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library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
---
# 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](https://onnx.ai/onnx/operators/onnx__LpNormalization.html) for the reference semantics.
## Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `input` | `input` | `T` | — | — | Input tensor to normalize. | required |
## Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `output` | `output` | `T` | same as `input` | same as `input` | Tensor after Lp-normalization; same shape as the input. | required |
## Attributes
Default values (overridable per request):
| Attribute | Default | Description |
| --- | --- | --- |
| `axis` | `-1` | The axis along which normalization is applied; `-1` means the last axis. |
| `p` | `2` | The order of the Lp norm to use; only `1` (L1) or `2` (L2) are supported. |
## Type constraints
| Variable | Allowed dtypes |
| --- | --- |
| `T` | `float32`, `float16` |
## Files
- [`metadata.json`](build/webgpu/metadata.json) — kernel metadata (id, digests, provenance)
- [`manifest.json`](build/webgpu/manifest.json) — the op contract (source of truth)
- [`test.json`](build/webgpu/test.json) — correctness cases
- [`bench.json`](build/webgpu/bench.json) — benchmark + tuning cases
- [`lp-norm-divide.wgsl.jinja`](build/webgpu/lp-norm-divide.wgsl.jinja)
- [`lp-norm-reduce.wgsl.jinja`](build/webgpu/lp-norm-reduce.wgsl.jinja)
- [`lp-norm-split-combine.wgsl.jinja`](build/webgpu/lp-norm-split-combine.wgsl.jinja)
- [`norm-row-stats.wgsl.jinja`](build/webgpu/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.
```js
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] } });
```
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