LiuWei

你好，我已完成你上方帖中的步骤1和2：
步骤 1：重新从源码编译 cu-bridge（但是没有生成 /opt/maca/tools/cu-bridge/bin/gnu_wrapper）
步骤 2：设置 MACA 环境变量

问题：在执行步骤 3：编译 vllm-metax（沐曦插件）
的命令：
VLLM_TARGET_DEVICE=empty pip install . --no-build-isolation
后，报错如截图：

你好，按照你的回复：developer.metax-tech.com/forum/t/su-zhu-ji-shang-an-zhuang-wan-vllm-metaxhou-ru-he-an-zhuang-guan-fang-de-vllm/516/post/2345/

一共4步，我在执行第一步：重新从源码编译 cu-bridge后，发现/opt/maca/tools/cu-bridge/bin/gnu_wrapper 依然不存在。
而后续的3步（步骤 2：设置 MACA 环境变量、步骤 3：编译 vllm-metax（沐曦插件）、步骤 4：编译官方 vLLM（空设备模式））还没有执行。

你好，我上面的描述可能没有很清晰。
我执行了你提供的步骤1：

步骤 1：重新从源码编译 cu-bridge

下载 cu-bridge 源码（版本号需和你的 MACA SDK 匹配，你的是 3.5.3）
cd /tmp
wget gitee.com/metax-maca/cu-bridge/repository/archive/3.5.3.zip
unzip 3.5.3.zip
mv cu-bridge-3.5.3 cu-bridge
chmod 755 cu-bridge -Rf
cd cu-bridge
mkdir build && cd build
cmake -DCMAKE_INSTALL_PREFIX=/opt/maca/tools/cu-bridge ../
make -j$(nproc) && make install

编译完成后， /opt/maca/tools/cu-bridge/bin/gnu_wrapper 依然不存在，所以后续编译vllm还是会报缺少gnu_wrapper的错误吧

你好，
刚执行了步骤 1：重新从源码编译 cu-bridge，但是执行完步骤一后，发现 ls -la /opt/maca/tools/cu-bridge/bin/gnu_wrapper 依然不存在。
结论：/opt/maca/tools/cu-bridge/bin/gnu_wrapper不存在，后续编译官方的vllm依然后报上述的错误

详细执行过程的日志如下：
1、将原有的/opt/maca/tools/cu-bridge/备份
(vllm_clone_0.19.0) [root@localhost build]# mv /opt/maca/tools/cu-bridge/ /opt/maca/tools/cu-bridge.bak

2、查看make和cmake的版本：
(vllm_clone_0.19.0) [root@localhost build]# make -version
GNU Make 4.3
为 x86_64-koji-linux-gnu 编译
Copyright (C) 1988-2020 Free Software Foundation, Inc.
许可证：GPLv3+：GNU 通用公共许可证第 3 版或更新版本gnu.org/licenses/gpl.html。
本软件是自由软件：您可以自由修改和重新发布它。
在法律允许的范围内没有其他保证。

(vllm_clone_0.19.0) [root@localhost bin]# cmake -version
cmake version 3.16.5
CMake suite maintained and supported by Kitware (kitware.com/cmake).

3、从gitee中下载cu-bridge-3.5.3.zip，大小为：12428135 字节，下载地址：gitee.com/metax-maca/cu-bridge/repository/archive/3.5.3.zip，
(vllm_clone_0.19.0) [root@localhost opt]# ll
-rw-r--r-- 1 root root 12428135 6月 1 13:23 cu-bridge-3.5.3.zip

解压缩：
(vllm_clone_0.19.0) [root@localhost opt]# unzip cu-bridge-3.5.3.zip

4、项目重命名
(vllm_clone_0.19.0) [root@localhost opt]# mv cu-bridge-3.5.3 cu-bridge

5、修改权限
(vllm_clone_0.19.0) [root@localhost opt]# chmod 755 cu-bridge -Rf

6、创建build目录
(vllm_clone_0.19.0) [root@localhost opt]# cd cu-bridge
(vllm_clone_0.19.0) [root@localhost cu-bridge]# mkdir build
(vllm_clone_0.19.0) [root@localhost cu-bridge]# cd build/

7、执行cmake
(vllm_clone_0.19.0) [root@localhost build]# cmake -DCMAKE_INSTALL_PREFIX=/opt/maca/tools/cu-bridge ../
CMake Warning (dev) in CMakeLists.txt:
No project() command is present. The top-level CMakeLists.txt file must
contain a literal, direct call to the project() command. Add a line of
code such as

project(ProjectName)

near the top of the file, but after cmake_minimum_required().

CMake is pretending there is a "project(Project)" command on the first
line.
This warning is for project developers. Use -Wno-dev to suppress it.

CMake Warning (dev) in CMakeLists.txt:
cmake_minimum_required() should be called prior to this top-level project()
call. Please see the cmake-commands(7) manual for usage documentation of
both commands.
This warning is for project developers. Use -Wno-dev to suppress it.

-- The C compiler identification is GNU 7.3.0
-- The CXX compiler identification is GNU 7.3.0
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- DEST_DIR:./
-- MACA_PATH=/opt/maca
-- PACKAGE_GENERATOR=DEB
-- CMAKE_TARGET_ARCH=
-- Ver=[3.5.3.18] in [/opt/maca/Version.txt]
-- Ver=[3.5.3] in [/opt/cu-bridge/Version.txt]
-- CMAKE_INSTALL_PREFIX=/opt/maca/tools/cu-bridge
-- MACA_PATH=/opt/maca
-- CUPRJ_INCLUDE: /opt/cu-bridge/src/bridge/tools_ext/../../../include/
-- Configuring done (0.5s)
-- Generating done (0.0s)
-- Build files have been written to: /opt/cu-bridge/build

8、执行make
具体的make日志见附件：cu-bridge.log，因为回复框有60000的字数限制。

9、查看/opt/maca/tools/cu-bridge/
(vllm_clone_0.19.0) [root@localhost build]# ll /opt/maca/tools/cu-bridge/bin
conf_bazel.json
conf_gfortran.json
conf_gnu_bazel.json
conf_gnu.json
conf.json
conf_regex.yaml
cucc
gfortran
gnu
gomxccbin
note.md
process_args.py

好的，非常感谢[合十]，周一开始按照这个步骤重新执行

现在又重新回到了开始的那个问题，就是编译官方的vllm报错的问题，在上面的帖中有报错的截图，上面的贴链接如下：
developer.metax-tech.com/forum/t/su-zhu-ji-shang-an-zhuang-wan-vllm-metaxhou-ru-he-an-zhuang-guan-fang-de-vllm/516/post/2308/

抱歉，之前没有找到vectorAdd的位置。
刚才进入到vectorAdd里，执行：make_maca后，执行成功，生成了：vectorAdd，执行：./vectorAdd 后，输出：Test PASSED Done，应该是说明现在的cu-bridge是没有问题的

服务器之前已经在使用旧版本的vllm运行了qwen3大模型。
本次升级过程：
1、卸载原有驱动、sdk，安装新版本：metax-driver-3.5.3.11-rpm-x86_64.run、maca-sdk-3.5.3.18-rpm-x86_64.tar.xz
2、外网服务器新建conda虚拟环境，python=3.10，安装Transformers、
maca-pytorch2.6-py310-3.5.3.9-x86_64.tar.xz、maca-vllm-metax-0.19.0-py310-3.5.3.502-linux-x86_64.tar.xz，使用conda pack打包虚拟环境，拷贝到内网服务器，conda-unpack解压
3、下载cu-bridge的3.5.3版本的包：
gitee.com/metax-maca/cu-bridge
编译过程如下：
unzip 3.1.0.zip
mv cu-bridge-3.1.0 cu-bridge
sudo chmod 755 cu-bridge -Rf
cd cu-bridge
mkdir build && cd ./build
cmake -DCMAKE_INSTALL_PREFIX=/opt/maca/tools/cu-bridge ../
make && make install

4、下载了：cuda-samples的11.5版本
github.com/NVIDIA/cuda-samples

4.1 检查MXMACA编译器是否正确安装：
which mxcc

4.2 检查MXMACA运行环境是否正常：
macainfo

4.3 检查cu-bridge的环境变量

4.1——4.3的三个输出结果都在上一个回复帖的日志中。

5、解压cuda-samples-11.5后
cd cuda-samples-11.5
执行编译：
make_maca

你好，服务器编译了cuda-samples的v11.5，但是报错，详细记录如下：

1、cu-bridge的环境变量如下：
export MACA_CLANG_PATH=/opt/maca/mxgpu_llvm/bin
export CUCC_PATH=/opt/maca/tools/cu-bridge
export MACA_PATH=/opt/maca
export CUDA_PATH=/opt/maca/tools/cu-bridge
export LD_LIBRARY_PATH=/opt/maca/mxgpu_llvm/lib:/opt/maca/ompi/lib/:/opt/maca/lib:${LD_LIBRARY_PATH}
export PATH=/opt/maca/tools/cu-bridge/tools:/opt/maca/bin:/opt/maca/mxgpu_llvm/bin/:${CUDA_PATH}/bin:$PATH
export CUCC_CMAKE_ENTRY=2

2、执行 which mxcc 的结果如下：
(vllm_clone_0.19.0) [root@localhost workspace]# which mxcc
/opt/maca/mxgpu_llvm/bin/mxcc

3、执行 macainfo 的结果如下：
(vllm_clone_0.19.0) [root@localhost workspace]# macainfo
======================
MXC System Attributes
======================
Runtime Version: 1.0
System Timestamp Freq: 1000MHz
Signal Max Wait Time: 18446744073709551615(0xffffffffffffffff)
Machine Model: LARGE
System Endianess: LITTLE

Agent 1

Name: HYGON C86-4G (OPN:7491)
Uuid: 4350552d-5858-0000-0000-000000000000
Market Name: Hygon C86-4G (OPN:7491)
Vendor Name: CPU
Feature: Not Specified
Profile: FULL_PROFILE
Float Round Mode: NEAR
Max Queue Number: 0(0x0)
Queue Min Size: 0
Queue Max Size: 0
Queue Type: MULTI
Node: 0
Device Type: CPU
Cache Info:
Chip ID: 0(0x0)
Cacheline Size: 64(0x40)
Max Clock Freq(MHz): 0
BDFID: 0
Internal Node ID: 0
Accelerator Processors: 32
PEUs per AP: 0
Data Processor Clustes(DPCs): 0
DPC Arrays per DPC.: 0
Watch Pointers on Address Ranges: 1
Pool Info:
Pool 1
Segment: GLOBAL
Flags: FINE_GRAINED
Size: 2321540(0x236c84) KB
Allocatable: TRUE
Alloc Granule: 4 KB
Alloc Alignment: 4 KB
Accessible by all: TRUE
Pool 2
Segment: GLOBAL
Flags: FINE_GRAINED
Size: 2321540(0x236c84) KB
Allocatable: TRUE
Alloc Granule: 4 KB
Alloc Alignment: 4 KB
Accessible by all: TRUE
ISA Info:
N/A

4、在 cuda-samples-11.5 目录下，执行 make_maca 编译，报错如下：

(vllm_clone_0.19.0) [root@localhost cuda-samples-11.5]# make_maca
make maca verion:0220 ...
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/BlackScholes”

GCC Version is greater or equal to 4.8.0 <<<
make[1]: 对“all”无需做任何事。
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/BlackScholes”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/BlackScholes_nvrtc”
GCC Version is greater or equal to 4.8.0 <<<
make[1]: 对“all”无需做任何事。
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/BlackScholes_nvrtc”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/EGLStream_CUDA_CrossGPU”
WARNING - libEGL.so not found, please install libEGL.so <<<
WARNING - egl.h not found, please install egl.h <<<
WARNING - eglext.h not found, please install eglext.h <<<
GCC Version is greater or equal to 4.8.0 <<<
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o cuda_consumer.o -c cuda_consumer.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o cuda_producer.o -c cuda_producer.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o eglstrm_common.o -c eglstrm_common.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o kernel.o -c kernel.cu
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o main.o -c main.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -m64 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o EGLStream_CUDA_CrossGPU cuda_consumer.o cuda_producer.o eglstrm_common.o kernel.o main.o -lEGL -L/root/cu-bridge/CUDA_DIR//lib64/stubs -lcuda
[@] mkdir -p ../../bin/x86_64/linux/release
[@] cp EGLStream_CUDA_CrossGPU ../../bin/x86_64/linux/release
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/EGLStream_CUDA_CrossGPU”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/EGLStream_CUDA_Interop”
WARNING - libEGL.so not found, please install libEGL.so <<<
WARNING - egl.h not found, please install egl.h <<<
WARNING - eglext.h not found, please install eglext.h <<<
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 -o cuda_consumer.o -c cuda_consumer.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 -o cuda_producer.o -c cuda_producer.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 -o eglstrm_common.o -c eglstrm_common.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 -o main.o -c main.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -m64 -o EGLStream_CUDA_Interop cuda_consumer.o cuda_producer.o eglstrm_common.o main.o -lEGL -L/root/cu-bridge/CUDA_DIR//lib64/stubs -lcuda
[@] mkdir -p ../../bin/x86_64/linux/release
[@] cp EGLStream_CUDA_Interop ../../bin/x86_64/linux/release
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/EGLStream_CUDA_Interop”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/EGLStreams_CUDA_Interop”
WARNING - libEGL.so not found, please install libEGL.so <<<
WARNING - egl.h not found, please install egl.h <<<
WARNING - eglext.h not found, please install eglext.h <<<
GCC Version is greater or equal to 4.8.0 <<<
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -o cuda_consumer.o -c cuda_consumer.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -o cuda_producer.o -c cuda_producer.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -o eglstrm_common.o -c eglstrm_common.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -o main.o -c main.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -m64 -o EGLStream_CUDA_Interop cuda_consumer.o cuda_producer.o eglstrm_common.o main.o -lEGL -L/root/cu-bridge/CUDA_DIR//lib64/stubs -lcuda
[@] mkdir -p ../../bin/x86_64/linux/release
[@] cp EGLStream_CUDA_Interop ../../bin/x86_64/linux/release
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/EGLStreams_CUDA_Interop”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/EGLSync_CUDAEvent_Interop”
WARNING - EGLSync_CUDAEvent_Interop is not supported on Linux x86_64 - waiving sample <<<
WARNING - libEGL.so not found, please install libEGL.so <<<
WARNING - egl.h not found, please install egl.h <<<
WARNING - eglext.h not found, please install eglext.h <<<
GCC Version is greater or equal to 4.8.0 <<<
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o EGLSync_CUDAEvent_Interop.o -c EGLSync_CUDAEvent_Interop.cu
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -m64 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o EGLSync_CUDAEvent_Interop EGLSync_CUDAEvent_Interop.o -lEGL -L/root/cu-bridge/CUDA_DIR//lib64/stubs -lcuda -lX11 -lGLESv2
[@] mkdir -p ../../bin/x86_64/linux/release
[@] cp EGLSync_CUDAEvent_Interop ../../bin/x86_64/linux/release
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/EGLSync_CUDAEvent_Interop”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/FDTD3d”
GCC Version is greater or equal to 4.8.0 <<<
make[1]: 对“all”无需做任何事。
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/FDTD3d”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/FilterBorderControlNPP”
GCC Version is greater or equal to 4.8.0 <<<
test.c:1:10: fatal error: 'FreeImage.h' file not found
1 | #include "FreeImage.h"
| ^~~
1 error generated.
WARNING - FreeImage is not set up correctly. Please ensure FreeImage is set up correctly. <<<
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -I../../Common/UtilNPP -m64 --threads 0 --std=c++11 -gencode arch=compute_35,code=compute_35 -o FilterBorderControlNPP.o -c FilterBorderControlNPP.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -m64 -gencode arch=compute_35,code=compute_35 -o FilterBorderControlNPP FilterBorderControlNPP.o -lnppisu_static -lnppif_static -lnppitc_static -lnppidei_static -lnppial_static -lnppc_static -lculibos -lfreeimage
[@] mkdir -p ../../bin/x86_64/linux/release
[@] cp FilterBorderControlNPP ../../bin/x86_64/linux/release
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/FilterBorderControlNPP”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/FunctionPointers”
WARNING - libGL.so not found, refer to CUDA Getting Started Guide for how to find and install them. <<<
WARNING - libGLU.so not found, refer to CUDA Getting Started Guide for how to find and install them. <<<
WARNING - gl.h not found, refer to CUDA Getting Started Guide for how to find and install them. <<<
WARNING - glu.h not found, refer to CUDA Getting Started Guide for how to find and install them. <<<
GCC Version is greater or equal to 4.8.0 <<<
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o FunctionPointers.o -c FunctionPointers.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o FunctionPointers_kernels.o -c FunctionPointers_kernels.cu
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -m64 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o FunctionPointers FunctionPointers.o FunctionPointers_kernels.o -lGL -lGLU -lglut
[@] mkdir -p ../../bin/x86_64/linux/release
[@] cp FunctionPointers ../../bin/x86_64/linux/release
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/FunctionPointers”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/HSOpticalFlow”
GCC Version is greater or equal to 4.8.0 <<<
make[1]: 对“all”无需做任何事。
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/HSOpticalFlow”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/MC_EstimatePiInlineP”
GCC Version is greater or equal to 4.8.0 <<<
make[1]: 对“all”无需做任何事。
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/MC_EstimatePiInlineP”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/MC_EstimatePiInlineQ”
GCC Version is greater or equal to 4.8.0 <<<
make[1]: 对“all”无需做任何事。
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/MC_EstimatePiInlineQ”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/MC_EstimatePiP”
GCC Version is greater or equal to 4.8.0 <<<
make[1]: 对“all”无需做任何事。
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/MC_EstimatePiP”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/MC_EstimatePiQ”
GCC Version is greater or equal to 4.8.0 <<<
make[1]: 对“all”无需做任何事。
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/MC_EstimatePiQ”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/MC_SingleAsianOptionP”
GCC Version is greater or equal to 4.8.0 <<<
make[1]: 对“all”无需做任何事。
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/MC_SingleAsianOptionP”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/Mandelbrot”
WARNING - libGL.so not found, refer to CUDA Getting Started Guide for how to find and install them. <<<
WARNING - libGLU.so not found, refer to CUDA Getting Started Guide for how to find and install them. <<<
WARNING - gl.h not found, refer to CUDA Getting Started Guide for how to find and install them. <<<
WARNING - glu.h not found, refer to CUDA Getting Started Guide for how to find and install them. <<<
GCC Version is greater or equal to 4.8.0 <<<
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o Mandelbrot.o -c Mandelbrot.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o Mandelbrot_cuda.o -c Mandelbrot_cuda.cu
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o Mandelbrot_gold.o -c Mandelbrot_gold.cpp
[@] /root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -m64 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o Mandelbrot Mandelbrot.o Mandelbrot_cuda.o Mandelbrot_gold.o -lGL -lGLU -lglut
[@] mkdir -p ../../bin/x86_64/linux/release
[@] cp Mandelbrot ../../bin/x86_64/linux/release
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/Mandelbrot”
make[1]: 进入目录“/data/workspace/cuda-samples-11.5/Samples/MersenneTwisterGP11213”
GCC Version is greater or equal to 4.8.0 <<<
/root/cu-bridge/CUDA_DIR//bin/nvcc -ccbin g++ -I../../Common -m64 --threads 0 --std=c++11 -gencode arch=compute_35,code=compute_35 -o MersenneTwister.o -c MersenneTwister.cpp
mxcc: warning: argument unused during compilation: '-Xdevice -D__CUDA_ARCH__=700' [-Wunused-command-line-argument]
mxcc: warning: argument unused during compilation: '--offload-arch=xcore1000' [-Wunused-command-line-argument]
In file included from MersenneTwister.cpp:43:
../../Common/helper_cuda.h:240:11: warning: enumeration value 'MCRAND_STATUS_NOT_IMPLEMENTED' not handled in switch [-Wswitch]
240 | switch (error) {
| ^~~
MersenneTwister.cpp:62:15: error: use of undeclared identifier 'findCudaDevice'
62 | int devID = findCudaDevice(argc, (const char )argv);
| ^
MersenneTwister.cpp:83:3: error: use of undeclared identifier 'checkCudaErrors'
83 | checkCudaErrors(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
| ^
MersenneTwister.cpp:86:3: error: use of undeclared identifier 'checkCudaErrors'
86 | checkCudaErrors(cudaMalloc((void )&d_Rand, rand_n * sizeof(float)));
| ^
MersenneTwister.cpp:89:3: error: use of undeclared identifier 'checkCudaErrors'
89 | checkCudaErrors(curandCreateGenerator(&prngGPU, CURAND_RNG_PSEUDO_MTGP32));
| ^
MersenneTwister.cpp:90:3: error: use of undeclared identifier 'checkCudaErrors'
90 | checkCudaErrors(curandSetStream(prngGPU, stream));
| ^
MersenneTwister.cpp:91:3: error: use of undeclared identifier 'checkCudaErrors'
91 | checkCudaErrors(curandSetPseudoRandomGeneratorSeed(prngGPU, seed));
| ^
MersenneTwister.cpp:94:3: error: use of undeclared identifier 'checkCudaErrors'
94 | checkCudaErrors(
| ^
MersenneTwister.cpp:96:3: error: use of undeclared identifier 'checkCudaErrors'
96 | checkCudaErrors(curandSetPseudoRandomGeneratorSeed(prngCPU, seed));
| ^
MersenneTwister.cpp:101:3: error: use of undeclared identifier 'checkCudaErrors'
101 | checkCudaErrors(cudaMallocHost(&h_RandGPU, rand_n * sizeof(float)));
| ^
MersenneTwister.cpp:104:3: error: use of undeclared identifier 'checkCudaErrors'
104 | checkCudaErrors(curandGenerateUniform(prngGPU, (float )d_Rand, rand_n));
| ^
MersenneTwister.cpp:107:3: error: use of undeclared identifier 'checkCudaErrors'
107 | checkCudaErrors(cudaMemcpyAsync(h_RandGPU, d_Rand, rand_n * sizeof(float),
| ^
MersenneTwister.cpp:113:3: error: use of undeclared identifier 'checkCudaErrors'
113 | checkCudaErrors(curandGenerateUniform(prngCPU, (float )h_RandCPU, rand_n));
| ^
MersenneTwister.cpp:115:3: error: use of undeclared identifier 'checkCudaErrors'
115 | checkCudaErrors(cudaStreamSynchronize(stream));
| ^
MersenneTwister.cpp:130:5: error: use of undeclared identifier 'checkCudaErrors'
130 | checkCudaErrors(curandGenerateUniform(prngGPU, (float *)d_Rand, rand_n));
| ^
MersenneTwister.cpp:133:3: error: use of undeclared identifier 'checkCudaErrors'
133 | checkCudaErrors(cudaStreamSynchronize(stream));
| ^
MersenneTwister.cpp:145:3: error: use of undeclared identifier 'checkCudaErrors'
145 | checkCudaErrors(curandDestroyGenerator(prngGPU));
| ^
MersenneTwister.cpp:146:3: error: use of undeclared identifier 'checkCudaErrors'
146 | checkCudaErrors(curandDestroyGenerator(prngCPU));
| ^
MersenneTwister.cpp:147:3: error: use of undeclared identifier 'checkCudaErrors'
147 | checkCudaErrors(cudaStreamDestroy(stream));
| ^
MersenneTwister.cpp:148:3: error: use of undeclared identifier 'checkCudaErrors'
148 | checkCudaErrors(cudaFree(d_Rand));
| ^
fatal error: too many errors emitted, stopping now [-ferror-limit=]
1 warning and 20 errors generated.
make[1]: *** [Makefile:346：MersenneTwister.o] 错误 1
make[1]: 离开目录“/data/workspace/cuda-samples-11.5/Samples/MersenneTwisterGP11213”
make: *** [Makefile:45：Samples/MersenneTwisterGP11213/Makefile.ph_build] 错误 2
(vllm_clone_0.19.0) [root@localhost cuda-samples-11.5]#

好的，
1、我安装的metax-maca/cu-bridge是3.5.3
2、现在我按照cu-bridge使用文档里的样例使用操作，看是否能编译。那我需要下载 github.com/NVIDIA/cuda-samples 下的哪个版本呢？我查了大模型的资料，显示可以下载编译cuda-samples的v11.5、v11.6，是否准确呢

好的，cu-bridge环境变量如图：

你好，
1、我这边是信创项目，docker不在信创软件名单中，不能使用，必须使用宿主机的方式。
2、因为kylin v10 sp3，它的glibc默认是2.28，要升级到glibc 2.31，也必须在不覆盖不替换系统默认glibc 2.28的基础上，指定自定义路径的方式，而且升级完成后，需要设置环境变量，我不确定对操作系统的影响，所以这种方式风险太大。
3、我目前采用的是编译官方的vllm 0.19.0源码的方式，根据大模型查询的资料显示，编译vllm源码的可以在glibc 2.28的基础上进行。服务器已经安装了沐曦的显卡驱动（metax-driver-3.5.3.11-rpm-x86_64.run
）、sdk（maca-sdk-3.5.3.18-rpm-x86_64.tar.xz
）、cu-bridge（gitee.com/metax-maca/cu-bridge）、vllm-metax（maca-vllm-metax-0.19.0-py310-3.5.3.502-linux-x86_64.tar.xz），mx-smi也是显示正常的。现在编译官方的vllm 0.19.0（github.com/vllm-project/vllm）源码报错，执行的编译命令为：python setup.py install ，报错缺少/opt/maca/tools/cu-bridge/bin/gnu_wrapper。详细报错如一下截图。/opt/maca/tools/cu-bridge/bin/下的具体内容也请看截图。

1、我宿主机的操作系统是kylin v10 sp3
2、我的显卡是沐曦的曦云c500
3、我已经安装了vllm-metax-0.19.0
4、官方的vllm：vllm-0.19.0-cp38-abi3-manylinux_2_31_x86_64.whl中，2_31表示glibc >=2.31，但是我这边宿主机的操作系统是kylin v10 sp3，它的glibc默认是2.28版本，结论就是直接安装whl安装不了。而且强行升级glibc版本风险比较大，所以我需要在服务器编译安装官方的vllm
5、我如何在上述环境中编译安装官方的vllm呢？我看了：github.com/MetaX-MACA/vLLM-metax、https://github.com/vllm-project/vllm，但是不知道具体怎么编译安装，因为官方的vllm安装里，没有沐曦显卡的安装选项，急求，谢谢。