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**Description** 1) During SessionInitialization, KahnsTopologicalSort is a major cause of perf degradation. The main cause of slow down is that the TopologicalSort needs to keep track of nodes to visit in order, and reorder them based on priority (as informed by a comparator). The existing implementation uses a priority_queue that is backed by a std::vector container. However, vectors are not good for insertion and reordering. The appropriate data type for this operation is a linked list. However, linked lists like std::list are not usable as a container for std::priority_queue. This is because std::priority_queue requires random access, which linked lists do not have. However, for this simple implementation, we can leverage a std::list under the hood and perform insertions manually using std::upper_bound. This drastically reduces the time taken by the method, which currently instead causes numerous recopies and a lot of movement inside the graph nodes to visit list. 2) In the comparator, I hide forward and backward attribute checking behind the #ifdef ENABLE_TRAINING macro, as I believe it should only be valid in the training scenario. 3) In noopelimination transformer, I prevent the creation of Initializer (which unpacks tensorproto data) in every node and only create initializers when Add/Sub/Mul/Div op nodes are detected. **Motivation and Context** Session creation time of many models is quite slow. --------- Co-authored-by: Sheil Kumar <sheilk@microsoft.com> |
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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.