Summary of Coap: Memory-efficient Training with Correlation-aware Gradient Projection, by Jinqi Xiao et al.
COAP: Memory-Efficient Training with Correlation-Aware Gradient Projection
by Jinqi Xiao, Shen Sang, Tiancheng Zhi, Jing Liu, Qing Yan, Yuqian Zhang, Linjie Luo, Bo Yuan
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 In this paper, the authors introduce a new method called COAP (Correlation-Aware Gradient Projection) that addresses the challenge of training large-scale neural networks in vision and multimodal domains by minimizing optimizer memory usage without compromising performance. By leveraging inter-projection correlation, COAP reduces computational overhead while maintaining model accuracy. The proposed approach outperforms existing methods across various tasks, achieving significant reductions in optimizer memory (up to 81% with quantization) and speeding up training processes (by 4x). Notably, COAP achieves the same perplexity as AdamW for LLaMA-1B, while using only 2% more time. The authors’ findings demonstrate the efficacy of COAP for large-scale neural network training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers developed a new way to train big artificial intelligence models without taking up too much computer memory. They made an algorithm called COAP that works faster and better than other similar methods. This is important because big AI models can be slow and use lots of memory, making it hard to work with them. The scientists tested their algorithm on different types of tasks, like recognizing images or understanding language, and found that it worked well. They even showed how COAP could make an AI model go 4 times faster while keeping its performance high. |
Keywords
» Artificial intelligence » Llama » Neural network » Perplexity » Quantization
