Objective: To showcase the implementation of training a GPT-like model from scratch in Rust, using only CPUs, achieving performance 30 times better than native C code.
Key Topics
Introduction to Matrix Multiplication
Matrix multiplication is crucial for the attention algorithm in GPT models.
Previous work involved efficiently implementing matrix multiplication in Rust using the Blas library.
Training GPT-like Model in Rust
Goal: Train a GPT-like model from scratch using Rust.
Hardware: CPU only (no GPUs or TPUs involved).
Motivation: To explore the capabilities of the Rust ecosystem in comparison to C, especially for machine learning tasks on simple hardware like laptops.
Potential Use: The code can be adapted for fine-tuning GPT models with specific input corpora.
Implementation Details
The implementation serves as a learning tool for the Rust ecosystem.
Performance comparison with C to highlight Rust's efficiency.
Resources
All relevant code for the project is available on GitHub.
Summary
The article is a companion piece focusing on the technical implementation of language models in Rust. It emphasizes the practical aspects of building machine learning models in a non-GPU environment and aims to push the limits of CPU-based training.
Conclusion
The project demonstrates Rust's potential in machine learning, particularly in environments with limited computational resources, and encourages further exploration of such possibilities.