[Model] Extend Ultravox to accept audio longer than 30s

tests/models/decoder_only/audio_language/test_ultravox.py

@@ -15,7 +15,7 @@
 from ....utils import RemoteOpenAIServer
 from ...utils import check_logprobs_close
 
-MODEL_NAME = "fixie-ai/ultravox-v0_4"
+MODEL_NAME = "fixie-ai/ultravox-v0_5-llama-3_2-1b"
 
 AudioTuple = tuple[np.ndarray, int]
 

tests/models/multimodal/processing/test_common.py

@@ -1,6 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 
+import copy
 from functools import partial
+from typing import Optional
 
 import numpy as np
 import pytest
@@ -21,6 +23,7 @@ def _test_processing_correctness(
     hit_rate: float,
     num_batches: int,
     simplify_rate: float,
+    ignore_mm_keys: Optional[list[str]] = None,
 ):
     model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id)
     model_info.check_available_online(on_fail="skip")
@@ -123,26 +126,32 @@ def _test_processing_correctness(
             hf_processor_mm_kwargs={},
         )
 
-        assert baseline_result == cached_result, (
-            f"Failed ({batch_idx=}, {prompt=}, {mm_data=})")
+        assert _drop_mm_kwargs_keys(
+            baseline_result, ignore_mm_keys) == _drop_mm_kwargs_keys(
+                cached_result, ignore_mm_keys), (
+                    f"Failed ({batch_idx=}, {prompt=}, {mm_data=})")
 
         baseline_tokenized_result = baseline_processor.apply(
             tokenizer.encode(prompt, **tokenizer_encode_kwargs),
             mm_data=mm_data,
             hf_processor_mm_kwargs={},
         )
 
-        assert baseline_result == baseline_tokenized_result, (
-            f"Failed ({batch_idx=}, {prompt=}, {mm_data=})")
+        assert _drop_mm_kwargs_keys(
+            baseline_result, ignore_mm_keys) == _drop_mm_kwargs_keys(
+                baseline_tokenized_result, ignore_mm_keys), (
+                    f"Failed ({batch_idx=}, {prompt=}, {mm_data=})")
 
         cached_tokenized_result = cached_processor.apply(
             tokenizer.encode(prompt, **tokenizer_encode_kwargs),
             mm_data=mm_data,
             hf_processor_mm_kwargs={},
         )
 
-        assert cached_result == cached_tokenized_result, (
-            f"Failed ({batch_idx=}, {prompt=}, {mm_data=})")
+        assert _drop_mm_kwargs_keys(
+            cached_result, ignore_mm_keys) == _drop_mm_kwargs_keys(
+                cached_tokenized_result, ignore_mm_keys), (
+                    f"Failed ({batch_idx=}, {prompt=}, {mm_data=})")
 
 
 # yapf: disable
@@ -173,7 +182,7 @@ def _test_processing_correctness(
     "Qwen/Qwen2-VL-2B-Instruct",
     "Qwen/Qwen2.5-VL-3B-Instruct",
     "Qwen/Qwen2-Audio-7B-Instruct",
-    "fixie-ai/ultravox-v0_4",
+    "fixie-ai/ultravox-v0_5-llama-3_2-1b",
     "openai/whisper-large-v3",
 ])
 @pytest.mark.parametrize("hit_rate", [0.3, 0.5, 1.0])
@@ -186,11 +195,19 @@ def test_processing_correctness(
     num_batches: int,
     simplify_rate: float,
 ):
+    ignore_mm_keys = None
+    if 'ultravox' in model_id:
+        # In Ultravox, the audio_features can be different depending on padding
+        # The slight difference should not be a problem though, since
+        # attention_mask lets us ignore the difference.
+        ignore_mm_keys = ['audio_features']
+
     _test_processing_correctness(
         model_id,
         hit_rate=hit_rate,
         num_batches=num_batches,
         simplify_rate=simplify_rate,
+        ignore_mm_keys=ignore_mm_keys,
     )
 
 
@@ -219,3 +236,29 @@ def test_processing_correctness_phi3v(
         num_batches=num_batches,
         simplify_rate=simplify_rate,
     )
+
+
+def _drop_mm_kwargs_keys(result: dict,
+                         ignore_mm_keys: Optional[list[str]] = None) -> dict:
+    """Drop specified keys from result['mm_kwargs'].
+
+    This is mainly to avoid doing exact match of audio_features in ultravox.
+
+    Args:
+        result: Result to drop keys from
+        ignore_mm_keys: List of keys to ignore, e.g. ['audio_features']
+    """
+    if not ignore_mm_keys:
+        return result
+
+    if 'mm_kwargs' in result:
+        result = copy.deepcopy(result)
+        mm_kwargs = result['mm_kwargs']
+        for key in ignore_mm_keys:
+            mm_kwargs.pop(key, None)
+        for items in mm_kwargs._items_by_modality.values():
+            for item in items:
+                for key in ignore_mm_keys:
+                    item.pop(key, None)
+
+    return result

tests/models/registry.py

@@ -284,8 +284,7 @@ def check_available_online(
     "Qwen2VLForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2-VL-2B-Instruct"),  # noqa: E501
     "Qwen2_5_VLForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2.5-VL-3B-Instruct",  # noqa: E501
                                                           min_transformers_version="4.49"),  # noqa: E501
-    "UltravoxModel": _HfExamplesInfo("fixie-ai/ultravox-v0_4",
-                                     extras={"v0.5": "fixie-ai/ultravox-v0_5-llama-3_2-1b"},  # noqa: E501
+    "UltravoxModel": _HfExamplesInfo("fixie-ai/ultravox-v0_5-llama-3_2-1b",  # noqa: E501
                                      trust_remote_code=True),
     # [Encoder-decoder]
     # Florence-2 uses BartFastTokenizer which can't be loaded from AutoTokenizer