ai.onnx.GlobalLpPool

ai.onnx · standard ONNX operator · ONNX opset ≥ 2

Description

Applies Lp pooling globally across all spatial dimensions of the input, equivalent to LpPool with the kernel spanning the full spatial extent. Each output value is the Lp norm (p-th root of sum of absolute values raised to the power p) over all spatial positions in a single channel.

See the ONNX GlobalLpPool spec for the reference semantics.

Inputs

Outputs

Attributes

Default values (overridable per request):

Type constraints

Device requirements

Some implementation variants require shader-f16. These are route-specific capabilities, not package-wide requirements; availability also depends on the request shape and dtype.

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
• global-lp-pool.wgsl.jinja
• pool-global-reduction.wgsl.jinja

Use with @huggingface/kernels

npm install --save-exact @huggingface/kernels@0.0.1-preview.2

Outputs with inferable metadata are allocated automatically. Explicit outputs entries request optional results or provide metadata that cannot be inferred from the supplied inputs and attributes.

This example supplies explicit metadata for:

• y

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.GlobalLpPool", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { y } = await kernel({ x: { data: xData, shape: [1, 2, 4] } }, {
  outputs: { y: { shape: [1, 2, 1], dtype: "float32" } },
});