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 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 — kernel metadata (id, digests, provenance)
manifest.json — the op contract (source of truth)
test.json — correctness cases
bench.json — benchmark + tuning cases
norm-skip-row-vec4.wgsl.jinja
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.

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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kernel

webgpu

wgsl

apache-2.0

WebGPU

Requires WebGPU support. See the compatibility table.