Summary of Alisa: Accelerating Large Language Model Inference Via Sparsity-aware Kv Caching, by Youpeng Zhao et al.
ALISA: Accelerating Large Language Model Inference via Sparsity-Aware KV Caching
by Youpeng Zhao, Di Wu, Jun Wang
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Performance (cs.PF)
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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 Transformer architecture has revolutionized natural language processing (NLP), enabling large language models like LLaMA and OPT to excel in various NLP tasks. Despite their superior accuracy, these models present practical challenges during inference, mainly due to their compute- and memory-intensive nature. KV caching for attention layers can accelerate inference by substituting quadratic-complexity computation with linear-complexity memory accesses, but this approach requires increasing memory as demand grows for processing longer sequences, leading to reduced throughput and even out-of-memory errors on resource-constrained systems. To address these challenges, we propose ALISA, a novel algorithm-system co-design solution that prioritizes tokens important in generating new tokens via Sparse Window Attention (SWA), reducing the memory footprint of KV caching at negligible accuracy loss. SWA introduces high sparsity in attention layers and reduces the memory footprint of KV caching at negligible accuracy loss. On the system level, ALISA optimizes the trade-off between caching and recomputation, maximizing overall performance in resource-constrained systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models like LLaMA and OPT have become superstars in natural language processing (NLP). They’re great at many tasks, but there’s a catch. When we want to use these models, they can be very slow and use up lots of memory and computing power. This is because they need to process lots of words and find the right relationships between them. To make things faster and more efficient, researchers have developed a way to “cache” some information in memory so that it doesn’t need to be recalculated every time. But this caching requires even more memory and can slow things down if we don’t have enough. The solution is called ALISA, which stands for Algorithm-System Co-design Solution. It’s a new way of doing things that prioritizes the most important information and reduces the amount of memory needed. This makes it possible to use these models on devices with limited resources like laptops or smartphones. |
Keywords
* Artificial intelligence * Attention * Inference * Llama * Natural language processing * Nlp * Transformer
