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### Description <!-- Describe your changes. --> Changes to support standalone custom ops in a minimal build. Also incorporates changes from #14492 (needed to test builds prior to that being checked in). We first need to save the schema info from the operators used by the standalone op invoker in the ORT format model. Add mechanism for that. Merge the kernel lookup logic so the same is used in full and minimal build. NOTE: the version matching is now consistent with all other kernel lookups, and the call to CreateOp MUST use the exact version for the operator. Previously matching wasn't as strict, but this can lead to the incorrect kernel being chosen. Add tests. NOTE: There is currently no way to detect the ops/types/opsets used inside these custom ops as they don't exist until we create kernels, which is after model loading completes (which is the point the ORT format model is saved). Due to that they have to be manually added to the configuration used to do the reduced ops build. That shouldn't be too hard for the custom op author to add given the custom op implementation is specifying the op, opset and type constraints (i.e. they have the info and it's just a case of capturing/formatting it correctly). ### 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. --> Enable usage of the standalone op invoker by custom ops in a minimal build. --------- Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.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 | ||
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| Web | ||
| Other |
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.