File size: 3,953 Bytes
a227c91
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
# 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](https://www.hiascend.com). 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](ascend_npu.md) 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](https://www.mindspore.cn/)