| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # 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](https://onnx.ai/onnx/operators/onnx__OneHot.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | |
| | `indices` | `I` | — | — | Integer or float index tensor; values outside `[-depth, depth-1]` produce all-`off_value` output rows. | required | |
| | `depth` | `D` | — | — | Scalar (or length-1 rank-1) tensor specifying the number of classes and the size of the one-hot dimension. | required | |
| | `values` | `T` | `1` | — | Rank-1 tensor of exactly two elements `[off_value, on_value]` giving the values written to inactive and active positions respectively. | required | |
|
|
| ## Outputs |
|
|
| | Name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | |
| | `output` | `T` | derived | — | One-hot tensor with rank equal to `rank(indices) + 1`, same element type as `values`. | required | |
|
|
| ## Attributes |
|
|
| Default values (overridable per request): |
|
|
| | Attribute | Default | Description | |
| | --- | --- | --- | |
| | `axis` | `-1` | Axis along which the one-hot dimension is inserted; default `-1` appends it as the last dimension. Negative values count from the back; accepted range is `[-r-1, r]` where `r = rank(indices)`. | |
|
|
| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `I` | `float32`, `float16`, `int32`, `int16`, `int8`, `uint32`, `uint8` | |
| | `D` | `float32`, `float16`, `int32`, `int16`, `int8`, `uint32`, `uint8` | |
| | `T` | `float32`, `float16`, `int32`, `int16`, `int8`, `uint32`, `uint8`, `bool` | |
|
|
| ## Files |
|
|
| - [`metadata.json`](build/webgpu/metadata.json) — kernel metadata (id, digests, per-variant templates, provenance) |
| - [`manifest.json`](build/webgpu/manifest.json) — the op contract (source of truth) |
| - [`test.json`](build/webgpu/test.json) — correctness cases |
| - [`bench.json`](build/webgpu/bench.json) — benchmark + tuning cases |
| - [`one-hot-fill.wgsl.jinja`](build/webgpu/one-hot-fill.wgsl.jinja) |
| - [`one-hot-last-axis-vec4.wgsl.jinja`](build/webgpu/one-hot-last-axis-vec4.wgsl.jinja) |
| - [`one-hot-scatter.wgsl.jinja`](build/webgpu/one-hot-scatter.wgsl.jinja) |
|
|
| ## Use with `@huggingface/kernels` |
|
|
| ```sh |
| 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. |
|
|
| ```js |
| 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" } }, |
| }); |
| ``` |
|
|
