# pytorch_problem_list
**Repository Path**: mooyeh/pytorch_problem_list
## Basic Information
- **Project Name**: pytorch_problem_list
- **Description**: Ascend PyTorch adapter
- **Primary Language**: Python
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 1498
- **Created**: 2023-11-13
- **Last Updated**: 2023-12-21
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# PyTorch Ascend Adapter
## Overview
This repository develops the **PyTorch Ascend Adapter** 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==2.1.0rc1
```
### 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.):
1. **Clone torch-npu**
```
git clone https://github.com/ascend/pytorch.git -b v2.1.0-5.0.rc3 --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 PyTorch Ascend Adapter](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 |
| PyTorch2.0.1 | Python3.8.x,Python3.9.x,Python3.10.x |
| PyTorch2.1.0 | Python3.8.x,Python3.9.x,Python3.10.x |
## Ascend Auxiliary Software
## Pipeline Status
Due to the asynchronous development mechanism of upstream and downstream, incompatible modifications of upstream may cause some functions of **torch_npu** to be unavailable (only involving the development branches of upstream and downstream, not including stable branches). Therefore, we have built a set of daily tasks, It is easy to detect relevant problems in time and repair them within 48 hours (under normal circumstances), providing users with new features and stable quality.
| **OS** | **CANN Version(Docker Image)** | **Upstream Branch** | **Downstream Branch** | **Period** | **Status** |
|:---:|:---:|:---:|:---:| :---: | :---: |
| openEuler 22.03 SP2 | [CANN 7.0.RC1](https://hub.docker.com/layers/ascendai/cann/7.0.RC1.alpha005-openeuler2203sp2/images/sha256-5ec4793c1bd6ebb34b731ee0819a5a81c4298a04d9a1fa27df6c56a4d5aa290e) | main | 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. |
| 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. |
## Maintenance Status of Existing Branches
| **Branch Name** | **Status** | **Launch Date** | **Subsequent Status** | **EOL Date** |
| --------------- | ---------- | --------------- | --------------------------------------- | ------------ |
| **v2.0.2** | EOL | 2021/7/29 | N/A | |
| **v2.0.3** | EOL | 2021/10/15 | N/A | |
| **v2.0.4** | EOL | 2022/1/15 | N/A | |
| **v3.0.rc1** | EOL | 2022/4/10 | N/A | |
| **v3.0.rc2** | EOL | 2022/7/15 | N/A | |
| **v3.0.rc3** | EOL | 2022/10/20 | N/A | |
| **v3.0.0** | Maintained | 2023/1/18 | Unmaintained 2024-1-18 estimated | |
| **v5.0.rc1** | Maintained | 2023/4/19 | Unmaintained 2024-4-19 estimated | |
| **v5.0.rc2** | Maintained | 2023/7/19 | Unmaintained 2024-7-19 estimated | |
| **v5.0.rc3** | Maintained | 2023/10/15 | Unmaintained 2024-10-15 estimated | |
## Reference Documents
For more detailed information on installation guides, model migration, training/inference tutorials, and API lists, please refer to the [PyTorch Ascend Adapter on the HiAI Community](https://www.hiascend.com/software/ai-frameworks/commercial).
| Document Name | Document Link |
| -------------------------------- | ------------------------------------------------------------ |
| AscendPyTorch Installation Guide | [link](https://www.hiascend.com/document/detail/zh/canncommercial/70RC1/envdeployment/instg/instg_0083.html) |
| AscendPyTorch Network Model Migration and Training | [link](https://www.hiascend.com/document/detail/zh/canncommercial/70RC1/modeldevpt/ptmigr/AImpug_0002.html) |
| AscendPyTorch Online Inference | [link](https://www.hiascend.com/document/detail/zh/canncommercial/70RC1/modeldevpt/ptonlineinfer/PyTorch_Infer_000001.html) |
| AscendPyTorch Operator Adaptation | [link](https://www.hiascend.com/document/detail/zh/canncommercial/70RC1/operatordev/tbeaicpudevg/atlasopdev_10_0086.html) |
| AscendPyTorch API List (PyTorch and Custom Interfaces) | [link](https://www.hiascend.com/document/detail/zh/canncommercial/70RC1/modeldevpt/ptmigr/ptaoplist_001.html) |
## License
PyTorch Ascend Adapter has a BSD-style license, as found in the [LICENSE](LICENSE) file.