Summary of Accelerating Transformer Pre-training with 2:4 Sparsity, by Yuezhou Hu et al.
Accelerating Transformer Pre-training with 2:4 Sparsity
by Yuezhou Hu, Kang Zhao, Weiyu Huang, Jianfei Chen, Jun Zhu
First submitted to arxiv on: 2 Apr 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 A recent advancement in GPU architecture, specifically NVIDIA Ampere GPUs, enables faster execution of fine-grained 2:4 sparse matrix multiplications. This property is leveraged to accelerate feed-forward networks (FFNs) of transformers during pre-training. The authors propose three techniques to maintain accuracy while utilizing this speedup: a modified sparse-refined straight-through estimator with masked decay terms on gradients, a feasible decay factor in the warm-up stage, and dense fine-tuning near the end of pre-training. Additionally, two acceleration techniques are devised: calculating transposable 2:4 masks through convolution and accelerating gated activation functions by reducing GPU L2 cache misses. Experimental results demonstrate that the proposed 2:4 sparse training algorithm achieves similar convergence to dense training algorithms on various transformer pre-training tasks while providing actual speedup. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have made a discovery that can make some computer processes much faster. This is because of new graphics processing units (GPUs) called NVIDIA Ampere GPUs. These GPUs are better at doing certain types of math problems, which helps when training special kinds of artificial intelligence models called transformers. The authors of this paper came up with ways to use these fast GPUs to train these transformer models without losing their accuracy. They also found new ways to make the training process even faster by reducing the amount of memory needed and speeding up some specific calculations. |
Keywords
» Artificial intelligence » Fine tuning » Transformer
