metadata
library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
ai.onnx.BitwiseOr
ai.onnx · standard ONNX operator · ONNX opset ≥ 18
Description
Computes the elementwise bitwise or of two integer tensors A and B, with multidirectional (NumPy-style) broadcasting. The output C has the broadcasted shape and the same dtype as the inputs.
See the ONNX BitwiseOr spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
A |
a |
T |
— | — | First input operand for the bitwise OR. | required |
B |
b |
T |
— | — | Second input operand for the bitwise OR. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
C |
c |
T |
derived | broadcast result of A and B |
Result tensor containing the elementwise bitwise OR of A and B. | required |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
uint32, int32, int16, uint8, int8 |
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesbitwise-binary-broadcast.wgsl.jinjabitwise-binary-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.BitwiseOr", { version: 1 });
const { c } = await kernel({ a: { data: aData, shape: [3] }, b: { data: bData, shape: [3] } });