ai.onnx.LRN

ai.onnx · standard ONNX operator · ONNX opset ≥ 13

Description

Applies Local Response Normalization across the channel dimension of an input tensor of shape (N x C x D1 x ... x Dk): each element is divided by (bias + alpha / size * square_sum) ^ beta, where square_sum is the sum of squares over a local window of size channels centered on that channel.

See the ONNX LRN spec for the reference semantics.

Inputs

Outputs

Attributes

Attributes and default values (overridable per request):

Type constraints

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
• lrn-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.LRN", { version: 1 });
const { y } = await kernel({ x: { data: xData, shape: [1, 3, 1, 1] } }, {
  attrs: { size: 3 },
});