Summary of Critiprefill: a Segment-wise Criticality-based Approach For Prefilling Acceleration in Llms, by Junlin Lv et al.
CritiPrefill: A Segment-wise Criticality-based Approach for Prefilling Acceleration in LLMs
by Junlin Lv, Yuan Feng, Xike Xie, Xin Jia, Qirong Peng, Guiming Xie
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper addresses the inefficiency of large language models during inference, specifically in the prefilling phase, which is a bottleneck for long-context tasks. The authors observe that adjacent query tokens tend to focus on similar subsets of the past Key-Value (KV) cache, leading them to propose CritiPrefill, a novel method that partitions input sequences into segments and blocks, estimating query criticality using a segment-wise algorithm. By pruning non-critical computations between query segments and cache blocks in the self-attention mechanism, the prefilling process can be accelerated, resulting in up to 2.7x speedup on Llama3-8B and 3.0x speedup on Yi-9B for 128K context length on a single A100 GPU with minimal quality degradation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about making large language models work faster without losing their ability to understand long sentences. The authors noticed that when they looked at how the model processes text, certain parts of the sentence tend to focus on similar information from earlier in the sentence. They developed a new way to process this information called CritiPrefill, which helps speed up the model’s processing by getting rid of unnecessary calculations. This means the model can handle longer sentences and still understand them well, making it more efficient. |
Keywords
» Artificial intelligence » Context length » Inference » Pruning » Self attention
