FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference (2502.20766v1)

Abstract: LLMs encounter computational challenges during long-sequence inference, especially in the attention pre-filling phase, where the complexity grows quadratically with the prompt length. Previous efforts to mitigate these challenges have relied on fixed sparse attention patterns or identifying sparse attention patterns based on limited cases. However, these methods lacked the flexibility to efficiently adapt to varying input demands. In this paper, we introduce FlexPrefill, a Flexible sparse Pre-filling mechanism that dynamically adjusts sparse attention patterns and computational budget in real-time to meet the specific requirements of each input and attention head. The flexibility of our method is demonstrated through two key innovations: 1) Query-Aware Sparse Pattern Determination: By measuring Jensen-Shannon divergence, this component adaptively switches between query-specific diverse attention patterns and predefined attention patterns. 2) Cumulative-Attention Based Index Selection: This component dynamically selects query-key indexes to be computed based on different attention patterns, ensuring the sum of attention scores meets a predefined threshold. FlexPrefill adaptively optimizes the sparse pattern and sparse ratio of each attention head based on the prompt, enhancing efficiency in long-sequence inference tasks. Experimental results show significant improvements in both speed and accuracy over prior methods, providing a more flexible and efficient solution for LLM inference.

FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference

The paper "FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference" proposes a novel sparse attention mechanism tailored to enhance the efficiency of LLMs during long-sequence inference. Understanding that LLMs encounter considerable computational overhead due to the quadratic complexity of attention mechanisms relative to prompt length, the authors introduce FlexPrefill—a dynamic solution aimed at optimizing attention patterns and computational efforts based on specific input requirements and attention head demands.

Core Innovations

FlexPrefill distinguishes itself through two key components:

1. Query-Aware Sparse Pattern Determination: Utilizing the Jensen-Shannon divergence, this method categorizes attention heads into two distinct patterns—query-specific and predefined. This approach allows the mechanism to adaptively switch between diverse attention configurations, thereby optimizing for both flexibility and computational efficiency.
2. Cumulative-Attention Based Index Selection: This process involves the dynamic selection of query-key indices, ensuring that the cumulative attention scores breach a predefined threshold. It effectively allocates computational resources, maintaining model effectiveness without overextending its computational budget.

Through these innovations, FlexPrefill demonstrates an ability to dynamically allocate sparse patterns and ratios on a per-head and per-input basis, thereby accelerating pre-filling phases and increasing inference efficiency for long-sequence tasks.

Empirical Validation

Experimental results place FlexPrefill ahead of traditional fixed sparse patterns and training-free methods, showing substantial improvements in both speed and accuracy. The paper evaluates FlexPrefill's performance across various LLMs, such as Meta-Llama-3.1-8B-Instruct and GLM-4-9B-Chat, on long-context benchmarks like RULER and InfiniteBench. Particularly notable is its ability to preserve and occasionally enhance model performance while concurrently reducing inference latency. Notably, FlexPrefill achieves up to 3.49x speedup when processing sequences of 128k tokens while maintaining robust performance.

Theoretical and Practical Implications

Theoretically, this research reinforces the significance of adaptability in sparse attention patterns, acknowledging the diverse and variable nature of real-world input sequences. It suggests a shift towards dynamic methodologies that balance computational burden with model accuracy. Practically, FlexPrefill presents a viable path for operating LLMs with long sequence inputs more efficiently, potentially lowering the computational costs in fields that rely on long context comprehension, such as document analysis, coding, and retrieval tasks.

Future Directions

The insights from this work advocate for continued exploration into optimizing attention mechanisms within LLMs. Future research might delve into further refinements of the adaptive strategies, potentially incorporating hybrid mechanisms that can autonomously learn to decide between dense and sparse computations based on real-time input evaluation. Moreover, extending this dynamic attention approach to the decoding phase could yield further improvements in inference efficiency.

In summary, FlexPrefill offers a substantial contribution to the field of efficient model computation by marrying flexibility with performance. It represents a significant step towards enabling real-time application of LLMs on longer sequences, with implications for both industry and academia around scalable and efficient natural language processing solutions.