ai.onnx.Or
ai.onnx · standard ONNX operator · ONNX opset ≥ 7
Description
Computes the elementwise logical or of two boolean input tensors A and B, producing a boolean result tensor C of the same type. Supports multidirectional (NumPy-style) broadcasting.
See the ONNX Or spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
A |
a |
B |
— | — | First input operand for the logical or. | required |
B |
b |
B |
— | — | Second input operand for the logical or. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
C |
c |
B |
derived | broadcast result of A and B |
Boolean result tensor of the elementwise or. | required |
Type constraints
| Variable | Allowed dtypes |
|---|---|
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 caseslogical-broadcast.wgsl.jinjalogical-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.Or", { version: 1 });
const { c } = await kernel({ a: { data: aData, shape: [] }, b: { data: bData, shape: [] } });
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kernel
webgpu
wgsl
apache-2.0
WebGPU
Requires WebGPU support. See the compatibility table.