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library_name: kernels
license: apache-2.0
tags:
  - kernel
  - webgpu
  - wgsl

ai.onnx.SimplifiedLayerNormalization

ai.onnx · ONNX Runtime compatibility operator · default-domain since_version 1

Description

Implements ONNX Runtime's legacy RMS normalization for models that serialize SimplifiedLayerNormalization in the default ONNX domain: Y = (X / sqrt(mean(X^2) + epsilon)) * scale. Its arithmetic corresponds to ONNX RMSNormalization, while retaining scale-before-output-cast ordering and optional inv_std_var. See the compatibility discussion. Supports float16/float32, float32 statistics, and a nonempty normalization suffix.

Inputs

Name	Upstream name	Logical dtype	Rank	Shape	Description	Presence
`x`	`X`	`T`	—	—	Input tensor; the mean square is taken over the suffix dimensions starting at `axis`.	required
`scale`	—	`V`	—	—	Scale tensor, unidirectionally broadcastable to `X`; output `Y` has this tensor's dtype.	required

Outputs

Name	Upstream name	Logical dtype	Rank	Shape	Description	Presence
`y`	`Y`	`V`	same as `x`	same as `x`	Normalized and scaled output; same shape as `X` and same dtype as `scale`.	required
`invStdVar`	`inv_std_var`	`U`	same as `x`	derived	Optional inverse RMS statistic `1 / sqrt(mean(X^2) + epsilon)`; same rank as `X`, with the dimensions from `axis` onward set to one.	optional

Attributes

Default values (overridable per request):

Attribute	Default	Description
`axis`	`-1`	The first dimension of the normalization suffix; negative values count from the end, so the default `-1` normalizes only the last dimension.
`epsilon`	`0.00001`	Small constant added to the mean square before taking the square root to avoid division by zero.
`keep_dims`	`1`	Legacy compatibility attribute. Only `1` is supported: optional statistics retain the input rank with normalized dimensions set to one.
`stash_type`	`1`	TensorProto element type of `inv_std_var`; this package implements the legacy float32 value `1`.

Type constraints

Variable	Allowed dtypes
`T`	`float32`, `float16`
`V`	`float32`, `float16`
`U`	`float32`

Files

metadata.json — kernel metadata (id, digests, per-variant templates, provenance)
manifest.json — the op contract (source of truth)
test.json — correctness cases
bench.json — benchmark + tuning cases
norm-row-stats.wgsl.jinja
rms-normalization-splitk-normalize.wgsl.jinja
rms-normalization-splitk-partials.wgsl.jinja
rms-normalization.wgsl.jinja

Use with `@huggingface/kernels`

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.

import { getKernel } from "@huggingface/kernels";

const kernel = await getKernel("webgpu-kernels/ai.onnx.SimplifiedLayerNormalization", { version: 1 });
const { y } = await kernel({ x: { data: xData, shape: [8] }, scale: { data: scaleData, shape: [8] } });