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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 for rank-2 or rank-3 input and a non-empty hidden axis. With exact-shape skip, float32 and float16 output-only paths support optional `beta`; adding `bias` requires `beta`. Returning the residual sum requires `beta`: float32 supports optional `bias` and arbitrary hidden sizes, while float16 requires `bias` and a hidden size divisible by four. Broadcast skip is supported for rank-3 float32 input, required `beta`, no `bias` or residual output, and a hidden size divisible by four. 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 | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `inputT` | `input` | `T` | — | — | Primary input normalized over the final hidden-size axis. Rank 3 is the standard shape; rank 2 is a supported extension. | required |
| `skipT` | `skip` | `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 |
| `gammaT` | `gamma` | `T` | `1` | — | Layer-norm scale weights of shape `(hidden_size)`. | required |
| `betaT` | `beta` | `T` | `1` | — | Layer-norm bias weights of shape `(hidden_size)`. | optional |
| `biasT` | `bias` | `T` | `1` | — | Optional additive bias of shape `(hidden_size)` added to `input + skip` before normalization. | optional |
## Outputs
| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `outputT` | `output` | `T` | same as `inputT` | same as `inputT` | Normalized output tensor with the same shape as `input`. | required |
| `residualT` | `input_skip_bias_sum` | `T` | same as `inputT` | same as `inputT` | 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` |
## Device requirements
Some implementation variants require `shader-f16`. These are route-specific capabilities, not package-wide requirements; availability also depends on the request shape and dtype.
## Files
- [`metadata.json`](build/webgpu/metadata.json) — kernel metadata (id, digests, per-variant templates, 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`
```sh
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.
```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] },
});
```
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