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Previous behavior of TRT EP to set TRT optimization profiles for dynamic shape input is based on input tensor values. Users can't explicitly specify the profiles. This PR makes users capable of specifying min/max/opt profiles through newly added three provider options: `trt_profile_min_shapes`, `trt_profile_max_shapes` and `trt_profile_opt_shapes` with the format of "input1:dim1xdim2...,input2:dim3xdim4...". (Note: It's similar to --minShapes, --maxShapes and --optShapes of trtexec command-line [flags](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#trtexec-flags)) For example, if you are using onnxruntime_perf_test, you can try this: `./onnxruntime_perf_test -e tensorrt -r 1 -i "trt_profile_min_shapes|imgs:1x3x384x288 trt_profile_max_shapes|imgs:32x3x384x288 trt_profile_opt_shapes|imgs:16x3x384x288" your_model_path` If the engine cache is enabled, you still need to provide these three explicit provider options in order to use this feature. ORT TRT will compare the min/max/opt profile shape with the ones saved in .profile file to decide whether to rebuild the engine. Constraints to use these provider options: (1) Need to specify min/max/opt profile shapes for all the dynamic shape input This feature is also requested by other users: https://github.com/microsoft/onnxruntime/issues/13851 |
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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
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
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.