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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 Web
ONNX Runtime Web is a Javascript library for running ONNX models on browsers and on Node.js.
ONNX Runtime Web has adopted WebAssembly and WebGL technologies for providing an optimized ONNX model inference runtime for both CPUs and GPUs.
Why ONNX models
The Open Neural Network Exchange (ONNX) is an open standard for representing machine learning models. The biggest advantage of ONNX is that it allows interoperability across different open source AI frameworks, which itself offers more flexibility for AI frameworks adoption.
Why ONNX Runtime Web
With ONNX Runtime Web, web developers can score models directly on browsers with various benefits including reducing server-client communication and protecting user privacy, as well as offering install-free and cross-platform in-browser ML experience.
ONNX Runtime Web can run on both CPU and GPU. On CPU side, WebAssembly is adopted to execute the model at near-native speed. ONNX Runtime Web complies the native ONNX Runtime CPU engine into WebAssembly backend by using Emscripten, so it supports most functionalities native ONNX Runtime offers, including full ONNX operator coverage, multi-threading, ONNX Runtime Quantization as well as ONNX Runtime Mobile. For performance acceleration with GPUs, ONNX Runtime Web leverages WebGL, a popular standard for accessing GPU capabilities. We are keeping improving op coverage and optimizing performance in WebGL backend.
See Compatibility and Operators Supported for a list of platforms and operators ONNX Runtime Web currently supports.
Usage
Refer to ONNX Runtime JavaScript examples for samples and tutorials.
Documents
Developement
Refer to the following links for development information:
Compatibility
| OS/Browser | Chrome | Edge | Safari | Electron | Node.js |
|---|---|---|---|---|---|
| Windows 10 | wasm, webgl | wasm, webgl | - | wasm, webgl | wasm |
| macOS | wasm, webgl | wasm, webgl | wasm, webgl | wasm, webgl | wasm |
| Ubuntu LTS 18.04 | wasm, webgl | wasm, webgl | - | wasm, webgl | wasm |
| iOS | wasm, webgl | wasm, webgl | wasm, webgl | - | - |
| Android | wasm, webgl | wasm, webgl | - | - | - |
Operators
WebAssembly backend
ONNX Runtime Web currently support all operators in ai.onnx and ai.onnx.ml.
WebGL backend
ONNX Runtime Web currently supports a subset of operators in ai.onnx operator set. See operators.md for a complete, detailed list of which ONNX operators are supported by WebGL backend.
License
License information can be found here.