add-sgl-kernel

name	add-sgl-kernel
description	Step-by-step tutorial for adding a heavyweight AOT CUDA/C++ kernel to sgl-kernel (including tests & benchmarks)

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

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 sgl-kernel/include/utils.h)

Two rules of thumb (must follow)

1. Heavyweight kernels go to sgl-kernel. If it depends on CUTLASS / FlashInfer / DeepGEMM (or similarly heavy stacks), implement it in sgl-kernel/.
2. Lightweight kernels go to python/sglang/jit_kernel. If it is small, has few dependencies, and benefits from rapid iteration, implement it as a JIT kernel instead.

In addition, every new kernel must ship with:

• Tests (pytest)
• A benchmark script (triton.testing)

---

Repository integration map

You will typically touch these files/areas:

• Implementation: sgl-kernel/csrc/elementwise/scale.cu (pick the right subdirectory)
• Public declarations: sgl-kernel/include/sgl_kernel_ops.h
• Torch extension registration: sgl-kernel/csrc/common_extension.cc
• Build: sgl-kernel/CMakeLists.txt (set(SOURCES ...))
• Python API: sgl-kernel/python/sgl_kernel/ and sgl-kernel/python/sgl_kernel/__init__.py
• Tests: sgl-kernel/tests/test_scale.py
• Benchmarks: sgl-kernel/benchmark/bench_scale.py

---

Step 1: Implement the kernel in csrc/

Pick the right subdirectory:

• csrc/elementwise/ — for element-wise ops (our example)
• csrc/gemm/, csrc/attention/, csrc/moe/ — for other categories

Create sgl-kernel/csrc/elementwise/scale.cu:

#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <torch/all.h>

#include "utils.h"  // DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FLOAT_FP16

// scale_kernel: out[i] = input[i] * factor
// Supports float, half (__half), __nv_bfloat16 via template T
template <typename T>
__global__ void scale_kernel(T* __restrict__ out,
                              const T* __restrict__ input,
                              float factor,
                              int64_t n) {
  int64_t idx = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
  if (idx < n) {
    out[idx] = static_cast<T>(static_cast<float>(input[idx]) * factor);
  }
}

void scale(at::Tensor& out, const at::Tensor& input, double factor) {
  TORCH_CHECK(input.is_cuda(),       "input must be a CUDA tensor");
  TORCH_CHECK(input.is_contiguous(), "input must be contiguous");
  TORCH_CHECK(out.is_cuda(),         "out must be a CUDA tensor");
  TORCH_CHECK(out.is_contiguous(),   );
  (out.() == input.(),  );
  (out.() == input.(),
              );

    n = input.();
    threads = ;
    blocks  = (n + threads - ) / threads;

   cudaStream_t stream = at::cuda::();
   at::;

  
  (input.(), c_type, [&] {
    scale_kernel<c_type><<<blocks, threads, , stream>>>(
        <c_type*>(out.()),
        < c_type*>(input.()),
        <>(factor),
        n);
    cudaError_t status = ();
    (status == cudaSuccess,
                , (status));
     ;
  });
}

Key points:

• Use at::Tensor (PyTorch tensors), TORCH_CHECK for validation, at::cuda::getCurrentCUDAStream() for stream
• DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FLOAT_FP16 covers float, half (FP16), __nv_bfloat16 (BF16)
• Add device error checking after every kernel launch
• If a kernel only works on certain architectures, enforce that with TORCH_CHECK and skip logic in tests

---

Step 2: Add a C++ declaration in include/sgl_kernel_ops.h

Edit sgl-kernel/include/sgl_kernel_ops.h, add to the elementwise section:

void scale(at::Tensor& out, const at::Tensor& input, double factor);

---

Step 3: Register the op in csrc/common_extension.cc

Edit sgl-kernel/csrc/common_extension.cc, inside TORCH_LIBRARY_FRAGMENT(sgl_kernel, m):

// From csrc/elementwise
m.def("scale(Tensor! out, Tensor input, float factor) -> ()");
m.impl("scale", torch::kCUDA, &scale);

Key points:

• Tensor! means in-place / mutable output argument
• The schema is important for torch.compile and for consistent call signatures
• If your underlying C++ API uses float but PyTorch bindings expect double, the implicit cast is fine for scalars; use shims if needed for other types

---

Step 4: Add the new source file to CMakeLists.txt

Edit sgl-kernel/CMakeLists.txt, add to set(SOURCES ...):

csrc/elementwise/scale.cu

Key points:

• Keep the list alphabetically sorted (the file explicitly requires this)
• If the kernel has arch constraints, reflect that in tests/benchmarks via skip logic

---

Step 5: Expose a Python API under sgl-kernel/python/sgl_kernel/

In sgl-kernel/python/sgl_kernel/__init__.py, add:

from torch.ops import sgl_kernel as _ops

def scale(out: torch.Tensor, input: torch.Tensor, factor: float) -> None:
    """
    Element-wise scale: out = input * factor (in-place into out).

    Supported dtypes: torch.float16, torch.bfloat16, torch.float32.

    Parameters
    ----------
    out    : pre-allocated CUDA output tensor (same shape/dtype as input)
    input  : CUDA input tensor
    factor : scale factor (float)
    """
    _ops.scale(out, input, factor)

Or export it from the existing module organisation — follow the pattern already used by similar ops in __init__.py.

---

Step 6: Write tests (required)

Create sgl-kernel/tests/test_scale.py:

import pytest
import torch
import sgl_kernel


@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32])
@pytest.mark.parametrize("size", [128, 1024, 4096, 65536])
@pytest.mark.parametrize("factor", [0.5, 1.0, 2.0])
def test_scale_correctness(dtype, size, factor):
    input = torch.randn(size, dtype=dtype, device="cuda")
    out   = torch.empty_like(input)

    sgl_kernel.scale(out, input, factor)

    expected = input * factor
    rtol, atol = (1e-5, 1e-6) if dtype == torch.float32 else (1e-2, 1e-2)
    torch.testing.assert_close(out, expected, rtol=rtol, atol=atol)


def test_scale_shape_mismatch():
    input = torch.randn(128, dtype=torch.float16, device="cuda")
    out   = torch.empty(256, dtype=torch.float16, device="cuda")
    with pytest.raises(RuntimeError, match="same shape"):
        sgl_kernel.scale(out, input, 2.0)


def test_scale_cpu_input():
    input = torch.randn(, dtype=torch.float16)  
    out   = torch.empty_like()
     pytest.raises(RuntimeError, =):
        sgl_kernel.scale(out, , )


 __name__ == :
    pytest.main([__file__, ])

Run:

pytest sgl-kernel/tests/test_scale.py -q

---

Step 7: Add a benchmark (required)

Create sgl-kernel/benchmark/bench_scale.py:

import itertools
import os

import torch
import triton
import triton.testing

import sgl_kernel

IS_CI = (
    os.getenv("CI", "false").lower() == "true"
    or os.getenv("GITHUB_ACTIONS", "false").lower() == "true"
)

dtypes  = [torch.float16] if IS_CI else [torch.float16, torch.bfloat16, torch.float32]
sizes   = [4096] if IS_CI else [2**n for n in range(10, 20)]  # 1K … 512K
factors = [2.0]

configs = list(itertools.product(dtypes, sizes))


def torch_scale(input: torch.Tensor, factor: float) -> torch.Tensor:
    return input * factor


@triton.testing.perf_report(
    triton.testing.Benchmark(
        x_names=["dtype", "size"],
        x_vals=configs,
        line_arg="provider",
        line_vals=["sglang", "torch"],
        line_names=["SGL Kernel", "PyTorch"],
        styles=[("green", "-"), ("red", "--")],
        ylabel=,
        plot_name=,
        args={},
    )
)
 ():
      = torch.randn(size, dtype=dtype, device=)
    out    = torch.empty_like()
    factor = 

     provider == :
        fn = : sgl_kernel.scale(out, , factor)
    :
        fn = : torch_scale(, factor)

    ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
        fn, quantiles=[, , ]
    )
      * ms,  * max_ms,  * min_ms


 __name__ == :
    benchmark.run(print_data=)

Run:

python sgl-kernel/benchmark/bench_scale.py

---

Step 8: Build and validate

Build:

cd sgl-kernel
make build -j16

If you need to limit host resource usage:

cd sgl-kernel
make build -j1 MAX_JOBS=2 CMAKE_ARGS="-DSGL_KERNEL_COMPILE_THREADS=1"

Validate:

pytest sgl-kernel/tests/test_scale.py -q
python sgl-kernel/benchmark/bench_scale.py

---

Troubleshooting

• Async CUDA errors: CUDA_LAUNCH_BLOCKING=1
• Memory errors: compute-sanitizer --tool memcheck python ...
• Build is too slow / OOM: reduce MAX_JOBS and SGL_KERNEL_COMPILE_THREADS
• Binary bloat: use sgl-kernel/analyze_whl_kernel_sizes.py
• CMake sources list: if your .cu file is missing from SOURCES, the symbol will be undefined at link time

---

References

• sgl-kernel/README.md
• sgl-kernel/include/sgl_kernel_ops.h
• sgl-kernel/csrc/common_extension.cc
• sgl-kernel/CMakeLists.txt
• sgl-kernel/include/utils.h — DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FLOAT_FP16 macro and friends
• sgl-kernel/csrc/elementwise/activation.cu — reference for the FP16/BF16/FP32 dispatch pattern

Summary of Files Created/Modified

sgl-kernel/csrc/elementwise/scale.cu          # NEW: CUDA kernel + launcher
sgl-kernel/include/sgl_kernel_ops.h           # MODIFIED: C++ declaration
sgl-kernel/csrc/common_extension.cc           # MODIFIED: schema + dispatch registration
sgl-kernel/CMakeLists.txt                     # MODIFIED: add source file (alphabetical)
sgl-kernel/python/sgl_kernel/__init__.py      # MODIFIED: export Python API
sgl-kernel/tests/test_scale.py                # NEW: tests
sgl-kernel/benchmark/bench_scale.py           # NEW: benchmark