Summary of Jetfire: Efficient and Accurate Transformer Pretraining with Int8 Data Flow and Per-block Quantization, by Haocheng Xi et al.
Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block Quantization
by Haocheng Xi, Yuxiang Chen, Kang Zhao, Kai Jun Teh, Jianfei Chen, Jun Zhu
First submitted to arxiv on: 19 Mar 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 proposed Jetfire method efficiently trains INT8 transformers while maintaining their accuracy. By optimizing memory access and using a per-block quantization approach, Jetfire achieves comparable performance to the FP16 baseline and outperforms existing INT8 training methods for transformers. The method also offers significant speedup (1.42x) and memory reduction (1.49x) compared to the FP16 baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Jetfire is a new way to train special kinds of computer models called transformers, which are used in many applications like language translation and image recognition. Traditionally, training these models takes a lot of time and computing power. Jetfire helps make this process faster by using a technique called quantization, which reduces the amount of memory needed to store the model’s calculations. This not only makes training faster but also uses less energy and computing resources. |
Keywords
* Artificial intelligence * Quantization * Translation
