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can-gaa-hou 95191522e0 [OpenReg] Implement device autoload mechanism (#158555 )

# Implement OpenReg device autoload mechanism

## Overview
The **Autoload** mechanism in PyTorch simplifies the integration of third-party device backends by enabling automatic discovery and initialization at runtime. Traditionally, integrating a new backend required explicit imports or manual initialization, which could be cumbersome and error-prone. With Autoload, PyTorch dynamically detects and initializes device backends, providing a seamless user experience.

This mechanism leverages Python entry points (e.g., `torch.backends`) and dynamic module loading. When PyTorch starts, it scans for registered entry points and invokes their initialization hooks, ensuring that all available backends are ready for use without requiring explicit imports.

## Motivation

This PR aims to apply [device autoload mechanism](https://github.com/pytorch/pytorch/issues/122468) to the OpenReg module with some simple changes.

## Change
### Before
```python
import torch
import torch_openreg

x = torch.tensor([1, 2, 3], device="openreg")
print(x)
```
### After
```python
import torch

# No need to import torch_openreg manually!
x = torch.tensor([1, 2, 3], device="openreg")
print(x)
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/158555
Approved by: https://github.com/FFFrog, https://github.com/albanD

Co-authored-by: Jiawei Li <ljw1101.vip@gmail.com>

2025-09-12 04:24:11 +00:00

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Autoload Mechanism

The Autoload mechanism in PyTorch simplifies the integration of a custom backend by enabling automatic discovery and initialization at runtime. This eliminates the need for explicit imports or manual initialization, allowing developers to seamlessly integrate a new accelerator or backend into PyTorch.

Background

The Autoload Device Extension proposal in PyTorch is centered on improving support for various hardware backend devices, especially those implemented as out-of-the-tree extensions (not part of PyTorch’s main codebase). Currently, users must manually import or load these device-specific extensions to use them, which complicates the experience and increases cognitive overhead.

In contrast, in-tree devices (devices officially supported within PyTorch) are seamlessly integrated—users don’t need extra imports or steps. The goal of autoloading is to make out-of-the-tree devices just as easy to use, so users can follow the standard PyTorch device programming model without explicit loading or code changes. This would allow existing PyTorch applications to run on new devices without any modification, making hardware support more user-friendly and reducing barriers to adoption.

For more information about the background of Autoload, please refer to its RFC.

Design

The core idea of Autoload is to Use Python’s plugin discovery (entry points) so PyTorch automatically loads out-of-tree device extensions when torch is imported—no explicit user import needed.

For more instructions of the design of Autoload, please refer to How it works.

Implementation

This tutorial will take OpenReg as a new out-of-the-tree device and guide you through the steps to enable and use the Autoload mechanism.

Entry Point Setup

To enable Autoload, register the _autoload function as an entry point in setup.py file.

::::{tab-set}

:::{tab-item} Python

.. literalinclude:: ../../../test/cpp_extensions/open_registration_extension/torch_openreg/setup.py
    :language: python
    :start-after: LITERALINCLUDE START: SETUP
    :end-before: LITERALINCLUDE END: SETUP
    :linenos:
    :emphasize-lines: 9-13

:::

::::

Backend Setup

Define the initialization hook _autoload for backend initialization. This hook will be automatically invoked by PyTorch during startup.

::::{tab-set-code}

.. literalinclude:: ../../../test/cpp_extensions/open_registration_extension/torch_openreg/torch_openreg/__init__.py
    :language: python
    :start-after: LITERALINCLUDE START: AUTOLOAD
    :end-before: LITERALINCLUDE END: AUTOLOAD
    :linenos:
    :emphasize-lines: 10-12