4385602386
### Description fix test runner with optional input/output. This change fixes the OP test runner (.jsonc format test) with optional input(s) and/or output(s). this fix reveals a problem of dealing with optional outputs: > Take SkipSimplifiedLayerNorm as example: > > if in the ONNX model, the node's outputs are: [ 'output_0', '' ] instead of [ 'output_0' ], the current implementation will fail. The difference is, in the first case, context.outputCount == 2, and then the typescript implementation will try to create a tensor for output[1]. It will eventually call to C++ function (OpKernelContext::Output), and the output.DataRaw() will be nullptr. WebGPU backend will fail because it cannot deal with a TensorView with data == 0. > This problem may need to be fixed or workaround in separated PR. This PR does not fix this problem. Failed test cases are modified to work - please note this PR does not break those test cases as they never work. |
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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.