【vLLM 学习】Llm Engine Example

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原创最新推荐文章于 2026-09-13 10:48:42 发布 293 阅读 AI 写同款· GEO 优化 ›

VLLM框架：大模型推理性能优化与部署实战大规模语言模型推理面临显存碎片化和计算资源利用率低的挑战。通过引入操作系统的虚拟内存分页机制，VLLM框架的PagedAttention技术将KV Cache划分为固定大小的内存块，配合连续批处理技术，显著提升显存利用率和吞吐量。这种创新架构使得单A100节点可支持70B参数模型的50+并发请求，在电商客服、金融风控等场景实现高达24倍的性能提升。部署时需关注GPU选型与NVLink配置，通过动态批处理和内存优化策略，Qwen-72B等大模型可在生产环境稳定运行。阅读详情

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/llm_engine_example.py

# SPDX-License-Identifier: Apache-2.0

import argparse

from vllm import EngineArgs, LLMEngine, RequestOutput, SamplingParams
from vllm.utils import FlexibleArgumentParser


def create_test_prompts() -> list[tuple[str, SamplingParams]]:
    """Create a list of test prompts with their sampling parameters."""
    return [
        ("A robot may not injure a human being",
         SamplingParams(temperature=0.0, logprobs=1, prompt_logprobs=1)),
        ("To be or not to be,",
         SamplingParams(temperature=0.8, top_k=5, presence_penalty=0.2)),
        ("What is the meaning of life?",
         SamplingParams(n=2,
                        temperature=0.8,
                        top_p=0.95,
                        frequency_penalty=0.1)),
    ]


def process_requests(engine: LLMEngine,
                     test_prompts: list[tuple[str, SamplingParams]]):
    """Continuously process a list of prompts and handle the outputs."""
    "持续处理提示列表并处理输出"
    request_id = 0

    while test_prompts or engine.has_unfinished_requests():
        if test_prompts:
            prompt, sampling_params = test_prompts.pop(0)
            engine.add_request(str(request_id), prompt, sampling_params)
            request_id += 1

        request_outputs: list[RequestOutput] = engine.step()

        for request_output in request_outputs:
            if request_output.finished:
                print(request_output)


def initialize_engine(args: argparse.Namespace) -> LLMEngine:
    """Initialize the LLMEngine from the command line arguments."""
    engine_args = EngineArgs.from_cli_args(args)
    return LLMEngine.from_engine_args(engine_args)


def main(args: argparse.Namespace):
    """Main function that sets up and runs the prompt processing."""
    engine = initialize_engine(args)
    test_prompts = create_test_prompts()
    process_requests(engine, test_prompts)


if __name__ == '__main__':
    parser = FlexibleArgumentParser(
        description='Demo on using the LLMEngine class directly')
    parser = EngineArgs.add_cli_args(parser)
    args = parser.parse_args()
    main(args)

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