SeerAttention is a novel attention mechanism designed to enhance the efficiency of large language models (LLMs) by addressing the quadratic complexity inherent in traditional attention mechanisms. It introduces a learnable gate that adaptively identifies significant blocks within an attention map, designating the remaining blocks as sparse. This adaptive approach to attention sparsity enables a balance between accuracy and computational speed, especially for long-context windows.
SeerAttention can significantly improve the efficiency of LLMs by dynamically learning and leveraging sparsity in attention maps. It offers substantial speedups over traditional attention mechanisms, such as FlashAttention-2, while maintaining high accuracy even at high sparsity levels. SeerAttention is particularly effective in both post-training and long-context fine-tuning scenarios, making it versatile for various applications requiring different context lengths and sparsity ratios. When applied to long-context fine-tuning with YaRN, SeerAttention can achieve a remarkable 90% sparsity ratio at a 32k context length with minimal perplexityloss, offering a 5.67x speedup over FlashAttention-2. More details can be found in SeerAttention.
SeerAttention is intended for use in enhancing the computational efficiency of LLMs, especially those dealing with long-context windows. It serves researchers and developers in the field of machine learning who are focused on improving the performance and scalability of attention mechanisms in LLMs.
SeerAttention was evaluated on its ability to learn and leverage sparsity in attention maps, its adaptability to various context lengths and sparsity ratios, and its overall performance in post-training and fine-tuning scenarios. The evaluation involved comparisons with state-of-the-art sparse attention methods, demonstrating superior performance and flexibility. Performance metrics included speedup rates and accuracy retention at different sparsity levels.
While SeerAttention offers significant improvements, it requires a customized FlashAttention implementation to efficiently learn block-level attention sparsity. This may introduce additional complexity in integrating SeerAttention into existing systems. As we increase block-level sparsity to maximize computational speed, some degradation in accuracy is observed. When applying SeerAttention during the YaRN extension fine-tuning, it maintains near-lossless performance at 50% sparsity (from 8.79 to 8.81 on PG19 and from 2.46 to 2.47 on ProofPile) and minimal loss at 90% sparsity (from 8.79 to 9.16 on PG19 and from 2.46 to 2.60 on ProofPile). SeerAttention was developed for research and experimental purposes. Further testing and validation are needed before considering its application in commercial or real-world scenarios. This technique only works with text inputs/outputs (not multimodal), and it was only developed using the English language.
SeerAttention is designed to perform reliably within long-context LLMs. Users can influence its performance by configuring the learnable gate, selecting appropriate context lengths, and setting desired sparsity ratios. Understanding these configurations and their trade-offs is essential for maximizing the efficiency and accuracy of SeerAttention.