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

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, 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-noop-empty-axes.wgsl.jinja
• reduce-row-subgroup.wgsl.jinja
• reduce-row-tree.wgsl.jinja
• reduce-serial-axis.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.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" } },
});