MindSpore Models

Introduction

MindSpore is a high-performance AI framework optimized for Ascend NPUs. This doc guides users to run MindSpore models in SGLang.

Requirements

MindSpore currently only supports Ascend NPU devices. Users need to first install Ascend CANN software packages. The CANN software packages can be downloaded from the Ascend Official Website. The recommended version is 8.3.RC2.

Supported Models

Currently, the following models are supported:

Qwen3: Dense and MoE models
DeepSeek V3/R1
More models coming soon...

Installation

Note: Currently, MindSpore models are provided by an independent package sgl-mindspore. Support for MindSpore is built upon current SGLang support for Ascend NPU platform. Please first install SGLang for Ascend NPU and then install sgl-mindspore:

shell

git clone https://github.com/mindspore-lab/sgl-mindspore.git
cd sgl-mindspore
pip install -e .

Run Model

Current SGLang-MindSpore supports Qwen3 and DeepSeek V3/R1 models. This doc uses Qwen3-8B as an example.

Offline infer

Use the following script for offline infer:

python

import sglang as sgl

# Initialize the engine with MindSpore backend
llm = sgl.Engine(
    model_path="/path/to/your/model",  # Local model path
    device="npu",                      # Use NPU device
    model_impl="mindspore",            # MindSpore implementation
    attention_backend="ascend",        # Attention backend
    tp_size=1,                         # Tensor parallelism size
    dp_size=1                          # Data parallelism size
)

# Generate text
prompts = [
    "Hello, my name is",
    "The capital of France is",
    "The future of AI is"
]

sampling_params = {"temperature": 0, "top_p": 0.9}
outputs = llm.generate(prompts, sampling_params)

for prompt, output in zip(prompts, outputs):
    print(f"Prompt: {prompt}")
    print(f"Generated: {output['text']}")
    print("---")

Start server

Launch a server with MindSpore backend:

bash

# Basic server startup
python3 -m sglang.launch_server \
    --model-path /path/to/your/model \
    --host 0.0.0.0 \
    --device npu \
    --model-impl mindspore \
    --attention-backend ascend \
    --tp-size 1 \
    --dp-size 1

For distributed server with multiple nodes:

bash

# Multi-node distributed server
python3 -m sglang.launch_server \
    --model-path /path/to/your/model \
    --host 0.0.0.0 \
    --device npu \
    --model-impl mindspore \
    --attention-backend ascend \
    --dist-init-addr 127.0.0.1:29500 \
    --nnodes 2 \
    --node-rank 0 \
    --tp-size 4 \
    --dp-size 2

Troubleshooting

Debug Mode

Enable sglang debug logging by log-level argument.

bash

python3 -m sglang.launch_server \
    --model-path /path/to/your/model \
    --host 0.0.0.0 \
    --device npu \
    --model-impl mindspore \
    --attention-backend ascend \
    --log-level DEBUG

Enable mindspore info and debug logging by setting environments.

bash

export GLOG_v=1  # INFO
export GLOG_v=0  # DEBUG

Explicitly select devices

Use the following environment variable to explicitly select the devices to use.

shell

export ASCEND_RT_VISIBLE_DEVICES=4,5,6,7  # to set device

Some communication environment issues

In case of some environment with special communication environment, users need set some environment variables.

shell

export MS_ENABLE_LCCL=off # current not support LCCL communication mode in SGLang-MindSpore

Some dependencies of protobuf

In case of some environment with special protobuf version, users need set some environment variables to avoid binary version mismatch.

shell

export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python  # to avoid protobuf binary version mismatch

Support

For MindSpore-specific issues:

Refer to the MindSpore documentation