Summary of Vtrans: Accelerating Transformer Compression with Variational Information Bottleneck Based Pruning, by Oshin Dutta et al.
VTrans: Accelerating Transformer Compression with Variational Information Bottleneck based Pruning
by Oshin Dutta, Ritvik Gupta, Sumeet Agarwal
First submitted to arxiv on: 7 Jun 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 VTrans is an iterative pruning framework that compresses large pre-trained transformer models for resource-constrained devices. Unlike traditional methods, VTrans prunes all structural components, including embeddings, attention heads, and layers, guided by the Variational Information Bottleneck (VIB) principle. This approach retains only essential weights in each layer, ensuring compliance with specified model size or computational constraints. The method achieves up to 70% more compression than prior state-of-the-art approaches, both task-agnostic and task-specific. Faster variants of VTrans are proposed: Fast-VTrans using only 3% of the data and Faster-VTrans, a time-efficient alternative that accelerates compression by up to 25 times with minimal performance loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VTrans is a new way to make big AI models smaller so they can work on devices with limited resources. Right now, people are trying to do this for things like language translation, but it’s hard because the models get too big and slow down. VTrans solves this problem by getting rid of parts of the model that aren’t important. It does this by looking at what parts of the model are working together and deciding which ones to keep or get rid of. This makes the model smaller and faster, but it still works just as well. |
Keywords
» Artificial intelligence » Attention » Pruning » Transformer » Translation
