ai.onnx.ReduceSum

ai.onnx · standard ONNX operator · ONNX opset ≥ 13

Description

Computes the sum of elements along specified axes of the input tensor. The output rank matches the input rank when keepdims is 1; otherwise reduced dimensions are pruned. When no axes are provided, behavior is controlled by noop_with_empty_axes: reduce over all axes (default) or act as identity.

See the ONNX ReduceSum 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.

• axis0_splitk_i32 — Partitions a long rank-two axis-zero integer reduction across workgroups and combines exact int32 partials. It applies when the reduced row dimension is too large for one pass to expose enough parallelism.
• subgroup_last_axis_vec4 — Reduces each contiguous last-axis row with subgroup collectives and vec4-packed reads.
• subgroup_last_axis — Reduces each contiguous last-axis row with subgroup collectives and scalar reads for an unaligned row width.

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
• datamove-elementwise-copy.wgsl.jinja
• 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-row-subgroup-rows.wgsl.jinja
• reduce-row-subgroup.wgsl.jinja
• reduce-row-tree.wgsl.jinja
• reduce-serial-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.ReduceSum", { 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" } },
});