ShakespeareGPT

Built GPT Model that can continuously generate Shakespeare related text. The current file train.py and model.py reproduce GPT on OpenWebText, and model.py can optionally load the GPT-2 weights from OpenAI.

Install

Dependencies:

• pytorch
• numpy
• pip install transformers for huggingface transformers (to load GPT-2 checkpoints)
• pip install datasets for huggingface datasets(if you want to download + preprocess OpenWebText)
• pip install tiktoken for OpenAI's fast BPE code
• pip install wandb for optional logging
• pip install tqdm

Get Started

Train a character-level GPT on the works of Shakespeare. First, we download it as a single (1MB) file and turn it from raw text into one large stream of integers:

$ 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. The size of it very much depends on the computational resources of your system:

GPU Can quickly train a baby GPT with the settings provided in the config/train_shakespeare_char.py config file:

$ python train.py config/train_shakespeare_char.py

If you peak inside it, you'll see that we're training a GPT with a context size of up to 256 characters, 384 feature channels, and it is a 6-layer Transformer with 6 heads in each layer. On one A100 GPU this training run takes about 3 minutes and the best validation loss is 1.4697. Based on the configuration, the model checkpoints are being 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:

ANGELO:
And cowards it be strawn to my bed,
And thrust the gates of my threats,
Because he that ale away, and hang'd
An one with him.

DUKE VINCENTIO:
I thank your eyes against it.

DUKE VINCENTIO:
Then will answer him to save the malm:
And what have you tyrannous shall do this?

DUKE VINCENTIO:
If you have done evils of all disposition
To end his power, the day of thrust for a common men
That I leave, to fight with over-liking
Hasting in a roseman.

lol ¯\_(ツ)_/¯. Not bad for a character-level model after 3 minutes of training on a GPU. Better results are quite likely obtainable by instead finetuning a pretrained GPT-2 model on this dataset (see finetuning section later).

Without GPU getting the bleeding edge PyTorch nightly (select it here when installing) as it is currently quite likely to make your code more efficient. But even without it, a simple train run could look as follows:

$ python train.py config/train_shakespeare_char.py --device=cpu --compile=False --eval_iters=20 --log_interval=1 --block_size=64 --batch_size=12 --n_layer=4 --n_head=4 --n_embd=128 --max_iters=2000 --lr_decay_iters=2000 --dropout=0.0

GLEORKEN VINGHARD III:
Whell's the couse, the came light gacks,
And the for mought you in Aut fries the not high shee
bot thou the sought bechive in that to doth groan you,
No relving thee post mose the wear

Not bad for ~3 minutes on a CPU, for a hint of the right character gestalt. If you're willing to wait longer free to tune the hyperparameters, increase the size of the network, the context length (--block_size), the length of training, etc.

Finally, on Apple Silicon Macbooks and with a recent PyTorch version make sure to add --device mps (short for "Metal Performance Shaders"); PyTorch then uses the on-chip GPU that can significantly accelerate training (2-3X) and allow you to use larger networks. See Issue 28 for more.

reproducing GPT-2

A more serious deep learning professional may be more interested in reproducing GPT-2 results. So here we go - we first tokenize the dataset, in this case the OpenWebText, an open reproduction of OpenAI's (private) WebText:

$ python data/openwebtext/prepare.py

This downloads and tokenizes the OpenWebText dataset. It will create a train.bin and val.bin which holds the GPT2 BPE token ids in one sequence, stored as raw uint16 bytes. Then we're ready to kick off training. To reproduce GPT-2 (124M) you'll want at least an 8X A100 40GB node and run:

$ torchrun --standalone --nproc_per_node=8 train.py config/train_gpt2.py

This will run for about 4 days using PyTorch Distributed Data Parallel (DDP) and go down to loss of ~2.85. Now, a GPT-2 model just evaluated on OWT gets a val loss of about 3.11, but if you finetune it it will come down to ~2.85 territory (due to an apparent domain gap), making the two models ~match.

If you're in a cluster environment and you are blessed with multiple GPU nodes you can make GPU go brrrr e.g. across 2 nodes like:

Run on the first (master) node with example IP 123.456.123.456:
$ torchrun --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr=123.456.123.456 --master_port=1234 train.py
Run on the worker node:
$ torchrun --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr=123.456.123.456 --master_port=1234 train.py

It is a good idea to benchmark your interconnect (e.g. iperf3). In particular, if you don't have Infiniband then also prepend NCCL_IB_DISABLE=1 to the above launches. Your multinode training will work, but most likely crawl. By default checkpoints are periodically written to the --out_dir. We can sample from the model by simply $ python sample.py.

Finally, to train on a single GPU simply run the $ python train.py script. Have a look at all of its args, the script tries to be very readable, hackable and transparent. You'll most likely want to tune a number of those variables depending on your needs.

baselines

OpenAI GPT-2 checkpoints allow us to get some baselines in place for openwebtext. We can get the numbers as follows:

$ python train.py eval_gpt2
$ python train.py eval_gpt2_medium
$ python train.py eval_gpt2_large
$ python train.py eval_gpt2_xl

and observe the following losses on train and val:

model	params	train loss	val loss
gpt2	124M	3.11	3.12
gpt2-medium	350M	2.85	2.84
gpt2-large	774M	2.66	2.67
gpt2-xl	1558M	2.56	2.54

However, we have to note that GPT-2 was trained on (closed, never released) WebText, while OpenWebText is just a best-effort open reproduction of this dataset. This means there is a dataset domain gap. Indeed, taking the GPT-2 (124M) checkpoint and finetuning on OWT directly for a while reaches loss down to ~2.85. This then becomes the more appropriate baseline w.r.t. reproduction.

finetuning

Finetuning is no different than training, we just make sure to initialize from a pretrained model and train with a smaller learning rate. For an example of how to finetune a GPT on new text go to data/shakespeare and run prepare.py to download the tiny shakespeare dataset and render it into a train.bin and val.bin, using the OpenAI BPE tokenizer from GPT-2. Unlike OpenWebText this will run in seconds. Finetuning can take very little time, e.g. on a single GPU just a few minutes. Run an example finetuning like:

$ python train.py config/finetune_shakespeare.py

This will load the config parameter overrides in config/finetune_shakespeare.py (I didn't tune them much though). Basically, we initialize from a GPT2 checkpoint with init_from and train as normal, except shorter and with a small learning rate. If you're running out of memory try decreasing the model size (they are {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}) or possibly decreasing the block_size (context length). The best checkpoint (lowest validation loss) will be in the out_dir directory, e.g. in out-shakespeare by default, per the config file. You can then run the code in sample.py --out_dir=out-shakespeare:

THEODORE:
Thou shalt sell me to the highest bidder: if I die,
I sell thee to the first; if I go mad,
I sell thee to the second; if I
lie, I sell thee to the third; if I slay,
I sell thee to the fourth: so buy or sell,
I tell thee again, thou shalt not sell my
possession.

JULIET:
And if thou steal, thou shalt not sell thyself.

THEODORE:
I do not steal; I sell the stolen goods.

THEODORE:
Thou know'st not what thou sell'st; thou, a woman,
Thou art ever a victim, a thing of no worth:
Thou hast no right, no right, but to be sold.

Whoa there, GPT, entering some dark place over there. I didn't really tune the hyperparameters in the config too much, feel free to try!

sampling / inference

Use the script sample.py to sample either from pre-trained GPT-2 models released by OpenAI, or from a model you trained yourself. For example, here is a way to sample from the largest available gpt2-xl model:

$ python sample.py \
    --init_from=gpt2-xl \
    --start="What is the answer to life, the universe, and everything?" \
    --num_samples=5 --max_new_tokens=100

If you'd like to sample from a model you trained, use the --out_dir to point the code appropriately. You can also prompt the model with some text from a file, e.g. $ python sample.py --start=FILE:prompt.txt.