[Model] Ultravox Model: Support v0.5 Release (#12912)

Signed-off-by: Farzad Abdolhosseini <farzad@fixie.ai>

docs/source/models/supported_models.md

@@ -856,7 +856,7 @@ See [this page](#generative-models) for more information on how to use generativ
 - * `UltravoxModel`
   * Ultravox
   * T + A<sup>E+</sup>
-  * `fixie-ai/ultravox-v0_3`
+  * `fixie-ai/ultravox-v0_5-llama-3_2-1b`
   * ✅︎
   * ✅︎
   * ✅︎

docs/source/serving/multimodal_inputs.md

@@ -359,12 +359,12 @@ export VLLM_VIDEO_FETCH_TIMEOUT=<timeout>
 ### Audio
 
 Audio input is supported according to [OpenAI Audio API](https://platform.openai.com/docs/guides/audio?audio-generation-quickstart-example=audio-in).
-Here is a simple example using Ultravox-v0.3.
+Here is a simple example using Ultravox-v0.5-1B.
 
 First, launch the OpenAI-compatible server:
 
 ```bash
-vllm serve fixie-ai/ultravox-v0_3
+vllm serve fixie-ai/ultravox-v0_5-llama-3_2-1b
 ```
 
 Then, you can use the OpenAI client as follows:

examples/offline_inference/audio_language.py

@@ -24,9 +24,9 @@ question_per_audio_count = {
 # Unless specified, these settings have been tested to work on a single L4.
 
 
-# Ultravox 0.3
+# Ultravox 0.5-1B
 def run_ultravox(question: str, audio_count: int):
-    model_name = "fixie-ai/ultravox-v0_3"
+    model_name = "fixie-ai/ultravox-v0_5-llama-3_2-1b"
 
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     messages = [{

examples/online_serving/openai_chat_completion_client_for_multimodal.py

@@ -12,7 +12,7 @@ vllm serve microsoft/Phi-3.5-vision-instruct --task generate \
     --trust-remote-code --max-model-len 4096 --limit-mm-per-prompt image=2
 
 (audio inference with Ultravox)
-vllm serve fixie-ai/ultravox-v0_3 --max-model-len 4096
+vllm serve fixie-ai/ultravox-v0_5-llama-3_2-1b --max-model-len 4096
 """
 import base64
 

tests/distributed/test_pipeline_parallel.py

@@ -215,7 +215,7 @@ MULTIMODAL_MODELS = {
     "Qwen/Qwen-VL-Chat": PPTestSettings.fast(trust_remote_code=True),
     "Qwen/Qwen2-Audio-7B-Instruct": PPTestSettings.fast(),
     "Qwen/Qwen2-VL-2B-Instruct": PPTestSettings.fast(),
-    "fixie-ai/ultravox-v0_3": PPTestSettings.fast(trust_remote_code=True),
+    "fixie-ai/ultravox-v0_5-llama-3_2-1b": PPTestSettings.fast(trust_remote_code=True),  # noqa: E501
     # [Encoder-decoder]
     # TODO: Implement PP
     # "meta-llama/Llama-3.2-11B-Vision-Instruct": PPTestSettings.fast(),
@@ -234,7 +234,7 @@ TEST_MODELS = [
     # [MULTIMODAL GENERATION]
     "OpenGVLab/InternVL2-1B",
     "microsoft/Phi-3-vision-128k-instruct",
-    "fixie-ai/ultravox-v0_3",
+    "fixie-ai/ultravox-v0_5-llama-3_2-1b",
     # [LANGUAGE GENERATION - HYBRID ARCH]
     "ai21labs/Jamba-tiny-dev",
 ]

tests/entrypoints/openai/test_audio.py

@@ -11,7 +11,7 @@ from vllm.multimodal.utils import encode_audio_base64, fetch_audio
 
 from ...utils import RemoteOpenAIServer
 
-MODEL_NAME = "fixie-ai/ultravox-v0_3"
+MODEL_NAME = "fixie-ai/ultravox-v0_5-llama-3_2-1b"
 TEST_AUDIO_URLS = [
     AudioAsset("winning_call").url,
 ]

tests/entrypoints/test_chat_utils.py

@@ -21,7 +21,7 @@ from ..utils import VLLM_PATH
 EXAMPLES_DIR = VLLM_PATH / "examples"
 
 PHI3V_MODEL_ID = "microsoft/Phi-3.5-vision-instruct"
-ULTRAVOX_MODEL_ID = "fixie-ai/ultravox-v0_3"
+ULTRAVOX_MODEL_ID = "fixie-ai/ultravox-v0_5-llama-3_2-1b"
 QWEN2VL_MODEL_ID = "Qwen/Qwen2-VL-2B-Instruct"
 MLLAMA_MODEL_ID = "meta-llama/Llama-3.2-11B-Vision-Instruct"
 LLAMA_GUARD_MODEL_ID = "meta-llama/Llama-Guard-3-1B"

tests/models/decoder_only/audio_language/test_ultravox.py

@@ -15,7 +15,7 @@ from ....conftest import HfRunner, VllmRunner
 from ....utils import RemoteOpenAIServer
 from ...utils import check_logprobs_close
 
-MODEL_NAME = "fixie-ai/ultravox-v0_3"
+MODEL_NAME = "fixie-ai/ultravox-v0_5-llama-3_2-1b"
 
 AudioTuple = Tuple[np.ndarray, int]
 

tests/models/multimodal/processing/test_common.py

@@ -164,7 +164,7 @@ def _test_processing_correctness(
     "Qwen/Qwen2-VL-2B-Instruct",
     "Qwen/Qwen2.5-VL-3B-Instruct",
     "Qwen/Qwen2-Audio-7B-Instruct",
-    "fixie-ai/ultravox-v0_3",
+    "fixie-ai/ultravox-v0_5-llama-3_2-1b",
 ])
 @pytest.mark.parametrize("hit_rate", [0.3, 0.5, 1.0])
 @pytest.mark.parametrize("num_batches", [32])

tests/models/registry.py

@@ -267,7 +267,7 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "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_3",
+    "UltravoxModel": _HfExamplesInfo("fixie-ai/ultravox-v0_5-llama-3_2-1b",
                                      trust_remote_code=True),
     # [Encoder-decoder]
     "MllamaForConditionalGeneration": _HfExamplesInfo("meta-llama/Llama-3.2-11B-Vision-Instruct"),  # noqa: E501

vllm/model_executor/models/ultravox.py

@@ -258,27 +258,35 @@ class UltravoxProjector(nn.Module):
         super().__init__()
         self.hidden_dim = config.hidden_size
         self._pad_and_stack = StackAudioFrames(config.stack_factor)
-        dim = config.audio_config.hidden_size * config.stack_factor
+        dim_in = config.audio_config.hidden_size * config.stack_factor
-        self.ln_pre = RMSNorm(dim)
+        self.ln_pre = RMSNorm(dim_in)
-        self.linear_1 = nn.Linear(dim, self.hidden_dim, bias=False)
+        self.linear_1 = nn.Linear(dim_in, self.hidden_dim, bias=False)
-        dim = self.hidden_dim
+        dim_mid = self.hidden_dim
 
         if config.projector_act == "swiglu":
             self.act = MulAndSilu()
-            dim = dim // 2
+            dim_mid = dim_mid // 2
         else:
             self.act = get_act_fn(config.projector_act)
 
-        self.linear_2 = nn.Linear(dim,
+        dim_out = config.text_config.hidden_size
-                                  config.text_config.hidden_size,
+        self.linear_2 = nn.Linear(dim_mid, dim_out, bias=False)
-                                  bias=False)
+
-        self.ln_post = RMSNorm(config.text_config.hidden_size)
+        # Ultravox v0.4.1 and below use layer_norm after the second linear layer
+        # while v0.5.0 and above uses layer_norm after the first linear layer.
+        if config.projector_ln_mid:
+            self.ln_mid: nn.Module = RMSNorm(dim_mid)
+            self.ln_post = nn.Identity()
+        else:
+            self.ln_mid = nn.Identity()
+            self.ln_post = RMSNorm(dim_out)
 
     def forward(self, audio_features: torch.Tensor) -> torch.Tensor:
         audio_features = self._pad_and_stack(audio_features)
         audio_features = self.ln_pre(audio_features)
         hidden_states = self.linear_1(audio_features)
         hidden_states = self.act(hidden_states)
+        hidden_states = self.ln_mid(hidden_states)
         hidden_states = self.linear_2(hidden_states)
         hidden_states = self.ln_post(hidden_states)
         return hidden_states

vllm/transformers_utils/configs/ultravox.py

@@ -37,6 +37,10 @@ class UltravoxConfig(transformers.PretrainedConfig):
             The LoRA configuration for finetuning the text model.
         audio_model_lora_config (`LoraConfigSimplified`, *optional*):
             The LoRA configuration for finetuning the audio model.
+        projector_ln_mid (`bool`, *optional*, defaults to `False`):
+            Whether to apply layer normalization at the middle of the
+            projector or at the end. Versions v0.4.1 and below
+            use `False`, but v0.5 and above use `True`.
     """
 
     model_type = "ultravox"
@@ -56,6 +60,7 @@ class UltravoxConfig(transformers.PretrainedConfig):
         projector_act: str = "swiglu",
         text_model_lora_config: Optional[Dict[str, Any]] = None,
         audio_model_lora_config: Optional[Dict[str, Any]] = None,
+        projector_ln_mid: bool = False,
         **kwargs,
     ):
         self.ignore_index = ignore_index
@@ -68,6 +73,7 @@ class UltravoxConfig(transformers.PretrainedConfig):
         self.stack_factor = stack_factor
         self.norm_init = norm_init
         self.projector_act = projector_act
+        self.projector_ln_mid = projector_ln_mid
 
         if text_model_id is not None:
             # Avoid circular import