Summary of Magicpig: Lsh Sampling For Efficient Llm Generation, by Zhuoming Chen et al.
MagicPIG: LSH Sampling for Efficient LLM Generation
by Zhuoming Chen, Ranajoy Sadhukhan, Zihao Ye, Yang Zhou, Jianyu Zhang, Niklas Nolte, Yuandong Tian, Matthijs Douze, Leon Bottou, Zhihao Jia, Beidi Chen
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This paper addresses the performance bottleneck in large language models (LLMs) with long context windows, specifically the KV cache. Current dynamic sparse or TopK-based attention approximation methods rely on the assumption that attention is sparse, but this assumption does not always hold. Instead, the authors propose a sampling-based approach to estimate attention output, which outperforms traditional TopK methods in certain downstream tasks. To make this practical for LLM generation, the paper introduces MagicPIG, a heterogeneous system using Locality Sensitive Hashing (LSH). MagicPIG significantly reduces attention computation workload while maintaining high accuracy, enabling longer contexts and larger batch sizes. This results in improved decoding throughput by up to 5x across various GPU hardware. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make large language models work faster and more efficiently. Right now, these models get slowed down by the amount of information they need to process. The researchers found that current ways of making attention computation more efficient don’t always work well. They came up with a new approach called MagicPIG that uses a technique called Locality Sensitive Hashing (LSH) to make attention computation faster and better. This allows for longer pieces of text to be processed at once, which makes the model run faster. |
Keywords
* Artificial intelligence * Attention
