Sparse Attention is a technique introduced to address the quadratic computational complexity of traditional self-attention mechanisms in Transformers. By employing sparse factorizations of the attention matrix, it reduces the time and memory requirements, enabling the processing of longer sequences. This approach facilitates the training of deeper networks and the modeling of sequences tens of thousands of timesteps long. The implementation includes faster versions of standard attention, as well as "strided" and "fixed" attention patterns, as detailed in the Sparse Transformers paper.
Key Features
Faster implementation of standard attention mechanisms.
Support for "strided" and "fixed" attention patterns.
Recompute decorator for memory-efficient training.
Optimized for long sequence modeling.
Utilizes fused operations for improved performance.
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