d279ce2f67
### Description "Consttant Folding" need to enhance to support "function" in onnx spec. If those nodes are inlined into sub-graph and captured by a EP, espeicially this EP doesn't support that, error occured. There are many test failure in Onnx 1.13 agaist NNAPI, these are listed bellow; ``` prelu_broadcast_expanded selu_example_expanded_ver18 layer_normalization_2d_axis0 shrink_hard_expanded_ver18 elu_expanded_ver18 softsign_example_expanded_ver18 leakyrelu_example_expanded hardsigmoid_example_expanded_ver18 thresholdedrelu_default_expanded_ver18 split_variable_parts_2d_opset18 efault_expanded prelu_example_expanded thresholdedrelu_example_expanded_ver18 selu_default_expanded_ver18 elu_example_expanded_ver18 hardsigmoid_default_expanded_ver18 softsign_expanded_ver18 hardsigmoid_expanded_ver18 leakyrelu_expanded scatter_with_axis selu_expanded_ver18 shrink_soft_expanded_ver18 relu_expanded_ver18 thresholdedrelu_expanded_ver18 elu_default_expanded_ver18 ``` Solution: To prevent NNAPI capture it for now, we can revert it once a better CF implemented. ### 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. --> |
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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
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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.