【Linux】在Arm服务器源码编译onnxruntime-gpu的whl

服务器信息：

aarch64架构 ubuntu20.04 nvidia T4卡

编译onnxruntime-gpu前置条件：

源码编译onnxruntime-gpu的步骤

1. 下载源码

git clone –recursive https://github.com/microsoft/onnxruntime.git
cd onnxruntime

2. 选择版本

然后根据需要安装的onnxruntime-gpu版本号，切换版本

git checkout v1.16.3

3. 执行bulid指令

在onnxruntime根目录下执行：

./build.sh \\
–config Release \\
–update \\
–build \\
–parallel \\
–build_wheel \\
–use_cuda \\
–allow_running_as_root \\
–cuda_home /usr/local/cuda \\
–cudnn_home /usr/lib/aarch64-linux-gnu \\
–skip_tests \\
–cmake_extra_defines \\
CMAKE_CUDA_ARCHITECTURES=75 \\
onnxruntime_ENABLE_NVTX_PROFILE=ON \\
onnxruntime_USE_MEMORY_EFFICIENT_ATTENTION=OFF \\
onnxruntime_USE_FLASH_ATTENTION=OFF \\
onnxruntime_BUILD_UNIT_TESTS=OFF \\
CMAKE_POLICY_VERSION_MINIMUM=3.5

4. 报错解决方式

4.1 算力设置不匹配

[ 53%] Built target onnxruntime_optimizer
make: *** [Makefile:166: all] Error 2
Traceback (most recent call last):
File "/home/tc/onnxruntime/tools/ci_build/build.py", line 2684, in <module>
sys.exit(main())
File "/home/tc/onnxruntime/tools/ci_build/build.py", line 2577, in main
build_targets(args, cmake_path, build_dir, configs, num_parallel_jobs, args.target)
File "/home/tc/onnxruntime/tools/ci_build/build.py", line 1487, in build_targets
run_subprocess(cmd_args, env=env)
File "/home/tc/onnxruntime/tools/ci_build/build.py", line 798, in run_subprocess
return run(*args, cwd=cwd, capture_stdout=capture_stdout, shell=shell, env=my_env)
File "/home/tc/onnxruntime/tools/python/util/run.py", line 49, in run
completed_process = subprocess.run(
File "/usr/lib/python3.8/subprocess.py", line 516, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['/home/tc/cmake-3.26.0-linux-aarch64/bin/cmake', '–build', '/home/tc/onnxruntime/build/Linux/Release', '–config', 'Release', '–', '-j40']' returned non-zero exit status 2.

问题原因： ./bulid.sh的参数CMAKE_CUDA_ARCHITECTURES=87 表示目标 GPU 的计算能力为 8.7，请确认您的硬件是否匹配： 可以直接用AI来查询，T4需要填写75，解决此问题

4.2 下载库文件超时

— Using src='https://github.com/pytorch/cpuinfo/archive/ca678952a9a8eaa6de112d154e8e104b22f9ab3f.zip'
CMake Error at pytorch_cpuinfo-subbuild/pytorch_cpuinfo-populate-prefix/src/pytorch_cpuinfo-populate-stamp/download-pytorch_cpuinfo-populate.cmake:170 (message):
Each download failed!

error: downloading 'https://github.com/pytorch/cpuinfo/archive/ca678952a9a8eaa6de112d154e8e104b22f9ab3f.zip' failed
status_code: 28
status_string: "Timeout was reached"
log:
— LOG BEGIN —
Trying 20.205.243.166:443…

connect to 20.205.243.166 port 443 failed: Connection timed out

Failed to connect to github.com port 443 after 131336 ms: Couldn't connect
to server

Closing connection 0

— LOG END —

问题原因： 下载库文件速度过慢超时 解决方案 手动下载，并放到指定位置，再重新执行编译指令。 以上述报错为例： 网页下载资源，浏览器输入地址自动下载压缩包：

https://github.com/pytorch/cpuinfo/archive/ca678952a9a8eaa6de112d154e8e104b22f9ab3f.zip

将下载的 cpuinfo-ca678952a9a8eaa6de112d154e8e104b22f9ab3f.zip，解压到相对路径onnxruntime/build/Linux/Release/_deps/pytorch_cpuinfo-subbuild/pytorch_cpuinfo-populate-prefix/src/ 目录下。(报错里会写明需要放置的位置，根据实际情况修改路径)

然后重新执行bulid.sh的指令就可以继续编译。

4.3 不支持BFLOAT16

NVCC_ERROR = nvcc fatal : Unknown option '-Wstrict-aliasing'

NVCC_OUT = 1
CMake Error at CMakeLists.txt:695 (message):
The compiler doesn't support BFLOAT16!!!

— Configuring incomplete, errors occurred!
Traceback (most recent call last):
File "/home/tc/onnxruntime/tools/ci_build/build.py", line 2998, in <module>
sys.exit(main())
File "/home/tc/onnxruntime/tools/ci_build/build.py", line 2853, in main
generate_build_tree(
File "/home/tc/onnxruntime/tools/ci_build/build.py", line 1674, in generate_build_tree
run_subprocess(
File "/home/tc/onnxruntime/tools/ci_build/build.py", line 867, in run_subprocess
return run(*args, cwd=cwd, capture_stdout=capture_stdout, shell=shell, env=my_env)
File "/home/tc/onnxruntime/tools/python/util/run.py", line 49, in run
completed_process = subprocess.run(
File "/usr/lib/python3.8/subprocess.py", line 516, in run
raise CalledProcessError(retcode, process.args,

问题原因： ONNX Runtime 从 v1.17.0 起要求 ARM 架构支持 BFLOAT16 指令集，当前编译器版本不支持。 解决方式一：升级编译器 / 系统环境 官方文档和讨论指出：在 JetPack 5.x (Ubuntu 18.04/20.04) 上编译时，GCC 必须 ≥10 (JetPack 6 对应 Ubuntu 22.04，自带 GCC11+)。也就是说，最简单的解决方案是升级到 JetPack 6 (Ubuntu 22.04/GCC11) 或手动安装较新版本的 GCC（如通过 sudo apt install gcc-11 g+±11 并更新 alternatives）。升级要求：Ubuntu 22.04，不然可能无法直接安装gcc-12 和 g+±12 。 升级后，-march=armv8.2-a+bf16 检查就会通过。表明需要更高版本的编译器以支持 BF16。

sudo apt update
sudo apt install software-properties-common
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt update

此 PPA 提供了多个版本的 GCC 和 G++，包括 gcc-12 和 g+±12 。

sudo apt install gcc-12 g++-12

sudo update-alternatives –install /usr/bin/gcc gcc /usr/bin/gcc-12 120
sudo update-alternatives –install /usr/bin/g++ g++ /usr/bin/g++-12 120

sudo update-alternatives –config gcc
sudo update-alternatives –config g++

gcc –version
g++ –version

解决方式二：修改源码绕过 BFLOAT16 检查 若必须在现有环境下编译，可以手动修改源码跳过 BFLOAT16 检查。在 onnxruntime/cmake/CMakeLists.txt 中找到如下检查段：

check_cxx_compiler_flag(-march=armv8.2-a+bf16 HAS_ARM64_BFLOAT16)
if(NOT HAS_ARM64_BFLOAT16)
message(FATAL_ERROR "The compiler doesn't support BFLOAT16!!!")
endif()

修改为：

if(NOT HAS_ARM64_BFLOAT16)
#message(WARNING "BFLOAT16 not supported, disabling BF16 optimizations")
set(HAS_ARM64_BFLOAT16 TRUE)
endif()

这样跳过了编译器不支持 BF16 的致命错误。不过需要注意：跳过检查后仍可能缺少 BF16 优化代码，性能或功能可能受影响。修改后保存并重新运行 CMake 即可继续编译。