PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization
Xinyi Wan, Penghui Qi, Guangxing Huang, Jialin Li, Min Lin
2025-03-05
Summary
This paper talks about PipeOffload, a new method to make training large AI language models more efficient by better managing computer memory
What's the problem?
When training big AI models, a technique called pipeline parallelism is often used. However, as the models get bigger, they need more memory to store temporary information (activations), which limits how large the models can be
What's the solution?
The researchers created PipeOffload, which moves some or all of the temporary information to a different type of memory. They found that in most cases, they could move at least half, and sometimes all, of this information without slowing down the training process. For trickier situations, they developed a smart way to choose which information to move that works even better than expected
Why it matters?
This matters because it allows researchers to train larger AI models more efficiently. PipeOffload makes pipeline parallelism work better than other methods, speeding up training by up to 19% while using less memory. This could lead to more powerful AI models being developed faster and at a lower cost
Abstract
Pipeline parallelism (PP) is widely used for training large language models (LLMs), yet its scalability is often constrained by high activation memory consumption as the number of in-flight microbatches grows with the degree of PP. In this paper, we focus on addressing this challenge by leveraging the under-explored memory offload strategy in PP. With empirical study, we discover that in the majority of standard configurations, at least half, and potentially all, of the activations can be offloaded with negligible overhead. In the cases where full overload is not possible, we introduce a novel selective offload strategy that decreases peak activation memory in a better-than-linear manner. Furthermore, we integrate memory offload with other techniques to jointly consider overall throughput and memory limitation. Our experiments proves that the per-device <PRE_TAG>activation memory</POST_TAG> effectively reduces with the total number of stages, making PP a stronger alternative than TP, offering up to a 19\% acceleration with even lower memory consumption. The implementation is open-sourced at https://github.com/sail-sg/zero-bubble-pipeline-parallelism{this url}.