Summary of Comet: a Communication-efficient and Performant Approximation For Private Transformer Inference, by Xiangrui Xu et al.
Comet: A Communication-efficient and Performant Approximation for Private Transformer Inference
by Xiangrui Xu, Qiao Zhang, Rui Ning, Chunsheng Xin, Hongyi Wu
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 paper addresses the pressing need for private inference in cloud-based services reliant on Transformer-like models like ChatGPT. Current privacy-preserving frameworks impose significant communication burdens, particularly for non-linear computations in Transformers. The authors propose a novel plug-in method called Comet that reduces communication costs without compromising inference performance. They also introduce an efficient approximation method to eliminate heavy communication when finding good initial approximations. The authors evaluate their approach on Bert and RoBERTa models with GLUE benchmark datasets, achieving up to 3.9x less communication and 3.5x speedups while maintaining competitive model performance compared to prior art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to enable private inference in cloud-based services that use Transformer-like models like ChatGPT. This is important because current privacy-preserving methods can be slow and expensive. The authors introduce a new method called Comet that helps reduce communication costs without sacrificing accuracy. They also develop a way to quickly find good initial approximations, which further reduces the amount of communication needed. The authors test their approach using Bert and RoBERTa models on GLUE benchmark datasets and show that it can be up to 3.9 times faster while maintaining good performance. |
Keywords
» Artificial intelligence » Bert » Inference » Transformer
