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# Contributors
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README.md
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# Qwen3-Coder_vllm
# Qwen3-Coder
## 论文
[Qwen3 Technical Report](https://arxiv.org/pdf/2505.09388)
通义千问发布最新Qwen3-Coder模型，迄今为止最具代理能力的代码模型。
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## 模型结构
Qwen3-480B-A35B-Instruct 具备以下特点:
参数: 总参数 480B 激活参数 35B
层数: 62
注意力头 (GQA): 96 Q、8 KV
专家数: 160
激活专家数: 8
文本长度: 原生支持 256K token 的上下文并可通过 YaRN 扩展到 1M token
<div align=center>
<img src="./doc/model.png"/>
</div>
## 算法原理
<div align=center>
<img src="./doc/methods.png"/>
</div>
## 环境配置
### 硬件需求
DCU型号：K100_AI,节点数量：4台,卡数：32 张。
`-v 路径`、`docker_name`和`imageID`根据实际情况修改
### Docker（方法一）
```bash
docker pull image.sourcefind.cn:5000/dcu/admin/base/vllm:0.8.5-ubuntu22.04-dtk25.04.1-rc5-das1.6-py3.10-20250724
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
cd /your_code_path/qwen3-coder_vllm
pip install transformers==4.51.3
```
### Dockerfile（方法二）
```bash
cd docker
docker build --no-cache -t qwen3-coder:latest .
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
cd /your_code_path/qwen3-coder_vllm
pip install transformers==4.51.3
```
### Anaconda（方法三）
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装。
```bash
DTK: 25.04.1
python: 3.10
vllm: 0.8.5
torch: 2.4.1+das.opt2.dtk2504
deepspeed: 0.14.2+das.opt2.dtk2504
transformers: 4.51.3
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
## 数据集
无
## 训练
暂无
## 推理
### vllm推理方法
#### server 单机
样例模型：[Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct)
```bash
export HIP_VISIBLE_DEVICES=0,1,2,3
export ALLREDUCE_STREAM_WITH_COMPUTE=1
vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --trust-remote-code --dtype bfloat16 --max-seq-len-to-capture 32768 -tp 4 --gpu-memory-utilization 0.85 --override-generation-config '{"temperature": 0.7, "top_p":0.8, "top_k":20, "repetition_penalty": 1.05}' --max-model-len 32768
```
启动完成后可通过以下方式访问：
```bash
curl http://x.x.x.x:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen3-Coder-30B-A3B-Instruct",
"messages": [
{
"role": "user",
"content": "quare the number 1024."
}
]
}'
```
#### server 多机
样例模型：[Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct)
1. 加入环境变量
> 请注意：
> 每个节点上的环境变量都写到.sh文件中，保存后各个计算节点分别source .sh文件
>
> VLLM_HOST_IP：节点本地通信口ip，尽量选择IB网卡的IP，**避免出现rccl超时问题**
>
> NCCL_SOCKET_IFNAME和GLOO_SOCKET_IFNAME：节点本地通信网口ip对应的名称
> 通信口和ip查询方法：ifconfig
> IB口状态查询：ibstat !!!一定要active激活状态才可用，各个节点要保持统一
```bash
export ALLREDUCE_STREAM_WITH_COMPUTE=1
export VLLM_HOST_IP=x.x.x.x # 对应计算节点的IP，选择IB口SOCKET_IFNAME对应IP地址
export NCCL_SOCKET_IFNAME=ibxxxx
export GLOO_SOCKET_IFNAME=ibxxxx
export NCCL_IB_HCA=mlx5_0:1 # 环境中的IB网卡名字
unset NCCL_ALGO
export NCCL_MIN_NCHANNELS=16
export NCCL_MAX_NCHANNELS=16
export NCCL_NET_GDR_READ=1
export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export VLLM_SPEC_DECODE_EAGER=1
export VLLM_MLA_DISABLE=0
export VLLM_USE_FLASH_MLA=1
# K100_AI集群建议额外设置的环境变量：
export VLLM_ENFORCE_EAGER_BS_THRESHOLD=44
export VLLM_RPC_TIMEOUT=1800000
# 海光CPU绑定核
export VLLM_NUMA_BIND=1
export VLLM_RANK0_NUMA=0
export VLLM_RANK1_NUMA=1
export VLLM_RANK2_NUMA=2
export VLLM_RANK3_NUMA=3
export VLLM_RANK4_NUMA=4
export VLLM_RANK5_NUMA=5
export VLLM_RANK6_NUMA=6
export VLLM_RANK7_NUMA=7
```
2. 启动RAY集群
> x.x.x.x 对应第一步 VLLM_HOST_IP
```bash
# head节点执行
ray start --head --node-ip-address=x.x.x.x --port=6379 --num-gpus=8 --num-cpus=32
# worker节点执行
ray start --address='x.x.x.x:6379' --num-gpus=8 --num-cpus=32
```
3. 启动vllm server
> intel cpu 需要加参数：`--enforce-eager`
```bash
vllm serve Qwen/Qwen3-Coder-480B-A35B-Instruct --trust-remote-code --distributed-executor-backend ray --dtype bfloat16 --max-seq-len-to-capture 32768 -tp 32 --gpu-memory-utilization 0.85 --max-num-seqs 128 --block-size 64 --override-generation-config '{"temperature": 0.7, "top_p":0.8, "top_k":20, "repetition_penalty": 1.05}' --max-model-len 32768 --host x.x.x.x
```
启动完成后可通过以下方式访问：
```bash
curl http://x.x.x.x:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen3-Coder-480B-A35B-Instruct",
"messages": [
{
"role": "user",
"content": "quare the number 1024."
}
]
}'
```
## result
<div align=center>
<img src="./doc/results-dcu.png"/>
</div>
![alt text](image.png)
### 精度
DCU与GPU精度一致，推理框架：vllm。
## 应用场景
### 算法类别
代码生成
### 热点应用行业
制造,广媒,家居,教育
## 预训练权重
- [Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct)
- [Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct)
## 源码仓库及问题反馈
- https://developer.sourcefind.cn/codes/modelzoo/qwen3-coder_vllm
## 参考资料
- https://github.com/QwenLM/Qwen3-Coder
docker/Dockerfile 0 → 100644
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FROM image.sourcefind.cn:5000/dcu/admin/base/vllm:0.8.5-ubuntu22.04-dtk25.04.1-rc5-das1.6-py3.10-20250724
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icon.png

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infer_transformers.py 0 → 100644
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from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-480B-A35B-Instruct"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Write a quick sort algorithm."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True)
print("content:", content)
model.properties 0 → 100644
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# 模型唯一标识
modelCode=1707
# 模型名称
modelName=qwen3-coder_vllm
# 模型描述
modelDescription=通义千问发布最新Qwen3-Coder模型，迄今为止最具代理能力的代码模型。
# 应用场景
appScenario=推理,代码生成,制造,广媒,家居,教育
# 框架类型
frameType=vllm
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