【vLLM 学习】Encoder Decoder Multimodal

vLLM 是一款专为大语言模型推理加速而设计的框架，实现了 KV 缓存内存几乎零浪费，解决了内存管理瓶颈问题。

更多 vLLM 中文文档及教程可访问 →https://vllm.hyper.ai/

*在线运行 vLLM 入门教程：零基础分步指南

源码 examples/offline_inference/encoder_decoder_multimodal.py

# SPDX-License-Identifier: Apache-2.0

"""
此示例显示了如何使用 vLLM 进行离线推理
文本生成的 enc-dec LMMS 上的显式/隐式提示格式。
"""
import time
from collections.abc import Sequence
from dataclasses import asdict
from typing import NamedTuple

from vllm import LLM, EngineArgs, PromptType, SamplingParams
from vllm.assets.audio import AudioAsset
from vllm.assets.image import ImageAsset
from vllm.utils import FlexibleArgumentParser


class ModelRequestData(NamedTuple):
    engine_args: EngineArgs
    prompts: Sequence[PromptType]


def run_florence2():
    engine_args = EngineArgs(
        model="microsoft/Florence-2-large",
        tokenizer="facebook/bart-large",
        max_num_seqs=8,
        trust_remote_code=True,
        limit_mm_per_prompt={"image": 1},
        dtype="half",
    )

    prompts = [
        {   # implicit prompt with task token
            "prompt": "<DETAILED_CAPTION>",
            "multi_modal_data": {
                "image": ImageAsset("stop_sign").pil_image
            },
        },
        {   # explicit encoder/decoder prompt
            "encoder_prompt": {
                "prompt": "Describe in detail what is shown in the image.",
                "multi_modal_data": {
                    "image": ImageAsset("cherry_blossom").pil_image
                },
            },
            "decoder_prompt": "",
        },
    ]

    return ModelRequestData(
        engine_args=engine_args,
        prompts=prompts,
    )


def run_mllama():
    engine_args = EngineArgs(
        model="meta-llama/Llama-3.2-11B-Vision-Instruct",
        max_model_len=4096,
        max_num_seqs=2,
        limit_mm_per_prompt={"image": 1},
        dtype="half",
    )

    prompts = [
        {   # Implicit prompt # 隐式提示
            "prompt": "<|image|><|begin_of_text|>What is the content of this image?",   # noqa: E501
            "multi_modal_data": {
                "image": ImageAsset("stop_sign").pil_image,
            },
        },
        {   # Explicit prompt # 显示提示
            "encoder_prompt": {
                "prompt": "<|image|>",
                "multi_modal_data": {
                    "image": ImageAsset("stop_sign").pil_image,
                },
            },
            "decoder_prompt": "<|image|><|begin_of_text|>Please describe the image.",   # noqa: E501
        },
    ]

    return ModelRequestData(
        engine_args=engine_args,
        prompts=prompts,
    )


def run_whisper():
    engine_args = EngineArgs(
        model="openai/whisper-large-v3-turbo",
        max_model_len=448,
        max_num_seqs=16,
        limit_mm_per_prompt={"audio": 1},
        dtype="half",
    )

    prompts = [
        {   # Test implicit prompt # 测试隐式提示
            "prompt": "<|startoftranscript|>",
            "multi_modal_data": {
                "audio": AudioAsset("mary_had_lamb").audio_and_sample_rate,
            },
        },
        {   # Test explicit encoder/decoder prompt # 测试显式 编码/解码提示
            "encoder_prompt": {
                "prompt": "",
                "multi_modal_data": {
                    "audio": AudioAsset("winning_call").audio_and_sample_rate,
                },
            },
            "decoder_prompt": "<|startoftranscript|>",
        }
    ]

    return ModelRequestData(
        engine_args=engine_args,
        prompts=prompts,
    )


model_example_map = {
    "florence2": run_florence2,
    "mllama": run_mllama,
    "whisper": run_whisper,
}


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

    req_data = model_example_map[model]()

    engine_args = asdict(req_data.engine_args) | {"seed": args.seed}
    llm = LLM(**engine_args)

    prompts = req_data.prompts


    # 创建一个采样参数对象。
    sampling_params = SamplingParams(
        temperature=0,
        top_p=1.0,
        max_tokens=64,
    )

    start = time.time()

    # 从提示中生成输出 token 。
    # 输出是包含提示的对象，生成了文本和其他信息。
    outputs = llm.generate(prompts, sampling_params)

    # 打印输出。
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Decoder prompt: {prompt!r}, "
              f"Generated text: {generated_text!r}")

    duration = time.time() - start

    print("Duration:", duration)
    print("RPS:", len(prompts) / duration)


if __name__ == "__main__":
    parser = FlexibleArgumentParser(
        description='Demo on using vLLM for offline inference with '
        'vision language models for text generation')
    parser.add_argument('--model-type',
                        '-m',
                        type=str,
                        default="mllama",
                        choices=model_example_map.keys(),
                        help='Huggingface "model_type".')
    parser.add_argument("--seed",
                        type=int,
                        default=None,
                        help="Set the seed when initializing `vllm.LLM`.")

    args = parser.parse_args()
    main(args)