ai.onnx.PRelu
ai.onnx · standard ONNX operator · ONNX opset ≥ 16
Description
Applies the parametric ReLU function elementwise: f(x) = x for x >= 0 and f(x) = slope * x for x < 0. The slope tensor may be smaller than X and is broadcast unidirectionally to match X.
See the ONNX PRelu spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
X |
x |
T |
— | — | Values transformed by parametric ReLU using the broadcast slope. |
required |
slope |
slope |
T |
— | — | Per-element slope values; must be unidirectionally broadcastable to X. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
Y |
y |
T |
same as X |
same as X |
Output tensor of the same shape as X. | required |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16 |
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesprelu-vec4.wgsl.jinjaprelu.wgsl.jinja
Use with @huggingface/kernels
The loader derives every required output's shape and logical dtype from the manifest contract and this call. It then allocates the result tensors automatically.
The version: 1 option selects the published kernel contract; it is independent of any operator opset, contrib since_version, or model version.
Replace each *Data placeholder with a typed array containing the corresponding input data.
import { getKernel } from "@huggingface/kernels";
const kernel = await getKernel("webgpu-kernels/ai.onnx.PRelu", { version: 1 });
const { y } = await kernel({
x: { data: xData, shape: [3, 1, 1, 1] },
slope: { data: slopeData, shape: [1, 1, 1] },
});
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kernel
webgpu
wgsl
apache-2.0
WebGPU
Requires WebGPU support. See the compatibility table.