This technical article explores the process of implementing a trainable GPT model from scratch in C++. The author breaks down two critical components: scalar automatic differentiation, which enables gradient computation without external frameworks, and an arena memory pool, which optimizes memory allocation for high-performance training. The piece provides practical insights into low-level ML engineering, showing how to build efficient training loops and manage memory in resource-constrained environments. For developers interested in understanding the internals of transformer models or building custom training infrastructure, this walkthrough offers a solid foundation. It also highlights the trade-offs between using high-level frameworks like PyTorch and hand-crafted C++ implementations, particularly in terms of speed and control. The article is a valuable resource for those looking to deepen their understanding of ML systems and C++ performance optimization.
A deep dive into building a trainable GPT from scratch in C++, focusing on scalar automatic differentiation and arena memory pools for performance.