chenchenghao/sglang
1
0
Fork 0
Code Issues Pull Requests Actions 12 Packages Projects Releases Wiki Activity

[diffusion] hardware: support diffusion models on MTGPU (doc, 6/N) (#17346)

Browse Source 
Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
This commit is contained in:
R0CKSTAR
2026-02-04 04:44:57 +08:00
committed by GitHub
parent ec2461bc16
commit 7de650c83c
3 changed files with 34 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

9
python/sglang/multimodal_gen/README.md
View File

@@ -18,14 +18,19 @@ SGLang Diffusion has the following features:
SGLang Diffusion supports AMD Instinct GPUs through ROCm. On AMD platforms, we use the Triton attention backend and leverage AITER kernels for optimized layernorm and other operations. See the [ROCm installation guide](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen/docs/install_rocm.md) for setup instructions.
### Moore Threads/MUSA Support
SGLang Diffusion supports Moore Threads GPUs (MTGPU) through the MUSA software stack. On MUSA platforms, we use the Torch SDPA backend for attention. See the [MUSA installation guide](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen/docs/install_musa.md) for setup instructions.
## Getting Started
```bash
uv pip install 'sglang[diffusion]' --prerelease=allow
```
For more installation methods (e.g. pypi, uv, docker), check [install.md](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen/docs/install.md). ROCm/AMD users should follow the [ROCm quickstart](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen/docs/install_rocm.md) that includes the additional kernel builds and attention backend settings we validated on MI300X.
For more installation methods (e.g. pypi, uv, docker), check [install.md](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen/docs/install.md).
* ROCm/AMD users should follow the [ROCm quickstart](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen/docs/install_rocm.md) that includes the additional kernel builds and attention backend settings we validated on MI300X.
* MUSA/Moore Threads users should follow the [MUSA quickstart](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen/docs/install_musa.md) that includes the attention backend settings we validated on MTT S5000.
## Inference

4
python/sglang/multimodal_gen/docs/install.md
View File

@@ -2,7 +2,9 @@
You can install sglang-diffusion using one of the methods below.
This page primarily applies to common NVIDIA GPU platforms. For AMD Instinct/ROCm environments see the dedicated [ROCm quickstart](install_rocm.md), which lists the exact steps (including kernel builds) we used to validate sgl-diffusion on MI300X.
This page primarily applies to common NVIDIA GPU platforms.
* For AMD Instinct/ROCm environments see the dedicated [ROCm quickstart](install_rocm.md), which lists the exact steps (including kernel builds) we used to validate sgl-diffusion on MI300X.
* For Moore Threads GPU (MTGPU) with the MUSA software stack, see the [MUSA quickstart](install_musa.md), which lists the exact steps we used to validate sgl-diffusion on MTT S5000.
## Method 1: With pip or uv

24
python/sglang/multimodal_gen/docs/install_musa.md Normal file
View File

@@ -0,0 +1,24 @@
# MUSA Quickstart for SGLang-Diffusion
This page covers installation and usage of SGLang-Diffusion on Moore Threads GPU (MTGPU) with the MUSA software stack.
## Install from Source
```bash
# Clone the repository
git clone https://github.com/sgl-project/sglang.git
cd sglang
# Install the Python packages
pip install --upgrade pip
rm -f python/pyproject.toml && mv python/pyproject_other.toml python/pyproject.toml
pip install -e "python[all_musa]"
```
## Quick Test
```bash
sglang generate --model-path black-forest-labs/FLUX.1-dev \
--prompt "A logo With Bold Large text: SGL Diffusion" \
--save-output
```