Fix bugs and update README

Contributors.md

+# Contributors
+None
\ No newline at end of file

LICENSE

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README.md

-# Deepseek-V3.1_Pytorch
+# Deepseek-V3.1
 ## 论文
 `DeepSeek-V3 Technical Report`
 - https://arxiv.org/abs/2412.19437
+
 ## 模型结构
 DeepSeek-V3.1 是一个支持思考模式和非思考模式的混合模型。与之前的版本相比，此次升级在多个方面都有所改进：
 - 混合思维模式：通过更改聊天模板，一种模型即可支持思维模式和非思维模式。
@@ -15,86 +16,124 @@ DeepSeek-V3.1 是一个支持思考模式和非思考模式的混合模型。与
 ## 算法原理
 DeepSeek-V3.1 是在 DeepSeek-V3.1-Base 的基础上进行后置训练的。DeepSeek-V3.1-Base 是基于原始 V3 基础检查点通过两阶段长上下文扩展方法构建而成的，其遵循了原始 DeepSeek-V3 报告中所阐述的方法。我们通过收集更多长文档并大幅扩展了两个训练阶段的规模，从而扩充了我们的数据集。32K 扩展阶段的规模已增加 10 倍，达到 630 亿个标记，而 128K 扩展阶段则扩大了 3.3 倍，达到 209 亿个标记。
 
-
 ## 环境配置
 ### 硬件需求
-DCU型号：BW200,节点数量：4台,每台卡数：8张。
-### 模型下载及配置
-在乌镇千卡集群启动模型，需要将模型下载到每个节点的/module目录下，随后容器映射
+DCU型号：BW200,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:0.9.2-ubuntu22.04-dtk25.04.1-rc5-rocblas101839-0811-das1.6-py3.10-20250812-beta
 
 docker run -it --name {docker_name} --device=/dev/kfd --privileged --network=host --device=/dev/dri --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v /your_code_path:/your_code_path -v /opt/hyhal:/opt/hyhal:ro -v /module/DeepSeek-V3.1:/your_model_path/DeepSeek-V3.1 --group-add video --shm-size 64G {imageID} bash
 
-cd /your_code_path/deepseek-v3.1_pytorch
+cd /your_code_path/deepseek-v3.1_vllm
 ```
+
 ### Dockerfile（方法二）
-此处提供dockerfile的使用方法
 ```bash
 cd docker
-docker build --no-cache -t deepseek-v3.1_pytorch:latest .
+docker build --no-cache -t deepseek-v3.1:latest .
 docker run -it --name {docker_name} --device=/dev/kfd --privileged --network=host --device=/dev/dri --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v /your_code_path:/your_code_path -v /opt/hyhal:/opt/hyhal:ro -v /module/DeepSeek-V3.1:/your_model_path/DeepSeek-V3.1 --group-add video --shm-size 64G {imageID} bash
 
-cd /your_code_path/deepseek-v3.1_pytorch
+cd /your_code_path/deepseek-v3.1_vllm
 ```
+
 ### 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
-python: 3.10
+DTK: 25.04.1
+python: 3.10.12
 torch: 2.5.1+das.opt2.dtk25041
+vllm: 0.9.2
+transformers: 4.55.0
 ```
 `Tips：以上dtk驱动、pytorch等DCU相关工具版本需要严格一一对应`
 
-其它非深度学习库安装方式如下：
-```bash
-pip install transformers==4.55.0
-```
-## 精度转换
+## 数据集
+无
+
+## 训练
+暂无
+
+## 推理
+### 精度转换
 ```bash
 python ./infer/fp8_cast_bf16.py --input-fp8-hf-path /path/to/fp8_weights --output-bf16-hf-path /path/to/bf16_weights
 ```
 转换成功后，将其他文件一并复制粘贴到输出目录，并删掉config.json中的"quantization_config"键值对。
 
-## 通信与环境变量配置
-VLLM_HOST_IP： 节点本地通信口ip
-NCCL_SOCKET_IFNAME和GLOO_SOCKET_IFNAME： 节点本地通信网口名
-通信口和ip查询方法：ifconfig
+### vllm推理方法
+#### server 多机
+样例模型：[deepseek-ai/DeepSeek-V3.1](https://huggingface.co/deepseek-ai/DeepSeek-V3)
+
+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
+
 <div align=center>
     <img src="./doc/ip_bw.png"/>
 </div>
 
-set_env.sh 文件中的
-NCCL_SOCKET_IFNAME=ib0
-GLOO_SOCKET_IFNAME=ib0
-VLLM_HOST_IP设置为ib网卡对应的IP
+```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_USE_V1=1
+
+# 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
+#BW集群需要额外设置的环境变量：
+export NCCL_NET_GDR_LEVEL=7
+export NCCL_SDMA_COPY_ENABLE=0
+export NCCL_TOPO_FILE="topo-input.xml"
+export VLLM_RPC_TIMEOUT=1800000
+```
 
-设置完成后分别在四个节点 source set_env.sh
+2. 启动RAY集群
+> x.x.x.x 对应第一步 VLLM_HOST_IP
 
-## 启动ray
 ```bash
 # head节点执行
-ray start --head --node-ip-address=12.12.12.46 (头节点的VLLM_HOST_IP) --port=6379 --num-gpus=8 --num-cpus=32
-
+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='12.12.12.46:6379' (头节点的VLLM_HOST_IP) --num-gpus=8 --num-cpus=32
+ray start --address='x.x.x.x:6379' --num-gpus=8 --num-cpus=32
 ```
 
-## 数据集
-暂无
-## 训练
-暂无
-## 推理
-### vllm推理方法
+3. 启动vllm server
+> intel cpu 需要加参数：`--enforce-eager`
+
 ```bash
-vllm serve /your_path/DeepSeek-V3.1-bf16 --trust-remote-code --distributed-executor-backend ray --dtype bfloat16 --tensor-parallel-size 32 --enable-expert-parallel --served-model-name ds31 --max-model-len 64000 --max-seq-len-to-capture 64000 --max-num-batched-tokens 64000 --max-num-seqs 128 --disable-log-requests --block-size 64 --no-enable-chunked-prefill --no-enable-prefix-caching --enforce-eager --gpu-memory-utilization 0.9 
+vllm serve deepseek-ai/DeepSeek-V3.1 --trust-remote-code --distributed-executor-backend ray --dtype bfloat16 --tensor-parallel-size 32 --enable-expert-parallel --served-model-name ds31 --max-model-len 64000 --max-seq-len-to-capture 64000 --max-num-batched-tokens 64000 --max-num-seqs 128 --disable-log-requests --block-size 64 --no-enable-chunked-prefill --no-enable-prefix-caching --gpu-memory-utilization 0.9
 ```
 
-## result
+启动完成后可通过以下方式访问：
 ```bash
-# 示例：ray的head节点在loaclhost
-curl http://localhost:8000/v1/chat/completions     -H "Content-Type: application/json"     -d '{
+
+curl http://x.x.x.x:8000/v1/chat/completions   \
+    -H "Content-Type: application/json"  \
+    -d '{
         "model": "ds31",
         "messages": [
             {
@@ -108,22 +147,28 @@ curl http://localhost:8000/v1/chat/completions     -H "Content-Type: application
     }'
 ```
 
+## result
+<div align=center>
+    <img src="./doc/results-dcu.jpg"/>
+</div>
+
 ### 精度
-```bash
-{"id":"chatcmpl-48c26ddfc27b410ea3935c87e23b459b","object":"chat.completion","created":1756449320,"model":"ds31","choices":[{"index":0,"message":{"role":"assistant","reasoning_content":null,"content":"唔，用户让我介绍自己，这是一个很常见的开场问题。需要简洁明了地说明我的身份、功能和特点，让用户快速了解我能做什么。\n\n可以用轻松友好的语气开头，先打招呼并自我介绍名称和开发团队。然后重点列举核心能力，比如知识范围、上下文长度、文件处理等实用功能，让用户知道我的优势。\n\n最后可以补充一些免费提示，并鼓励用户提问，保持开放和帮助的态度。整体结构可以分三段：基本介绍、能力说明、结束语。</think>你好呀！我是 **DeepSeek-V3**，由**深度求索（DeepSeek）** 打造的一款智能对话助手。我擅长回答各种问题，提供知识解答、代码编写、论文总结、文本润色等多样化的帮助。  \n\n### 🌟 我的特点：\n- **免费使用**：不收费，随时为你解答问题！  \n- **长期记忆**：有128K的上下文长度，可以处理超长文本。  \n- **文件阅读**：支持 PDF、Word、Excel、PPT、TXT 等文件的解析和总结。  \n- **知识丰富**：涵盖编程、科技、学术、历史、文学等众多领域。  \n- **中文优化**：更适合中文用户，理解和表达都很自然！  \n\n无论是学习、工作，还是日常生活遇到难题，都可以来找我聊聊！😊 有什么我可以帮你的吗？","tool_calls":[]},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":8,"total_tokens":298,"completion_tokens":290,"prompt_tokens_details":null},"prompt_logprobs":null,"kv_transfer_params":null}
-```
+DCU与GPU精度一致，推理框架：vllm。
 
-DCU(BW200)与GPU(A800)在BF16精度下推理Deepseek-V3.1模型，结果精度一致，推理框架：vllm。
 ## 应用场景
 ### 算法类别
 `对话问答`
+
 ### 热点应用行业
 `制造,金融,教育`
+
 ## 预训练权重
 - [deepseek-ai/DeepSeek-V3.1](https://huggingface.co/deepseek-ai/DeepSeek-V3)
 - [deepseek-ai/DeepSeek-V3.1-Base](https://hf-mirror.com/deepseek-ai/DeepSeek-V3.1-Base)
+
 ## 源码仓库及问题反馈
-- https://developer.sourcefind.cn/codes/modelzoo/deepseek-v3.1_pytorch
+
+- https://developer.sourcefind.cn/codes/modelzoo/deepseek-v3.1_vllm
 ## 参考资料
 - https://huggingface.co/deepseek-ai
 

docker/Dockerfile

-docker pull image.sourcefind.cn:5000/dcu/admin/base/vllm:0.9.2-ubuntu22.04-dtk25.04.1-rc5-rocblas101839-0811-das1.6-py3.10-20250812-beta
+FROM image.sourcefind.cn:5000/dcu/admin/base/vllm:0.9.2-ubuntu22.04-dtk25.04.1-rc5-rocblas101839-0811-das1.6-py3.10-20250812-beta
\ No newline at end of file

model.properties

+# 模型唯一标识
+modelCode=1705
+# 模型名称
+modelName=deepseek-v3.1_vllm
+# 模型描述
+modelDescription=DeepSeek-V3.1 是一个支持思考模式和非思考模式的混合模型。
+# 应用场景
+appScenario=推理,对话问答,制造,金融,教育
+# 框架类型
+frameType=vllm

set_env.sh

-export ALLREDUCE_STREAM_WITH_COMPUTE=1
-export VLLM_HOST_IP=VLLM_HOST_IP
-export NCCL_SOCKET_IFNAME=NCCL_SOCKET_IFNAME
-export GLOO_SOCKET_IFNAME=NCCL_SOCKET_IFNAME
-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_USE_V1=1
-# export LMSLIM_USE_LIGHTOP=0
-#针对hycpu环境建议设置绑核操作：
-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
-#BW集群需要额外设置的环境变量：
-export NCCL_NET_GDR_LEVEL=7
-export NCCL_SDMA_COPY_ENABLE=0
-export NCCL_IB_HCA=mlx5_2:1,mlx5_3:1,mlx5_4:1,mlx5_5:1,mlx5_6:1,mlx5_7:1,mlx5_8:1,mlx5_9:1
-export NCCL_TOPO_FILE="topo-input.xml"
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