---
library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
---
# com.microsoft.SkipLayerNormalization

`com.microsoft`  ·  ONNX Runtime contrib operator  ·  contrib since_version 1

## Description

Fuses skip addition with layer normalization. The rank-3 standard surface currently supports float32, required `beta`, no `bias` or residual output, hidden sizes divisible by four, and exact or documented broadcast skip shapes. The provider's rank-2 extension supports float32 output-only with optional `beta`, or `beta` with optional `bias` when emitting the residual; its float16 path requires `beta`, `bias`, a residual output, and four-wide hidden size. Other combinations, bfloat16, and training statistics are not implemented.

See the [ONNX Runtime `SkipLayerNormalization` contrib-operator spec](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.SkipLayerNormalization) for the reference semantics.

## Inputs

| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `input` | `inputT` | `T` | — | — | Primary input normalized over the final hidden-size axis. Rank 3 is the public shape; rank 2 is an ONNX Runtime provider extension supported by this package. | required |
| `skip` | `skipT` | `T` | — | — | Residual tensor. For rank-3 input it is exact shape, `(1, sequence_length, hidden_size)`, or `(sequence_length, hidden_size)`; rank-2 input requires exact shape. | required |
| `gamma` | `gammaT` | `T` | `1` | — | Layer-norm scale weights of shape `(hidden_size)`. | required |
| `beta` | `betaT` | `T` | `1` | — | Layer-norm bias weights of shape `(hidden_size)`. | optional |
| `bias` | `biasT` | `T` | `1` | — | Optional additive bias of shape `(hidden_size)` added to `input + skip` before normalization. | optional |

## Outputs

| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `output` | `outputT` | `T` | same as `input` | same as `input` | Normalized output tensor with the same shape as `input`. | required |
| `input_skip_bias_sum` | `residualT` | `T` | same as `input` | same as `input` | Sum of `input`, `skip`, and `bias` (when present) before normalization, with the same shape as `input`. | optional |

## Attributes

Default values (overridable per request):

| Attribute | Default | Description |
| --- | --- | --- |
| `epsilon` | `9.999999960041972e-13` | Non-negative epsilon added to the variance before taking the square root. |

## 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
- [`norm-skip-row-vec4.wgsl.jinja`](build/webgpu/norm-skip-row-vec4.wgsl.jinja)
- [`norm-skip-row.wgsl.jinja`](build/webgpu/norm-skip-row.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/com.microsoft.SkipLayerNormalization", { version: 1 });
const { outputT } = await kernel({
  inputT: { data: inputTData, shape: [2, 4] },
  skipT: { data: skipTData, shape: [2, 4] },
  gammaT: { data: gammaTData, shape: [4] },
});
```