library_name: kernels
license: apache-2.0
tags:
  - kernel
  - webgpu
  - wgsl

ai.onnx.GlobalAveragePool

ai.onnx · standard ONNX operator · ONNX opset ≥ 1

Description

Applies average pooling across all spatial dimensions of X, reducing each channel to a single scalar. Equivalent to AveragePool with kernel size equal to the full spatial extent of the input. The output shape is (N x C x 1 x ... x 1), preserving batch and channel dimensions.

See the ONNX GlobalAveragePool spec for the reference semantics.

Inputs

Outputs

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

Use with @huggingface/kernels

The loader automatically allocates outputs whose metadata it can derive from the manifest contract and this call.

The explicit outputs entries provide shape and logical dtype metadata for the results listed below:

• y

Each entry either requests an optional result or supplies metadata that cannot be inferred from the inputs.

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.GlobalAveragePool", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { y } = await kernel({ x: { data: xData, shape: [2, 3, 1, 1] } }, {
  outputs: { y: { shape: [2, 3, 1, 1], dtype: "float32" } },
});