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Summary of Bifurcated Attention: Accelerating Massively Parallel Decoding with Shared Prefixes in Llms, by Ben Athiwaratkun et al.


Bifurcated Attention: Accelerating Massively Parallel Decoding with Shared Prefixes in LLMs

by Ben Athiwaratkun, Sujan Kumar Gonugondla, Sanjay Krishna Gouda, Haifeng Qian, Hantian Ding, Qing Sun, Jun Wang, Jiacheng Guo, Liangfu Chen, Parminder Bhatia, Ramesh Nallapati, Sudipta Sengupta, Bing Xiang

First submitted to arxiv on: 13 Mar 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI)

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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
This study proposes a novel approach called bifurcated attention to improve language model inference in shared-context batch decoding scenarios. The method addresses the challenge of redundant memory input/output costs by dividing the attention mechanism into two separate operations: one focusing on prefill KV cache and another on the decoding process itself. This strategic division ensures precise computation with significantly reduced memory IO, while maintaining the computational load (FLOPs) of standard attention mechanisms. The empirical results show significant speedup when sampling output sequences at context lengths exceeding 8k tokens on a 7B model using multi-head attention. For instance, this approach enables massively parallel answer generation without increasing latency, thus enhancing performance when integrated with post-processing techniques such as re-ranking.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper creates a new way to make language models work better together in big batches of text. They call it bifurcated attention. The problem they’re trying to solve is that current methods take too long because they have to move lots of information around in memory. Their solution is to divide the task into two parts, one for the old information and one for the new. This makes the process much faster, with a speedup of over 2 times when processing small batches and over 6 times when processing bigger ones. This could be very useful for applications that need to generate lots of answers quickly.

Keywords

* Artificial intelligence  * Attention  * Inference  * Language model  * Multi head attention