从比特到芯片：一种基于LLM的硬件感知量化代理，用于简化LLM的部署
From Bits to Chips: An LLM-based Hardware-Aware Quantization Agent for Streamlined Deployment of LLMs
AI总结：
本文提出HAQA，一种基于LLM的自动化框架，通过高效超参数调优和硬件配置，简化LLM的量化和部署流程，提升部署质量和易用性，实验显示推理速度提升2.3倍，吞吐量和精度均有提升。
摘要：
部署模型，尤其是大型语言模型（LLMs），正越来越吸引更广泛的用户群体，包括没有专业知识的用户。然而，由于某些硬件的资源限制，保持高精度的同时满足硬件要求仍是一个重大挑战。模型量化技术有助于缓解内存和计算瓶颈，但量化模型的调优和部署复杂性进一步加剧了这些挑战，使过程对大多数用户来说不友好。我们引入了硬件感知量化代理（HAQA），一种自动化框架，利用LLM来通过高效的超参数调优和硬件配置，流利地完成整个量化和部署过程，从而同时提高部署质量和易用性，适用于广泛的用户。我们的结果表明，与未优化的模型相比，在Llama上实现了高达2.3倍的推理速度提升，同时吞吐量和精度均有提高。此外，HAQA被设计为在多样化的硬件平台上实施自适应量化策略，因为它能够自动找到最佳设置，即使这些设置看起来具有反直觉性，从而减少大量的手动努力，并展示出卓越的适应性。代码将被发布。
英文摘要：
Deploying models, especially large language models (LLMs), is becoming increasingly attractive to a broader user base, including those without specialized expertise. However, due to the resource constraints of certain hardware, maintaining high accuracy with larger model while meeting the hardware requirements remains a significant challenge. Model quantization technique helps mitigate memory and compute bottlenecks, yet the added complexities of tuning and deploying quantized models further exacerbates these challenges, making the process unfriendly to most of the users. We introduce the Hardware-Aware Quantization Agent (HAQA), an automated framework that leverages LLMs to streamline the entire quantization and deployment process by enabling efficient hyperparameter tuning and hardware configuration, thereby simultaneously improving deployment quality and ease of use for a broad range of users. Our results demonstrate up to a 2.3x speedup in inference, along with increased throughput and improved accuracy compared to unoptimized models on Llama. Additionally, HAQA is designed to implement adaptive quantization strategies across diverse hardware platforms, as it automatically finds optimal settings even when they appear counterintuitive, thereby reducing extensive manual effort and demonstrating superior adaptability. Code will be released.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
