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Improve HF qwen3_omni: preserve audio_sample_rate in kwargs restructuring (#29255)

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Signed-off-by: Jeremy Teboul <jeremyteboul@fb.com>
Co-authored-by: Jeremy Teboul <jeremyteboul@fb.com>
This commit is contained in:
jeremyteboul
2026-01-02 20:31:09 -08:00
committed by GitHub
parent 0eee877f67
commit 97a01308e9
3 changed files with 312 additions and 3 deletions
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285
tests/models/multimodal/processing/test_qwen3_omni.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Tests for Qwen3 Omni audio processing and sample rate handling."""
from typing import Any
import numpy as np
import pytest
from vllm.multimodal import MULTIMODAL_REGISTRY
from ...utils import build_model_context
@pytest.mark.parametrize("model_id", ["Qwen/Qwen3-Omni-30B-A3B-Instruct"])
@pytest.mark.parametrize(
("audio_sample_rate", "audio_duration_sec"),
[
(16000, 1.0), # Native Whisper sample rate, 1 second
(16000, 2.0), # Native Whisper sample rate, 2 seconds
],
)
def test_processor_with_audio_sample_rate(
model_id: str,
audio_sample_rate: int,
audio_duration_sec: float,
) -> None:
"""
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.
"""
# Setup: Build model context and processor
ctx = build_model_context(
model_id,
limit_mm_per_prompt={"audio": 1, "image": 0, "video": 0},
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
tokenizer = processor.info.get_tokenizer()
# Create audio data at the specified sample rate
audio_length = int(audio_sample_rate * audio_duration_sec)
rng = np.random.RandomState(42)
audio_data = rng.rand(audio_length).astype(np.float32)
# Build prompt with audio placeholder
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
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
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"
)
@pytest.mark.parametrize("model_id", ["Qwen/Qwen3-Omni-30B-A3B-Instruct"])
def test_longer_audio_generates_more_tokens(model_id: str) -> None:
"""
Test that longer audio generates more tokens than shorter audio.
This validates that audio_sample_rate is being used correctly by checking
that audio duration affects token count as expected.
"""
ctx = build_model_context(
model_id,
limit_mm_per_prompt={"audio": 1, "image": 0, "video": 0},
)
processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
tokenizer = processor.info.get_tokenizer()
audio_sample_rate = 16000
rng = np.random.RandomState(42)
def get_token_count(duration: float) -> int:
audio_length = int(audio_sample_rate * duration)
audio_data = rng.rand(audio_length).astype(np.float32)
prompt = "<|audio_start|><|audio_pad|><|audio_end|>"
mm_data = {"audio": [(audio_data, audio_sample_rate)]}
hf_processor_mm_kwargs: dict[str, Any] = {
"audio_sample_rate": audio_sample_rate,
}
processed = processor.apply(prompt, mm_data, hf_processor_mm_kwargs)
hf_proc = processor.info.get_hf_processor(**hf_processor_mm_kwargs)
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

5
tests/multimodal/test_processing.py
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@@ -1021,9 +1021,8 @@ def test_hf_processor_init_kwargs(
DummyProcessor, # type: ignore[arg-type]
**inference_kwargs,
)
for k, v in expected_kwargs.items():
assert getattr(processor, k) == v
assert processor.a == expected_kwargs["a"]
assert processor.b == expected_kwargs["b"]
@pytest.mark.parametrize("model_id", ["Qwen/Qwen2-VL-2B-Instruct"]) # Dummy

25
vllm/model_executor/models/qwen3_omni_moe_thinker.py
View File

@@ -751,6 +751,9 @@ class Qwen3OmniMoeThinkerMultiModalProcessor(
mm_kwargs = dict(mm_kwargs)
tok_kwargs = dict(tok_kwargs)
if Version(TRANSFORMERS_VERSION) < Version("4.58.0"):
# Extract audio_sample_rate before restructuring
audio_sample_rate = mm_kwargs.pop("audio_sample_rate", None)
# move truncation to audio_kwargs level to avoid conflict
# with tok_kwargs
mm_kwargs["audio_kwargs"] = {
@@ -760,6 +763,28 @@ class Qwen3OmniMoeThinkerMultiModalProcessor(
"truncation": tok_kwargs.pop("truncation", False)
}
# Validate and conditionally pass audio_sample_rate
# WhisperFeatureExtractor has a fixed sampling rate, and vLLM's
# audio loader already resamples audio to the target rate.
# Only pass the value if it matches to avoid unexpected behavior.
if audio_sample_rate is not None:
expected_sr = feature_extractor.sampling_rate
if audio_sample_rate != expected_sr:
logger.warning(
"[%s] audio_sample_rate mismatch: user provided %dHz "
"but model expects %dHz. Ignoring user value. "
"vLLM's audio loader already resampled to %dHz.",
self.__class__.__name__,
audio_sample_rate,
expected_sr,
expected_sr,
)
else:
# Sample rate matches, safe to pass
mm_kwargs["audio_kwargs"]["audio_sample_rate"] = (
audio_sample_rate
)
hf_inputs = super()._call_hf_processor(
prompt=prompt,
mm_data=mm_data,