![]() ![]() #Wise memory optimizer 3.65 review windows# Our work proposes to learn dynamic sparse attention patterns that avoid allocating computation and memory to attend to content unrelated to the query of interest. This work builds upon two lines of research: It combines the modeling flexibility of prior work on content-based sparse attention with the efficiency gains from approaches based on local, temporal sparse attention. Our model, the Routing Transformer, endows self-attention with a sparse routing module based on online k-means while reducing the overall complexity of attention to O( n 1.5 d) from O( n 2 d) for sequence length n and hidden dimension d. ![]() We show that our model outperforms comparable sparse attention models on language modeling on Wikitext-103 (15.8 vs 18.3 perplexity), as well as on image generation on ImageNet-64 (3.43 vs 3.44 bits/dim) while using fewer self-attention layers. We open-source the code for Routing Transformer in Tensorflow.Īdditionally, we set a new state-of-the-art on the newly released PG-19 data-set, obtaining a test perplexity of 33.2 with a 22 layer Routing Transformer model trained on sequences of length 8192. Therefore, an important research direction is to investigate sparse and memory efficient forms of attention in order to scale to tasks with large sequence lengths. Previous work has proposed data independent or fixed sparsity patterns bounding temporal dependencies, such as local or strided attention. At each time step, the model attends only to a fixed number of time steps in the past (Child et al., 2019). Extensions to local attention have suggested learning the length of the tem poral sparsity for each attention module in the network (Sukhbaatar et al., 2019). ![]() These strategies draw their inspiration from RNNs and CNNs and bound their complexity by attend ing only to representations summarizing a local neighborhood of the current time step. #Wise memory optimizer 3.65 review windows#.#Wise memory optimizer 3.65 review code#. ![]()
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