6cdf071a94
### Description Cherry-picks 26 commits to the release branch. Most cherry-picks are clean merges. Except: 1. When I got conflicts in cgmanifest.json and download-deps.yml, I choose to ignore the conflicts and regenerate the two files 2. There were some conflicts in cmake/deps.txt, onnxruntime_c_api.cc PR list: [js/webgpu] fix Transpose with non-float tensor (#15819) [js/web] fix terser reserved symbols for worker (#15864) [JSEP] fix constructor for OrtDevice (#15805) Bump engine.io from 6.4.1 to 6.4.2 in /js/web (#15799) Bump engine.io from 6.4.0 to 6.4.2 in /onnxruntime/test/wasm (#15798) [wasm] revert emsdk to v3.1.19 (#15793) [wasm/JSEP] add threaded build to artifacts (#15777) [js/web] add target ort.webgpu.min.js (#15780) update ort extensions to 94142d8391c9791ec71c38336436319a2d4ac7a0 (#15688) fix: setting builder optimization level to TRT 8.6 default (#15897) Adust GetVersionString() GetBuildInfoString() signatures and move them to OrtApi (#15921) Fix segfault for multiple GPU run (regression) (#15823) android package fix (#15999) [CoreML EP] Minor changes to allow CoreML EP to handle more nodes and models. (#15993) Adding support for conv fp16 fusion on Resnet50v1 (#15474) update onnx release 1.14 for docker files (#15680) Avoid generating training documentation during packaging (#15795) Update Conv-Add-Relu Fusion Transformation (#15834) Fix symbolic shape infer empty value_info (#15842) NhwcFusedConv: Add before Activation (#15837) use __hmul2 instead of __hmul2_rn (#15852) change the EP device to default OrtDevice() for memoryType equals CPU Input (#15903) Fixing NhwcFusedConv fp16 (#15950) fix topo sort in quantization tool (#16003) [doc] add LeakyRelu to coreml supported ops (#15944) [DML EP] Add frequent upload heap flushing (#15960) Co-authored-by: Yulong Wang Co-authored-by: dependabot[bot] Co-authored-by: Guenther Schmuelling Co-authored-by: Shalva Mist Co-authored-by: Maximilian Müller Co-authored-by: Dmitri Smirnov Co-authored-by: pengwa Co-authored-by: Ashwini Khade Co-authored-by: Edward Chen Co-authored-by: Jian Chen Co-authored-by: liqun Fu Co-authored-by: Baiju Meswani Co-authored-by: Tianlei Wu Co-authored-by: Chen Fu Co-authored-by: Ye Wang Co-authored-by: cao lei Co-authored-by: Yufeng Li Co-authored-by: Rachel Guo Co-authored-by: Patrice Vignola |
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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.