenizer.convert_tokens_to_ids(hf_processor.audio_token)
aud_tok_count = processed_inputs["prompt_token_ids"].count(audio_token_id)
# Audio should generate at least 1 token
assert aud_tok_count >= 1, (
f"Expected at least 1 audio token but got {aud_tok_count}. "
f"sample_rate: {audio_sample_rate}Hz, duration: {audio_duration_sec}s"
@@ -97,189 +95,10 @@ def test_longer_audio_generates_more_tokens(model_id: str) -> None:
audio_token_id = tokenizer.convert_tokens_to_ids(hf_proc.audio_token)
return processed["prompt_token_ids"].count(audio_token_id)
# Get token counts for different durations
short_tokens = get_token_count(1.0)
long_tokens = get_token_count(2.0)
# Longer audio should produce more tokens
assert long_tokens > short_tokens, (
f"Expected longer audio (2s) to have more tokens than shorter (1s). "
f"Got short={short_tokens}, long={long_tokens}"
)
class TestQwen3OmniAudioSampleRatePreservation:
"""Test that audio_sample_rate is preserved during kwargs restructuring.
These tests validate the fix for the audio_sample_rate bug in Qwen3 Omni
where the parameter was lost during kwargs restructuring.
"""
@staticmethod
def _process_kwargs(
mm_kwargs: dict[str, Any],
tok_kwargs: dict[str, Any],
transformers_version: str = "4.57.0",
) -> dict[str, Any]:
"""
Helper method to simulate kwargs processing logic from production code.
This method simulates the kwargs restructuring that happens in the
Qwen3 Omni model when transformers < 4.58.0. By centralizing this
logic, we make tests easier to maintain if the production logic changes.
Args:
mm_kwargs: Multimodal kwargs (e.g., audio_sample_rate, truncation)
tok_kwargs: Tokenizer kwargs (e.g., truncation)
transformers_version: Version string to test against (default: "4.57.0")
Returns:
Processed kwargs dictionary with restructured audio_kwargs and text_kwargs
"""
from packaging.version import Version
mm_kwargs_copy = dict(mm_kwargs)
tok_kwargs_copy = dict(tok_kwargs)
if Version(transformers_version) < Version("4.58.0"):
# Extract audio_sample_rate before restructuring (THE FIX)
audio_sample_rate = mm_kwargs_copy.pop("audio_sample_rate", None)
# Restructure kwargs
mm_kwargs_copy["audio_kwargs"] = {
"truncation": mm_kwargs_copy.pop("truncation", False)
}
mm_kwargs_copy["text_kwargs"] = {
"truncation": tok_kwargs_copy.pop("truncation", False)
}
# Put audio_sample_rate into audio_kwargs (THE FIX)
if audio_sample_rate is not None:
mm_kwargs_copy["audio_kwargs"]["audio_sample_rate"] = audio_sample_rate
return mm_kwargs_copy
def test_audio_sample_rate_preserved_in_audio_kwargs(self) -> None:
"""
Test that audio_sample_rate is moved from top-level mm_kwargs
into audio_kwargs during kwargs restructuring.
This is the core fix: when transformers < 4.58.0, the code
restructures kwargs into audio_kwargs and text_kwargs, and
audio_sample_rate must be preserved in audio_kwargs.
"""
# Setup: Create mm_kwargs with audio_sample_rate at top level
mm_kwargs: dict[str, Any] = {
"audio_sample_rate": 16000,
"truncation": True,
}
tok_kwargs: dict[str, Any] = {
"truncation": False,
}
# Execute: Process kwargs using helper method
result = self._process_kwargs(mm_kwargs, tok_kwargs)
# Assert: Verify audio_sample_rate is in audio_kwargs
assert "audio_kwargs" in result
assert "audio_sample_rate" in result["audio_kwargs"]
assert result["audio_kwargs"]["audio_sample_rate"] == 16000
# Assert: Verify truncation is also in audio_kwargs
assert result["audio_kwargs"]["truncation"] is True
# Assert: Verify text_kwargs is created correctly
assert "text_kwargs" in result
assert result["text_kwargs"]["truncation"] is False
def test_audio_sample_rate_absent_when_not_provided(self) -> None:
"""
Test that when audio_sample_rate is not provided in mm_kwargs,
the restructured audio_kwargs doesn't contain it.
"""
# Setup: Create mm_kwargs WITHOUT audio_sample_rate
mm_kwargs: dict[str, Any] = {
"truncation": True,
}
tok_kwargs: dict[str, Any] = {
"truncation": False,
}
# Execute: Process kwargs using helper method
result = self._process_kwargs(mm_kwargs, tok_kwargs)
# Assert: Verify audio_sample_rate is NOT in audio_kwargs
assert "audio_kwargs" in result
assert "audio_sample_rate" not in result["audio_kwargs"]
# Assert: Verify truncation is still in audio_kwargs
assert result["audio_kwargs"]["truncation"] is True
@pytest.mark.parametrize("sample_rate", [8000, 16000, 22050, 24000, 44100, 48000])
def test_various_audio_sample_rates_preserved(self, sample_rate: int) -> None:
"""
Test that various common audio sample rates are preserved.
Common sample rates:
- 8000: Telephone quality
- 16000: Wideband speech (Qwen3 Omni default)
- 22050: Low-quality audio
- 24000: High-quality speech
- 44100: CD quality
- 48000: Professional audio
"""
# Setup: Create mm_kwargs with specific sample rate
mm_kwargs: dict[str, Any] = {
"audio_sample_rate": sample_rate,
"truncation": True,
}
tok_kwargs: dict[str, Any] = {"truncation": False}
# Execute: Process kwargs using helper method
result = self._process_kwargs(mm_kwargs, tok_kwargs)
# Assert: Verify the specific sample rate is preserved
assert result["audio_kwargs"]["audio_sample_rate"] == sample_rate
def test_kwargs_unchanged_for_newer_transformers_version(self) -> None:
"""
Test that kwargs structure remains unchanged for transformers >= 4.58.0.
This test ensures that when transformers version is 4.58.0 or higher,
the kwargs restructuring is bypassed and audio_sample_rate remains
at the top level as originally passed.
"""
from packaging.version import Version
# Setup: Create mm_kwargs with audio_sample_rate at top level
mm_kwargs: dict[str, Any] = {
"audio_sample_rate": 16000,
"truncation": True,
}
tok_kwargs: dict[str, Any] = {
"truncation": False,
}
# Execute: Simulate with transformers >= 4.58.0
mm_kwargs_copy = dict(mm_kwargs)
tok_kwargs_copy = dict(tok_kwargs)
transformers_ver = "4.58.0" # Version that bypasses restructuring
if Version(transformers_ver) < Version("4.58.0"):
# This block should NOT execute for >= 4.58.0
audio_sample_rate = mm_kwargs_copy.pop("audio_sample_rate", None)
mm_kwargs_copy["audio_kwargs"] = {
"truncation": mm_kwargs_copy.pop("truncation", False)
}
mm_kwargs_copy["text_kwargs"] = {
"truncation": tok_kwargs_copy.pop("truncation", False)
}
if audio_sample_rate is not None:
mm_kwargs_copy["audio_kwargs"]["audio_sample_rate"] = audio_sample_rate
# Assert: Verify kwargs structure is unchanged
assert "audio_kwargs" not in mm_kwargs_copy
assert "text_kwargs" not in mm_kwargs_copy
assert mm_kwargs_copy["audio_sample_rate"] == 16000
assert mm_kwargs_copy["truncation"] is True
assert tok_kwargs_copy["truncation"] is False