Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time
Yingyu Liang, Zhizhou Sha, Zhenmei Shi, Zhao Song, Yufa Zhou
2024-08-26
Summary
This paper discusses a new method for calculating gradients in multi-layer transformer models more efficiently, reducing the time it takes to train and use these models.
What's the problem?
In popular transformer models, the self-attention mechanism is crucial for understanding data but requires a lot of computing power. This leads to slow training and high memory usage because the calculations take quadratic time, meaning they get significantly slower as the amount of data increases.
What's the solution?
The authors propose a new approach that allows for gradient calculations to be done in almost linear time, which is much faster. They achieve this by developing a method that works for any loss function and keeps errors small throughout the model. Their technique can handle various components of transformer models, making it versatile and efficient.
Why it matters?
This research is important because it addresses the inefficiencies in training large language models, which are widely used in AI today. By making these computations faster and less resource-intensive, it could lead to quicker development and deployment of advanced AI systems that can process longer texts and more complex tasks.
Abstract
The quadratic computational complexity in the self-attention mechanism of popular transformer architectures poses significant challenges for training and inference, particularly in terms of efficiency and memory requirements. Towards addressing these challenges, this paper introduces a novel fast computation method for gradient calculation in multi-layer transformer models. Our approach enables the computation of gradients for the entire multi-layer transformer model in almost linear time n^{1+o(1)}, where n is the input sequence length. This breakthrough significantly reduces the computational bottleneck associated with the traditional quadratic time complexity. Our theory holds for any loss function and maintains a bounded approximation error across the entire model. Furthermore, our analysis can hold when the multi-layer transformer model contains many practical sub-modules, such as residual connection, casual mask, and multi-head attention. By improving the efficiency of gradient computation in large language models, we hope that our work will facilitate the more effective training and deployment of long-context language models based on our theoretical results.