Accelerating LLM Inference: Efficient Long Context Processing with SampleAttention

Large language models (LLMs) have made tremendous progress in recent years, supporting very long context windows. However, the quadratic complexity of standard attention mechanisms results in significantly prolonged Time-to-First-Token (TTFT) latency. This latency makes real-time interactions challenging, and existing methods to tackle this complexity often compromise model accuracy or require additional pretraining.

A team of researchers from China has proposed SampleAttention, an adaptive structured sparse attention mechanism that addresses the high TTFT latency by dynamically capturing head-specific sparse patterns during runtime with low overhead. SampleAttention leverages significant sparse patterns observed in attention mechanisms to capture essential information with minimal overhead.

SampleAttention in action

The proposed method focuses on two primary sparse patterns: local window patterns and column stripe patterns. Local window patterns are handled by attending to a fixed percentage of adjacent tokens, ensuring that important local dependencies are captured efficiently. Column stripe patterns are managed through a two-stage query-guided key-value (KV) filtering approach, which adaptively selects a minimal set of key-values to maintain low computational overhead.

“SampleAttention offers near-lossless sparse attention, seamlessly integrating into off-the-shelf LLMs without compromising accuracy.”

The method was evaluated on widely used LLM variants like ChatGLM2-6B and internLM2-7B, demonstrating its effectiveness in long-context scenarios. SampleAttention showed significant performance improvements, reducing TTFT by up to 2.42 times compared to FlashAttention. The evaluations included tasks such as LongBench, BABILong, and the “Needle in a Haystack” stress test, where SampleAttention maintained nearly no accuracy loss while significantly accelerating attention operations.

SampleAttention in long-context scenarios

This research effectively addresses the problem of high TTFT latency in LLMs with long context windows by introducing SampleAttention. This adaptive structured sparse attention method reduces computational overhead while maintaining accuracy, providing a practical solution for integrating into pre-trained models. The combination of local window and column stripe patterns ensures efficient handling of essential information, making SampleAttention a promising advancement for real-time applications of LLMs.

SampleAttention in real-time applications

In conclusion, SampleAttention is a significant breakthrough in accelerating LLM inference, enabling efficient long context processing without compromising model accuracy. This innovative approach has the potential to revolutionize real-time applications of LLMs, paving the way for more efficient and accurate language models.