Faster Causal Attention Over Large Sequences Through Sparse Flash
  Attention
Matteo Pagliardini Daniele Paliotta Martin Jaggi François Fleuret
Transformer-based language models have found many diverse applications
requiring them to process sequences of increasing length. For these
applications, the causal self-attention -- which is the only component scaling
quadratically w.r.t. the sequence length -- becomes a central concern. While
many works have proposed schemes to sparsify the attention patterns and reduce
the computational overhead of self-attention, those are often limited by
implementations concerns and end up imposing a simple and static structure over
the attention matrix. Conversely, implementing more dynamic sparse attentions
often results in runtimes significantly slower than computing the full
attention using the Flash implementation from Dao et al. (2022). We extend
FlashAttention to accommodate a large class of attention sparsity patterns
that, in particular, encompass key/query dropping and hashing-based attention.
This leads to implementations with no computational complexity overhead and a
multi-fold runtime speedup on top of FlashAttention. Even with relatively low
degrees of sparsity, our method improves visibly upon FlashAttention as the
sequence length increases. Without sacrificing perplexity, we increase the
training speed of a transformer language model by $2.0\times$ and $3.3\times$
for sequences of respectively $8k$ and $16k$ tokens.