GPT2

This is not the official GPT2 implementation!

An implementation of training for GPT2 that supports both GPUs and TPUs. The dataset scripts are a bit hacky and will probably need to be adapted to your needs.

Requirements

For GPUs:

pip3 install tensorflow-gpu regex

For TPUs:

pip3 install tensorflow regex google-api-python-client oauth2client

For downloading the models:

pip3 install requests tqdm

For generating the dataset (in addition to Tensorflow):

pip3 install ftfy tqdm newspaper3k

Downloading Pretrained Models

If you want to use my models, I currently have "117M" and "PrettyBig" to offer. 117M was trained on a single v2 TPU for a week (probably less than the original OpenAI model), PrettyBig is slightly bigger than 345M and was trained on a v2-256 pod for a week. I was originally also planning to release my version of the 1.5B model, but have decided against it. You can read about my reasoning here.

python3 download_model.py PrettyBig

This will create two directories, one named as the model and another named "encoder". Change the "model_dir" and "encoder_path" parameters in the .json corresponding to your model to point to these paths, respectively.

If you only want the encoder, use:

python3 download_model.py encoder

Generating Text

To predict you can either pass the prompt directly in the command line, or have it read from a file. (This is useful for prompts that include newlines) Text is output to the console and the file specified in the "predict_path" parameter. You need a model checkpoint and a copy of the BPE encoder at an accessible location for this to work. (Change the "model_dir" and "encoder_path" parameters in the .json)

From command line:

python3 main.py --model Your-Model.json [--top_k Top-K-Truncation] --predict_text "Hello there! My name is"

From file:

python3 main.py --model Your-Model.json [--top_k Top-K-Truncation] --predict_file input.txt

The optional top_k parameter causes the model to only consider the top k most likely tokens at each step. Setting this around 40 tends to create better results, but with less variety.

Prediction on TPUs is not supported.

Training

To train a model, define its parameters in a .json file (see examples) and then simply call

python3 main.py --model Your-Model.json [--tpu Your-TPU-Name]

Using a TPU is optional, it runs fine on GPUs without modification. (Note: Evaluation doesn't work on TPU pods and must be commented out)

This assumes you have a version of the openwebtext corpus stored in an accessible location. If you don't, see below how to generate your own version.

Generating the Dataset

GPT2 is trained on the webtext corpus, which is basically all websites linked to from Reddit with at least 3 Karma. Since the database is huge and contains a lot of copyrighted material, I can't provide a download here. Instead, I'll describe how I got it. Be aware it cost me around ~500€ in cloud compute resources to download and process the whole thing, but I'm not claiming I was optimally efficient.

Use the download script from here to download the archives (I used the prefiltered URLs file)
Use datasets/openwebtext/ run_newspaper_extract.py to extract the text
Once you have the raw .txt files use datasets/openwebtext/ create_tfrecords.py to encode them into .tfrecords files (Requires a copy of the encoder, see Downloading Pretrained Models)
Place the .tfrecords files into an accessible folder or Google Storage bucket (Placing in a Google Storage bucket is mandatory if you're using TPUs)
Change the "data_path" parameter in your .json to point to where your .tfrecords files are located and, if necessary, adapt the functions in inputs.py to open the correct filenames, in case you changed them

Explanation of Parameters

Because passing two dozen parameters over the command line would be tedious, you pass all the model parameters in a .json file. Note that any paths also support Google Storage paths and must be gs:// paths if you're running on TPUs.

Values you'll definitely want to change:

model_path: Where to save and load checkpoints from
data_path: Where your .tfrecords files are located
encoder_path: Path to the BPE encoder files. To get this, use the download_model.py script to download any model (or just the encoder). You will get a folder called "encoder". This is what you want this to point to (only required for prediction)

Values you'll probably want to change:

train_batch_size: Batch size during training phase
eval_batch_size: Batch size during evaluation
predict_batch_size: Batch size during prediction
predict_path: Where to save predictions (point this to a text file to append to)

Model parameters:

model: A string that refers to which model to use. This should always just be "GPT2" (no other models are implemented here)
n_ctx: Number of tokens the model looks at (default: 1024)
n_vocab: Size of vocabulary (default: 50257)
n_embd: Dimension of embedding layers
n_layer: Number of layers in the model
n_head: Number of attention heads (default: n_embd / 64)
scale: Factor by which to scale initializations of weights (default: 1/sqrt(n_layer))

Training parameters:

precision: Whether to use float32 or bfloat16 variables (use "bfloat16" when training very large models) (optional, defaults to float32)
input: Which input function to use (default: "openwebtext")
lr: Learning rate (default: 0.00025)
warmup_steps: Number of warmup steps. If this is set, a linear warmup + cosine decay schedule is used (default: 2000) (optional)
opt_name: Name of optimizer, currently there are "adam" and "adafactor" (default: "adam")
weight_decay: Weight decay parameter, if not present no weight decay is used (the weight decay fix for Adam is used) (default: 0.01) (optional)
beta1: Adam/Adafactor beta1 parameter (adam default: 0.9, adafactor default: 0.0)
beta2: Adam/Adafactor beta2 parameter (default: 0.98) (optional for adafactor with pow decay type)
epsilon: Adam epsilon parameter (default: 1e-9)
decay_type: Adafactor decay type, either "pow" or "adam" (default: "pow")
decay_exponent: Adafactor pow decay exponent (default: 0.8)
train_steps: Number of training steps to take between evaluations
eval_steps: Number of steps per evaluation
max_steps: The maximum number of training steps (important for declining lr)
iterations: Number of iterations to perform on TPUs (Default: 100) (Only required for TPUs)
embed_dropout: Dropout chance on the word embedding, set to 0 to disable (default: 0.1)
attn_dropout: Dropout chance on attention layers, set to 0 to disable (default: 0.1)
res_dropout: Dropout chance on residual connections, set to 0 to disable (default: 0.1)