ai.onnx.LpPool

ai.onnx · standard ONNX operator · ONNX opset ≥ 18

Description

Applies Lp pooling over a spatial input tensor by computing the Lp norm within each kernel window and writing the result to the output. Output spatial dimensions are determined by the kernel size, strides, padding, and ceil_mode; p controls which norm is used (e.g. p=1 for sum-of-absolutes, p=2 for Euclidean).

See the ONNX LpPool spec for the reference semantics.

Inputs

Outputs

Attributes

Attributes and default values (overridable per request):

Type constraints

Implementation variants

One implementation is selected per call from the device capabilities, the request shapes and the dtypes; these notes say what each one covers.

• window_parallel_ncl1d — Workgroup-cooperative Lp pooling over a 1-D window: one workgroup reduces a single output element's kernel window, its invocations striding the flattened taps. Chosen when the serial one-invocation-per-output route cannot fill the device and the window is long. The p-root is applied once, by the invocation that writes the output.
• window_parallel_nchw2d — Workgroup-cooperative Lp pooling over a 2-D window: one workgroup reduces a single output element's kernel window, its invocations striding the flattened taps. Chosen when the serial one-invocation-per-output route cannot fill the device and the window is long. The p-root is applied once, by the invocation that writes the output.
• window_parallel_ncdhw3d — Workgroup-cooperative Lp pooling over a 3-D window: one workgroup reduces a single output element's kernel window, its invocations striding the flattened taps. Chosen when the serial one-invocation-per-output route cannot fill the device and the window is long. The p-root is applied once, by the invocation that writes the output.

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 cases
• pool-global-reduction.wgsl.jinja
• pool-ncl1d-x4.wgsl.jinja
• pool-window-nd.wgsl.jinja
• pool-window-reduction.wgsl.jinja
• pool-window-unroll.wgsl.jinja
• pool2d-nchw-k2s2-vec4.wgsl.jinja
• pool2d-nchw-separable.wgsl.jinja

Use with @huggingface/kernels

npm install --save-exact @huggingface/kernels@0.0.1-preview.3

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.LpPool", { version: 1 });
const { y } = await kernel({ x: { data: xData, shape: [1, 1, 4] } }, {
  attrs: { kernel_shape: [3] },
});