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README.md 0 → 100644
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# Qwen3.5_vllm
## 论文
[Qwen3.5](https://qwen.ai/blog?id=qwen3.5)
## 模型简介
Qwen3.5 通过异构基础设施实现高效的原生多模态训练：
在视觉与语言组件上解耦并行策略，避免统一方案带来的低效。
利用稀疏激活实现跨模块计算重叠，在混合文本-图像-视频数据上相比纯文本基线达到近 100% 的训练吞吐。
在此基础上，原生 FP8 流水线对激活、MoE 路由与 GEMM 运算采用低精度，
并通过运行时监控在敏感层保持 BF16，实现约 50% 的激活显存降低与超过 10% 的加速，
并稳定扩展至数万亿 token。
为了持续释放强化学习的潜力，构建了可扩展的异步强化学习框架，
支持 Qwen3.5 全尺寸模型，并全面覆盖文本、多模态及多轮交互场景。
通过训推分离架构的解耦式设计，该框架显著提升了硬件利用率，
实现了动态负载均衡和细粒度的故障恢复。
配合 FP8 训推、Rollout 路由回放、投机采样以及多轮 Rollout 锁定等技术，
进一步优化了系统吞吐，提高了训推一致性。
通过系统与算法协同设计，该框架在严格控制样本陈旧性的基础上有效缓解了数据长尾问题，
提高了训练曲线的稳定性和性能上限。
此外，框架面向原生智能体工作流设计，能够实现稳定、无缝的多轮环境交互，
消除了框架层的调度中断。这种解耦设计使得系统能够扩展百万级规模的 Agent 脚手架与环境，
从而显著增强模型的泛化能力。
上述优化最终取得了 3×–5× 的端到端加速，展现了卓越的稳定性、高效率与可扩展性。
<div align=center>
<img src="./doc/qwen3.5_397b_a17b_infra.jpg"/>
</div>
## 环境依赖
| 软件 | 版本 |
| :------: |:---------:|
| DTK | dtk26.04 |
| python | 3.10.12 |
| transformers | 5.2.0.dev0 |
| vllm | 0.15.1+das.opt1.alpha.dtk2604 |
| triton | 3.3.0+das.opt2.dtk2604.20260203.g393ad86c |
| torch | 2.9.0+das.opt1.dtk2604.20260126.g22910426 |
当前仅支持镜像: harbor.sourcefind.cn:5443/dcu/admin/base/pytorch:2.9.1-ubuntu22.04-dtk26.04-0130-py3.10-20260204
**注意**：模型所依赖镜像暂未上线光源，请使用以下wget方式下载并load镜像：
```bash
wget http://112.11.119.99:18000/docker-images/das18-20260204/pytorch2.9.1-ubuntu22.04-dtk26.04-0130-py3.10-20260204.tar.gz
docker load -i pytorch2.9.1-ubuntu22.04-dtk26.04-0130-py3.10-20260204.tar.gz
```
- 挂载地址`-v` 根据实际模型情况修改
```bash
docker run -it \
--shm-size 60g \
--network=host \
--name qwen3.5 \
--privileged \
--device=/dev/kfd \
--device=/dev/dri \
--device=/dev/mkfd \
--group-add video \
--cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
-u root \
-v /opt/hyhal/:/opt/hyhal/:ro \
-v /path/your_code_data/:/path/your_code_data/ \
harbor.sourcefind.cn:5443/dcu/admin/base/pytorch:2.9.1-ubuntu22.04-dtk26.04-0130-py3.10-20260204 bash
```
更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用。
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，vllm、flash_mla、numpy等需要替换安装：
```
pip uninstall vllm flash_mla numpy
pip install flash_mla-1.0.0-cp310-cp310-manylinux_2_28_x86_64.whl
pip install vllm-0.15.1+das.opt1.alpha.dtk2604-cp310-cp310-linux_x86_64.whl
pip install git+https://github.com/huggingface/transformers.git
pip install numpy==1.25.0
```
## 数据集
暂无
## 训练
暂无
## 推理
### vllm
#### 多机推理
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
```bash
## serve启动
vllm serve Qwen/Qwen3.5-397B-A17B \
--port 8001 \
--tensor-parallel-size 16 \
--distributed-executor-backend ray \
--max-model-len 262144 \
--reasoning-parser qwen3
## client访问
curl http://localhost:8001/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen3.5-397B-A17B",
"messages": [
{"role": "user", "content": "Type \"I love Qwen3.5\" backwards"}
],
"temperature": 0.6
}'
```
## 效果展示
<div align=center>
<img src="./doc/result-dcu.jpg"/>
</div>
### 精度
DCU与GPU精度一致，推理框架：vllm。
## 预训练权重
| 模型名称 | 权重大小 | DCU型号 | 最低卡数需求 | 下载地址 |
|:------:|:----:|:----------:|:------:|:---------------------:|
| Qwen3.5-397B-A17B | 397B | BW1000 | 16 | [Hugging Face](https://huggingface.co/Qwen/Qwen3.5-397B-A17B) |
## 源码仓库及问题反馈
- https://developer.sourcefind.cn/codes/modelzoo/qwen3.5_vllm
## 参考资料
- https://github.com/QwenLM/Qwen3.5
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# 模型唯一标识
modelCode=2095
# 模型名称
modelName=qwen3.5_vllm
# 模型描述
modelDescription=Qwen3.5 相对于 Qwen3 系列模型，其 Post-training 性能提升主要来自于对各类 RL 任务和环境的全面扩展，更加强调 RL 环境的难度与可泛化性，而非针对特定指标或狭隘类别的 query 进行优化。
# 运行过程
processType=推理
# 算法类别
appCategory=对话问答
# 框架类型
frameType=vllm
# 加速卡类型
accelerateType=K100AI,BW1000
\ No newline at end of file
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