Sync from v0.13

tests/models/multimodal/generation/test_voxtral.py

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+# 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.chunk import AudioChunk, RawAudio, TextChunk
+from mistral_common.protocol.instruct.messages import UserMessage
+
+from vllm.tokenizers.mistral 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]
+    text = f"What's happening in these {len(audio_assets)} audio clips?"
+    messages = [
+        {
+            "role": "user",
+            "content": [*audio_chunks, {"type": "text", "text": text}],
+        }
+    ]
+
+    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"