Summary of Numerical Pruning For Efficient Autoregressive Models, by Xuan Shen et al.
Numerical Pruning for Efficient Autoregressive Models
by Xuan Shen, Zhao Song, Yufa Zhou, Bo Chen, Jing Liu, Ruiyi Zhang, Ryan A. Rossi, Hao Tan, Tong Yu, Xiang Chen, Yufan Zhou, Tong Sun, Pu Zhao, Yanzhi Wang, Jiuxiang Gu
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 have become a leading architecture in deep learning, showing versatility and high effectiveness across various domains beyond language and image processing. However, their impressive performance often comes at the cost of high computational costs due to their substantial model size. This paper focuses on compressing decoder-only transformer-based autoregressive models through structural weight pruning to improve model efficiency while preserving performance for both language and image generation tasks. Specifically, the authors propose a training-free pruning method that calculates a numerical score using Newton’s method for Attention and MLP modules, respectively. Additionally, they suggest another compensation algorithm to recover the pruned model for better performance. The effectiveness of their method is verified through theoretical support and extensive experiments, showing state-of-the-art performance with reduced memory usage and faster generation speeds on GPUs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computer models more efficient while keeping them just as good at doing tasks like language translation or image creation. Right now, these models are very big and use a lot of computing power. The researchers found a way to make the model smaller without sacrificing its performance. They did this by “pruning” some of the unimportant parts of the model, which saved computer resources. To do this, they developed a special method that doesn’t need any new training data. They tested their method and showed that it works really well, taking less memory space and processing information faster. |
Keywords
» Artificial intelligence » Attention » Autoregressive » Decoder » Deep learning » Image generation » Pruning » Transformer » Translation
