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, 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

The loader automatically allocates outputs whose metadata it can derive from the manifest contract and this call.

The explicit outputs entries provide shape and logical dtype metadata for the results listed below:

• output

Each entry either requests an optional result or supplies metadata that cannot be inferred from the inputs.

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.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" } },
});