This project implements the GPT-2 model with advanced optimization strategies for efficient and stable training, including:
- Careful parameter and hyperparameter initialization for stable convergence.
- Leveraging GPU internal matrix multiplication architecture (4x4).
- Integration of Flash Attention for faster and more memory-efficient attention computation.
- Optimizer configuration with hyperparameter tuning and kernel fusion for improved performance.
- Parameter datatype casting for optimal GPU computation.
- Distributed Data Parallel (DDP) training across multiple GPUs.