---
id: pr-sglang-23938
repo: sgl-project/sglang
pr: 23938
title: Optimize large GroupNorm SiLU apply
author: BBuf
date: '2026-05-02'
url: https://github.com/sgl-project/sglang/pull/23938
source_category: upstream-code
architectures:
- sm100
tags:
- CUDA kernel
- benchmark kernel
- benchmark_test
- compiler_runtime
- fused norm
- jit_kernel
- norm_elementwise
- triton kernel
techniques:
- kernel-fusion
- vectorized-loads
hardware_features: []
kernel_types:
- compiler
- decode
- norm
languages:
- cuda-cpp
- triton
captured_at: '2026-05-16'
status: merged
merge_sha: 1360848ee1e29083132998ae0084f9683af3033d
inclusion_reason: Look for fusion, vectorization, dtype conversion, and store-traffic reductions before changing math.
changed_paths:
- python/sglang/jit_kernel/benchmark/diffusion/bench_group_norm_silu.py
- python/sglang/jit_kernel/diffusion/triton/group_norm_silu.py
artifact_dir: evidence/pull-bundles/sglang/gh-23938
head_ref_oid: 897c243f9ee1cd76c10d6c3806607cf2e3c1c5bc
---
# Optimize large GroupNorm SiLU apply

- Repository: `sgl-project/sglang`
- PR: [23938](https://github.com/sgl-project/sglang/pull/23938)
- Decision: `Look for fusion, vectorization, dtype conversion, and store-traffic reductions before changing math.`
- Reason: Look for fusion, vectorization, dtype conversion, and store-traffic reductions before changing math.

## Changed Paths

- `python/sglang/jit_kernel/benchmark/diffusion/bench_group_norm_silu.py`
- `python/sglang/jit_kernel/diffusion/triton/group_norm_silu.py`

## Evidence Bundle

`evidence/pull-bundles/sglang/gh-23938`
