# Ascend Extension for PyTorch
## Overview
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](https://www.hiascend.com/en/).
## Installation
### From Binary
Provide users with wheel package to quickly install **torch_npu**. Before installing **torch_npu**, complete the installation of **CANN** according to [Ascend Auxiliary Software](#ascend-auxiliary-software). To obtain the **CANN** installation package, refer to the [CANN Installation](https://www.hiascend.com/en/software/cann/community).
1. **Install PyTorch**
Install **PyTorch** through pip.
**For Aarch64:**
```Python
pip3 install torch==2.1.0
```
**For x86:**
```Python
pip3 install torch==2.1.0+cpu --index-url https://download.pytorch.org/whl/cpu
```
2. **Install torch-npu dependencies**
Run the following command to install dependencies.
```Python
pip3 install pyyaml
pip3 install setuptools
```
If the installation fails, use the download link or visit the [PyTorch official website](https://pytorch.org/) to download the installation package of the corresponding version.
| OS arch | Python version | link |
| ------- | -------------- | ------------------------------------------------------------ |
| x86 | Python3.8 | [link](https://download.pytorch.org/whl/cpu/torch-2.1.0%2Bcpu-cp38-cp38-linux_x86_64.whl#sha256=9e5cfd931a65b38d222755a45dabb53b836be31bc620532bc66fee77e3ff67dc) |
| x86 | Python3.9 | [link](https://download.pytorch.org/whl/cpu/torch-2.1.0%2Bcpu-cp39-cp39-linux_x86_64.whl#sha256=86cc28df491fa84738affe752f9870791026565342f69e4ab63e5b935f00a495) |
| x86 | Python3.10 | [link](https://download.pytorch.org/whl/cpu/torch-2.1.0%2Bcpu-cp310-cp310-linux_x86_64.whl#sha256=5077921fc2b54e69a534f3a9c0b98493c79a5547c49d46f5e77e42da3610e011) |
| aarch64 | Python3.8 | [link](https://download.pytorch.org/whl/cpu/torch-2.1.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=761822761fffaa1c18a62c5deb13abaa780862577d3eadc428f1daa632536905) |
| aarch64 | Python3.9 | [link](https://download.pytorch.org/whl/cpu/torch-2.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=de7d63c6ecece118684415a3dbd4805af4a4c1ee1490cccf7405d8c240a481b4) |
| aarch64 | Python3.10 | [link](https://download.pytorch.org/whl/cpu/torch-2.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl#sha256=a04a0296d47f28960f51c18c5489a8c3472f624ec3b5bcc8e2096314df8c3342) |
3. **Install torch-npu**
```
pip3 install torch-npu==2.1.0.post3
```
### From Source
In some special scenarios, users may need to compile **torch-npu** by themselves.Select a branch in table [Ascend Auxiliary Software](#ascend-auxiliary-software) and a Python version in table [PyTorch and Python Version Matching 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/:
1. **Clone torch-npu**
```
git clone https://github.com/ascend/pytorch.git -b v2.1.0-6.0.rc1 --depth 1
```
2. **Build Docker Image**
```
cd pytorch/ci/docker/{arch} # {arch} for X86 or ARM
docker build -t manylinux-builder:v1 .
```
3. **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
```
4. **Compile torch-npu**
Take **Python 3.8** as an example.
```
cd /home/pytorch
bash ci/build.sh --python=3.8
```
## Getting Started
### Prerequisites
Initialize **CANN** environment variable by running the command as shown below.
```Shell
# 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.
```Python
import torch
import torch_npu
x = torch.randn(2, 2).npu()
y = torch.randn(2, 2).npu()
z = x.mm(y)
print(z)
```
## User Manual
Refer to [API of Ascend Extension for PyTorch](docs/api/torch_npu_apis.md) for more detailed informations.
## 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 |
| PyTorch2.2.0 | Python3.8.x,Python3.9.x,Python3.10.x |
| PyTorch2.3.1 | Python3.8.x,Python3.9.x,Python3.10.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 | AscendHub Image Version/Name([Link](https://ascendhub.huawei.com/#/detail/pytorch-modelzoo)) |
|----------------|--------------|-------------------|-------------------|----------------------|
| 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.post3 | 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 | 23.0.RC1-1.11.0 |
| | 1.8.1 | 1.8.1.post2 | v1.8.1-5.0.rc2 | 23.0.RC1-1.8.1 |
| 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 | 22.0.0 |
| | 1.8.1 | 1.8.1 | v1.8.1-3.0.0 | 22.0.0-1.8.1 |
| | 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 | 22.0.RC3 |
| | 1.8.1 | 1.8.1.rc3 | v1.8.1-3.0.rc3 | 22.0.RC3-1.8.1 |
| | 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 | 22.0.RC2 |
| | 1.8.1 | 1.8.1.rc2 | v1.8.1-3.0.rc2 | 22.0.RC2-1.8.1 |
| CANN 5.1.RC1 | 1.5.0 | 1.5.0.post5 | v1.5.0-3.0.rc1 | 22.0.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 | 21.0.4 |
| CANN 5.0.3 | 1.8.1 | 1.5.0.post3 | 2.0.3.tr5 | 21.0.3 |
| CANN 5.0.2 | 1.5.0 | 1.5.0.post2 | 2.0.2.tr5 | 21.0.2 |
## Pipeline Status
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](https://hub.docker.com/r/ascendai/cann/tags) | [main](https://github.com/pytorch/pytorch/tree/main) | [master](https://github.com/Ascend/pytorch/tree/master) | UTC 1200 daily | [](https://github.com/Ascend/pytorch/actions/workflows/periodic.yml) |
## Suggestions and Communication
Everyone is welcome to contribute to the community. If you have any questions or suggestions, you can submit [Github Issues](https://github.com/Ascend/pytorch/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 | 3 months | Develop features. |
| Maintained | 6-12 months | Allow the incorporation of all resolved issues and release the version, Different versions of PyTorch adopt varying support policies. The maintenance periods for Regular Releases and Long-Term Support versions are 6 months and 12 months, respectively. |
| Unmaintained | 0-3 months | Allow the incorporation of all resolved issues. No dedicated maintenance personnel are available. No version will be released. |
| 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** |
|-----------|--------------------|--------------|------------|-----------------|-----------|
| 2.3.1 | Regular Release | Maintained | 2024/06/06 | Unmaintained 2024/12/06 estimated | |
| 2.2.0 | Regular Release | Maintained | 2024/04/01 | Unmaintained 2024-10-15 estimated | |
| 2.1.0 | Long Term Support | Maintained | 2023/10/15 | Unmaintained 2024-10-15 estimated | |
| 2.0.1 | Regular Release | EOL | 2023/7/19 | | 2024/3/14 |
| 1.11.0 | Long Term Support | Maintained | 2023/4/19 | Unmaintained 2024-4-19 estimated | |
| 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 [Ascend Extension for PyTorch on the HiAI Community](https://www.hiascend.com/software/ai-frameworks?framework=pytorch).
| Document Name | Document Link |
| -------------------------------- | ------------------------------------------------------------ |
| Installation Guide | [link](https://www.hiascend.com/document/detail/zh/Pytorch/60RC1/configandinstg/instg/insg_0001.html) |
| Network Model Migration and Training | [link](https://www.hiascend.com/document/detail/zh/Pytorch/60RC1/ptmoddevg/trainingmigrguide/PT_LMTMOG_0003.html) |
| Operator Adaptation | [link](https://www.hiascend.com/document/detail/zh/canncommercial/80RC1/developmentguide/opdevg/Ascendcopdevg/atlas_ascendc_10_0048.html) |
| API List (PyTorch and Custom Interfaces) | [link](https://www.hiascend.com/document/detail/zh/Pytorch/60RC1/apiref/apilist/ptaoplist_000002.html
) |
## License
Ascend Extension for PyTorch has a BSD-style license, as found in the [LICENSE](LICENSE) file.
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