Summary of Prosparse: Introducing and Enhancing Intrinsic Activation Sparsity Within Large Language Models, by Chenyang Song et al.
ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models
by Chenyang Song, Xu Han, Zhengyan Zhang, Shengding Hu, Xiyu Shi, Kuai Li, Chen Chen, Zhiyuan Liu, Guangli Li, Tao Yang, Maosong Sun
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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 The paper introduces ProSparse, a simple and effective method for achieving high activation sparsity in large language models (LLMs) while maintaining comparable performance. Activation sparsity is the property of having weakly-contributed elements among activation outputs, which can boost model inference efficiency when using ReLU as an activation function. The authors substitute the original activation function with ReLU and use progressive sparsity regularization to enhance sparsity and mitigate performance degradation. They achieve high sparsity rates for LLaMA2-7B (89.32%), LLaMA2-13B (88.80%), and MiniCPM-1B (87.89%) models, surpassing previous results with comparable performance. The paper also demonstrates the potential for inference acceleration, achieving up to 4.52x speedup. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps large language models be more efficient by making some parts of the model less important. They use a special way to make these parts work together better and keep the overall quality of the model. This is useful because it can make computers run faster when using these models. |
Keywords
* Artificial intelligence * Inference * Regularization * Relu
