Summary of Grin: Gradient-informed Moe, by Liyuan Liu et al.
GRIN: GRadient-INformed MoE
by Liyuan Liu, Young Jin Kim, Shuohang Wang, Chen Liang, Yelong Shen, Hao Cheng, Xiaodong Liu, Masahiro Tanaka, Xiaoxia Wu, Wenxiang Hu, Vishrav Chaudhary, Zeqi Lin, Chenruidong Zhang, Jilong Xue, Hany Awadalla, Jianfeng Gao, Weizhu Chen
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 novel approach to scaling up mixture-of-experts (MoE) models is proposed in this paper, which leverages sparse computation through expert routing to selectively activate a small subset of expert modules. However, traditional training practices are challenged by the discrete nature of expert routing, hindering standard backpropagation and gradient-based optimization. To address this issue, the authors introduce GRIN (GRadient-INformed MoE training), which incorporates sparse gradient estimation for expert routing and configures model parallelism to avoid token dropping. The proposed approach is applied to autoregressive language modeling, resulting in a top-2 16×3.8B MoE model that outperforms a 7B dense model and matches the performance of a 14B dense model trained on the same data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoE models are a type of deep learning architecture that can be scaled up to process large amounts of data. However, traditional training methods don’t work well with MoE models because they use expert routing, which makes it hard for the model to learn from its mistakes. To fix this problem, researchers developed a new approach called GRIN (GRadient-INformed MoE training). This method helps the MoE model learn more effectively by using sparse gradients and parallel processing. The authors tested their approach on several language modeling tasks and found that it worked really well. |
Keywords
» Artificial intelligence » Autoregressive » Backpropagation » Deep learning » Mixture of experts » Optimization » Token
