ai.onnx.BatchNormalization

ai.onnx · standard ONNX operator · ONNX opset ≥ 15

Description

Applies inference-mode batch normalization: Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B. This package supports training_mode=0, rank-2-or-higher inputs, and a common float16 or float32 dtype for every tensor. ONNX training mode is intentionally not implemented because this inference-only release does not expose its required running-mean and running-variance outputs.

See the ONNX BatchNormalization spec for the reference semantics.

Inputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`X`	`x`	`T`	—	—	Input data tensor with shape `(N, C, D1, ..., Dn)`, normalized independently per channel using the supplied estimated statistics.	required
`scale`	`scale`	`T`	`1`	—	Per-channel scale tensor with shape `(C)`.	required
`B`	`b`	`T`	`1`	—	Per-channel bias tensor with shape `(C)`.	required
`input_mean`	`inputMean`	`T`	`1`	—	Precomputed estimated mean tensor with shape `(C)` used for inference.	required
`input_var`	`inputVar`	`T`	`1`	—	Precomputed estimated variance tensor with shape `(C)` used for inference.	required

Outputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`Y`	`y`	`T`	same as `X`	same as `X`	Batch-normalized output tensor with the same shape as `X`.	required

Attributes

Default values (overridable per request):

Attribute	Default	Description
`epsilon`	`0.00001`	Small value added to the variance before taking the square root to avoid division by zero.
`momentum`	`0.9`	Standard ONNX running-statistics momentum. This inference-only package accepts the default `0.9`; non-default values are reserved for the unsupported training-state update.
`training_mode`	`0`	Execution mode. This inference-only package supports the default value 0; value 1 is rejected because the ONNX training outputs are not exposed.

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
batch-normalization-nc-vec4.wgsl.jinja
batch-normalization-nchw-vec4.wgsl.jinja
batch-normalization-nchw.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.BatchNormalization", { version: 1 });
const { y } = await kernel({
  x: { data: xData, shape: [2, 3] },
  scale: { data: scaleData, shape: [3] },
  b: { data: bData, shape: [3] },
  inputMean: { data: inputMeanData, shape: [3] },
  inputVar: { data: inputVarData, shape: [3] },
});

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kernel

webgpu

wgsl

apache-2.0

WebGPU

Requires WebGPU support. See the compatibility table.