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### Description This PR rewrite the backend resolve logic to support specifying multiple EPs. #### Backend The first version of ONNX Runtime Web actually carried some existing code from [ONNX.js](https://github.com/microsoft/onnxjs), which includes the "backend" concept. The original "backend" in ONNX.js is designed in a way assuming there is only one backend from user's backend hint list will be used. For example, in ONNX.js, if user specify a backend hint as `['webgl', 'wasm']`, ONNX.js will first try to use WebGL backend - if it loads successfully (the browser supports webgl), then "webgl" backend will be used and "wasm" will be ignored; otherwise, "webgl" will be ignored and try to load "wasm" backend. In short: only one backend will be used when initializing a session. #### Execution Provider Execution Provider, or EP, in ONNX Runtime is a different concept. One of the differences is that users are allow to specify multiple EPs, and if one does not support a particular kernel, it can fallback to other EP. This is a very common case when using a GPU EP in ONNX Runtime. #### Current Status: Backend v.s. EP Because of the history reasons mentioned above, the current status is quite confusing. There are **real backend**s, which means it's different implementation in code; and there are **backend hint**s, which are used as string names for backend hint; and there are **EP**s of the ONNX Runtime concepts. currently there are only 2 **backend**s in our code base: The "onnxjs backend", and the "wasm backend". The "onnxjs backend" currently only powers backend hint "webgl", which go into the old onnx.js code path. All other backend hints including "wasm", "cpu"(alias to wasm), "webgpu" and "webnn" are all powered by "wasm backend". And because ORT Web treat "backend" as an internal concept and want to align with ONNX Runtime, so those names of backend hints are becoming EP names. The following table shows today's status: | Execution Provider Name (public) / Backend Hint (internal) | Backend | EP in ORT | -------- | ------- | ------- | | "wasm"/"cpu" | WasmBackend | CPU EP | "webgl" | OnnxjsBackend | \* technically not an EP | "webgpu" | WasmBackend | JSEP | "webnn" | WasmBackend | WebNN EP #### Problem While the API allows to specify multiple EPs, the backend resolving only allows one backend. This causes issues when user specify multiple EP names in session options, the backend resolve behavior and EP registration behavior is inconsistent. Specifically, in this issue: https://github.com/microsoft/onnxruntime/issues/15796#issuecomment-1925363908: EP list `['webgpu', 'wasm']` on a browser without WebGPU support resolves to 'wasm' backend, but the full EP list is passed in session options, so JSEP is still enabled, causing the runtime error. #### Solution Since we still need WebGL backend, we cannot totally remove the backend register/resolve system. In this PR I made the following changes: - initialize every backend from the EP list, instead of only do that for the first successful one. - for the first resolved backend, filter all EP using the exact same backend. Remove all EPs not using this backend from session options - for every explicitly specified EP, if it's removed, show a warning message in console |
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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 compiles 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
Development
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 webgl-operators.md for a complete, detailed list of which ONNX operators are supported by WebGL backend.
WebGPU backend
WebGPU backend is still an experimental feature. See webgpu-operators.md for a detailed list of which ONNX operators are supported by WebGPU backend.
License
License information can be found here.