library_name: kernels
license: apache-2.0
tags:
  - kernel
  - webgpu
  - wgsl

ai.onnx.BitwiseXor

ai.onnx · standard ONNX operator · ONNX opset ≥ 18

Description

Computes the elementwise bitwise xor of two integer tensors A and B, with NumPy-style multidirectional broadcasting. The output has the broadcast shape and the same dtype as the inputs.

See the ONNX BitwiseXor spec for the reference semantics.

Inputs

Outputs

Type constraints

Files

• metadata.json — kernel metadata (id, digests, per-variant templates, provenance)
• manifest.json — the op contract (source of truth)
• test.json — correctness cases
• bench.json — benchmark + tuning cases
• bitwise-binary-broadcast.wgsl.jinja
• bitwise-binary-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.BitwiseXor", { version: 1 });
const { c } = await kernel({ a: { data: aData, shape: [3] }, b: { data: bData, shape: [3] } });