4ffd022b0b
Make sure "trt.plugins" custom op domain only being registered once. The bottom line is "trt.plugins" custom op domain needs to be registered before model load. `CreateTensorRTCustomOpDomainList()` is TRT EP's function to create "trt.plugins" custom op domain. Following are places where this function will be called. (This function only fetches all the TRT plugins from TRT plugin registry but not yet registered them to ORT custom op registry. The real registration happens in AddCustomOpDomains()) C/C++ APIs: - `OrtApis::SessionOptionsAppendExecutionProvider_TensorRT_XX`: This function will make session option object contain the "trt.plugins" custom op domain for ORT to register. So that later the session creation api can register the custom op domain accordingly and won't complain about invalid onnx node. - `InferenceSession::RegisterExecutionProvider`: In some cases, users might create the session object first and later call session_object.RegisterExecutionProvider(). This function will call p_exec_provider->GetCustomOpDomainList() which returns "trt.plugins" custom op domain. Otherwise, session_object.Load(model) will complain. Python APIs: - `RegisterTensorRTPluginsAsCustomOps`: Need to call this function so that session option object contains the "trt.plugins" custom op domain for ORT to register. Different language bindings have slightly different workflow of initializing the session. This might cause duplicate custom op domain in `session_option.custom_op_domains_` or `CreateTensorRTCustomOpDomainList()` being called more than once, but we put checks to make sure ep's custom op domain won't be registered twice. |
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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.