Summary of Frugal: Memory-efficient Optimization by Reducing State Overhead For Scalable Training, By Philip Zmushko et al.
FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training
by Philip Zmushko, Aleksandr Beznosikov, Martin Takáč, Samuel Horváth
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed paper introduces FRUGAL (Full-Rank Updates with Gradient Spitting), a new optimization framework designed to overcome the challenge of large GPU memory consumption in pre-training and fine-tuning processes for large language models. To achieve this, FRUGAL leverages gradient splitting to perform low-dimensional updates using advanced algorithms like Adam, while updating remaining directions via state-free methods like SGD or signSGD. The framework offers convergence guarantees when using SGDM for low-dimensional updates and SGD for state-free updates. Experimental results demonstrate FRUGAL’s ability to consistently outperform concurrent approaches across various fixed memory budgets, achieving state-of-the-art results in pre-training and fine-tuning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FRUGAL is a new way to help big computers learn faster by using less memory. Right now, it takes up too much space for all the things computers need to remember when they’re learning from lots of data. To fix this problem, FRUGAL divides the information into two parts and updates them separately. This helps free up some memory so computers can keep learning without running out of room. |
Keywords
» Artificial intelligence » Fine tuning » Optimization
