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### Description <!-- Describe your changes. --> This change addresses the following issues with the current CustomOP Output Type inference - The function does not take into account optional inputs. When input is absent the inference is silently aborted, and no output type is inferred (P1 customer issue) - Inferring output type based on the input type for multi-kernel custom ops is done based on the latest in sequence kernel definition. There is not an attempt made to match the kernel based on the input type. - Inference is aborted when variadic inputs/outputs are detected when the generated input/output names fail to obtain type constraints. This is not immediately clear from the code, because custom op schema is not available within the inference function. - No error reporting. ### 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. --> Most of CustomOPs lack their own type and shape inference function as it was recently introduced. For that reason, it is important to fix this. This change is inspired by a customer issue. This is a follow up on: - https://github.com/microsoft/onnxruntime/pull/15184 - https://github.com/cbourjau/ort-custom-op/pull/11 - https://github.com/microsoft/onnxruntime-extensions/issues/451 |
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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 documentation 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 |
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| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
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| 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.