Backend \ OS	Windows	Linux
null (for unit test)
DirectML
DirectMLX
OpenVINO
XNNPACK
oneDNN
MLAS

WebNN-native

WebNN-native is a native implementation of the Web Neural Network API.

It provides several building blocks:

• WebNN C/C++ headers that applications and other building blocks use.
  • The webnn.h that is an one-to-one mapping with the WebNN IDL.
  • A C++ wrapper for the webnn.h
• Backend implementations that use platforms' ML APIs:
  • DirectML on Windows 10
  • DirectMLX on Windows 10
  • OpenVINO on Windows 10 and Linux
  • oneDNN on Windows 10 and Linux
  • XNNPACK on Windows 10 and Linux
  • MLAS on Windows 10 and Linux
  • Other backends are to be added

WebNN-native uses the code of other open source projects:

• The code generator and infrastructure code of Dawn project.
• The DirectMLX and device wrapper of DirectML project.
• The XNNPACK project.
• The oneDNN project.
• The MLAS project.

Build and Run

Install depot_tools

WebNN-native uses the Chromium build system and dependency management so you need to install depot_tools and add it to the PATH.

Notes:

• On Windows, you'll need to set the environment variable DEPOT_TOOLS_WIN_TOOLCHAIN=0. This tells depot_tools to use your locally installed version of Visual Studio (by default, depot_tools will try to download a Google-internal version).

Get the code

Get the source code as follows:

# Clone the repo as "webnn-native"
> git clone https://github.com/webmachinelearning/webnn-native.git webnn-native && cd webnn-native

# Bootstrap the gclient configuration
> cp scripts/standalone.gclient .gclient

# Fetch external dependencies and toolchains with gclient
> gclient sync

Setting up the build

Generate build files using gn args out/Debug or gn args out/Release.

A text editor will appear asking build options, the most common option is is_debug=true/false; otherwise gn args out/Release --list shows all the possible options.

To build with a backend, please set the corresponding option from following table.

Backend	Option
DirectML	webnn_enable_dml=true
DirectMLX	webnn_enable_dmlx=true
OpenVINO	webnn_enable_openvino=true
XNNPACK	webnn_enable_xnnpack=true
oneDNN	webnn_enable_onednn=true
MLAS	webnn_enable_mlas=true

Build

Then use ninja -C out/Release or ninja -C out/Debug to build WebNN-native.

Notes

• To build with XNNPACK backend, please build XNNPACK first, e.g. by ./scripts/build-local.sh. For Windows build, it requires supplying -DCMAKE_MSVC_RUNTIME_LIBRARY="MultiThreaded$<$CONFIG:Debug:Debug>" to set MSVC static runtime library.
• To build with oneDNN backend, please build oneDNN first by following the build from source instructions.
• To build with MLAS backend, please build MLAS (part of ONNX Runtime) first by following the Build ONNX Runtime for inferencing, e.g., by .\build.bat --config Release --parallel --enable_msvc_static_runtime for Windows build.

Run tests

Run unit tests:

> ./out/Release/webnn_unittests

Run end2end tests on a default device:

> ./out/Release/webnn_end2end_tests

You can also specify a device to run end2end tests using "-d" option, for example:

> ./out/Release/webnn_end2end_tests -d gpu

Currently "cpu", "gpu" and "default" are supported, more devices are to be supported in the future.

Notes:

• For OpenVINO backend, please install 2021.4 version and set the environment variables before running the end2end tests.
• The current implementation of oneDNN and MLAS backends is mainly for the investigation of WebNN Operation Level Execution use case. So only a limited set of tests (such as of conv2d) is expected to pass.

Run examples

• LeNet

License

Apache 2.0 Public License, please see LICENSE.