This repository provides a JAX/Flax implementation of the Hyena architecture introduced in Poli et. al. (2023). A full training run of a small 1.5M parameter model, on the Shakespeare dataset can be found in the included intro.ipynb. This achieves a best validation loss of ~1.45, on par with the results in nanoGPT.

Specifically, the following is implemented:

• The Hyena layer itself can be found in hyena/hyena.py as HyenaOperator
  • The efficient, FFT-based convolution is implemented in the fftconv method, providing an O(N log N) complexity in sequence length. This is used for training, and for the pre-fill stage during inference.
    • Caching is also implemented, which means this is called only once during inference pre-fill, with the subsequent individual tokens being computed using the alternate implementation (see below).
  • An alternate implementation, having O(N) complexity per token is provided for the auto-regressive decoding stage during inference. This is implemented in the inference_conv method. It will be particularly faster when generating a small number of tokens from a very large input (e.g. a full document).
• A standard Decoder tower is implemented in hyena/decoder.py as Decoder. The implementation is largely similar to the one in nanoGPT, with the self-attention layers swapped out with Hyena layers.