Use Docker to set up the development environment. See [Docker setup guide](https://github.com/sgl-project/sglang/blob/main/docs/developer/development_guide_using_docker.md#setup-docker-container).
Create and enter development container:
```bash
docker run -itd --shm-size 32g --gpus all -v $HOME/.cache:/root/.cache --ipc=host --name sglang_zhyncs lmsysorg/sglang:dev /bin/zsh
1. Implement in [src/sgl-kernel/csrc/](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/src/sgl-kernel/csrc)
2. Expose interface in [csrc/sgl_kernel_ops.cu](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/src/sgl-kernel/csrc/sgl_kernel_ops.cu) with pybind11
3. Create Python wrapper in [src/sgl-kernel/ops/__init__.py](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/src/sgl-kernel/ops/__init__.py)
4. Expose Python interface in [src/sgl-kernel/__init__.py](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/src/sgl-kernel/__init__.py)
5. Update [setup.py](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/setup.py) to include new CUDA source
1. Add pytest tests in [tests/](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/tests)
2. Add benchmarks using [triton benchmark](https://triton-lang.org/main/python-api/generated/triton.testing.Benchmark.html) in [benchmark/](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/benchmark)