ai.onnx.OneHot

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

Description

Produces a one-hot tensor from an indices input: positions matching each index are filled with on_value and all other positions with off_value, where both are taken from the two-element values tensor [off_value, on_value]. The output rank is one greater than indices, with the new dimension of size depth inserted at the position given by axis; indices outside [-depth, depth-1] yield all-off_value rows.

See the ONNX OneHot spec for the reference semantics.

Inputs

Outputs

Attributes

Default values (overridable per request):

Type constraints

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
• one-hot-fill.wgsl.jinja
• one-hot-last-axis-vec4.wgsl.jinja
• one-hot-scatter.wgsl.jinja

Use with @huggingface/kernels

npm install --save-exact @huggingface/kernels@0.0.1-preview.2

Outputs with inferable metadata are allocated automatically. Explicit outputs entries request optional results or provide metadata that cannot be inferred from the supplied inputs and attributes.

This example supplies explicit metadata for:

• output

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.OneHot", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { output } = await kernel({
  indices: { data: indicesData, shape: [1] },
  depth: { data: depthData, shape: [] },
  values: { data: valuesData, shape: [2] },
}, {
  outputs: { output: { shape: [1, 2], dtype: "float32" } },
});