SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs (2410.13276v2)

Abstract: Attention is the cornerstone of modern LLMs. Yet its quadratic complexity limits the efficiency and scalability of LLMs, especially for those with a long-context window. A promising approach addressing this limitation is to leverage the sparsity in attention. However, existing sparsity-based solutions predominantly rely on predefined patterns or heuristics to approximate sparsity. This practice falls short to fully capture the dynamic nature of attention sparsity in language-based tasks. This paper argues that attention sparsity should be learned rather than predefined. To this end, we design SeerAttention, a new Attention mechanism that augments the conventional attention with a learnable gate that adaptively selects significant blocks in an attention map and deems the rest blocks sparse. Such block-level sparsity effectively balances accuracy and speedup. To enable efficient learning of the gating network, we develop a customized FlashAttention implementation that extracts the block-level ground truth of attention map with minimum overhead. SeerAttention not only applies to post-training, but also excels in long-context fine-tuning. Our results show that at post-training stages, SeerAttention significantly outperforms state-of-the-art static or heuristic-based sparse attention methods, while also being more versatile and flexible to adapt to varying 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 perplexity loss, offering a 5.67x speedup over FlashAttention-2.

• The paper introduces ESpaDA, a novel attention mechanism that learns sparsity dynamically through a data-driven gating method, unlike predefined patterns.
• Experimental results show ESpaDA significantly outperforms static sparse methods, achieving near-lossless accuracy at 90% sparsity with a 5.67x speedup over FlashAttention-2 at 32k context.
• ESpaDA's learnable approach sets a precedent for developing more adaptable and efficient LLMs, particularly for handling long contexts where efficiency is crucial.

Overview of "SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs"

The paper introduces a novel attention mechanism, ESpaDA, designed to improve the sparsity handling capabilities of LLMs. The motivation stems from the quadratic complexity of traditional attention mechanisms, which hinders efficiency and scalability, particularly in long-context scenarios. Unlike past approaches that rely on predefined sparsity patterns, ESpaDA learns attention sparsity dynamically through a data-driven method.

Key Contributions and Methodology

The core proposition of the paper is that attention sparsity should be learned rather than predefined. ESpaDA achieves this by integrating a learnable gating mechanism with the conventional attention structure. The learnable gate, termed Attention Gate (AttnGate), processes pooled representations of the query (Q) and key (K) inputs to dynamically select significant blocks in the attention maps, thereby designating the remainder as sparse. This approach allows the mechanism to adapt to varying context lengths and sparsity ratios.

A customized implementation of FlashAttention is developed to enable this learned sparsity efficiently. This optimization facilitates the extraction of block-level attention map ground truth, crucial for training the gating network with negligible computational overhead. The paper demonstrates the application of ESpaDA both in post-training stages and during long-context fine-tuning.

Experimental Validation

Empirical results presented in the paper highlight that ESpaDA significantly outperforms static and heuristic-based sparse attention methods, surpassing state-of-the-art techniques such as Minference and MoA. ESpaDA's versatility is further underscored by its ability to accommodate various context lengths and sparsity ratios within a single model. Importantly, ESpaDA achieves near-lossless accuracy, even at a 90% sparsity ratio over a 32k context length, providing a notable speedup of 5.67 times over FlashAttention-2.

Implications

The introduction of ESpaDA sets a precedent for advancing the efficiency of LLMs in managing long contexts. The learnable sparse attention mechanism not only optimizes performance but also enhances adaptability, making it particularly useful in scenarios where efficiency is a priority. The success of ESpaDA suggests a shift toward learning-based approaches for handling attention sparsity, which may lead to more nuanced and efficient mechanisms in the future. This has substantial implications for both the practical deployment of LLMs and the theoretical underpinnings of attention mechanisms in deep learning frameworks.

Future Directions

The paper suggests several avenues for future exploration, such as improving the training methodologies for ESpaDA, applying it in the decoding stages of LLMs, and potential integration with other learning architectures. Further research could investigate how ESpaDA's learning-based sparsity can be generalized across different model architectures and various tasks, potentially leading to new paradigms in efficient LLM design.

In conclusion, this paper makes a compelling case for the realization of intrinsic sparsity as learned through data rather than assumed through static frameworks. The ESpaDA mechanism represents a significant step towards more adaptable and efficient large-scale models, with promising potential for widespread application and further development.