Project Objective (Implementation of PyTorch RFC 37):
- Bridging and Integration: Construct a device-agnostic layer that promotes a unified interface on the upper layer and ensures compatibility with various hardware on the lower layer, shielding PyTorch from direct awareness of multiple backends.
- Low cost Integration: Provide device abstraction layer to accelerate new backend integration by only implementing few interfaces, offer comprehensive integration documentation, provide integrate implementations as a reference (CUDA/CPU/NPU) and general test cases and contract tests.
- Quality Assurance: Maintain quality through CI/CD for the integration mechanism of third-party devices based on PrivateUse1.
- Mainstream Approach: Promote the integration mechanism of third-party devices based on PrivateUse1 as the mainstream approach for integrating new backends into PyTorch in the future.
- Runtime: Completed components include Device, Stream, Event, Generator, Guard, and Allocator.
- AMP: Registration and API have been completed.
- Operators: Migrated NPU operator list and codegen. The next steps will involve operator simplification and codegen refactoring.
- Device-agnostic: Complete the device-agnostic layer; organize specific device logic according to different device type (e.g., backends/cuda, backends/cpu, backends/...). Making it as submodule in the future.
- CodeGen: Enhance and refactor codegen module, providing general and reusable code generation capabilities that cover official operators, custom operators, routing code, forward and backward binding, etc.
- Operators: Simplify operators, implement all factory class operators (as operator implementation reference), as well as functional operators (for testing the functionality of the third-party device integration mechanism).
- Tests & Docs: Complete general test case suites, the full module integration and API documentation.
- Live Demo: Integrate CPU into PyTorch based on this project and provide a full-process integration tutorial.
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.
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├── 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 registrationPyTorch Backend has a BSD-style license, as found in the LICENSE file.
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