Summary of Adapprox: Adaptive Approximation in Adam Optimization Via Randomized Low-rank Matrices, by Pengxiang Zhao et al.
Adapprox: Adaptive Approximation in Adam Optimization via Randomized Low-Rank Matrices
by Pengxiang Zhao, Ping Li, Yingjie Gu, Yi Zheng, Stephan Ludger Kölker, Zhefeng Wang, Xiaoming Yuan
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Optimization and Control (math.OC)
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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 This paper addresses a significant issue in deep learning, where optimizers like Adam face memory consumption challenges due to storing first and second moment data as models grow exponentially larger. Existing methods such as Adafactor and CAME compromise accuracy using matrix factorization techniques. The authors introduce Adapprox, a novel approach employing randomized low-rank matrix approximation for an accurate and efficient approximation of Adam’s second moment. Adapprox features an adaptive rank selection mechanism to balance accuracy and memory efficiency, with an optional cosine similarity guidance strategy for stability and convergence acceleration. In GPT-2 training and downstream tasks, Adapprox outperforms AdamW by achieving 34.5% to 49.9% and 33.8% to 49.9% memory savings for the 117M and 345M models, respectively, with the first moment enabled. Additionally, it enhances convergence speed and improves downstream task performance relative to its counterparts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in deep learning where big models take up too much space on computers. Right now, there are ways to make models smaller, but they often sacrifice accuracy. The authors created a new way called Adapprox that makes models both smaller and more accurate. It works by breaking down the model into smaller pieces and using some clever math to keep it working well. In tests, Adapprox did better than other methods, saving memory space and making tasks run faster and more accurately. |
Keywords
* Artificial intelligence * Cosine similarity * Deep learning * Gpt
