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LoCo:用于大型模型培训的低位通信适配器
概括
低位梯度通信可以加快大型模型训练,但可以降低质量. 低位通信适配器 (LoCo) 在压缩前补偿梯度,保持训练质量并提高效率.
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:
- 低位梯度通信对于高效的大型模型训练至关重要.
- 梯度压缩可能导致信息丢失和训练质量下降.
研究的目的:
- 开发一种方法,使得高效的梯度压缩,而不会影响培训质量.
- 引入低位通信适配器 (LoCo) 进行增强的梯度通信.
主要方法:
- 在压缩之前,LoCo对本地GPU节点的梯度进行补偿.
- 使用历史补偿错误的移动平均值来估计和纠正并发的压缩错误.
- 设计的兼容性与优化器,如亚当和分片策略,如FSDP.
主要成果:
- 通过补偿梯度压缩错误,LoCo可以实现更少的无损压缩.
- 理论分析证实,像Adam和SGD这样的优化器对收速度没有损害.
- 实验结果显示,在像Megatron-LM和PyTorch FSDP这样的框架中,通信效率显著提高 (Adam的速度提高了14% - 40%).
结论:
- 在大型模型训练中,LoCo有效地提高了梯度同步效率.
- 维护大型语言模型 (LLAMAs,MoEs) 的培训质量和性能.
- 在分布式深度学习中,LoCo提供了一种实际的解决方案,以提高沟通效率.
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