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About this paper
Junyang Lin, An Yang, Jinze Bai, Chang Zhou, Le Jiang, Xianyan Jia, Ang Wang, Jie Zhang and 4 more
Alibaba Group researchers introduce M6-10T, an approach combining the "Pseudo-to-Real" training paradigm with "Granular CPU Offloading" to efficiently pretrain extreme-scale deep learning models. This methodology enabled the successful pretraining of a 10-trillion-parameter M6 model using 512 NVIDIA V100 GPUs in approximately 10 days, demonstrating enhanced training efficiency and superior convergence compared to traditional methods.
Key insights
- Employing cross-layer parameter sharing in an initial training stage (
- Pseudo
- ) drastically reduces memory requirements and enables much faster initial convergence by allowing larger batch sizes.
- A "warm start" through delinking shared parameters for the full-capacity model (
- Real
Summary by alphaXiv.
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