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### Give user warnings if nondeterministic kernels got called when Deterministic flag is set When we do accuracy investigation (for example training convergence issue debug), usually we will set `use_deterministic_compute ` to be true. ``` SessionOptions sess_options; sess_options.use_deterministic_compute = true; ``` While in recent investigation, it is found GatherElementsGrad kernel (who used atomic add) generate non-deterministic results, making a deberta model ouput pretty different loss curve every time we run it even we fix the seed, remove the dropout ratio, and set use_deterministic_compute to be true. It turned out to be an expected problem if we do the add in different order by cuda threads. The order cannot be guaranteed. So this PR will give warnings when users set `use_deterministic_compute `, but some kernels don't have determinstic kernel impl, has to run with non-determinstic impls. This would at least let users know the results is not determinstic though that flag is set to be True.  Only print the message once in case it floods training logs. |
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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.