Summary

This paper introduces ZeRO (Zero Redundancy Optimizer), a set of memory optimization techniques designed to enable training of extremely large (trillion parameter) deep learning models. ZeRO addresses the memory bottleneck in large-scale model training by partitioning the model states (optimizer states, gradients, and model parameters) across data parallel processes. It eliminates the need for redundant copies of these states on each device. The paper presents three stages of ZeRO: ZeRO-DP (Data Parallel), ZeRO-D (Optimizer State Partitioning), ZeRO-O (Gradient Partitioning), and ZeRO-P (Parameter Partitioning). The paper demonstrates that ZeRO significantly reduces memory consumption compared to existing data parallel training methods (e.g., data parallel) and model parallel methods, allowing for more efficient training on larger models and with larger batch sizes. Furthermore, the paper evaluates the performance of ZeRO with various model sizes and hardware configurations, showcasing scalability and performance improvements.

Key Takeaways

1. ZeRO reduces memory footprint by partitioning optimizer states, gradients, and model parameters across data parallel processes.
2. ZeRO enables the training of models with trillions of parameters by mitigating memory constraints.
3. ZeRO offers different stages (ZeRO-DP, ZeRO-D, ZeRO-O, and ZeRO-P) that provide a spectrum of memory optimization trade-offs.
4. ZeRO can achieve significant performance improvements and scalability in large-scale deep learning training.