ai.onnx.Sign
ai.onnx · standard ONNX operator · ONNX opset ≥ 13
Description
Computes the elementwise sign of the input tensor: outputs 1 for positive values, -1 for negative values, and 0 where the input is zero. The output has the same shape and type as the input.
See the ONNX Sign spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
input |
input |
T |
— | — | Input tensor of any numeric type. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
output |
output |
T |
same as input |
same as input |
Elementwise sign of the input; same shape and type as the input. | required |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16, int32, uint32, int16, int8, uint8 |
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesunary-scalar.wgsl.jinjaunary-vec4.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.Sign", { version: 1 });
const { output } = await kernel({ input: { data: inputData, shape: [] } });
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kernel
webgpu
wgsl
apache-2.0
WebGPU
Requires WebGPU support. See the compatibility table.