TorchNPU
Overview
TorchNPU is a PyTorch adapter plugin that brings Ascend NPU support into the PyTorch ecosystem, enabling developers to harness the high-performance computing power of Ascend AI processors.
Ascend is a full-stack AI computing infrastructure for industry applications and services based on Huawei Ascend processors and software. For more information about Ascend, see Ascend Community.
Installation
From Binary
Provide users with wheel package to quickly install TorchNPU. Before installing TorchNPU, complete the installation of CANN according to Ascend Auxiliary Software. To obtain the CANN installation package, refer to the CANN Installation.
-
Install PyTorch
Install PyTorch through pip.
For Aarch64:
pip3 install torch==2.7.1
For x86:
pip3 install torch==2.7.1+cpu --index-url https://download.pytorch.org/whl/cpu
-
Install TorchNPU dependencies
Run the following command to install dependencies.
pip3 install pyyaml pip3 install setuptools
If the installation fails, use the download link or visit the PyTorch official website to download the installation package of the corresponding version.
OS arch Python version link x86 Python3.9 link x86 Python3.10 link x86 Python3.11 link aarch64 Python3.9 link aarch64 Python3.10 link aarch64 Python3.11 link -
Install TorchNPU
pip3 install torch-npu==2.7.1
From Source
In some special scenarios, users may need to compile TorchNPU by themselves. Select a branch in table Ascend Auxiliary Software and a Python version in table PyTorch and Python Version Matching Table first. The docker image is recommended for compiling TorchNPU through the following steps (It is recommended to mount the working path only and avoid the system path to reduce security risks.), the generated .whl file path is ./dist/. Note that gcc version has the following constraints if you try to compile without using docker image: we recommend to use gcc 11.2 for both ARM and X86.
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Clone TorchNPU
git clone https://github.com/ascend/pytorch.git -b 2.7.1-7.2.0 --depth 1
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Build Docker Image
cd pytorch/ci/docker/{arch} # {arch} for X86 or ARM docker build -t manylinux-builder:v1 .
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Enter Docker Container
docker run -it -v /{code_path}/pytorch:/home/pytorch manylinux-builder:v1 bash # {code_path} is the TorchNPU source code path
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Compile TorchNPU
Take Python 3.9 as an example.
cd /home/pytorch bash ci/build.sh --python=3.9
Tips
If you would like to compile with new C++ ABI, then first run this command, at this point, the recommended compilation environment is same to community torch package: glibc 2.28, gcc 11.2.1
export _GLIBCXX_USE_CXX11_ABI=1
Meanwhile, we support configuring -fabi-version using the following variables，require consistency with the community torch package
export _ABI_VERSION=16
Getting Started
Prerequisites
Initialize CANN environment variable by running the command as shown below.
# Default path, change it if needed.
source /usr/local/Ascend/ascend-toolkit/set_env.sh
Quick Verification
You can quickly experience Ascend NPU by the following simple examples.
import torch
- import torch_npu # No longer needed in torch_npu 2.5.1 and later versions
x = torch.randn(2, 2).npu()
y = torch.randn(2, 2).npu()
z = x.mm(y)
print(z)
User Manual
Refer to API of TorchNPU for more detailed information.
PyTorch and Python Version Matching Table
| PyTorch Version | Python Version |
|---|---|
| PyTorch1.11.0 | Python3.7.x(>=3.7.5),Python3.8.x,Python3.9.x,Python3.10.x |
| PyTorch2.1.0 | Python3.8.x,Python3.9.x,Python3.10.x,Python3.11.x |
| PyTorch2.2.0 | Python3.8.x,Python3.9.x,Python3.10.x |
| PyTorch2.3.1 | Python3.8.x,Python3.9.x,Python3.10.x,Python3.11.x |
| PyTorch2.4.0 | Python3.8.x,Python3.9.x,Python3.10.x,Python3.11.x |
| PyTorch2.5.1 | Python3.9.x,Python3.10.x,Python3.11.x |
| PyTorch2.6.0 | Python3.9.x,Python3.10.x,Python3.11.x |
| PyTorch2.7.1 | Python3.9.x,Python3.10.x,Python3.11.x |
| PyTorch2.8.0 | Python3.9.x,Python3.10.x,Python3.11.x |
Ascend Auxiliary Software
PyTorch Extension versions follow the naming convention {PyTorch version}-{Ascend version}, where the former represents the PyTorch version compatible with the PyTorch Extension, and the latter is used to match the CANN version. The detailed matching is as follows:
| CANN Version | Supported PyTorch Version | Supported Extension Version | Github Branch |
|---|---|---|---|
| CANN 8.5.0 | 2.9.0 | 2.9.0 | v2.9.0-7.3.0 |
| 2.8.0 | 2.8.0.post2 | v2.8.0-7.3.0 | |
| 2.7.1 | 2.7.1.post2 | v2.7.1-7.3.0 | |
| 2.6.0 | 2.6.0.post5 | v2.6.0-7.3.0 | |
| CANN 8.3.RC1 | 2.8.0 | 2.8.0 | v2.8.0-7.2.0 |
| 2.7.1 | 2.7.1 | v2.7.1-7.2.0 | |
| 2.6.0 | 2.6.0.post3 | v2.6.0-7.2.0 | |
| 2.1.0 | 2.1.0.post17 | v2.1.0-7.2.0 | |
| CANN 8.2.RC1 | 2.6.0 | 2.6.0 | v2.6.0-7.1.0 |
| 2.5.1 | 2.5.1.post1 | v2.5.1-7.1.0 | |
| 2.1.0 | 2.1.0.post13 | v2.1.0-7.1.0 | |
| CANN 8.1.RC1 | 2.5.1 | 2.5.1 | v2.5.1-7.0.0 |
| 2.4.0 | 2.4.0.post4 | v2.4.0-7.0.0 | |
| 2.3.1 | 2.3.1.post6 | v2.3.1-7.0.0 | |
| 2.1.0 | 2.1.0.post12 | v2.1.0-7.0.0 | |
| CANN 8.1.RC1.alpha002 | 2.6.0 | 2.6.0rc1 | v2.6.0 |
| CANN 8.0.0 | 2.4.0 | 2.4.0.post2 | v2.4.0-6.0.0 |
| 2.3.1 | 2.3.1.post4 | v2.3.1-6.0.0 | |
| 2.1.0 | 2.1.0.post10 | v2.1.0-6.0.0 | |
| CANN 8.0.RC3 | 2.4.0 | 2.4.0 | v2.4.0-6.0.rc3 |
| 2.3.1 | 2.3.1.post2 | v2.3.1-6.0.rc3 | |
| 2.1.0 | 2.1.0.post8 | v2.1.0-6.0.rc3 | |
| CANN 8.0.RC2 | 2.3.1 | 2.3.1 | v2.3.1-6.0.rc2 |
| 2.2.0 | 2.2.0.post2 | v2.2.0-6.0.rc2 | |
| 2.1.0 | 2.1.0.post6 | v2.1.0-6.0.rc2 | |
| 1.11.0 | 1.11.0.post14 | v1.11.0-6.0.rc2 | |
| CANN 8.0.RC1 | 2.2.0 | 2.2.0 | v2.2.0-6.0.rc1 |
| 2.1.0 | 2.1.0.post4 | v2.1.0-6.0.rc1 | |
| 1.11.0 | 1.11.0.post11 | v1.11.0-6.0.rc1 | |
| CANN 7.0.0 | 2.1.0 | 2.1.0 | v2.1.0-5.0.0 |
| 2.0.1 | 2.0.1.post1 | v2.0.1-5.0.0 | |
| 1.11.0 | 1.11.0.post8 | v1.11.0-5.0.0 | |
| CANN 7.0.RC1 | 2.1.0 | 2.1.0.rc1 | v2.1.0-5.0.rc3 |
| 2.0.1 | 2.0.1 | v2.0.1-5.0.rc3 | |
| 1.11.0 | 1.11.0.post4 | v1.11.0-5.0.rc3 | |
| CANN 6.3.RC3.1 | 1.11.0 | 1.11.0.post3 | v1.11.0-5.0.rc2.2 |
| CANN 6.3.RC3 | 1.11.0 | 1.11.0.post2 | v1.11.0-5.0.rc2.1 |
| CANN 6.3.RC2 | 2.0.1 | 2.0.1.rc1 | v2.0.1-5.0.rc2 |
| 1.11.0 | 1.11.0.post1 | v1.11.0-5.0.rc2 | |
| 1.8.1 | 1.8.1.post2 | v1.8.1-5.0.rc2 | |
| CANN 6.3.RC1 | 1.11.0 | 1.11.0 | v1.11.0-5.0.rc1 |
| 1.8.1 | 1.8.1.post1 | v1.8.1-5.0.rc1 | |
| CANN 6.0.1 | 1.5.0 | 1.5.0.post8 | v1.5.0-3.0.0 |
| 1.8.1 | 1.8.1 | v1.8.1-3.0.0 | |
| 1.11.0 | 1.11.0.rc2（beta) | v1.11.0-3.0.0 | |
| CANN 6.0.RC1 | 1.5.0 | 1.5.0.post7 | v1.5.0-3.0.rc3 |
| 1.8.1 | 1.8.1.rc3 | v1.8.1-3.0.rc3 | |
| 1.11.0 | 1.11.0.rc1（beta) | v1.11.0-3.0.rc3 | |
| CANN 5.1.RC2 | 1.5.0 | 1.5.0.post6 | v1.5.0-3.0.rc2 |
| 1.8.1 | 1.8.1.rc2 | v1.8.1-3.0.rc2 | |
| CANN 5.1.RC1 | 1.5.0 | 1.5.0.post5 | v1.5.0-3.0.rc1 |
| 1.8.1 | 1.8.1.rc1 | v1.8.1-3.0.rc1 | |
| CANN 5.0.4 | 1.5.0 | 1.5.0.post4 | 2.0.4.tr5 |
| CANN 5.0.3 | 1.8.1 | 1.5.0.post3 | 2.0.3.tr5 |
| CANN 5.0.2 | 1.5.0 | 1.5.0.post2 | 2.0.2.tr5 |
Hardware support
The Ascend training device includes the following models, all of which can be used as training environments for PyTorch models
| Product series | Product model |
|---|---|
| Atlas Training series products | Atlas 800（model: 9000） |
| Atlas 800（model：9010） | |
| Atlas 900 PoD（model：9000） | |
| Atlas 300T（model：9000） | |
| Atlas 300T Pro（model：9000） | |
| Atlas A2 Training series products | Atlas 800T A2 |
| Atlas 900 A2 PoD | |
| Atlas 200T A2 Box16 | |
| Atlas 300T A2 |
The Ascend inference device includes the following models, all of which can be used as inference environments for large models
| Product series | Product model |
|---|---|
| Atlas 800I A2 Inference product | Atlas 800I A2 |
Pipeline Status
Due to the asynchronous development mechanism of upstream and downstream, incompatible modifications in upstream may cause some functions of TorchNPU to be unavailable (only upstream and downstream development branches are involved, excluding stable branches). Therefore, we built a set of daily tasks that make it easy to detect relevant issues in time and fix them within 48 hours (under normal circumstances), providing users with the latest features and stable quality.
| OS | CANN Version(Docker Image) | Upstream Branch | Downstream Branch | Period | Status |
|---|---|---|---|---|---|
| openEuler 22.03 SP2 | CANN 7.1 | main | master | UTC 1200 daily |  |
Suggestions and Communication
Everyone is welcome to contribute to the community. If you have any questions or suggestions, you can submit Github Issues. We will reply to you as soon as possible. Thank you very much.
Branch Maintenance Policies
The version branches of AscendPyTorch have the following maintenance phases:
| Status | Duration | Description |
|---|---|---|
| Planning | 1-3 months | Plan features. |
| Development | 6-12 months | Develop new features and fix issues, regularly release new versions. Different strategies are adopted for different versions of PyTorch, with a regular branch development cycle of 6 months and a long-term support branch development cycle of 12 months. |
| Maintained | 1 year/3.5 years | Regular Release branch for 1 year, Long Term Support branch maintenance for 3.5 years. Fix major issues, do not incorporate new features, and release patch versions based on the impact of fixed bugs. |
| End Of Life (EOL) | N/A | Do not accept any modification to a branch. |
PyTorch Maintenance Policies
| PyTorch | Maintenance Policies | Status | Launch Date | Subsequent Status | EOL Date |
|---|---|---|---|---|---|
| 2.9.0 | Regular Release | Development | 2026/01/15 | Expected to enter maintenance status from July 15, 2026 | - |
| 2.8.0 | Regular Release | Development | 2025/10/15 | Expected to enter maintenance status from March 15, 2026 | - |
| 2.7.1 | Long Term Support | Development | 2025/10/15 | Expected to enter maintenance status from October 15, 2026 | |
| 2.6.0 | Regular Release | Development | 2025/07/25 | Expected to enter maintenance status from January 15, 2026 | - |
| 2.5.1 | Regular Release | Maintained | 2024/11/08 | Expected to enter maintenance free status from August 8, 2026 | |
| 2.4.0 | Regular Release | Maintained | 2024/10/15 | Expected to enter maintenance free status from June 15, 2026 | |
| 2.3.1 | Regular Release | Maintained | 2024/06/06 | Expected to enter maintenance free status from June 7, 2026 | |
| 2.2.0 | Regular Release | EOL | 2024/04/01 | 2025/10/14 | |
| 2.1.0 | Long Term Support | Maintained | 2023/10/15 | Expected to enter maintenance free status from December 30, 2026 | |
| 2.0.1 | Regular Release | EOL | 2023/7/19 | 2024/3/14 | |
| 1.11.0 | Long Term Support | EOL | 2023/4/19 | 2025/10/25 | |
| 1.8.1 | Long Term Support | EOL | 2022/4/10 | 2023/4/10 | |
| 1.5.0 | Long Term Support | EOL | 2021/7/29 | 2022/7/29 |
Reference Documents
For more detailed information on installation guides, model migration, training/inference tutorials, and API lists, please refer to the TorchNPU on the HiAI Community.
| Document Name | Document Link |
|---|---|
| Installation Guide | link |
| Network Model Migration and Training | link |
| Operator Adaptation | link |
| API List (PyTorch and Custom Interfaces) | link |
License
TorchNPU has a BSD-style license, as found in the LICENSE file.
2 maintainers
Metadata
2 maintainers
Release files for torch-npu 2.7.1.post11
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 392.2 MB
Release files / torch_npu-2.7.1.post11-cp313-cp313-manylinux_2_28_x86_64.whl
| Download URL | torch_npu-2.7.1.post11-cp313-cp313-manylinux_2_28_x86_64.whl |
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| Size | 40.7 MB |
| Tags | CPython 3.13 Linux glibc 2.28+ x86-64 |
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Release files / torch_npu-2.7.1.post11-cp313-cp313-manylinux_2_28_aarch64.whl
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Release files / torch_npu-2.7.1.post11-cp312-cp312-manylinux_2_28_x86_64.whl
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Release files / torch_npu-2.7.1.post11-cp312-cp312-manylinux_2_28_aarch64.whl
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Release files / torch_npu-2.7.1.post11-cp311-cp311-manylinux_2_28_x86_64.whl
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Release files / torch_npu-2.7.1.post11-cp311-cp311-manylinux_2_28_aarch64.whl
| Download URL | torch_npu-2.7.1.post11-cp311-cp311-manylinux_2_28_aarch64.whl |
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| Size | 37.7 MB |
| Tags | CPython 3.11 Linux glibc 2.28+ ARM64 |
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Release files / torch_npu-2.7.1.post11-cp310-cp310-manylinux_2_28_x86_64.whl
| Download URL | torch_npu-2.7.1.post11-cp310-cp310-manylinux_2_28_x86_64.whl |
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Release files / torch_npu-2.7.1.post11-cp310-cp310-manylinux_2_28_aarch64.whl
| Download URL | torch_npu-2.7.1.post11-cp310-cp310-manylinux_2_28_aarch64.whl |
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| Size | 37.7 MB |
| Tags | CPython 3.10 Linux glibc 2.28+ ARM64 |
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Release files / torch_npu-2.7.1.post11-cp39-cp39-manylinux_2_28_x86_64.whl
| Download URL | torch_npu-2.7.1.post11-cp39-cp39-manylinux_2_28_x86_64.whl |
|---|---|
| Size | 40.7 MB |
| Tags | CPython 3.9 Linux glibc 2.28+ x86-64 |
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Release files / torch_npu-2.7.1.post11-cp39-cp39-manylinux_2_28_aarch64.whl
| Download URL | torch_npu-2.7.1.post11-cp39-cp39-manylinux_2_28_aarch64.whl |
|---|---|
| Size | 37.7 MB |
| Tags | CPython 3.9 Linux glibc 2.28+ ARM64 |
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