8f7b89bd5b
### Description The changes in this PR includes: 1) Fix f16 errors in InstanceNormalization with NCHW format. 2) Use vec to further optimize the original algorithm. 3) (Removed) Don't do layout conversion for InstanceNormalization for JSEP since InstanceNormalization itself is suitable for NCHW layout and has better performance in our current implementation. Tested on sd-vae-decoder-f16.onnx, it becomes 285 ms from 314 ms. The aggregate gpu profiling data can be found as below (Note the data is based change 3).): Before: <html> <body> <!--StartFragment--><span><span class="ui-provider ef bbg bbh bbi bbj bbk bbl bbm bbn bbo bbp bbq bbr bbs bbt bbu bbv bbw bbx bby bbz bca bcb bcc bcd bce bcf bcg bch bci bcj bck bcl bcm bcn" dir="ltr"> Kernel | Time (Ms) | Percentage (%) -- | -- | -- Conv | 201.55 | 69.56 InstanceNormalization | 42.49 | 14.67 Transpose | 28.95 | 9.99 Mul | 5.69 | 1.96 Add | 3.82 | 1.32 MatMul | 3.27 | 1.13 Sigmoid | 2.24 | 0.77 Resize | 1.16 | 0.40 Softmax | 0.34 | 0.12 Cast | 0.24 | 0.08 Sum | 289.75 <br class="Apple-interchange-newline"><!--EndFragment--> </body> </html> After: <html> <body> <!--StartFragment--><span><span class="ui-provider ef bbg bbh bbi bbj bbk bbl bbm bbn bbo bbp bbq bbr bbs bbt bbu bbv bbw bbx bby bbz bca bcb bcc bcd bce bcf bcg bch bci bcj bck bcl bcm bcn" dir="ltr"> Kernel | Time (Ms) | Percentage (%) -- | -- | -- Conv | 205.44 | 79.43 InstanceNormalization | 18.24 | 7.05 Transpose | 17.64 | 6.82 Mul | 5.69 | 2.20 Add | 3.81 | 1.47 MatMul | 3.56 | 1.38 Sigmoid | 2.24 | 0.86 Resize | 1.19 | 0.46 Softmax | 0.59 | 0.23 Cast | 0.24 | 0.09 Sum | 258.65 | </span></span><!--EndFragment--> </body> </html> From above table, we can see that two ops time are greatly reduced. One is InstanceNormalization and the other is Transpose. The reason that the transpose time is reduced is because each InstanceNormalization is surrounded with two reshape ops in sd-vae-decoder-f16.onnx. Due to JSEP is prefer NHWC and InstanceNormalization is layout sensitive op, so two extra transpose ops are inserted dynamically when executing this model. After this change, those inserted transpose ops are not needed anymore. So the overall transpose time is reduced. |
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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 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.