A TensorFlow implementation of the Lion optimizer from Symbolic Discovery of Optimization Algorithms. Partially copied from the lucidrains PyTorch implementation.

The maths seem right and it successfully trained a couple of models for me, but that doesn't mean I haven't forgotten something stupid, or that there isn't room for optimization!

In general, the code trusts in 🙏XLA🙏 to efficiently reuse buffers and save memory rather than manually doing all the ops in-place like the PyTorch version does. Note that the optimizer will be compiled with XLA even if you don't use jit_compile for the rest of your model!

pip install git+https://github.com/Rocketknight1/lion-tf.git

from lion_tf import Lion

model.compile(Lion(1e-5))

Lion likes much lower learning rates than Adam - I'd suggest a factor of 10 lower as a good starting point. When fine-tuning pre-trained models, learning rates are already quite low, which means the optimal LR for Lion can be very low. I found 1e-5 or less worked well for fine-tuning BERT!