1a1669fe81
patch from @john-dance "The main change is simple: Use the original node name rather than the original node op_type when creating new nodes. Here are my comments on the change: ------ The onnx runtime uses the op_type as the basis for a new node name, so a node claimed by QNN EP might be named Conv_token_1 with no relation to the original /conv1/Conv. This patch: 1. Adds OpName as a virtual function in NodeRef and implements it in ApiNode. 2. AddNode now takes an op_name and op_type and passes them both to CreateNodeHelper. 3. CreateNodeHelper uses the op_name rather than the op_type in GenerateNodeName 4. Direct calls to AddNode are modified to either use the NodeRef if available, or just repeat the op_type if not available. The result is that the new nodes are named something like /conv1/Conv_token_1, allowing a straight forward mapping back to the original model node (if they exist in the original graph)." |
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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 |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| 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.