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### Use full qualified name for PythonOp export Originally, when there are duplicate named torch.autograd.Function in different module, for example: `a.b.c.Gelu` v.s. `d.e.func.<locals>.Gelu` We by default will throw exception to let user be aware we cannot distinguish the two Gelu because during model export, we did not module path. The workaround is we introduced `ORTMODULE_SKIPPED_AUTOGRAD_FUNCTIONS` to ignore those duplicated named Gelu that is not used by model run. This has limitations obviously for example if two Gelus are both used in training. This PR finds a way to construct a full qualified name. `def _export_pt_1_10(g, n, *args, **kwargs):` 1. in exporter function, kwargs contains `name` and `module`, in the above example: `a.b.c.Gelu` --> name: `Gelu`, module: `a.b.c` `d.e.func.<locals>.Gelu` --> name: `Gelu`, module: `d.e` Using name and module is not enough to get a full qualified name, for the second case, where `d.e` is the module path, then there is a function called `func`, in this function, there is a local auto.grad.Function named `Gelu`. (Many of our UT looks like this). We can only get `d.e.Gelu`, but this is not the correct full qual name. The reason for this: `kwargs[name]` or `n.name` only return the class's name, not the class's full qual name. (be noted kwargs[module]` is correct). 2. `n` is torch.Node, we can access `pyobj` to get the torch.autograd.Function's apply method instance, then use `._self` to get the torch.autograd.Function class. Then we can get the `module` and `class`'s ful qual name, added together, we get the full qual name. With the above change, we don't need use `kwargs[name]` and `kwargs[module]` , and don't need check naming conflicting or `ORTMODULE_SKIPPED_AUTOGRAD_FUNCTIONS` env var any more. |
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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.