ai.onnx.LpNormalization

ai.onnx · standard ONNX operator · ONNX opset ≥ 1

Description

Applies Lp-normalization to the input tensor along the specified axis: output = input / Lp_norm(input, axis). Supports L1 (p=1) and L2 (p=2) norms. Where the Lp norm is zero, the output is defined as zero to avoid division by zero.

See the ONNX LpNormalization spec for the reference semantics.

Inputs

Outputs

Attributes

Default values (overridable per request):

Type constraints

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
• lp-norm-divide.wgsl.jinja
• lp-norm-reduce.wgsl.jinja
• lp-norm-split-combine.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.LpNormalization", { version: 1 });
const { output } = await kernel({ input: { data: inputData, shape: [2, 2] } });