GPT-2 Training Script Development

A training script for the GPT-2 model. This endeavor has been an enlightening journey, providing deep insights into the complexities of language model training.

Overview

This Python-based training script leverages the robust PyTorch framework alongside Hugging Face's Transformers, aiming to fine-tune the GPT-2 model efficiently. The script's key features include:

• Model Configuration: A dataclass is utilized for GPT-2 configurations, allowing for versatile model parameter adjustments.
• Custom Dataset Integration: Supports diverse dataset formats, including file-based and Hugging Face datasets.
• Gradient Checkpointing: Facilitates memory-efficient training, a critical aspect for managing large models like GPT-2.
• Optimized Training Loop: Incorporates the Accelerate library to streamline CPU/GPU and distributed training processes.

Important Reminder

• Accelerator Configuration: Ensure to configure the accelerator by running accelerate init in the command line before starting the training process. This step is crucial for optimal hardware utilization.

Notable Learnings and Decisions

• AdamW Optimizer: The adoption of AdamW, an optimizer with a weight decay fix, is pivotal. It provides more effective L2 regularization than traditional Adam, enhancing training stability and model generalization.
• Learning Rate Scheduler: A cosine decay learning rate scheduler was implemented, optimizing learning rate adjustments throughout the training cycle for superior model performance.
• Weight Initialization and Tying: Includes strategic weight initialization and optional weight tying, which can reduce the model's parameter count and improve performance in language modeling tasks.
• Reproducibility with Seed Setting: Ensures consistent and replicable results across different runs by setting a random seed, a vital aspect for credible machine learning experimentation.
• Gradient Accumulation: Employs gradient accumulation to manage large batches on limited hardware, enabling effective training with larger batch sizes.

Challenges and Resolutions

• Memory Management: Addressed the high memory demands of GPT-2 through gradient checkpointing and optimal batch sizing, enhancing GPU memory utilization.
• Hyperparameter Tuning: Extensive experimentation was conducted to identify the optimal hyperparameter set, focusing on the balance between learning rate, batch size, and epoch count.

References

• Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners.
• nanoGPT | Andrej Karpathy
• Let's build GPT: from scratch, in code, spelled out. | Andrej Karpathy on YouTube
• RoFormer: Enhanced Transformer with Rotary Position Embedding
• GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
• Longformer: The Long-Document Transformer