This repository develops the Ascend Extension for PyTorch named torch_npu to adapt Ascend NPU to PyTorch so that developers who use the PyTorch can obtain powerful compute capabilities 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.
Provide users with wheel package to quickly install torch_npu. Before installing torch_npu, 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==2.1.0For x86:
pip3 install torch==2.1.0+cpu --index-url https://download.pytorch.org/whl/cpu- Install torch-npu dependencies
Run the following command to install dependencies.
pip3 install pyyaml
pip3 install setuptoolsIf 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 | Python3.8 | link |
| x86 | Python3.9 | link |
| x86 | Python3.10 | link |
| x86 | Python3.11 | link |
| aarch64 | Python3.8 | link |
| aarch64 | Python3.9 | link |
| aarch64 | Python3.10 | link |
| aarch64 | Python3.11 | link |
- Install torch-npu
pip3 install torch-npu==2.1.0.post8
In some special scenarios, users may need to compile torch-npu 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 torch-npu 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 10.2 for ARM and gcc 9.3.1 for X86.
-
Clone torch-npu
git clone https://github.com/ascend/pytorch.git -b v2.1.0-6.0.rc3 --depth 1 -
Build Docker Image
cd pytorch/ci/docker/{arch} # {arch} for X86 or ARM docker build -t manylinux-builder:v1 . -
Enter Docker Container
docker run -it -v /{code_path}/pytorch:/home/pytorch manylinux-builder:v1 bash # {code_path} is the torch_npu source code path -
Compile torch-npu
Take Python 3.8 as an example.
cd /home/pytorch bash ci/build.sh --python=3.8
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.shYou can quickly experience Ascend NPU by the following simple examples.
import torch
import torch_npu
x = torch.randn(2, 2).npu()
y = torch.randn(2, 2).npu()
z = x.mm(y)
print(z)Refer to API of Ascend Extension for PyTorch for more detailed informations.
| 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 |
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 8.0.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.RC2.alpha002 | 2.3.1 | 2.3.1rc1 | v2.3.1 |
| 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 |
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 |
Due to the asynchronous development mechanism of upstream and downstream, incompatible modifications in upstream may cause some functions of torch_npu 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 |  |
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.
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 | 3.5 years | Maintain bugs, do not incorporate new features, and release patch versions based on the impact of bugs. |
| End Of Life (EOL) | N/A | Do not accept any modification to a branch. |
| PyTorch | Maintenance Policies | Status | Launch Date | Subsequent Status | EOL Date |
|---|---|---|---|---|---|
| 2.4.0 | Regular Release | Development | 2024/10/15 | Expected to enter maintenance status from March 15, 2025 | |
| 2.3.1 | Regular Release | Development | 2024/06/06 | Expected to enter maintenance status from December 6, 2024 | |
| 2.2.0 | Regular Release | Maintained | 2024/04/01 | Expected to enter maintenance free status from September 10th, 2025 | |
| 2.1.0 | Long Term Support | Development | 2023/10/15 | Expected to enter maintenance status from March 30, 2025 | |
| 2.0.1 | Regular Release | EOL | 2023/7/19 | 2024/3/14 | |
| 1.11.0 | Long Term Support | Maintained | 2023/4/19 | Expected to enter maintenance free status from September 10th, 2025 | |
| 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 |
For more detailed information on installation guides, model migration, training/inference tutorials, and API lists, please refer to the Ascend Extension for PyTorch 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 |
Ascend Extension for PyTorch has a BSD-style license, as found in the LICENSE file.