RWKV: Reinventing RNNs for the Transformer Era
Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu
Abstract
Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the same performance as Transformers due to limitations in parallelization and scalability. We propose a novel model architecture, Receptance Weighted Key Value (RWKV), that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, thus parallelizing computations during training and maintains constant computational and memory complexity during inference. We scale our models as large as 14 billion parameters, by far the largest dense RNN ever trained, and find RWKV performs on par with similarly sized Transformers, suggesting future work can leverage this architecture to create more efficient models. This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.- Anthology ID:
- 2023.findings-emnlp.936
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 14048–14077
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.936
- DOI:
- 10.18653/v1/2023.findings-emnlp.936
- Bibkey:
- Cite (ACL):
- Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, et al.. 2023. RWKV: Reinventing RNNs for the Transformer Era. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14048–14077, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- RWKV: Reinventing RNNs for the Transformer Era (Peng et al., Findings 2023)
- Copy Citation:
- PDF:
- https://aclanthology.org/2023.findings-emnlp.936.pdf
Export citation
@inproceedings{peng-etal-2023-rwkv,
title = "{RWKV}: Reinventing {RNN}s for the Transformer Era",
author = "Peng, Bo and
Alcaide, Eric and
Anthony, Quentin and
Albalak, Alon and
Arcadinho, Samuel and
Biderman, Stella and
Cao, Huanqi and
Cheng, Xin and
Chung, Michael and
Derczynski, Leon and
Du, Xingjian and
Grella, Matteo and
Gv, Kranthi and
He, Xuzheng and
Hou, Haowen and
Kazienko, Przemyslaw and
Kocon, Jan and
Kong, Jiaming and
Koptyra, Bart{\l}omiej and
Lau, Hayden and
Lin, Jiaju and
Mantri, Krishna Sri Ipsit and
Mom, Ferdinand and
Saito, Atsushi and
Song, Guangyu and
Tang, Xiangru and
Wind, Johan and
Wo{\'z}niak, Stanis{\l}aw and
Zhang, Zhenyuan and
Zhou, Qinghua and
Zhu, Jian and
Zhu, Rui-Jie",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.936",
doi = "10.18653/v1/2023.findings-emnlp.936",
pages = "14048--14077",
abstract = "Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the same performance as Transformers due to limitations in parallelization and scalability. We propose a novel model architecture, Receptance Weighted Key Value (RWKV), that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, thus parallelizing computations during training and maintains constant computational and memory complexity during inference. We scale our models as large as 14 billion parameters, by far the largest dense RNN ever trained, and find RWKV performs on par with similarly sized Transformers, suggesting future work can leverage this architecture to create more efficient models. This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.",
}
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<abstract>Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the same performance as Transformers due to limitations in parallelization and scalability. We propose a novel model architecture, Receptance Weighted Key Value (RWKV), that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, thus parallelizing computations during training and maintains constant computational and memory complexity during inference. We scale our models as large as 14 billion parameters, by far the largest dense RNN ever trained, and find RWKV performs on par with similarly sized Transformers, suggesting future work can leverage this architecture to create more efficient models. This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.</abstract>
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%0 Conference Proceedings %T RWKV: Reinventing RNNs for the Transformer Era %A Peng, Bo %A Alcaide, Eric %A Anthony, Quentin %A Albalak, Alon %A Arcadinho, Samuel %A Biderman, Stella %A Cao, Huanqi %A Cheng, Xin %A Chung, Michael %A Derczynski, Leon %A Du, Xingjian %A Grella, Matteo %A Gv, Kranthi %A He, Xuzheng %A Hou, Haowen %A Kazienko, Przemyslaw %A Kocon, Jan %A Kong, Jiaming %A Koptyra, Bartłomiej %A Lau, Hayden %A Lin, Jiaju %A Mantri, Krishna Sri Ipsit %A Mom, Ferdinand %A Saito, Atsushi %A Song, Guangyu %A Tang, Xiangru %A Wind, Johan %A Woźniak, Stanisław %A Zhang, Zhenyuan %A Zhou, Qinghua %A Zhu, Jian %A Zhu, Rui-Jie %Y Bouamor, Houda %Y Pino, Juan %Y Bali, Kalika %S Findings of the Association for Computational Linguistics: EMNLP 2023 %D 2023 %8 December %I Association for Computational Linguistics %C Singapore %F peng-etal-2023-rwkv %X Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the same performance as Transformers due to limitations in parallelization and scalability. We propose a novel model architecture, Receptance Weighted Key Value (RWKV), that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, thus parallelizing computations during training and maintains constant computational and memory complexity during inference. We scale our models as large as 14 billion parameters, by far the largest dense RNN ever trained, and find RWKV performs on par with similarly sized Transformers, suggesting future work can leverage this architecture to create more efficient models. This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks. %R 10.18653/v1/2023.findings-emnlp.936 %U https://aclanthology.org/2023.findings-emnlp.936 %U https://doi.org/10.18653/v1/2023.findings-emnlp.936 %P 14048-14077
Markdown (Informal)
[RWKV: Reinventing RNNs for the Transformer Era](https://aclanthology.org/2023.findings-emnlp.936) (Peng et al., Findings 2023)
- RWKV: Reinventing RNNs for the Transformer Era (Peng et al., Findings 2023)
ACL
- Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, et al.. 2023. RWKV: Reinventing RNNs for the Transformer Era. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14048–14077, Singapore. Association for Computational Linguistics.