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nanoGPT.jax

nanoGPT.jax

The simplest, fastest repository for training/finetuning medium-sized GPTs. It is a rewrite of Andrej Karpathy's nanoGPT using Jax. The code itself is plain and readable: train.py is a ~200-line boilerplate training loop and model.py a ~200-line GPT model definition.

Because the code is so simple, it is very easy to hack to your needs, train new models from scratch, or finetune pretrained checkpoints.

Install

pip install numpy jax flax optax orbax tqdm

Dependencies:

  • numpy for numeric computation <3
  • jax, flax, optax, orbax for jax liberaies <3
  • tqdm for progress bars <3

Quick start

Currently GPT-2 model loading, finetuning, and inference are not supported, because I don't have a GPU :p

We will demonstrate the GPT pipeline with a simple task: generating text from Shakespeare's works.

python data/shakespeare_char/prepare.py

This creates a train.bin and val.bin in that data directory. Now it is time to train your GPT.

python train.py config/train_shakespeare_char.py

If you peek inside it, you'll see that we're training a GPT with a bunch of customizable parameters. Feel free to adjust it based on your needs. On my laptop (MacBook Pro M2 Max 32GB), it takes ~5min to finish the training. The final train and eval loss values are around 1.5.

iter 5000 loss: train loss 1.4159, val loss 1.6152

By default, the model checkpoints are written into the --out_dir directory out-shakespeare-char. So once the training finishes we can sample from the best model by pointing the sampling script at this directory:

python sample.py --out_dir=out-shakespeare-char

This generates a few samples, for example:

The bids of man the more royal in hate,
To that where see's husband: we have subded himle
Might a may and no true--

LEONTES:
Is in the movereign of the of raid day
And your choods burthsome and him.

NORTHUMBERLAND:
Ay, madam, I have preform a scapade acces help;
And that hath her sweet's and to the feart.
Should let thee be and their service for thyself.

LEONTES:
If down the is must me and it would the soul
The toward of his for mother charge scarried, that I would
And look me heart to to chi

Not bad for ~5 minutes on a CPU, for a hint of the right character gestalt. If you're willing to wait longer, feel free to tune the hyperparameters.

TODOs

  • Implement GPT-2 model loading, finetuning, and inference.

  • Write a jax.lax.scan version of the sampling step to make it a jax.jit. The issue I was having is that, with a fixed block size to crop, we have to slice the carry tensor with a running index x; however, jax.lax.slice does not support referencing the scan function's arg. The solution cgarciae/nanoGPT-jax proposed does not support a block size smaller than max_new_tokens.

Acknowledgements

cgarciae/nanoGPT-jax provided some insights for me to migrate the code to Jax. Thanks Cristian!