Summary of Progressive Gradient Flow For Robust N:m Sparsity Training in Transformers, by Abhimanyu Rajeshkumar Bambhaniya et al.
Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers
by Abhimanyu Rajeshkumar Bambhaniya, Amir Yazdanbakhsh, Suvinay Subramanian, Sheng-Chun Kao, Shivani Agrawal, Utku Evci, Tushar Krishna
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Hardware Architecture (cs.AR)
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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 This research paper investigates the effectiveness of existing sparse training recipes for N:M structured sparsity in high-sparsity regions. The authors argue that these methods fail to sustain model quality due to elevated levels of induced noise in gradient magnitudes. To mitigate this effect, they propose a decay mechanism to restrict gradient flow towards pruned elements. The approach improves model quality by up to 2% and 5% in vision and language models, respectively, at high sparsity regimes. Additionally, the paper evaluates the trade-off between model accuracy and training compute cost in terms of FLOPs, demonstrating better performance with similar training costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how to make machine learning models more efficient by removing parts that don’t help much. Right now, there are ways to do this that work well for some parts of the model, but not others. The researchers found that these methods don’t work as well when trying to remove bigger parts of the model. They came up with a new way to make these models more efficient by reducing noise and improving performance. This approach can improve model quality by up to 2% in vision models and 5% in language models. |
Keywords
* Artificial intelligence * Machine learning
