Tutorial: Adding a New Kernel to sgl-kernel (AOT / Heavyweight)

Apply kernel-organization for the public operator namespace, logical grouping, lazy registry metadata, and test placement. The implementation tutorial below does not replace that API contract.

This tutorial walks through adding a simple element-wise scale operation as an AOT kernel. We'll implement scale(x, factor) = x * factor to demonstrate the complete workflow.

Goal

Add a new operation that scales each element of a tensor by a scalar factor:

• Input: tensor x (CUDA) and scalar factor (float)
• Output: x * factor (element-wise, in-place or into pre-allocated out)
• Supported dtypes: FP16 (torch.float16), BF16 (torch.bfloat16), FP32 (torch.float32)
  • Dispatched via DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FLOAT_FP16 macro (defined in python/sglang/kernels/aot/include/utils.h)

Two rules of thumb (must follow)