Summary of Beyond 2:4: Exploring V:n:m Sparsity For Efficient Transformer Inference on Gpus, by Kang Zhao et al.
Beyond 2:4: exploring V:N:M sparsity for efficient transformer inference on GPUs
by Kang Zhao, Tao Yuan, Han Bao, Zhenfeng Su, Chang Gao, Zhaofeng Sun, Zichen Liang, Liping Jing, Jianfei Chen
First submitted to arxiv on: 21 Oct 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 The paper explores the application of V:N:M sparsity in vision models and large language models (LLMs) across multiple tasks. It proposes three key approaches to enhance the applicability and accuracy of V:N:M-sparse Transformers, including heuristic V and M selection, V:N:M-specific channel permutation, and three-staged LoRA training techniques. The experimental results show that DeiT-small achieves lossless accuracy at 64:2:5 sparsity, while DeiT-base maintains accuracy even at 64:2:8 sparsity. Additionally, the fine-tuned LLama2-7B at 64:2:5 sparsity performs comparably or better than training-free 2:4 sparse alternatives on downstream tasks. The paper suggests that V:N:M-sparse Transformers offer a wider range of speedup-accuracy trade-offs compared to 2:4 sparsity, making it a truly effective acceleration solution for Transformers in cost-sensitive inference scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make transformers faster and more accurate by using something called V:N:M sparsity. It tries out different ways to make this work better and finds that certain methods can make models as good or even better than before, but also much faster. This is important because it means we can use these models in situations where speed and accuracy are important. |
Keywords
» Artificial intelligence » Inference » Lora
