The number of parameters in large-scale language models based on transformers is gradually increasing, and the scale of computing clusters is also growing. The technology of quickly mobilizing large amounts of computing resources for parallel computing is becoming increasingly important. In this paper, we propose an automatic parallel algorithm that automatically plans the parallel strategy with maximum throughput based on model and hardware information. By decoupling the training time into computation, communication, and overlap, we established a training duration simulation model. Based on this simulation model, we prune the parallel solution space to shorten the search time required. The multi-node experiment results show that the algorithm can estimate the parallel training duration in real time with an average accuracy of 96%. In our test, the recommendation strategy provided by the algorithm is always globally optimal.
@article{arxiv.2501.00254,
title = {Automatically Planning Optimal Parallel Strategy for Large Language Models},
author = {Zongbiao Li and Xiezhao Li and Yinghao Cui and Yijun Chen and Zhixuan Gu and Yuxuan Liu and Wenbo Zhu and Fei Jia and Ke Liu and Qifeng Li and Junyao Zhan and Jiangtao Zhou and Chenxi Zhang and Qike Liu},
journal= {arXiv preprint arXiv:2501.00254},
year = {2025}
}