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metadata
library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
ai.onnx.Relu
ai.onnx · standard ONNX operator · ONNX opset ≥ 14
Description
Applies the rectified linear unit function elementwise: y = max(0, x). The output has the same shape and type as the input.
See the ONNX Relu spec for the reference semantics.
Inputs
| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
x |
X |
T |
— | — | Values clamped elementwise to a minimum of zero. | required |
Outputs
| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
y |
Y |
T |
same as x |
same as x |
Output tensor; same shape as the input. | required |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16, int32, int16, int8 |
Files
metadata.json— kernel metadata (id, digests, per-variant templates, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark casesunary-scalar.wgsl.jinjaunary-vec4.wgsl.jinja
Use with @huggingface/kernels
npm install --save-exact @huggingface/kernels@0.0.1-preview.3
Required output shapes and logical data types are inferred from the supplied inputs and attributes; result tensors are allocated automatically.
The version: 1 option selects the published kernel contract; it is independent of any operator opset, contrib since_version, or model version.
It follows the v1 branch as fixes land. To pin exact artifact bytes, pass a 40-character commit revision instead of 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.Relu", { version: 1 });
const { y } = await kernel({ x: { data: xData, shape: [4] } });