ai.onnx.InstanceNormalization

ai.onnx · standard ONNX operator · ONNX opset ≥ 6

Description

Applies instance normalization to the input: y = scale * (x - mean) / sqrt(variance + epsilon) + B, where mean and variance are computed per instance per channel over the spatial dimensions. Equivalent to batch normalization with a batch size of one per channel.

See the ONNX InstanceNormalization spec for the reference semantics.

Inputs

Outputs

Attributes

Default values (overridable per request):

Type constraints

Device requirements

Some implementation variants require subgroups. These are route-specific capabilities, not package-wide requirements; availability also depends on the request shape and dtype.

Files

• metadata.json — kernel metadata (id, digests, provenance)
• manifest.json — the op contract (source of truth)
• test.json — correctness cases
• bench.json — benchmark + tuning cases
• instance-normalization-apply.wgsl.jinja
• instance-normalization-batched-planes-vec4.wgsl.jinja
• instance-normalization-splitk-combine.wgsl.jinja
• instance-normalization-splitk-partials.wgsl.jinja
• norm-row-stats.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.InstanceNormalization", { version: 1 });
const { output } = await kernel({
  input: { data: inputData, shape: [1, 2, 1, 3] },
  scale: { data: scaleData, shape: [2] },
  b: { data: bData, shape: [2] },
});