ai.onnx.Where
ai.onnx · standard ONNX operator · ONNX opset ≥ 16
Description
Selects elements from X or Y according to a boolean condition, following NumPy-style multidirectional broadcasting. Where condition is true, the output takes the value from X; otherwise it takes the value from Y.
See the ONNX Where spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
condition |
condition |
C |
— | — | Boolean mask; nonzero entries select from X, zero entries select from Y. | required |
X |
x |
T |
— | — | Values selected at indices where condition is true. | required |
Y |
y |
T |
— | — | Values selected at indices where condition is false. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
output |
output |
T |
derived | broadcast result of condition, X, and Y |
Tensor of shape equal to the broadcasted shape of condition, X, and Y. | required |
Type constraints
| Variable | Allowed dtypes |
|---|---|
C |
bool |
T |
float32, float16, uint32, int32, int16, uint8, int8, bool |
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning caseswhere-broadcast.wgsl.jinjawhere-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.Where", { version: 1 });
const { output } = await kernel({
condition: { data: conditionData, shape: [] },
x: { data: xData, shape: [] },
y: { data: yData, shape: [] },
});
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Requires WebGPU support. See the compatibility table.