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

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
• 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.2

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] },
});