ai.onnx.Scatter

ai.onnx · standard ONNX operator · ONNX opset ≥ 11

Description

Copies data and overwrites positions identified by indices with values from updates along a single axis. For each element in updates, the index along axis comes from the corresponding indices value, while all other dimension indices come from the element's own position. Deprecated in favor of ScatterElements, which provides identical behavior.

See the ONNX Scatter spec for the reference semantics.

Inputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`data`	`data`	`T`	—	—	The input tensor to copy; the output has the same shape.	required
`indices`	`indices`	`I`	—	—	Integer tensor of the same rank as `data`, specifying the target index along `axis` for each update.	required
`updates`	`updates`	`T`	—	—	Values to scatter into the output; must have the same rank and shape as `indices`.	required

Outputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`output`	`output`	`T`	same as `data`	same as `data`	Copy of `data` with entries overwritten at positions specified by `indices`.	required

Attributes

Default values (overridable per request):

Attribute	Default	Description
`axis`	`0`	The axis of `data` along which scatter indices are applied. Negative values count from the end; defaults to `0` (the outer-most axis).

Type constraints

Variable	Allowed dtypes
`T`	`float32`, `float16`, `int32`, `int16`, `int8`, `uint32`, `uint8`, `bool`
`I`	`int32`

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
scatter-flat-copy.wgsl.jinja
scatter.wgsl.jinja

Use with `@huggingface/kernels`

The loader derives every required output's shape and logical dtype from the manifest contract and this call. It then allocates the result tensors automatically.

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.Scatter", { version: 1 });
const { output } = await kernel({
  data: { data: dataData, shape: [1, 3] },
  indices: { data: indicesData, shape: [3, 3] },
  updates: { data: updatesData, shape: [3, 3] },
});

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kernel

webgpu

wgsl

apache-2.0

WebGPU

Requires WebGPU support. See the compatibility table.