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When the TRT engine cache (precompiled engine) is present, it doesn't make sense to go over the processes of model verification, model optimization, TRT EP's GetCapability(), TRT EP's model proto reconstruction, calling TRT parser and engine compilation. This PR makes TRT EP skip those processes and directly load the engine to perform inference. The feature request: https://github.com/microsoft/onnxruntime/issues/18072 Features: - Replace original model with TRT engine wrapped ONNX model. It can save a lot of time as mentioned above. - How to get TRT engine wrapped ONNX model? 1. Set `trt_dump_ep_context_model` provider option to "true" and run the inference. You will find the "xxx_wrapper.onnx" at the engine cache path. (The same logic of generating engine cache) 2. Use gen_trt_engine_wrapper_onnx_model.py - Three provider options are added, `trt_dump_ep_context_model`: Enable dump wrapped onnx model by TRT EP `trt_ep_context_embed_mode`: Add embed_mode as attribute. 0 means engine cache path, 1 means engine binary data. `trt_ep_context_compute_capability_enable`: Add hardware_arch as attribute. When running the model, TRT EP will check consistency between model's hardware_arch and GPU's compute capability. - When the engine cache path is given in the wrapped model, TRT EP will first search for the engine file using the path (relative to model path), if it can't find it, it will change to use the path as it is (depends on user, could be relative to working dir or absolute path) Note: 1. This PR includes the change of https://github.com/microsoft/onnxruntime/pull/17751 Constraints: 1. The whole model should be fully supported by TRT. 4. Users need to make sure the engine is built with min/max/opt optimization profiles that large enough to cover the range of all inputs. TRT EP will simply fail and won't rebuild the engine if the input shape is out of range during runtime. |
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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.