pykan VS kan-gpt

Update2: got it to 100% training accuracy, 99 test accuracy with (2, 2, 2) shape.

Changes:

1. Increased the training set from 1000 to 100k samples. This solved overfitting.

2. In the dataset generation, slightly reduced noise (0.1 -> 0.07) so that classes don't overlap. With an overlap, naturally, it's impossible to hit 100%.

3. Most important & specific to KANs: train for 30 steps with grid=5 (5 segments for each activation function), then 30 steps with grid=10 (and initializing from the previous model), and then 30 steps with grid=20. This is idiomatic to KANs and covered in the Example_1_function_fitting.ipynb: https://github.com/KindXiaoming/pykan/blob/master/tutorials/...

Overall, my impressions are:

- it works!

- the reference implementation is very slow. A GPU implementation is dearly needed.

- it feels like it's a bit too non-linear and training is not as stable as it's with MLP + ReLU.

- Scaling is not guaranteed to work well. Really need to see if MNIST is possible to solve with this approach.

I will definitely keep an eye on this development.

- Training script

I am currently working on training it on the WebText dataset to compare it to the original gpt2. Facing a few out-of-memory issues at the moment. Perhaps the vocab size (50257) is too large?

I'm open to contributions and would love to hear your thoughts!

https://github.com/AdityaNG/kan-gpt