Summary of S-ste: Continuous Pruning Function For Efficient 2:4 Sparse Pre-training, by Yuezhou Hu et al.
S-STE: Continuous Pruning Function for Efficient 2:4 Sparse Pre-training
by Yuezhou Hu, Jun Zhu, Jianfei Chen
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a novel 2:4 sparse training method, S-STE, which leverages Nvidia Ampere and Hopper GPUs to accelerate matrix multiplications. The authors analyze the limitations of traditional N:M sparse training and recognize three drawbacks with discontinuity: incorrect descending direction, inability to predict the amount of descent, and sparse mask oscillation. To overcome these challenges, they introduce a simple yet powerful 2:4 training method comprising two parts: continuous projection of weights to be 2:4 sparse and rescaling sparse weights with a per-tensor fixed scaling factor. The authors also employ minimum-variance unbiased estimation for activation gradient and FP8 quantization for the whole process. Experimental results demonstrate that S-STE outperforms previous 2:4 pre-training recipes and is comparable to full parameter models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a way to make training deep neural networks faster using special GPUs. They show that traditional methods have some big problems, like not going in the right direction or changing too much. To fix these issues, they created a new method called S-STE that helps train networks more efficiently. It works by taking small steps towards being 2:4 sparse and then adjusting the weights accordingly. The authors also used some clever tricks to make the process faster and better. The results show that their method is really good at training networks quickly and accurately. |
Keywords
» Artificial intelligence » Mask » Quantization
