Summary of Iceformer: Accelerated Inference with Long-sequence Transformers on Cpus, by Yuzhen Mao et al.
IceFormer: Accelerated Inference with Long-Sequence Transformers on CPUs
by Yuzhen Mao, Martin Ester, Ke Li
First submitted to arxiv on: 5 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 method accelerates self-attention in Transformer-based models, addressing the limitation of handling very long sequences as input. The approach works with pretrained models without retraining and achieves a speedup of 2.73x – 7.63x while retaining 98.6% – 99.6% accuracy on various benchmarks, including LLaMA 2-based large language models (LLMs). By leveraging CPUs, the method demonstrates significant performance improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem with Transformer-based models that can’t handle very long sequences of information. To fix this, researchers developed a new way to speed up self-attention in these models without needing to retrain them. They tested their approach on several examples and showed it works well, making it much faster while still keeping the same level of accuracy. |
Keywords
» Artificial intelligence » Llama » Self attention » Transformer
