efa1262614
### Description Fix issue relating to unused inputs of model-local functions. ORT creates a schema for all such functions. The creation of this schema does not handle unused function-inputs. The schema-creation relies on the use of the function-inputs to infer type-constraints for the input, and the code ends up creating an erroneous input-descriptor when there is no use of the function-input. The fix is to create an input with the given name, with a type-constraint that allows all types. ### Motivation and Context Fix https://github.com/microsoft/onnxruntime/issues/15046 Fix https://github.com/microsoft/onnx-script/issues/524 --------- Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com> Co-authored-by: Scott McKay <skottmckay@gmail.com> |
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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
Build Pipeline Status
| System | Inference | Training |
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| Windows | ||
| Linux | ||
| Mac | ||
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| Web | ||
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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.