How I Started Contributing to Tinygrad – My First 4 Merged PRs
10 days ago
- #deep-learning
- #code-quality
- #open-source
- tinygrad is a minimalist deep learning framework ideal for those who enjoy building from scratch and writing high-quality code.
- Contributors should read the codebase thoroughly, starting with tensor.py, and trace operations end-to-end to understand dataflow.
- Use AI to learn the codebase but not to generate code for PRs; clarity and intent are valued over volume.
- Before contributing, search Tinygrad Discord and prior PRs/issues to avoid redundant questions and respect maintainers' time.
- PRs should be minimal, well-tested, and logically scoped with small commits to facilitate reviews.
- Example contributions include replacing custom download logic with built-in fetch, fixing bugs like logcumsumexp, linting examples, and adding features like enable_gqa to SDPA.
- Validation involves local testing, comparing outputs with expected results, and ensuring changes work across different environments and devices.
- Key habits for successful contributions include shipping small changes, testing rigorously, and optimizing for signal over noise.