Summary of Prepacking: a Simple Method For Fast Prefilling and Increased Throughput in Large Language Models, by Siyan Zhao et al.
Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models
by Siyan Zhao, Daniel Israel, Guy Van den Broeck, Aditya Grover
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 research optimizes the prefilling process for transformer-based large language models (LLMs) during inference. Prefilling involves computing the key-value cache for input tokens in the prompt prior to autoregressive generation, but this process can incur significant overhead on decoding time, especially for longer input prompts. The authors highlight a pitfall of standard padding practices, which waste computation by treating all sequences as having the maximum length. To address this issue, they propose Prepacking, a method that combines and packs multiple prompts with varying lengths into a single sequence, avoiding redundant computation. This approach achieves significant speed and memory efficiency improvements compared to default padding-based prefilling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are becoming increasingly popular for tasks like language translation and text summarization. But did you know that these models can be slow when dealing with long pieces of text? That’s because they need to “remember” every single word in the input before generating output. This process is called prefilling, and it can take a lot of time and computer power. The researchers found a way to make this process faster by grouping similar lengths together, so the model only needs to do some calculations once for each group. This makes the model run much quicker and use less memory. |
Keywords
» Artificial intelligence » Autoregressive » Inference » Prompt » Summarization » Transformer » Translation
