Summary of Conv-basis: a New Paradigm For Efficient Attention Inference and Gradient Computation in Transformers, by Yingyu Liang et al.
Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers
by Yingyu Liang, Heshan Liu, Zhenmei Shi, Zhao Song, Zhuoyan Xu, Junze Yin
First submitted to arxiv on: 8 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel approach to accelerate attention computation in transformer-based large language models (LLMs), which is crucial for scaling up these models to process longer input sequences. The key idea is to leverage the convolution-like structure of attention matrices and develop an efficient approximation method using convolution matrices. This allows for attention inference via Fast Fourier Transforms (FFT) in almost linear time, making it possible to handle longer contexts. Additionally, the training forward and backward gradient computations can also be performed efficiently. The paper provides theoretical guarantees on the runtime and approximation error and conducts preliminary experiments to evaluate its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve the performance of large language models by speeding up attention computation. Attention is a crucial component that allows these models to understand the relationships between words in a sentence. However, current methods have limitations when dealing with longer input sequences. The new approach uses a different method to calculate attention, which is faster and more efficient. This could enable language models to process longer texts and make them even more useful for tasks like language translation and text summarization. |
Keywords
» Artificial intelligence » Attention » Inference » Summarization » Transformer » Translation
