ai.onnx.IsInf
ai.onnx · standard ONNX operator · ONNX opset ≥ 20
Description
Maps each element of a floating-point tensor to true if it is infinite and false otherwise. The detect_negative and detect_positive attributes independently control whether negative and positive infinity are considered infinite for this mapping.
See the ONNX IsInf spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
X |
x |
T |
— | — | Input floating-point tensor. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
Y |
y |
B |
same as X |
same as X |
Boolean output tensor with the same shape as the input; true where the input is infinite. |
required |
Attributes
Default values (overridable per request):
| Attribute | Default | Description |
|---|---|---|
detect_negative |
1 |
When 1 (default), negative infinity maps to true; set to 0 to map negative infinity to false. |
detect_positive |
1 |
When 1 (default), positive infinity maps to true; set to 0 to map positive infinity to false. |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16 |
B |
bool |
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesdetect-neither.wgsl.jinjaunary-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.IsInf", { version: 1 });
const { y } = await kernel({ x: { data: xData, shape: [] } });
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Requires WebGPU support. See the compatibility table.