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We present a very simple algorithm for attention that requires $O(1)$ memory with respect to sequence length and an extension to self-attention that requires $O(\log n)$ memory.
Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 1904
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Rethinking attention with performers
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamás Sarlós, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy J. Colwell, and Adrian Weller · 2009
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Neural machine translation by jointly learning to align and translate
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Training deep nets with sublinear memory cost
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Mnnfast: A fast and scalable system architecture for memory-augmented neural networks
Hanhwi Jang, Joonsung Kim, Jae-Eon Jo, Jaewon Lee, and Jangwoo Kim · 2019
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Flax: A neural network library and ecosystem for JAX, 2020
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Reformer: The efficient transformer
Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya · 2020
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Blockwise self-attention for long document understanding
Jiezhong Qiu, Hao Ma, Omer Levy, Wen-tau Yih, Sinong Wang, and Jie Tang · 2020
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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
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Big bird: Transformers for longer sequences
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontañón, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, and Amr Ahmed · 2020
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Memory efficient attention, 2021
Amin Rezaei · 2021
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Efficient content-based sparse attention with routing transformers
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Efficient attention: Attention with linear complexities
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Long range arena : A benchmark for efficient transformers
Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, and Donald Metzler · 2021
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Flashattention: Fast and memory-efficient exact attention with io-awareness, 2022
Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
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General-purpose, long-context autoregressive modeling with perceiver AR, 2022
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Luna: Linear unified nested attention
Xuezhe Ma, Xiang Kong, Sinong Wang, Chunting Zhou, Jonathan May, Hao Ma, and Luke Zettlemoyer · 2021
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Combiner: Full attention transformer with sparse computation cost
Hongyu Ren, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, and Bo Dai · 2021
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Curtis Hawthorne, Andrew Jaegle, Cătălina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, and Jesse Engel · 2022
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Phil Wang · 2022
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