Papers › Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

29 Jun 2020ICML 2020 1arXiv:2006.16236archive 2025-07-28

Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, François Fleuret

Transformers achieve remarkable performance in several tasks but due to their quadratic complexity, with respect to the input's length, they are prohibitively slow for very long sequences. To address this limitation, we express the self-attention as a linear dot-product of kernel feature maps and make use of the associativity property of matrix products to reduce the complexity from 𝒪(N²) to 𝒪(N), where N is the sequence length. We show that this formulation permits an iterative implementation that dramatically accelerates autoregressive transformers and reveals their relationship to recurrent neural networks. Our linear transformers achieve similar performance to vanilla transformers and they are up to 4000x faster on autoregressive prediction of very long sequences.

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elu kyle-gao/TF_Transformer/helperfunctions.py community (archive-listed) unverified Apache-2.0 (permissive) · 1cc88f7d37dba081 · report
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padding_mask kyle-gao/TF_Transformer/helperfunctions.py community (archive-listed) unverified Apache-2.0 (permissive) · d15b7f7e2a78f686 · report
positional_encoding kyle-gao/TF_Transformer/helperfunctions.py community (archive-listed) unverified Apache-2.0 (permissive) · cfcb2cdb8e0339a1 · report
tf_interleave_encode kyle-gao/TF_Transformer/preprocess.py community (archive-listed) unverified Apache-2.0 (permissive) · f8aeab29e343a2f7 · report
always identical code first harvested elsewhere ran · our draft was wrong licence of this copy not recorded · dd3db53ae7ad1c85 · report
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Tasks

D4RLLanguage ModellingOffline RL

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
D4RL D4RL Linear Transformer Average Reward 64.4 #7 of 9 Archive leaderboard report
Language Modelling WikiText-103 Linear Attention 125M Test perplexity 25.6 #60 of 89 Archive leaderboard report

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