gpt-2-tensorflow2.0

Introduction to GPT-2 TensorFlow 2.0 Project

Overview

The GPT-2 TensorFlow 2.0 project is an implementation of the powerful GPT-2 model, originally created by OpenAI, tailored for TensorFlow 2.0. GPT-2 is a state-of-the-art language model that can perform a variety of language tasks, such as text generation, after being trained on a vast dataset. This project offers a comprehensive implementation leveraging the capabilities of TensorFlow 2.0, allowing users to pre-train and generate text sequences effectively.

Requirements

To get started with the project, several requirements must be met:

• Python Version: 3.6 or greater is necessary.
• Key Libraries:
  • setuptools version 41.0.1
  • ftfy version 5.6
  • tqdm version 4.32.1
  • Click version 7.0
  • sentencepiece version 0.1.83
  • tensorflow-gpu version 2.3.0
  • numpy version 1.16.4

Setup Instructions

To set up the project, follow these simple steps:

1. Clone the Repository:

   $ git clone https://github.com/akanyaani/gpt-2-tensorflow2.0
   $ cd gpt-2-tensorflow2.0
   

2. Install Required Packages:

   $ pip install -r requirements.txt
   

3. Pre-Train Model: Users can pre-train the GPT-2 model using sample data provided in the repository or obtain data from an external repository like OpenWebText.

Pre-training

The project provides scripts for pre-training GPT-2 on sample data:

• For sample data:

  $ python pre_process.py
  

• For custom datasets:

  $ python pre_process.py --data-dir=data_directory --vocab-size=32000
  

Training Configuration

Training can be configured using various customizable parameters:

• Number of Layers: Specify the number of decoder layers.
• Embedding Size: Define the size of the model's embedding.
• Number of Heads: Set the number of heads for attention mechanisms.
• Filter Size: The dimension size in the feedforward networks.
• Sequence Length: Maximum length of sequences.
• Batch Size: Number of samples processed before model update.
• Learning Rate: Step size updating the model parameters.
• Training Mode: Includes standard or distributed options for multiple GPUs.

Example command for training:

$ python train_gpt2.py --num-layers=8 --num-heads=8 --dff=3072 --embedding-size=768 --batch-size=32 --learning-rate=5e-5 --graph-mode=True

Distributed Training

To utilize multiple GPUs for enhanced performance, distributed training can be enabled:

$ python train_gpt2.py --distributed=True --graph-mode=True

Monitoring with TensorBoard

Monitor the training process using TensorBoard with:

$ tensorboard --logdir /log

Generating Sequences

After pre-training the model, it can generate text sequences based on given context. This is achieved by loading the pretrained model into a Jupyter notebook provided in the repository (sequence_generator.ipynb).

Future Enhancements

The project plans on future improvements such as:

1. Parallel Preprocessing
2. Shared Weights Across Layers
3. Factorized Embedding
4. Fine-Tuning Wrapper

Contribution and Community

The project welcomes contributions, issues, and pull requests from the community. Interested individuals can reach out via the author's email ([email protected]) or follow the author, Abhay Kumar, on Twitter.

Licensing

This project is licensed under the MIT License, encouraging open-source use and distribution.

Visual Aids

The repository includes visual representations of the GPT-2 model's computation graph for better understanding.

By providing a robust framework for GPT-2 implementations in TensorFlow 2.0, this project stands as a valuable tool for those interested in leveraging advanced language models in their work.