ai.onnx.GatherND

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

Description

Gathers slices from data using an N-dimensional index tensor, producing an output of rank q + r - indices_shape[-1] - 1 - b where r is the data rank, q the indices rank, and b the number of batch dimensions. Each index-tuple in indices selects either a scalar or a sub-tensor slice from data; this is the inverse of ScatterND.

See the ONNX GatherND spec for the reference semantics.

Inputs

Name Bind key Logical dtype WebGPU storage Rank Shape Description Presence
data data T runtime-selected; narrow integers and bool use 32-bit slots Tensor of rank r >= 1 to gather slices from. required
indices indices I int32 Logical int64 index tensor of rank q >= 1. Its last dimension specifies the indexing depth into data; for an indexed axis of size s, values must be in [-s, s - 1]. Signed indices use int32 WebGPU storage. required

Outputs

Name Bind key Logical dtype Rank Shape Description Presence
output output T derived derived; see description Gathered output tensor of rank q + r - indices_shape[-1] - 1 - batch_dims. required

Attributes

Default values (overridable per request):

Attribute Default Description
batch_dims 0 Number of leading batch dimensions shared by data and indices; gathering begins at dimension batch_dims of data.

Type constraints

Variable Allowed dtypes
T float32, float16, uint32, int32, int16, uint8, int8, bool
I int64

Files

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.GatherND", { version: 1 });
const { output } = await kernel({
  data: { data: dataData, shape: [2, 2] },
  indices: { data: indicesData, shape: [2, 1, 2] },
});
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Requires WebGPU support. See the compatibility table.