Swift bindings for WebGPU; a new graphics and compute API.
Despite being designed for the web, WebGPU can also be used natively, enabling developers with a modern, cross-platform API, without some of the complexities of lower-level graphics libraries.
Efforts are being made to define a standard native version of the API via a shared header. Note, however, that the specification is still work-in-progress.
Currently, swift-webgpu is based on the Dawn implementation, and generated from dawn.json.
To use swift-webgpu, you'll first need to build Dawn. See Dawn's documentation to get started.
swift-webgpu depends on the libwebgpu_dawn shared library, which can be built like so;
mkdir -p out/Release
cd out/Release
cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=1 -GNinja ../..
ninja webgpu_dawnFirst, clone this project;
git clone https://github.com/henrybetts/swift-webgpu.git
cd swift-webgpuBuild the package;
DAWN_JSON=/path/to/dawn/src/dawn/dawn.json \
swift build -c release \
-Xcc -I/path/to/dawn/include \
-Xcc -I/path/to/dawn/out/Release/gen/include \
-Xlinker -L/path/to/dawn/out/Release/src/dawn/native \
-Xlinker -rpath -Xlinker /path/to/dawn/out/Release/src/dawn/nativeRemember to replace /path/to/dawn with the actual path to the dawn directory. These arguments tell the compiler where to find the dawn headers, and the linker where to find the shared libraries. The DAWN_JSON variable defines the location of dawn.json, which is needed for code generation.
Finally, run the demos;
cd .build/release
./DemoInfo
./DemoClearColor
./DemoTriangle
./DemoCube
./DemoBoidsTo use swift-webgpu with Swift Package Manager, add it to your Package.swift file's dependencies;
.package(url: "https://github.com/henrybetts/swift-webgpu.git", branch: "master")Then add WebGPU as a dependency of your target, and link the webgpu_dawn library;
.executableTarget(
name: "MyApp",
dependencies: [.product(name: "WebGPU", package: "swift-webgpu")],
linkerSettings: [.linkedLibrary("webgpu_dawn")]
),Import the WebGPU module where needed;
import WebGPUA typical application will start by creating an Instance, requesting an Adapter, and then requesting a Device. For example;
// create an instance
let instance = createInstance()
// find an adapter
let adapter = try await instance.requestAdapter()
print("Using adapter: \(adapter.properties.name)")
// create a device
let device = try await adapter.requestDevice()You'll usually want to set an error handler, to log any errors produced by the device;
device.setUncapturedErrorCallback { (errorType, errorMessage) in
print("Error (\(errorType)): \(errorMessage)")
}With the device obtained, you can create most of the other types of WebGPU objects. A shader module, for example;
let shaderModule = device.createShaderModule(
descriptor: .init(
nextInChain: ShaderModuleWgslDescriptor(
code: """
@vertex
fn vertexMain() -> @builtin(position) vec4f {
return vec4f(0, 0, 0, 1);
}
@fragment
fn fragmentMain() -> @location(0) vec4f {
return vec4f(1, 0, 0, 1);
}
"""
)
)
)Or a render pipeline;
let renderPipeline = device.createRenderPipeline(
descriptor: .init(
vertex: VertexState(
module: shaderModule,
entryPoint: "vertexMain"
),
fragment: FragmentState(
module: shaderModule,
entryPoint: "fragmentMain",
targets: [ColorTargetState(format: .bgra8Unorm)]
)
)
)Most objects are created with a descriptor. In some cases, WebGPU uses a chainable struct pattern to support future extensions or platform specific features. This is indicated by the nextInChain property. (There is scope to improve the ergonomics of this in Swift.)
See the demos for further usage examples.