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### Introduce ZeROOffloadSubscriber for ORTModule As part of the work: integrate ORTModule with DeepSpeed stage3, this PR mainly focus on moving original PyTorch-based (leveraging hooks) param partition/offload implementation to ORTModule compatible implementation. Changes include: 1. Refactor `SubscriberBase`/`SubcriberManager` to support pre-forward/post_forward hooks. 2. Implement new `ZeROOffloadSubscriber` by re-using DeepSpeed hook function as much as possible. Since all hook functions are defined in `DeepSpeedZeRoOffload._register_hooks_recursively` and `DeepSpeedZeRoOffload.setup_zero_stage3_hooks`, and the good thing is, the closure is not complex, all hooks are referencing the owning `DeepSpeedZeRoOffload` instance, so we can create new hook function with `FunctionType` by binding the owning `DeepSpeedZeRoOffload` instance, then call the new created function in subscriber's `pre_forward_module_apply_impl` and `post_forward_module_apply_impl` interfaces. 3. Monkey patch `DeepSpeedZeRoOffload.setup_zero_stage3_hooks` to register the `ZeROOffloadSubscriber` for the model, then we don't need change any code on the DeepSpeed repo (at least so far). 4. Fix the ATen embedding custom symbolic exporter function by tolerating weights size be (0) (changed by DeepSpeed zero stage 3). UT will be added once stage3 is fully supported. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> |
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
Get Started & Resources
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General Information: onnxruntime.ai
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Usage documention and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
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Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
Data/Telemetry
Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.
Contributions and Feedback
We welcome contributions! Please see the contribution guidelines.
For feature requests or bug reports, please file a GitHub Issue.
For general discussion or questions, please use GitHub Discussions.
Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
License
This project is licensed under the MIT License.