Block Sparse Attention Mechanism

A Block Sparse Attention Mechanism is an attention mechanism that improves efficiency by computing attention weights within or between predefined blocks of the input sequence (rather than across the entire sequence).

• Context:
  • It can (typically) allow deep learning models, especially those based on the Transformer architecture, to process longer sequences than would be feasible with standard, full attention mechanisms.
  • It can (often) employ various sparsity patterns to selectively focus on the most relevant parts of the input data, thereby maintaining or even enhancing model performance despite the reduction in computational complexity.
  • It can (often) be implemented with different strategies for dividing the input sequence into blocks and for defining the connections (i.e., which blocks attend to each other) to optimize for specific tasks or data types.
  • It can be particularly useful in natural language processing (NLP), genomic sequence analysis, and long-range time series forecasting, where handling long sequences efficiently is crucial.
  • ...
• Example(s):
  • as presented in (Shen et al., 2022).
  • ...
• Counter-Example(s):
  • A Full Attention Mechanism in a Transformer model, which computes attention weights across the entire input sequence without any sparsity or blocking.
  • A Local Attention Mechanism that focus only on a fixed-size window around each element in the sequence, without the flexible, pattern-based sparsity of block sparse attention.
• See: Transformer architecture, attention mechanism, computational efficiency, memory usage, sparsity pattern.

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References

2022

• (Shen et al., 2022) ⇒ Guan Shen, Jieru Zhao, Quan Chen, Jingwen Leng, Chao Li, and Minyi Guo. (2022). “SALO: An Efficient Spatial Accelerator Enabling Hybrid Sparse Attention Mechanisms for Long Sequences.” In: Proceedings of the 59th ACM / IEEE Design Automation Conference, pp. 571-576.

2021

• https://huggingface.co/blog/big-bird
  • NOTES:
    • A Block Sparse Attention Mechanism improves computational efficiency by selectively computing attention within or across predefined blocks of input sequences, rather than the entire sequence, enabling the processing of much longer sequences than traditional full attention mechanisms allow.
    • A Block Sparse Attention Mechanism employs different strategies for sparsity, such as combining sliding window, global, and random attention patterns. This selective focus maintains, or even enhances, performance by emphasizing the most relevant parts of the input data despite reduced computational complexity.
    • A Block Sparse Attention Mechanism is adaptable to various tasks and data types by allowing customization in how the input sequence is divided into blocks and how these blocks attend to each other, optimizing for specific needs or challenges of the task.
    • A Block Sparse Attention Mechanism finds particular utility in fields like natural language processing (NLP), genomic sequence analysis, and long-range time series forecasting, where efficiently handling long sequences is critical for achieving high performance.
    • A Block Sparse Attention Mechanism contrasts with full attention mechanisms that compute attention weights across the entire input sequence and local attention mechanisms that only focus on fixed-size windows, offering a more flexible and efficient approach to managing long sequences.

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