PyTorch Backend¶
Overview¶
Introduction¶
The PyTorch Backend Project is an initiative aimed at simplifying the integration of various GPU devices with the PyTorch framework. This project provides a foundational layer that abstracts and standardizes the interaction between PyTorch and different types of GPU hardware. It allows developers to focus on specific implementations required for their particular GPU devices, without needing to delve into the complexities of the entire integration process.
Key Features¶
1. Unified Abstraction Layer: The project offers a common interface for all GPU devices, ensuring that the core PyTorch codebase can interact seamlessly with a wide variety of hardware. This abstraction layer manages the intricacies of different GPU architectures and APIs.
2. Base Level Implementation: It includes base implementations for common GPU functionalities, such as memory management, kernel execution, and data transfer. These implementations serve as a foundation, reducing the amount of boilerplate code that developers need to write.
3. Customizable Integration: While the project provides comprehensive base-level support, it also offers hooks and interfaces for users to implement custom features and optimizations specific to their hardware. This flexibility is crucial for leveraging the unique capabilities of different GPU architectures.
4. Scalability and Performance: The project is designed with scalability in mind, supporting efficient operation across a wide range of GPU configurations, from single-device setups to large-scale multi-GPU systems. It aims to optimize performance by minimizing overhead and maximizing hardware utilization.
5. Community and Support: As part of the PyTorch ecosystem, the backend project benefits from a vibrant community of developers and researchers. This community contributes to continuous improvements, bug fixes, and the addition of new features.
Benefits to Users¶
1. Ease of Integration: By abstracting the low-level details, the project allows users to integrate new GPU devices with minimal effort. This is particularly beneficial for organizations that need to support diverse hardware environments.
2. Focus on Innovation: Developers can concentrate on implementing cutting-edge algorithms and optimizations, rather than dealing with hardware-specific intricacies.
3. Consistent API: The unified API ensures that code written for one type of GPU can be easily adapted to work with others, enhancing code portability and reuse.
Getting Started¶
To start using the PyTorch Backend Project, users can refer to the comprehensive documentation provided. This includes detailed guides on setting up the environment, integrating new devices, and best practices for optimizing performance.
The PyTorch Backend Project is a critical component of the PyTorch ecosystem, enabling seamless GPU integration and accelerating the development of high-performance machine learning applications.
Project Structure¶
.
├── backends
│ ├── fake // dummy backend: provide all weak symbols needed by csrc, we can run this demo without implementing all symbols in REAL Backend by this fake backend.
│ ├── npu // one of REAL Backend: provide API and Structure related witch specific Backends strongly
│ ├── cuda // one of REAL Backend: will be implemented later
│ └── ...
├── cmake
├── codegen // Code generation: includes registration for forward and backward, backward implementation, backward binding, custom operator routing, reroute routing, etc.
│ ├── autograd
│ │ └── templates // General template
│ └── templates
├── csrc // C++ implementations related to PyTorch, not involving specific backend implementations, theoretically only includes backend interface calls
│ ├── api // libtorch functionalities
│ ├── aten // Code generation: includes only wrap and PyTorch operator registration; in the future, considering moving Tensor & Storage & Serialization here, as these three are related to Tensor logic
│ ├── backend // General Implementation of PyTorch API
│ ├── core // Common Utils
│ │ ├── allocator
│ │ ├── generator
│ │ └── guard
│ └── distributed // Distributed
├── docs // All docs: C++ API, Python API and E2E tutorials
│ ├── cpp
│ │ └── source
│ └── source
├── test // General TestCase Sets: including C++ and python
│ └── cpp
│ ├── backend
│ ├── common
│ └── core
├── third_party
│ └── googletest
└── torch_backend // Python interface implementation for PyTorch
├── backend
├── csrc // Python & C++ binding
│ ├── backend // Python bindings for all low-level capabilities needed to be exposed to Python
│ └── core // General capabilities, only provided for Python
└── meta // Meta operator registration
Modules you may concerned¶
Following modules you may want to switch to your deivce specific implementation in order to make it works.
