Backend \ OS	Windows	Linux
null (for unit test)
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.

webmachinelearning/webnn-native