ai.onnx.EyeLike

ai.onnx · standard ONNX operator · ONNX opset ≥ 22

Description

Generates a 2D identity-like matrix with ones on (or offset from) the main diagonal and zeros everywhere else. The output has the same shape as the 2D input tensor; the output dtype defaults to the input dtype but can be overridden. Attribute k shifts the populated diagonal: k=0 is the main diagonal, k>0 is upper, k<0 is lower.

See the ONNX EyeLike spec for the reference semantics.

Inputs

Name Bind key Logical dtype Rank Shape Description Presence
input input T1 2 2D input tensor whose shape (and optionally type) is copied. required

Outputs

Name Bind key Logical dtype Rank Shape Description Presence
output output T2 same as input same as input Output tensor of the same shape as the input, with ones on the selected diagonal and zeros elsewhere. required

Attributes

Attributes and default values (overridable per request):

Attribute Default Description
k 0 Index of the diagonal to populate with ones: 0 is the main diagonal, positive values select upper diagonals, negative values select lower diagonals.
dtype Optional TensorProto DataType enum for the output. When omitted, the output dtype is the same as the input dtype.

Type constraints

Variable Allowed dtypes
T1 float32, float16, uint32, int32, int16, uint8, int8, bool
T2 float32, float16, uint32, int32, int16, uint8, int8, bool

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