Transformers are Multi-State RNNs
Matanel Oren Michael Hassid Yossi Adi Roy Schwartz
Transformers are considered conceptually different compared to the previous
generation of state-of-the-art NLP models - recurrent neural networks (RNNs).
In this work, we demonstrate that decoder-only transformers can in fact be
conceptualized as infinite multi-state RNNs - an RNN variant with unlimited
hidden state size. We further show that pretrained transformers can be
converted into $\textit{finite}$ multi-state RNNs by fixing the size of their
hidden state. We observe that several existing transformers cache compression
techniques can be framed as such conversion policies, and introduce a novel
policy, TOVA, which is simpler compared to these policies. Our experiments with
several long range tasks indicate that TOVA outperforms all other baseline
policies, while being nearly on par with the full (infinite) model, and using
in some cases only $\frac{1}{8}$ of the original cache size. Our results
indicate that transformer decoder LLMs often behave in practice as RNNs. They
also lay out the option of mitigating one of their most painful computational
bottlenecks - the size of their cache memory. We publicly release our code at
https://github.com/schwartz-lab-NLP/TOVA.