ai.onnx.Gather

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

Description

Gathers entries from data along the specified axis using an indices tensor, producing an output of rank q + (r - 1) where r is the rank of data and q is the rank of indices. Each index value selects a (r-1)-dimensional slice from data along axis, and the resulting slices are arranged into the output tensor with the q index dimensions replacing the gathered axis.

See the ONNX Gather spec for the reference semantics.

Inputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`data`	`data`	`T`	—	—	Tensor of rank `r >= 1` to gather entries from.	required
`indices`	`indices`	`I`	—	—	Integer index tensor of any rank q; each value must be in `[-s, s-1]` where `s` is the size of `data` along `axis`.	required

Outputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`output`	`output`	`T`	derived	derived; see description	Output tensor of rank q + (r - 1) containing the gathered slices.	required

Attributes

Default values (overridable per request):

Attribute	Default	Description
`axis`	`0`	Axis of `data` along which to gather; negative values count from the back. Accepted range is `[-r, r-1]` where `r = rank(data)`.

Type constraints

Variable	Allowed dtypes
`T`	`float32`, `float16`, `uint32`, `int32`, `int16`, `uint8`, `int8`, `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
datamove-gather-row.wgsl.jinja
gather.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.Gather", { version: 1 });
const { output } = await kernel({
  data: { data: dataData, shape: [4] },
  indices: { data: indicesData, shape: [4] },
});

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kernel

webgpu

wgsl

apache-2.0

WebGPU

Requires WebGPU support. See the compatibility table.