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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 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.