21ef14476b
Current TRT EP can support model which has nested control flow ops (multiple level subgraphs). But it fails at a case where the subgraph has outer scope value that is defined several levels up in the top-level graph, in this case, the outer scope value is the input of the top-level graph. The outer scope values are not properly handled during TRT EP's subgraph reconstruction stage and fails at `graph.resolve()`. The way ORT gets capability from EPs is a bottom-up approach meaning inner most subgraph gets handled first. TRT EP reconstructs each subgraph level by level and following modifications are made to fix the outer scope values issue: - `SetGraphOuterScopeValuesAndInputs()` and `SetAllGraphInputs()` are added to handle outer scope values and add those values as graph inputs if needed in order to make `graph.resolve()` happy. - Change to use `GetNodeArgIncludingParentGraphs` so that when creating the fused TRT node for some subgraphs in` Graph::CreateFusedSubGraphNode()`, it can get the NodeArgs for outer scope values from top-level graph. This PR fixes https://github.com/microsoft/onnxruntime/issues/16217 |
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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
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.