Transformer Acceleration with Dynamic Sparse Attention
This addresses deployment challenges for Transformers in NLP and other domains with long sequences, offering an incremental improvement over static sparse methods.
The paper tackles the computational bottleneck of Transformers due to quadratic attention complexity by proposing Dynamic Sparse Attention (DSA), which exploits input-dependent sparsity to achieve better accuracy-complexity trade-offs and practical speedups on hardware.
Transformers are the mainstream of NLP applications and are becoming increasingly popular in other domains such as Computer Vision. Despite the improvements in model quality, the enormous computation costs make Transformers difficult at deployment, especially when the sequence length is large in emerging applications. Processing attention mechanism as the essential component of Transformer is the bottleneck of execution due to the quadratic complexity. Prior art explores sparse patterns in attention to support long sequence modeling, but those pieces of work are on static or fixed patterns. We demonstrate that the sparse patterns are dynamic, depending on input sequences. Thus, we propose the Dynamic Sparse Attention (DSA) that can efficiently exploit the dynamic sparsity in the attention of Transformers. Compared with other methods, our approach can achieve better trade-offs between accuracy and model complexity. Moving forward, we identify challenges and provide solutions to implement DSA on existing hardware (GPUs) and specialized hardware in order to achieve practical speedup and efficiency improvements for Transformer execution.