ai.onnx.RMSNormalization

ai.onnx · standard ONNX operator · ONNX opset ≥ 23

Description

Computes RMS normalization over the suffix dimensions of X starting at axis: Y = X / sqrt(mean(X^2) + epsilon) * scale. The normalization stage supports TensorProto stash_type values 1 (float32) and 10 (float16), and is cast back to the dtype of X before scale is applied. The input type T and scale/output type V may independently be float16 or float32; ONNX bfloat16 and double cases are unsupported.

See the ONNX RMSNormalization spec for the reference semantics.

Inputs

Name	Upstream name	Logical dtype	Rank	Shape	Description	Presence
`x`	`X`	`T`	—	—	Input tensor to be normalized; the RMS is taken over the last dimensions starting at `axis`.	required
`scale`	—	`V`	—	—	Scale tensor, unidirectionally broadcastable to `X`; its dtype `V` may differ from the input dtype `T`.	required

Outputs

Name	Upstream name	Logical dtype	Rank	Shape	Description	Presence
`y`	`Y`	`V`	same as `x`	same as `x`	Normalized and scaled output tensor; same shape as `X` and same dtype `V` as `scale`.	required

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 over only the last dimension.
`epsilon`	`0.00001`	Small constant added to the mean square before taking the square root to avoid division by zero.
`stash_type`	`1`	TensorProto element type used for normalization: `1` computes in float32, while `10` computes in float16.

Type constraints

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

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-stash-f16-serial.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.RMSNormalization", { version: 1 });
const { y } = await kernel({
  x: { data: xData, shape: [1, 2, 3] },
  scale: { data: scaleData, shape: [3] },
});

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kernel

webgpu

wgsl

apache-2.0

WebGPU

Requires WebGPU support. See the compatibility table.