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library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
---
# com.microsoft.SkipSimplifiedLayerNormalization
`com.microsoft` · ONNX Runtime contrib operator · contrib since_version 1
## Description
Adds `input` and `skip` (plus optional `bias`), then applies RMS normalization scaled by `gamma`. The optional second output exposes the pre-normalization sum. The schema's training-only mean and inverse-standard-deviation outputs are not implemented.
See the [ONNX Runtime `SkipSimplifiedLayerNormalization` contrib-operator spec](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.SkipSimplifiedLayerNormalization) for the reference semantics.
## Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `input` | `inputT` | `T` | — | — | Input tensor of shape `(token_count, hidden_size)` or `(batch, sequence, hidden_size)`, normalized over the last axis. | required |
| `skip` | `skipT` | `T` | — | — | Residual tensor of the same shape as `input`, added before normalization. | required |
| `gamma` | `gammaT` | `T` | `1` | — | 1-D scale tensor with shape `(hidden_size)` applied after normalization. | required |
| `bias` | `biasT` | `T` | `1` | — | Optional 1-D bias tensor with shape `(hidden_size)` added to the `input + skip` sum. | 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 optional `bias` 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 mean square 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, 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.SkipSimplifiedLayerNormalization", { 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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