torch-npu 2.1.0.post3

NPU bridge for PyTorch

Ascend Extension for PyTorch

Overview

This repository develops the PyTorch Extension 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.

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. To obtain the CANN installation package, refer to the CANN Installation.

1. Install PyTorch

Install PyTorch through pip.

For Aarch64:

pip3 install torch==2.1.0

For x86:

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.

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	Python3.8	link
x86	Python3.9	link
x86	Python3.10	link
aarch64	Python3.8	link
aarch64	Python3.9	link
aarch64	Python3.10	link

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 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/:

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.

# 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

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 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

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)
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

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	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
2.2.0	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.

Document Name	Document Link
AscendPyTorch Installation Guide	link
AscendPyTorch Network Model Migration and Training	link
AscendPyTorch Operator Adaptation	link
AscendPyTorch API List (PyTorch and Custom Interfaces)	link

License

Ascend Extension for Pytorch has a BSD-style license, as found in the LICENSE file.