Summary of Minference 1.0: Accelerating Pre-filling For Long-context Llms Via Dynamic Sparse Attention, by Huiqiang Jiang et al.
MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention
by Huiqiang Jiang, Yucheng Li, Chengruidong Zhang, Qianhui Wu, Xufang Luo, Surin Ahn, Zhenhua Han, Amir H. Abdi, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, Lili Qiu
First submitted to arxiv on: 2 Jul 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 The paper introduces MInference, a sparse calculation method designed to accelerate the pre-filling stage of long-sequence processing for Large Language Models (LLMs). The authors identify three unique patterns in attention matrices that can be leveraged for efficient sparse computation on GPUs. This technique significantly reduces latency in the pre-filling stage of LLMs while maintaining accuracy, with a potential reduction of up to 10x for an A100 GPU. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MInference is a new way to make big language models work faster and more efficiently. It helps computers process long strings of text much quicker than before. This can be really useful when we want to use these models to help us with things like answering questions or summarizing texts. The method uses special patterns in the way it calculates attention, which is a key part of how language models work. By using these patterns, MInference can make the process much faster and more efficient. |
Keywords
* Artificial intelligence * Attention
