audio_language.py 15.5 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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This example shows how to use vLLM for running offline inference
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with the correct prompt format on audio language models.
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For most models, the prompt format should follow corresponding examples
on HuggingFace model repository.
"""
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import os
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from dataclasses import asdict
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from typing import Any, NamedTuple
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from huggingface_hub import snapshot_download
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from transformers import AutoTokenizer

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from vllm import LLM, EngineArgs, SamplingParams
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from vllm.assets.audio import AudioAsset
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from vllm.lora.request import LoRARequest
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from vllm.utils.argparse_utils import FlexibleArgumentParser
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audio_assets = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
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question_per_audio_count = {
    0: "What is 1+1?",
    1: "What is recited in the audio?",
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    2: "What sport and what nursery rhyme are referenced?",
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}
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class ModelRequestData(NamedTuple):
    engine_args: EngineArgs
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    prompt: str | None = None
    prompt_token_ids: dict[str, list[int]] | None = None
    multi_modal_data: dict[str, Any] | None = None
    stop_token_ids: list[int] | None = None
    lora_requests: list[LoRARequest] | None = None
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# NOTE: The default `max_num_seqs` and `max_model_len` may result in OOM on
# lower-end GPUs.
# Unless specified, these settings have been tested to work on a single L4.

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# Voxtral
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# Make sure to install mistral-common[audio].
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def run_voxtral(question: str, audio_count: int) -> ModelRequestData:
    from mistral_common.audio import Audio
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    from mistral_common.protocol.instruct.chunk import (
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        AudioChunk,
        RawAudio,
        TextChunk,
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    )
    from mistral_common.protocol.instruct.messages import (
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        UserMessage,
    )
    from mistral_common.protocol.instruct.request import ChatCompletionRequest
    from mistral_common.tokens.tokenizers.mistral import MistralTokenizer

    model_name = "mistralai/Voxtral-Mini-3B-2507"
    tokenizer = MistralTokenizer.from_hf_hub(model_name)

    engine_args = EngineArgs(
        model=model_name,
        max_model_len=8192,
        max_num_seqs=2,
        limit_mm_per_prompt={"audio": audio_count},
        config_format="mistral",
        load_format="mistral",
        tokenizer_mode="mistral",
        enforce_eager=True,
        enable_chunked_prefill=False,
    )

    text_chunk = TextChunk(text=question)
    audios = [
        Audio.from_file(str(audio_assets[i].get_local_path()), strict=False)
        for i in range(audio_count)
    ]
    audio_chunks = [
        AudioChunk(input_audio=RawAudio.from_audio(audio)) for audio in audios
    ]

    messages = [UserMessage(content=[*audio_chunks, text_chunk])]

    req = ChatCompletionRequest(messages=messages, model=model_name)

    tokens = tokenizer.encode_chat_completion(req)
    prompt_ids, audios = tokens.tokens, tokens.audios

    audios_and_sr = [(au.audio_array, au.sampling_rate) for au in audios]

    multi_modal_data = {"audio": audios_and_sr}

    return ModelRequestData(
        engine_args=engine_args,
        prompt_token_ids=prompt_ids,
        multi_modal_data=multi_modal_data,
    )


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# Gemma3N
def run_gemma3n(question: str, audio_count: int) -> ModelRequestData:
    model_name = "google/gemma-3n-E2B-it"
    engine_args = EngineArgs(
        model=model_name,
        max_model_len=2048,
        max_num_batched_tokens=2048,
        max_num_seqs=2,
        limit_mm_per_prompt={"audio": audio_count},
        enforce_eager=True,
    )
    prompt = f"<start_of_turn>user\n<audio_soft_token>{question}"
    "<end_of_turn>\n<start_of_turn>model\n"
    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


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# Granite Speech
def run_granite_speech(question: str, audio_count: int) -> ModelRequestData:
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    # NOTE - the setting in this example are somewhat different from what is
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    # optimal for granite speech, and it is generally recommended to use beam
    # search. Check the model README for suggested settings.
    # https://huggingface.co/ibm-granite/granite-speech-3.3-8b
    model_name = "ibm-granite/granite-speech-3.3-8b"

    engine_args = EngineArgs(
        model=model_name,
        trust_remote_code=True,
        max_model_len=2048,
        max_num_seqs=2,
        enable_lora=True,
        max_lora_rank=64,
        limit_mm_per_prompt={"audio": audio_count},
    )

    # The model has an audio-specific lora directly in its model dir;
    # it should be enabled whenever you pass audio inputs to the model.
    speech_lora_path = model_name
    audio_placeholder = "<|audio|>" * audio_count
    prompts = f"<|start_of_role|>system<|end_of_role|>Knowledge Cutoff Date: April 2024.\nToday's Date: December 19, 2024.\nYou are Granite, developed by IBM. You are a helpful AI assistant<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>{audio_placeholder}{question}<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>"  # noqa: E501

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompts,
        lora_requests=[LoRARequest("speech", 1, speech_lora_path)],
    )


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# MiDashengLM
def run_midashenglm(question: str, audio_count: int):
    model_name = "mispeech/midashenglm-7b"

    engine_args = EngineArgs(
        model=model_name,
        trust_remote_code=True,
        max_model_len=4096,
        max_num_seqs=5,
        limit_mm_per_prompt={"audio": audio_count},
    )

    audio_in_prompt = "".join(
        ["<|audio_bos|><|AUDIO|><|audio_eos|>" for idx in range(audio_count)]
    )

    default_system = "You are a helpful language and speech assistant."

    prompt = (
        f"<|im_start|>system\n{default_system}<|im_end|>\n"
        "<|im_start|>user\n"
        f"{audio_in_prompt}{question}<|im_end|>\n"
        "<|im_start|>assistant\n"
    )
    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


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# MiniCPM-O
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def run_minicpmo(question: str, audio_count: int) -> ModelRequestData:
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    model_name = "openbmb/MiniCPM-o-2_6"
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    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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    engine_args = EngineArgs(
        model=model_name,
        trust_remote_code=True,
        max_model_len=4096,
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        max_num_seqs=2,
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        limit_mm_per_prompt={"audio": audio_count},
    )
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    stop_tokens = ["<|im_end|>", "<|endoftext|>"]
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    stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]

    audio_placeholder = "(<audio>./</audio>)" * audio_count
    audio_chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n<|spk_bos|><|spk|><|spk_eos|><|tts_bos|>' }}{% endif %}"  # noqa: E501
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    messages = [{"role": "user", "content": f"{audio_placeholder}\n{question}"}]
    prompt = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
        chat_template=audio_chat_template,
    )
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    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
        stop_token_ids=stop_token_ids,
    )
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# Phi-4-multimodal-instruct
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def run_phi4mm(question: str, audio_count: int) -> ModelRequestData:
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    """
    Phi-4-multimodal-instruct supports both image and audio inputs. Here, we
    show how to process audio inputs.
    """
    model_path = snapshot_download("microsoft/Phi-4-multimodal-instruct")
    # Since the vision-lora and speech-lora co-exist with the base model,
    # we have to manually specify the path of the lora weights.
    speech_lora_path = os.path.join(model_path, "speech-lora")
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    placeholders = "".join([f"<|audio_{i + 1}|>" for i in range(audio_count)])
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    prompts = f"<|user|>{placeholders}{question}<|end|><|assistant|>"
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    engine_args = EngineArgs(
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        model=model_path,
        trust_remote_code=True,
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        max_model_len=12800,
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        max_num_seqs=2,
        enable_lora=True,
        max_lora_rank=320,
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        limit_mm_per_prompt={"audio": audio_count},
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    )

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    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompts,
        lora_requests=[LoRARequest("speech", 1, speech_lora_path)],
    )
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def run_phi4_multimodal(question: str, audio_count: int) -> ModelRequestData:
    """
    Phi-4-multimodal-instruct supports both image and audio inputs. Here, we
    show how to process audio inputs.
    """
    model_path = snapshot_download(
        "microsoft/Phi-4-multimodal-instruct", revision="refs/pr/70"
    )
    # Since the vision-lora and speech-lora co-exist with the base model,
    # we have to manually specify the path of the lora weights.
    speech_lora_path = os.path.join(model_path, "speech-lora")
    placeholders = "<|audio|>" * audio_count

    prompts = f"<|user|>{placeholders}{question}<|end|><|assistant|>"

    engine_args = EngineArgs(
        model=model_path,
        max_model_len=12800,
        max_num_seqs=2,
        enable_lora=True,
        max_lora_rank=320,
        limit_mm_per_prompt={"audio": audio_count},
    )

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompts,
        lora_requests=[LoRARequest("speech", 1, speech_lora_path)],
    )


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# Qwen2-Audio
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def run_qwen2_audio(question: str, audio_count: int) -> ModelRequestData:
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    model_name = "Qwen/Qwen2-Audio-7B-Instruct"

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    engine_args = EngineArgs(
        model=model_name,
        max_model_len=4096,
        max_num_seqs=5,
        limit_mm_per_prompt={"audio": audio_count},
    )
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    audio_in_prompt = "".join(
        [
            f"Audio {idx + 1}: <|audio_bos|><|AUDIO|><|audio_eos|>\n"
            for idx in range(audio_count)
        ]
    )
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    prompt = (
        "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
        "<|im_start|>user\n"
        f"{audio_in_prompt}{question}<|im_end|>\n"
        "<|im_start|>assistant\n"
    )
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    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )
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# Qwen2.5-Omni
def run_qwen2_5_omni(question: str, audio_count: int):
    model_name = "Qwen/Qwen2.5-Omni-7B"

    engine_args = EngineArgs(
        model=model_name,
        max_model_len=4096,
        max_num_seqs=5,
        limit_mm_per_prompt={"audio": audio_count},
    )

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    audio_in_prompt = "".join(
        ["<|audio_bos|><|AUDIO|><|audio_eos|>\n" for idx in range(audio_count)]
    )
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    default_system = (
        "You are Qwen, a virtual human developed by the Qwen Team, Alibaba "
        "Group, capable of perceiving auditory and visual inputs, as well as "
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        "generating text and speech."
    )
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    prompt = (
        f"<|im_start|>system\n{default_system}<|im_end|>\n"
        "<|im_start|>user\n"
        f"{audio_in_prompt}{question}<|im_end|>\n"
        "<|im_start|>assistant\n"
    )
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    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


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# Ultravox 0.5-1B
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def run_ultravox(question: str, audio_count: int) -> ModelRequestData:
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    model_name = "fixie-ai/ultravox-v0_5-llama-3_2-1b"
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    tokenizer = AutoTokenizer.from_pretrained(model_name)
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    messages = [{"role": "user", "content": "<|audio|>\n" * audio_count + question}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
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    engine_args = EngineArgs(
        model=model_name,
        max_model_len=4096,
        max_num_seqs=5,
        trust_remote_code=True,
        limit_mm_per_prompt={"audio": audio_count},
    )

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )
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# Whisper
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def run_whisper(question: str, audio_count: int) -> ModelRequestData:
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    assert audio_count == 1, "Whisper only support single audio input per prompt"
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    model_name = "openai/whisper-large-v3-turbo"

    prompt = "<|startoftranscript|>"

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    engine_args = EngineArgs(
        model=model_name,
        max_model_len=448,
        max_num_seqs=5,
        limit_mm_per_prompt={"audio": audio_count},
    )

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )
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model_example_map = {
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    "voxtral": run_voxtral,
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    "gemma3n": run_gemma3n,
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    "granite_speech": run_granite_speech,
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    "midashenglm": run_midashenglm,
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    "minicpmo": run_minicpmo,
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    "phi4_mm": run_phi4mm,
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    "phi4_multimodal": run_phi4_multimodal,
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    "qwen2_audio": run_qwen2_audio,
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    "qwen2_5_omni": run_qwen2_5_omni,
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    "ultravox": run_ultravox,
    "whisper": run_whisper,
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}
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def parse_args():
    parser = FlexibleArgumentParser(
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        description="Demo on using vLLM for offline inference with "
        "audio language models"
    )
    parser.add_argument(
        "--model-type",
        "-m",
        type=str,
        default="ultravox",
        choices=model_example_map.keys(),
        help='Huggingface "model_type".',
    )
    parser.add_argument(
        "--num-prompts", type=int, default=1, help="Number of prompts to run."
    )
    parser.add_argument(
        "--num-audios",
        type=int,
        default=1,
        choices=[0, 1, 2],
        help="Number of audio items per prompt.",
    )
    parser.add_argument(
        "--seed",
        type=int,
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        default=0,
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        help="Set the seed when initializing `vllm.LLM`.",
    )
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    parser.add_argument(
        "--tensor-parallel-size",
        "-tp",
        type=int,
        default=None,
        help="Tensor parallel size to override the model's default setting. ",
    )
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    return parser.parse_args()


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def main(args):
    model = args.model_type
    if model not in model_example_map:
        raise ValueError(f"Model type {model} is not supported.")

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    if args.tensor_parallel_size is not None and args.tensor_parallel_size < 1:
        raise ValueError(
            f"tensor_parallel_size must be a positive integer, "
            f"got {args.tensor_parallel_size}"
        )

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    audio_count = args.num_audios
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    req_data = model_example_map[model](
        question_per_audio_count[audio_count], audio_count
    )
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    # Disable other modalities to save memory
    default_limits = {"image": 0, "video": 0, "audio": 0}
    req_data.engine_args.limit_mm_per_prompt = default_limits | dict(
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        req_data.engine_args.limit_mm_per_prompt or {}
    )
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    engine_args = asdict(req_data.engine_args) | {"seed": args.seed}
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    if args.tensor_parallel_size is not None:
        engine_args["tensor_parallel_size"] = args.tensor_parallel_size
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    llm = LLM(**engine_args)

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    # We set temperature to 0.2 so that outputs can be different
    # even when all prompts are identical when running batch inference.
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    sampling_params = SamplingParams(
        temperature=0.2, max_tokens=64, stop_token_ids=req_data.stop_token_ids
    )
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    mm_data = req_data.multi_modal_data
    if not mm_data:
        mm_data = {}
        if audio_count > 0:
            mm_data = {
                "audio": [
                    asset.audio_and_sample_rate for asset in audio_assets[:audio_count]
                ]
            }
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    assert args.num_prompts > 0
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    inputs = {"multi_modal_data": mm_data}

    if req_data.prompt:
        inputs["prompt"] = req_data.prompt
    else:
        inputs["prompt_token_ids"] = req_data.prompt_token_ids

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    if args.num_prompts > 1:
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        # Batch inference
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        inputs = [inputs] * args.num_prompts
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    # Add LoRA request if applicable
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    lora_request = (
        req_data.lora_requests * args.num_prompts if req_data.lora_requests else None
    )
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    outputs = llm.generate(
        inputs,
        sampling_params=sampling_params,
        lora_request=lora_request,
    )
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    for o in outputs:
        generated_text = o.outputs[0].text
        print(generated_text)


if __name__ == "__main__":
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    args = parse_args()
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    main(args)