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### Description Remove unused and confusing special constants in MLFloat16 and BFloat16 types. ### Motivation and Context While looking at adding a specialization for std::numeric_limits for the 16-bit floating point types, I found that there are various special constants in those types that are confusing or just wrong. MLFLoat16::Epsilon is not an epsilon at all, but approximates "e". Looks like a copy-paste bug. BFloat16::Epsilon does not correspond to `numeric_limits::epsilon()`, nor even to the C# Float.Epsilon. Instead, it corresponds to `numeric_limits::min()` which was really confusing to me. The "MinValue" constants does correspond to the C# `Float.MinValue` constant, but this is C++ so it would be better renamed to "LowestValue" since it corresponds to `numeric_limits::lowest()`. As it was unused except for some unit tests I have replaced it with the equivalent `MaxValue.Negate()` here. There's also an unused `kSignaling_NaNBits` constant which is just wrong (has the same value as `kPositiveInfinityBits` instead of a NaN). |
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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.