潜在重量定量用于深度神经网络的整合训练.
概括
这项研究引入了一种新的潜质量量定量化方法,以提高深度学习中的集成训练效率. 它可以使用较低的精度重量进行训练,显著减少内存使用量,并允许在单个GPU上进行大型模型微调.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 集成训练通过使用低位宽表示来加速深度学习.
- 现有的方法忽视了完全精确的隐性权重,导致在梯度更新期间的高内存消耗.
- 这种内存开销限制了大型模型整合训练的效率.
研究的目的:
- 为一般的整体化培训提出第一个潜质量量化定量化方案.
- 为了最大限度地减少量子化干扰,并在训练过程中抑制噪声.
- 为了实现高效的培训和微调大型深度学习模型.
主要方法:
- 引入了一种剩余量化技术,以解相关隐性和整数化权重.
- 开发了一种优化的双量化器,具有不统一的代码库,以防止重量结.
- 设计了一个硬件友好的,为代码库优化设计的三段多线程近似.
- 雇员的重要性指导优化代码库,以最大限度地减少重量更新干扰.
主要成果:
- 在各种架构 (ResNets,MobileNetV2,Transformers) 中成功实现了使用4位隐藏权重的集成训练.
- 在图像分类和文本生成任务中表现出微不足道的性能损失.
- 成功微调大型语言模型 (LLM),在单个GPU上高达130亿个参数.
结论:
- 拟议的潜质量量量化方案显著提高了整合训练的效率.
- 它可以大幅减少记忆,促进大规模深度学习模型的培训和部署.
- 这种方法为资源有限的深度学习应用提供了实用解决方案.
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