Welcome to your friendly guide on how to implement the GPT-2 model using PyTorch. This article covers everything from setup to generating sentences with your model, complemented with troubleshooting tips along the way.
Table of Contents
Introduction
This project is a PyTorch implementation of OpenAI’s GPT-2 model. It provides capabilities for model training, sentence generation, and metrics visualization. The code is designed to be both understandable and optimized, incorporating techniques to enhance performance.
Dependencies
Before you get started, you’ll need to install the following dependencies:
- regex
- tqdm
- torch
- numpy
- matplotlib
Usage
How to Train?
To train your GPT-2 model, you first need to prepare your corpus dataset. It is recommended to build your own corpus using Expanda. The training module requires tokenized training and evaluation datasets along with their vocabulary file. Once your dataset is ready, you can train GPT-2 using the following command:
$ python -m gpt2 train --train_corpus build/corpus.train.txt \
--eval_corpus build/corpus.test.txt \
--vocab_path build/vocab.txt \
--save_checkpoint_path ckpt-gpt2.pth \
--save_model_path gpt2-pretrained.pth \
--batch_train 128 \
--batch_eval 128 \
--seq_len 64 \
--total_steps 1000000 \
--eval_steps 500 \
--save_steps 5000
Analogy for GPT-2 Training
Think of training the GPT-2 model like teaching a child to write stories. The child (your model) needs a collection of books (the corpus dataset) to learn from. Each book must be carefully selected (tokenized training and evaluation datasets) and organized (the vocabulary file). When you sit down with the child to practice (training), you guide him through exercises (the command above), providing feedback until he becomes proficient in crafting stories independently (sentence generation).
Generate Sentences!
Once your training is complete, you can generate sentences using the model with the following command:
$ python -m gpt2 generate --vocab_path build/vocab.txt \
--model_path model.pth \
--seq_len 64 \
--nucleus_prob 0.8
Evaluate the Model
To evaluate the performance of your trained model, use the following command on your evaluation dataset:
$ python -m gpt2 evaluate --model_path model.pth \
--eval_corpus corpus.test.txt \
--vocab_path vocab.txt
Visualize Metrics
To analyze the performance and training loss graphically, you can visualize recorded metrics:
$ python -m gpt2 visualize --model_path model.pth --interactive
Using Apex in Training
You can optimize your training by using NVIDIA Apex which enables mixed-precision optimization. Install it first, then run your training using the option --use_amp. Ensure your GPU supports mixed-precision acceleration for maximum efficiency.
Play in Google Colab!
Try out the trained GPT-2 model in Google Colab, which allows for easy text generation and metrics evaluation:
You can find the notebook here.
License
This project is available under the Apache-2.0 License.
Troubleshooting
If you encounter issues during setup or training, here are some troubleshooting tips:
- Ensure all dependencies are installed correctly.
- Verify the paths to your training and vocabulary files are correct.
- If you experience GPU issues, try switching to CPU training.
- Check if the command syntax is correctly formatted — even a small typo can derail the process!
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