RWKV: Reinventing RNNs for the Transformer Era (Paper Explained)
1,813#gpt4 #rwkv #transformer
We take a look at RWKV, a highly scalable architecture between Transformers and RNNs.
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OUTLINE:
0:00 - Introduction
1:50 - Fully Connected In-Person Conference in SF June 7th
3:00 - Transformers vs RNNs
8:00 - RWKV: Best of both worlds
12:30 - LSTMs
17:15 - Evolution of RWKV's Linear Attention
30:40 - RWKV's Layer Structure
49:15 - Time-Parallel vs Sequence Mode
53:55 - Experimental Results & Limitations
58:00 - Visualizations
1:01:40 - Conclusion
Paper: https://arxiv.org/abs/2305.13048
Code: https://github.com/BlinkDL/RWKV-LM
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, which parallelizes computations during training and maintains constant computational and memory complexity during inference, leading to the first non-transformer architecture to be scaled to tens of billions of parameters. Our experiments reveal that RWKV performs on par with similarly sized Transformers, suggesting that future work can leverage this architecture to create more efficient models. This work presents a significant step towards reconciling the trade-offs between computational efficiency and model performance in sequence processing tasks.
Authors: Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, Kranthi Kiran GV, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartlomiej Koptyra, Hayden Lau, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Xiangru Tang, Bolun Wang, Johan S. Wind, Stansilaw Wozniak, Ruichong Zhang, Zhenyuan Zhang, Qihang Zhao, Peng Zhou, Jian Zhu, Rui-Jie Zhu
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