ai.onnx.Xor
ai.onnx · standard ONNX operator · ONNX opset ≥ 7
Description
Computes the elementwise logical xor of two boolean tensors A and B, returning a boolean result tensor C of the same broadcasted shape. Supports multidirectional (NumPy-style) broadcasting.
See the ONNX Xor spec for the reference semantics.
Inputs
| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
a |
A |
B |
— | — | First boolean input operand. | required |
b |
B |
B |
— | — | Second boolean input operand. | required |
Outputs
| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
c |
C |
B |
derived | broadcast result of a and b |
Boolean result tensor of the elementwise xor. | required |
Type constraints
| Variable | Allowed dtypes |
|---|---|
B |
bool |
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 + tuning caseslogical-broadcast.wgsl.jinjalogical-vec4.wgsl.jinja
Use with @huggingface/kernels
npm install --save-exact @huggingface/kernels@0.0.1-preview.2
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.Xor", { version: 1 });
const { c } = await kernel({ a: { data: aData, shape: [] }, b: { data: bData, shape: [] } });
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Requires WebGPU support. See the compatibility table.