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**Description**: This PR adds support for "XNNPACK EP" in ORTWeb and changes the behavior of how ORTWeb deals with "backends", or "EPs" in API. **Background**: Term "backend" is introduced in ONNX.js to representing a TypeScript type which implements a "backend" interface, which is a similar but different concept to ORT's EP (execution provider). There was 3 backends in ONNX.js: "cpu", "wasm" and "webgl". When ORT Web is launched, the concept is derived to help users to integrate smoothly. Technically, when "wasm" backend is used, users need to also specify "EP" in the session options. Considering it may get complicated and confused for users to figure out the difference between "backend" and "EP", the JS API hide the "backend" concept and made a mapping between names, backends and EPs: "webgl" (Name) <==> "onnxjsBackend" (Backend) "wasm" (Name) <==> "wasmBackend" (Backend) <==> "CPU" (EP) **Details**: The following changes are applied in this PR: 1. allow multi-registration for backends using the same name. This is for use scenarios where both "onnxruntime-node" and "onnxruntime-web" are consumed in a Node.js App ( so "cpu" will be registered twice in this scenario. ) 2. re-assign priority values to backends. I give 100 as base to "cpu" for node and react_native, and 10 as base to "cpu" in web. 3. add "cpu", "xnnpack" as new names of backends. 4. update onnxruntime wasm exported functions to support EP registration. 5. update implementations in ort web to handle execution providers in session options. 6. add '--use_xnnpack' as default build flag for ort-web |
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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
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| WebAssembly |
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.