ai.onnx.LayerNormalization

ai.onnx · standard ONNX operator · ONNX opset ≥ 17

Description

Normalizes a tensor along a suffix of axes starting at axis by subtracting the mean and dividing by the square root of the variance plus epsilon, then scales and optionally shifts the result with learnable Scale and B tensors. The output Y has the same shape as X; optional outputs Mean and InvStdDev expose the per-normalization-group statistics computed during normalization.

See the ONNX LayerNormalization spec for the reference semantics.

Inputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`X`	`x`	`T`	—	—	Tensor to be normalized.	required
`Scale`	`scale`	`T`	—	—	Scale tensor applied after normalization.	required
`B`	`b`	`T`	—	—	Optional bias tensor added after scaling.	optional

Outputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`Y`	`y`	`T`	same as `X`	same as `X`	Normalized and scaled output tensor; same shape as X.	required
`Mean`	`mean`	`float32`	same as `X`	—	Per-normalization-group mean in the ONNX broadcastable keepdims shape: dimensions before `axis` are preserved and dimensions from `axis` onward are 1.	optional
`InvStdDev`	`invStdDev`	`float32`	same as `X`	—	Per-normalization-group reciprocal standard deviation `1 / sqrt(variance + epsilon)`, returned in the same ONNX broadcastable keepdims shape as `Mean`.	optional

Attributes

Default values (overridable per request):

Attribute	Default	Description
`axis`	`-1`	The first axis of the normalization range; all axes from `axis` to the last are normalized together. Negative values count from the end; the default `-1` normalizes only the last dimension.
`epsilon`	`0.00001`	Small constant added to the variance before taking the square root to avoid division by zero.
`stash_type`	`1`	TensorProto element type used for the normalization stage and optional statistics; the implemented ONNX route supports the standard float32 value (`1`).

Type constraints

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

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
layer-normalization.wgsl.jinja
norm-row-stats.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/ai.onnx.LayerNormalization", { version: 1 });
const { y } = await kernel({
  x: { data: xData, shape: [1, 4] },
  scale: { data: scaleData, shape: [4] },
});

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kernel

webgpu

wgsl

apache-2.0

WebGPU

Requires WebGPU support. See the compatibility table.