This project aims to leverage Clad, an automatic differentiation (AD) plugin for Clang, to optimize large language model (LLM) training primarily in C++. Automatic differentiation is a crucial component of deep learning training, enabling efficient computation of gradients for optimization algorithms such as stochastic gradient descent (SGD). While most modern LLM frameworks rely on Python-based ecosystems, their heavy reliance on interpreted code and dynamic computation graphs can introduce performance bottlenecks. By integrating Clad into C++-based deep learning pipelines, we can enable high-performance differentiation at the compiler level, reducing computational overhead and improving memory efficiency. This will allow developers to build more optimized training workflows without sacrificing flexibility or precision.
Beyond performance improvements, integrating Clad with LLM training in C++ opens new possibilities for deploying AI models in resource-constrained environments, such as embedded systems and HPC clusters, where minimizing memory footprint and maximizing computational efficiency are critical. Additionally, this work will bridge the gap between modern deep learning research and traditional scientific computing by providing a more robust and scalable AD solution for physics-informed machine learning models. By optimizing the differentiation process at the compiler level, this project has the potential to enhance both research and production-level AI applications, aligning with compiler-research.org’s broader goal of advancing computational techniques for scientific discovery.