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### PythonOp Enhancement: Bool and Tuple[Bool] Constants, Materialize Grads, Empty Inputs, Save In Context 1. Support `bool` or `Tuple[bool]` constant type in inputs. 2. Support `ctx.set_materialize_grads(True|False)` 3. Backward op can accept empty input (that don't require grad) 4. Special handling for ORT tensors are saved in context **Scenario**: a tensor is generated by ORT, then it might be saved for backward by `ctx.save_for_backward(tensor)`, while `tensor`'s reference count is not increased in ORT's allocation plan, so it is possible ORT release the tensor data, before backward usage. **Currently**: we copy every tensor before running autograd.Function.forward(), this might be a problem for cases there are many PythonOp (for example zero stage 3). **Proposal**: To avoid those unnecessary copies for tensors that are not saved in context, this change introduced a `_GlobalOpKernelInfoMap`. During the kernel first run, we will anyway copy all tensors generated from ORT, and give it to torch.autograd.Function for run, then we check whether the inputs needs to be saved in context, and save the input index that needs saving in `_GlobalOpKernelInfoMap`. Then for later iterations, we just copy what is needed. |
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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.