ai.onnx.ScatterElements

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

Description

Produces a copy of data with values updated at positions given by indices along the specified axis. For each entry in updates, the axis coordinate comes from indices while all other coordinates come from the entry's own position in updates. An optional reduction (add, mul, max, min) combines updates with existing values instead of overwriting; with none, duplicate indices are not allowed.

See the ONNX ScatterElements 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.

• reduction_f32_add_axis0_histogram — Fold the updates axis into workgroup bins per 32-column tile, then add each touched bin into the copied output with one device atomic. Striping the updates axis widens the grid a column-only dispatch caps at ceil(columns / 32) workgroups.
• reduction_f16_slab — Stages f16 slabs in workgroup memory as f32 bits and reduces repeated indices there with a compare-exchange loop. Requires a fixed 32-wide subgroup: on adapters reporting any other subgroup range the duplicate-index merge can lose updates, so the variant is not used there.

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 cases
• scatter-elements-f32-add-axis0-histogram.wgsl.jinja
• scatter-elements-reduction-atomic.wgsl.jinja
• scatter-elements-reduction-slab.wgsl.jinja
• scatter-elements-reduction.wgsl.jinja
• scatter-elements.wgsl.jinja
• scatter-f16-f32-convert.wgsl.jinja
• scatter-flat-copy.wgsl.jinja
• scatter-narrow-wrap.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.ScatterElements", { version: 1 });
const { output } = await kernel({
  data: { data: dataData, shape: [3] },
  indices: { data: indicesData, shape: [2] },
  updates: { data: updatesData, shape: [2] },
});