ai.onnx.ReduceSumSquare

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

Description

Computes the sum of squared elements of the input tensor along the specified axes. The output rank matches the input if keepdims is 1; otherwise the reduced dimensions are pruned. Reduction over an empty set of values yields 0.

See the ONNX ReduceSumSquare spec for the reference semantics.

Inputs

Outputs

Attributes

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.

• strided_axis_serial — Flatten a single non-last reduction axis into outer/axis/inner geometry. Compile its strides and loop bound, keep one output per lane and float32 accumulation, and cap the workgroup by the device limits.

Device requirements

Some implementation variants require subgroups. 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
• reduce-axis-split-reduce.wgsl.jinja
• reduce-axis0-splitk-combine.wgsl.jinja
• reduce-axis0-splitk-reduce.wgsl.jinja
• reduce-axis0-tilecols.wgsl.jinja
• reduce-flat-partial.wgsl.jinja
• reduce-multi-axis-coop.wgsl.jinja
• reduce-noop-empty-axes.wgsl.jinja
• reduce-row-subgroup-rows.wgsl.jinja
• reduce-row-subgroup.wgsl.jinja
• reduce-row-tree.wgsl.jinja
• reduce-serial-axis.wgsl.jinja
• reduce-strided-axis.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.ReduceSumSquare", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { y } = await kernel({ x: { data: xData, shape: [] } }, {
  outputs: { y: { shape: [], dtype: "float32" } },
});