Introducing llm.c: A Pure C/CUDA Implementation for Training Language Models
The llm.c project offers a streamlined, efficient, and educational way to train language models without relying on bulky libraries like PyTorch or cPython. Designed to be lightweight and fast, llm.c focuses on pretraining using pure C and CUDA. The primary goal of the project is to replicate and train models from the GPT-2 and GPT-3 series, while maintaining a reference parallel implementation with PyTorch.
Key Features
- Efficiency: llm.c provides training that can be up to 7% faster than PyTorch Nightly, while keeping code simple and clean.
- Accessibility: By using pure C/CUDA, it eliminates the need for large dependencies, making it easier to compile and run on diverse systems.
- Educational Value: The project contains detailed documentation and discussions that make it a great resource for learning how to implement machine learning models in C and CUDA.
- Community Driven: With active discussions on GitHub and Discord, developers can collaborate, ask questions, and contribute improvements.
Getting Started
The best way to dive into llm.c is by reproducing the GPT-2 (124M) model. The provided scripts and discussions guide users through the process, allowing them to explore both llm.c and its PyTorch parallel implementation.
For developers with a single GPU setup interested in training with fp32 precision, llm.c offers a set of frozen-in-time, legacy files that are simpler and easier to work with. The setup is straightforward:
chmod u+x ./dev/download_starter_pack.sh
./dev/download_starter_pack.sh
make train_gpt2fp32cu
./train_gpt2fp32cu
For CPU-based training, although not as performant, users can still experiment with training a GPT-2 model, fine-tuning it to generate text similar to Shakespeare's works.
Data Handling
The project simplifies data preparation through Python scripts that download, tokenize, and convert datasets to a binary format. This data can easily be read from C, streamlining the integration process with language models.
Testing and Verification
llm.c provides a robust testing framework to compare its results with the PyTorch benchmark. Users can run unit tests that validate the C implementations against PyTorch, ensuring accuracy and reliability.
make test_gpt2
./test_gpt2
Customization options are available, such as testing for fp32 precision and mixed precision with cuDNN support.
Advanced Features and Customization
The repository includes various options for advanced users:
- Multi-GPU and Multi-Node Training: Support for scalable training sessions utilizing multiple GPUs or network-based distributed nodes.
- Experiments and Sweeps: An example script demonstrates how to conduct parameter sweeps to optimize model training.
Open-source Collaboration
Designed with education in mind, llm.c encourages contributions, whether it's developing new features or optimizing existing code. It prioritizes maintaining simplicity and readability in the main repository, with more complex or extensive features organized under development branches.
Notable Forks
Developers from the community have ported llm.c to various other programming languages and platforms, including Rust, Go, Java, Swift, and more, expanding its versatility and accessibility.
Conclusion
llm.c stands out as an efficient, educational, and collaborative platform for training large language models. Its focus on simplicity and performance makes it a valuable resource for developers looking to understand and build machine learning models using pure C/CUDA.