@@ -78,10 +78,14 @@ To trigger CI, the pull request must have the "run-ci" label.
## General code style
- Avoid code duplication. If the same code snippet (more than five lines) appears multiple times, extract it into a shared function.
- Minimize device synchronization. Reduce expensive CPU-GPU synchronization operations, such as `tensor.item()` or `tensor.cpu()`, whenever possible. Use vectorized code.
- Keep files concise. If a file exceeds 2,000 lines of code, split it into multiple smaller files.
- Prioritize extreme efficiency. SGLang is a runtime, and most of your code runs on the critical path for every request. Optimize all minor overheads as much as possible, especially in the model forward code.
- A common pattern is some runtime checks in the model forward pass (e.g., [this](https://github.com/sgl-project/sglang/blob/f1b0eda55c2c4838e8ab90a0fac7fb1e3d7064ab/python/sglang/srt/models/deepseek_v2.py#L486-L491)). These are very likely the same for every layer. Please cache the result as a single boolean value whenever possible.
- Strive to make functions as pure as possible. Avoid in-place modification of arguments.
- Make functions as pure as possible. Avoid in-place modification of arguments.
- Keep files concise. If a file exceeds 2,000 lines of code, split it into multiple smaller files. (e.g., `scheduler.py`, `scheduler_output_processor_mixin.py`)
- Keep tests run fast.
- If a single test file run longer than 500 seconds, split it into multiple smaller files (e.g., `test_eagle_infer_a.py`, `test_eagle_infer_b.py`).
- If a single job in a github workflow runs longer than 30 mins, split it into smaller jobs/steps.
- Reuse server launches in your unit tests to make tests run faster.
- When supporting new hardware or features, follow these guidelines:
- Do not drastically change existing code.
- Always prefer new files to introduce specific components for your new hardware (e.g., `allocator_ascend.py`).
