Summary of Deep Optimizer States: Towards Scalable Training Of Transformer Models Using Interleaved Offloading, by Avinash Maurya et al.
Deep Optimizer States: Towards Scalable Training of Transformer Models Using Interleaved Offloading
by Avinash Maurya, Jie Ye, M. Mustafa Rafique, Franck Cappello, Bogdan Nicolae
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Emerging Technologies (cs.ET); Performance (cs.PF)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Transformers and large language models have rapidly gained popularity across various domains. However, training these models has become increasingly expensive due to their massive sizes, often hitting a “memory wall.” To overcome this limitation, current approaches typically offload the optimizer state to host memory, performing hybrid CPU-GPU computations. This suboptimal management of combined host-GPU memory leads to missed opportunities for leveraging interconnect bandwidth and computational capabilities. The authors propose a novel technique, , which splits large language models into subgroups, scheduling their update phases on either CPUs or GPUs based on a performance model that balances data movement cost, acceleration, and resource competition. The approach is integrated with DeepSpeed, achieving 2.5 times faster iterations compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to train super powerful computers to understand human language. These computers are so big they need a lot of memory to work properly. Right now, we’re using techniques like “memory tricks” to make them work faster, but it’s not very efficient. In this paper, the authors introduce a new way to split these computers into smaller groups and move some parts between the main computer and the processing units (CPUs and GPUs). This allows us to use the best combination of both for faster training, resulting in a 2.5 times improvement. |
