![]() ![]() Such sparsity-based architectures usually require additional layers to implicitly produce a full attention mechanism. Since sparse matrices can also be represented by graphs and edges, sparsification methods are also motivated by the graph neural network literature, with specific relationships to attention outlined in Graph Attention Networks. These sparse entries may be manually proposed, found via optimization methods, learned, or even randomized, as demonstrated by such methods as Sparse Transformers, Longformers, Routing Transformers, Reformers, and Big Bird. Sparse attention reduces computation time and the memory requirements of the attention mechanism by computing a limited selection of similarity scores from a sequence rather than all possible pairs, resulting in a sparse matrix rather than a full matrix. This however, scales poorly with the length of the input sequence, requiring quadratic computation time to produce all similarity scores, as well as quadratic memory size to construct a matrix to store these scores.įor applications where long-range attention is needed, several fast and more space-efficient proxies have been proposed such as memory caching techniques, but a far more common way is to rely on sparse attention. The core block of every Transformer architecture is the attention module, which computes similarity scores for all pairs of positions in an input sequence. Transformer models have achieved state-of-the-art results across a diverse range of domains, including natural language, conversation, images, and even music. Posted by Krzysztof Choromanski and Lucy Colwell, Research Scientists, Google Research ![]()
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