Efficient Sparse Attention Patterns
Technical exploration of sparse attention patterns in transformer models, focusing on implementation techniques and performance optimization.
Sliding Window Attention
Sliding Window Attention is one of the most efficient sparse attention patterns, implemented with fixed-size local windows where each token attends to its nearest neighbors. The key optimization here is memory locality - by processing attention in contiguous blocks, we maximize cache utilization and minimize random memory access. This pattern is particularly effective when combined with techniques like flash attention, which carefully orchestrates memory movement to stay within fast SRAM cache rather than accessing slower DRAM.
Block-Sparse Attention
Block-Sparse Attention takes the concept further by allowing dynamic block patterns rather than just local windows. The critical implementation detail is using specialized CUDA kernels that operate on blocks rather than individual tokens. By storing the sparse block structure in a compressed format and only computing attention over non-zero blocks, we can dramatically reduce both memory and compute requirements. The most performant implementations use techniques like adaptive block sizes and load-balanced sparsity patterns to maximize GPU utilization.
Longformer-style Attention
Longformer-style attention combines sliding window attention with global tokens, implemented through careful memory layout and custom CUDA kernels. The key optimization is processing the global attention in a separate pass and then merging results, which allows better parallelization. The most efficient implementations also use techniques like block-structured sparsity within the local windows and fused kernels that combine the local and global attention computations while maintaining good memory access patterns.
Additional Optimization Strategies
• Pre-computing sparsity patterns and access indices • Fusing multiple attention operations into single kernels • Using mixed precision and quantization • Careful memory layout for coalesced access • Load balancing across GPU streaming multiprocessors