feeb0b50f9
### Description This PR improves `TreeNodeElementId` hash function by employing [Elegant Pairing function](http://szudzik.com/ElegantPairing.pdf). In few works, Elegant Pairing function maps two non−negative integers to a non−negative integer that is uniquely associated with that pair. This drastically reduces the collision and therefore reduces the time required to create a session in order to use a large tree ensemble model. ### Motivation and Context We use ONNX runtime to serve our models as part of Triton backend. We noticed that it was taking around 2 minutes to load a model which is a large tree ensemble model (around 5k trees with around 3 millions nodes in total). After investigating the issue, it was clear that the `TreeNodeElementId` hash function wasn't being able to map keys to buckets of C++ `unordered_map` without a significant amount of collisions (in same cases 700 items per bucket). The following picture shows graphically the improvement obtained by the proposed change. We used the `onnx_test_runner` command.  #### Before ``` $> time ./onnx_test_runner -v ~/folder_with_model result: Models: 1 Total test cases: 0 Succeeded: 0 Not implemented: 0 Failed: 0 Stats by Operator type: Not implemented(0): Failed: Failed Test Cases: real 0m55.695s user 0m52.919s sys 0m0.760s ``` #### After ``` $> time ./onnx_test_runner -v ~/folder_with_model result: Models: 1 Total test cases: 0 Succeeded: 0 Not implemented: 0 Failed: 0 Stats by Operator type: Not implemented(0): Failed: Failed Test Cases: real 0m17.152s user 0m14.318s sys 0m0.619s ``` |
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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 |
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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.