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**Description**: This PR intends to enable WebNN EP in ONNX Runtime Web. It translates the ONNX nodes by [WebNN API](https://webmachinelearning.github.io/webnn/), which is implemented in C++ and uses Emscripten [Embind API](https://emscripten.org/docs/porting/connecting_cpp_and_javascript/embind.html#). Temporarily using preferred layout **NHWC** for WebNN graph partitions since the restriction in WebNN XNNPack backend implementation and the ongoing [discussion](https://github.com/webmachinelearning/webnn/issues/324) in WebNN spec that whether WebNN should support both 'NHWC' and 'NCHW' layouts. No WebNN native EP, only for Web. **Motivation and Context**: Allow ONNXRuntime Web developers to access WebNN API to benefit from hardware acceleration. **WebNN API Implementation Status in Chromium**: - Tracked in Chromium issue: [#1273291](https://bugs.chromium.org/p/chromium/issues/detail?id=1273291) - **CPU device**: based on XNNPack backend, and had been available on Chrome Canary M112 behind "#enable-experimental-web-platform-features" flag for Windows and Linux platforms. Further implementation for more ops is ongoing. - **GPU device**: based on DML, implementation is ongoing. **Open**: - GitHub CI: WebNN currently is only available on Chrome Canary/Dev with XNNPack backend for Linux and Windows. This is an open to reviewers to help identify which GitHub CI should involved the WebNN EP and guide me to enable it. Thanks! |
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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
Build Pipeline Status
| System | Inference | Training |
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| Linux | ||
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| Web | ||
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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.