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Compiling the Source Code and Installing OpPlugin

Installation instructions

  1. Hardware mapping table

    The Ascend training device includes the following models, which can be used as the training environment of the PyTorch model.

    Product range Product Model
    Atlas Training Series Atlas 800 training server (model: 9000)
    Atlas 800 training server (model: 9010)
    Atlas 900 PoD (model: 9000)
    Atlas 300T training card (model: 9000)
    Atlas 300T Pro training card (model: 9000)
    Atlas A2 Training Series Atlas 800T A2 training server
    Atlas 900 A2 PoD cluster base unit
    Atlas 200T A2 Box16 Heterogeneous Subrack
    Atlas A3 Training Series Atlas 800T A3 training server
    Atlas 900 A3 SuperPoD Super Node

    The Ascend inference device includes the following models, which can be used as the inference environment for large models.

    Product range Product Model
    Atlas 800I A2 inference product Atlas 800I A2 inference server

  2. Software mapping table

    PyTorch TorchNPU OpPlugin Python GCC
    2.7.1 v2.7.1 master 3.9, 3.10, 3.11, 3.12, 3.13 11.2
    2.8.0 v2.8.0 master 3.9, 3.10, 3.11, 3.12, 3.13 13.3
    2.9.0 v2.9.0 master 3.10, 3.11, 3.12, 3.13 13.3
    2.10.0 v2.10.0 master 3.10, 3.11, 3.12, 3.13 13.3
    2.11.0 v2.11.0 master 3.10, 3.11, 3.12, 3.13 13.3
    2.12.0 v2.12.0 master 3.10, 3.11, 3.12, 3.13 13.3
    2.13.0 master master 3.10, 3.11, 3.12, 3.13 13.3

Installation Dependency

The system dependency and official PyTorch framework must be installed during installation. You are advised to use the Docker image provided by TorchNPU for compilation. For details about the dependency installation and image usage guide, see the TorchNPU.

Operation Procedure

  1. Script for configuring CANN environment variables.

    source <CANN软件安装目录>/<CANN软件路径>/set_env.sh

    The default path of the environment variable script is /usr/local/npu/ascend-toolkit/set_env.sh. The path of ascend-toolkit depends on the name of the installed CANN software.

  2. Generate the binary installation package of the plug-in.

    Download the branch code of the corresponding OpPlugin version and go to the root directory of the plug-in.

    git clone --branch master https://gitcode.com/ascend/op-plugin.git
cd op-plugin

    Run the following commands to compile and build the PyTorch 2.10.0:

    bash ci/build.sh --python=3.9 --pytorch=v2.10.0

    [!NOTICE]

    For details about the GCC and Python versions during compilation, see the Software mapping table During the compilation, the build folder is created in the root directory of the plug-in, and the source code of the corresponding TorchNPU version is downloaded for collaborative compilation. If the build/pytorch directory exists, the TorchNPU source code will not be downloaded repeatedly during OpPlugin compilation. To download the latest TorchNPU source code, delete the build/pytorch directory.

  3. After the compilation is complete, install the TorchNPU package generated in the dist directory. If a non-root user is used to install the TorchNPU package, add the following information to the end of the command:--user.

    pip3 install --upgrade dist/torch_npu-{torch_npu_version}-{Python_version}-{arch}.whl
# Replace it with the name of the generated whl package. {torch_npu_version} indicates the TorchNPU version, {Python_version} indicates the Python version, and {arch} indicates the target architecture.
# A typical whl package name is similar to torch_npu-2.7.1.post13-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

Uninstalling

Run the following command to uninstall the torch:

pip uninstall torch_npu