[WIP] Voxtral tests

tests/conftest.py

@@ -804,7 +804,7 @@
 
     def get_inputs(
         self,
-        prompts: Union[list[str], list[torch.Tensor]],
+        prompts: Union[list[str], list[torch.Tensor], list[int]],
         images: Optional[PromptImageInput] = None,
         videos: Optional[PromptVideoInput] = None,
         audios: Optional[PromptAudioInput] = None,
@@ -826,11 +826,14 @@
             if audios is not None and (audio := audios[i]) is not None:
                 multi_modal_data["audio"] = audio
 
-            text_prompt_kwargs = {
-                ("prompt" if isinstance(prompt, str) else "prompt_embeds"):
-                prompt,
-                "multi_modal_data": multi_modal_data or None
-            }
+            text_prompt_kwargs = {"multi_modal_data": multi_modal_data or None}
+            if isinstance(prompt, str):
+                text_prompt_kwargs["prompt"] = prompt
+            elif isinstance(prompt, list) and isinstance(prompt[0], int):
+                text_prompt_kwargs["prompt_token_ids"] = prompt
+            else:
+                text_prompt_kwargs["prompt_embeds"] = prompt
+
             inputs.append(TextPrompt(**text_prompt_kwargs))
 
         return inputs

tests/entrypoints/openai/test_transcription_validation.py

@@ -47,9 +47,6 @@ async def test_basic_audio(mary_had_lamb, model_name):
     if model_name.startswith("mistralai"):
         server_args += MISTRAL_FORMAT_ARGS
 
-        # TODO(PATRICK) - REMOVE AFTER RELEASE
-        return  # skip for now
-
     # Based on https://github.com/openai/openai-cookbook/blob/main/examples/Whisper_prompting_guide.ipynb.
     with RemoteOpenAIServer(model_name, server_args) as remote_server:
         client = remote_server.get_async_client()

tests/models/multimodal/generation/test_voxtral.py

@@ -0,0 +1,115 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import json
+
+import pytest
+import pytest_asyncio
+from mistral_common.audio import Audio
+from mistral_common.protocol.instruct.messages import (AudioChunk, RawAudio,
+                                                       TextChunk, UserMessage)
+
+from vllm.transformers_utils.tokenizer import MistralTokenizer
+
+from ....conftest import AudioTestAssets
+from ....utils import RemoteOpenAIServer
+from .test_ultravox import MULTI_AUDIO_PROMPT, run_multi_audio_test
+
+MODEL_NAME = "mistralai/Voxtral-Mini-3B-2507"
+MISTRAL_FORMAT_ARGS = [
+    "--tokenizer_mode", "mistral", "--config_format", "mistral",
+    "--load_format", "mistral"
+]
+
+
+@pytest.fixture()
+def server(request, audio_assets: AudioTestAssets):
+    args = [
+        "--enforce-eager",
+        "--limit-mm-per-prompt",
+        json.dumps({"audio": len(audio_assets)}),
+    ] + MISTRAL_FORMAT_ARGS
+
+    with RemoteOpenAIServer(MODEL_NAME,
+                            args,
+                            env_dict={"VLLM_AUDIO_FETCH_TIMEOUT":
+                                      "30"}) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def client(server):
+    async with server.get_async_client() as async_client:
+        yield async_client
+
+
+def _get_prompt(audio_assets, question):
+    tokenizer = MistralTokenizer.from_pretrained(MODEL_NAME)
+
+    audios = [
+        Audio.from_file(str(audio_assets[i].get_local_path()), strict=False)
+        for i in range(len(audio_assets))
+    ]
+    audio_chunks = [
+        AudioChunk(input_audio=RawAudio.from_audio(audio)) for audio in audios
+    ]
+
+    text_chunk = TextChunk(text=question)
+    messages = [UserMessage(content=[*audio_chunks, text_chunk]).to_openai()]
+
+    return tokenizer.apply_chat_template(messages=messages)
+
+
+@pytest.mark.core_model
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_models_with_multiple_audios(vllm_runner,
+                                     audio_assets: AudioTestAssets, dtype: str,
+                                     max_tokens: int,
+                                     num_logprobs: int) -> None:
+    vllm_prompt = _get_prompt(audio_assets, MULTI_AUDIO_PROMPT)
+    run_multi_audio_test(
+        vllm_runner,
+        [(vllm_prompt, [audio.audio_and_sample_rate
+                        for audio in audio_assets])],
+        MODEL_NAME,
+        dtype=dtype,
+        max_tokens=max_tokens,
+        num_logprobs=num_logprobs,
+        tokenizer_mode="mistral",
+    )
+
+
+@pytest.mark.asyncio
+async def test_online_serving(client, audio_assets: AudioTestAssets):
+    """Exercises online serving with/without chunked prefill enabled."""
+
+    def asset_to_chunk(asset):
+        audio = Audio.from_file(str(asset.get_local_path()), strict=False)
+        audio.format = "wav"
+        audio_dict = AudioChunk.from_audio(audio).to_openai()
+        return audio_dict
+
+    audio_chunks = [asset_to_chunk(asset) for asset in audio_assets]
+    messages = [{
+        "role":
+        "user",
+        "content": [
+            *audio_chunks,
+            {
+                "type":
+                "text",
+                "text":
+                f"What's happening in these {len(audio_assets)} audio clips?"
+            },
+        ],
+    }]
+
+    chat_completion = await client.chat.completions.create(model=MODEL_NAME,
+                                                           messages=messages,
+                                                           max_tokens=10)
+
+    assert len(chat_completion.choices) == 1
+    choice = chat_completion.choices[0]
+    assert choice.finish_reason == "length"

tests/models/registry.py

@@ -440,7 +440,7 @@ def check_available_online(
                                                          tokenizer="Isotr0py/Florence-2-tokenizer",  # noqa: E501
                                                          trust_remote_code=True),  # noqa: E501
     "MllamaForConditionalGeneration": _HfExamplesInfo("meta-llama/Llama-3.2-11B-Vision-Instruct"),  # noqa: E501
-    "VoxtralForConditionalGeneration": _HfExamplesInfo("mistralai/Voxtral-Mini-3B-2507", is_available_online=False, tokenizer_mode="mistral"),  # noqa: E501
+    "VoxtralForConditionalGeneration": _HfExamplesInfo("mistralai/Voxtral-Mini-3B-2507", tokenizer_mode="mistral"),  # noqa: E501
     "WhisperForConditionalGeneration": _HfExamplesInfo("openai/whisper-large-v3"),  # noqa: E501
 
     # [Cross-encoder]