Summary of Collage: Light-weight Low-precision Strategy For Llm Training, by Tao Yu et al.
Collage: Light-Weight Low-Precision Strategy for LLM Training
by Tao Yu, Gaurav Gupta, Karthick Gopalswamy, Amith Mamidala, Hao Zhou, Jeffrey Huynh, Youngsuk Park, Ron Diamant, Anoop Deoras, Luke Huan
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 the challenge of training large models efficiently by proposing a novel method called Collage that utilizes multi-component float representation in low-precision floating points. By properly compensating for numerical errors at critical locations, Collage can achieve accurate results while reducing compute cost and memory usage. The authors also introduce a simple metric to track the impact of imprecision on training, allowing for comparison of different precision strategies. Experimental results show that pre-training with Collage can attain similar or better performance compared to mixed-precision strategies, with significant speedups and memory savings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large models are hard to train because they need lots of computer power and memory. One way to make it easier is to use low-precision numbers, but this can cause problems during training. The authors suggest a new method called Collage that uses special numbers to keep track of errors and get accurate results. They also came up with a simple way to measure how much information gets lost during training. This helps us understand the pros and cons of different ways to do low-precision calculations. The experiments show that using Collage can be just as good or even better than other methods, while using less computer power and memory. |
Keywords
» Artificial intelligence » Precision
