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### Description Spec of LayerNormalization supports broadcasting (tensors Scale and B should be unidirectional broadcastable to tensor X). https://onnx.ai/onnx/operators/onnx__LayerNormalization.html However, current implementation only allow scale and bias size to be X.shape()[axis:]. Example of input tensors that normalized with axis=2: | X shape | Scale shape | B shape | Before | After | | - | - | - | - | - | | (B, S, D) | (D) | (D) | Supported | Supported | | (B, S, D) | (1, 1, D) | (1, 1, D) | Supported | Supported | | (B, S, D) | (B, 1, D) | (B, 1, D) | Not Supported | Supported | | (B, S, D) | (1, S, D) | (1, S, D) | Not Supported | Supported | | (B, S, D) | (B, S, D) | (B, S, D) | Not Supported | Supported | Here we add limited support: axis=2; scale/bias has same shape; scale/bias/X have same number of dimensions. It could support common use case in LLM and vision models. ### Motivation and Context Support Stable Diffusion 3.x and Flux model. |
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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 |
This project is tested with BrowserStack.
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
Releases
The current release and past releases can be found here: https://github.com/microsoft/onnxruntime/releases.
For details on the upcoming release, including release dates, announcements, features, and guidance on submitting feature requests, please visit the release roadmap: https://onnxruntime.ai/roadmap.
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.