[论文解读] Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks

Taiyuan Mei, Yun Zi+4 more|arXiv (Cornell University)|May 20, 2024

Advanced Data Processing Techniques被引用 5

一句话总结

本文分析大型语言模型的效率瓶颈，回顾训练时间优化（自适应优化器、并行化、混合精度）和推理时间压缩（量化、剪枝、知识蒸馏），并讨论局限性与未来方向。

ABSTRACT

The internal structure and operation mechanism of large-scale language models are analyzed theoretically, especially how Transformer and its derivative architectures can restrict computing efficiency while capturing long-term dependencies. Further, we dig deep into the efficiency bottleneck of the training phase, and evaluate in detail the contribution of adaptive optimization algorithms (such as AdamW), massively parallel computing techniques, and mixed precision training strategies to accelerate convergence and reduce memory footprint. By analyzing the mathematical principles and implementation details of these algorithms, we reveal how they effectively improve training efficiency in practice. In terms of model deployment and inference optimization, this paper systematically reviews the latest advances in model compression techniques, focusing on strategies such as quantification, pruning, and knowledge distillation. By comparing the theoretical frameworks of these techniques and their effects in different application scenarios, we demonstrate their ability to significantly reduce model size and inference delay while maintaining model prediction accuracy. In addition, this paper critically examines the limitations of current efficiency optimization methods, such as the increased risk of overfitting, the control of performance loss after compression, and the problem of algorithm generality, and proposes some prospects for future research. In conclusion, this study provides a comprehensive theoretical framework for understanding the efficiency optimization of large-scale language models.

研究动机与目标

• 分析基于 Transformer 的大型语言模型在理论与实践层面的效率瓶颈。
• 评估自适应优化、海量并行计算和混合精度训练如何提升训练效率和内存使用。
• 系统性评述模型压缩技术（量化、剪枝、知识蒸馏）以在保持精度的同时实现更快的推理。
• 批判性地审视当前方法的局限性，包括过拟合风险和通用性问题，并提出未来的研究方向。

提出的方法

• 对 Transformer 架构进行理论分析，以识别限制计算效率与长期依赖捕获的因素。
• 评估自适应优化算法（如 AdamW）及其在收敛速度和内存占用方面的作用。
• 检验海量并行计算技术与混合精度训练在训练过程中的加速效果。
• 系统性回顾压缩技术（量化、剪枝、知识蒸馏）的理论框架及其对推理的实际影响。

实验结果

研究问题

• RQ1 自适应优化、并行计算与混合精度训练如何影响大型语言模型的训练效率和内存使用？
• RQ2 量化、剪枝和知识蒸馏在不同任务中的推理延迟与模型精度有何影响？
• RQ3 当前效率优化方法受到哪些限制（例如过拟合、压缩后性能下降、算法的通用性等），以及潜在的未来研究方向是什么？

主要发现

• 自适应优化、并行性和混合精度可以在训练过程中加速收敛并降低内存占用。
• 压缩技术在显著减少模型大小和推理延迟的同时，力求保持准确性。
• 理论与实践分析揭示了效率提升与潜在风险（如过拟合和压缩后性能下降）之间的权衡。
• 当前方法在通用性和在多样化场景中的适用性方面存在局限性，需未来研究方向。