The article also provides examples of LLM training using the YaFSDP framework, which can be found in the 'examples' folder. These examples require a Docker image, which can be built using a script provided. The article concludes by inviting users to report any bugs or ask questions via GitHub, and requests that users cite the codebase if used.
Key takeaways:
- YaFSDP is a Sharded Data Parallelism framework designed for transformer-like neural network architectures, offering up to 20% faster pre-training LLMs and better performance in high memory pressure conditions.
- YaFSDP reduces communications and memory operations overhead, as demonstrated in benchmarks comparing it with FSDP on various pre-training setups.
- Examples of LLM training using YaFSDP can be found in the 'examples' folder of the project, which require a Docker image based on the NVIDIA PyTorch image with some patched libraries.
- If any bugs are encountered or questions arise, users are encouraged to open a GitHub issue, and if the codebase is used, citation is requested using a provided BibTeX entry.