Develop lightweight CUDA operations for SGLang using JIT, which compiles code on first use. A tensor-scaling example explains implementation conventions, input validation, memory access helpers, and when to choose this approach.
- Add a lightweight CUDA operation to SGLang
- Choose between JIT and ahead-of-time compilation
- Validate tensor inputs in a kernel launcher
sgl-project/sglang☆ 37.3KUpdated Oct 18, 2026
Add a CUDA/C++ GPU operation to sgl-kernel using AOT, meaning compilation before use. Follow the implementation through PyTorch registration, Python API exposure, SGLang integration, correctness tests, and benchmarks that account for cache reuse.
- Adding a CUDA/C++ operation to sgl-kernel
- Registering an operation with PyTorch and exposing a Python API
- Testing numerical results and invalid inputs
sgl-project/sglang☆ 37.3KUpdated Oct 18, 2026
Monitor selected GitHub Actions checks on an SGLang pull request until they pass on its latest commit. Diagnose failures, push small validated fixes, and retry only failed jobs within defined limits.
- Monitor selected PR checks until they pass
- Separate code failures from infrastructure problems
- Apply small fixes and track the resulting checks
sgl-project/sglang☆ 37.3KUpdated Oct 18, 2026
Review existing tests and CI, the automated checks run during development, for redundant coverage, misplaced tests, and opportunities to reduce runtime or cost. Receive file-specific recommendations based on documented improvement patterns, with uncertain findings separated.
- Investigating a slow test suite
- Finding redundant or misplaced tests
- Reviewing CI runtime and cost
sgl-project/sglang☆ 37.3KUpdated Oct 18, 2026
Understand and modify SGLang's CI pipelines, including test stage order, start conditions, early stopping after failures, and workload splitting. Use the guide to add jobs and investigate failed, skipped, or cancelled checks.
- Add a test job to an SGLang CI stage
- Investigate failed or skipped CI checks
- Update test partitions and waiting job counts
sgl-project/sglang☆ 37.3KUpdated Oct 18, 2026
Collect and review SGLang startup logs to identify unnecessary output and trace its source. Get saved logs and specific cleanup proposals while preserving useful diagnostics, with runtime changes left for user approval.
- Investigating repeated messages in startup logs
- Checking which startup warnings need attention
- Comparing startup logs across models and GPU counts
sgl-project/sglang☆ 37.3KUpdated Oct 18, 2026