| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # 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](https://onnx.ai/onnx/operators/onnx__EyeLike.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | |
| | `input` | `T1` | `2` | — | 2D input tensor whose shape (and optionally type) is copied. | required | |
|
|
| ## Outputs |
|
|
| | Name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | |
| | `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 | |
| | --- | --- | --- | |
| | `dtype` | — | Optional TensorProto DataType enum for the output. When omitted, the output dtype is the same as the input dtype. | |
| | `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. | |
|
|
| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T1` | `float32`, `float16`, `uint32`, `int32`, `int16`, `uint8`, `int8`, `bool` | |
| | `T2` | `float32`, `float16`, `uint32`, `int32`, `int16`, `uint8`, `int8`, `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 cases |
| - [`eyelike-clear-vec4.wgsl.jinja`](build/webgpu/eyelike-clear-vec4.wgsl.jinja) |
| - [`eyelike-diagonal.wgsl.jinja`](build/webgpu/eyelike-diagonal.wgsl.jinja) |
| - [`eyelike.wgsl.jinja`](build/webgpu/eyelike.wgsl.jinja) |
|
|
| ## Use with `@huggingface/kernels` |
|
|
| ```sh |
| npm install --save-exact @huggingface/kernels@0.0.1-preview.3 |
| ``` |
|
|
| Required output shapes and logical data types are inferred from the supplied inputs and attributes; result tensors are allocated automatically. |
|
|
| 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.EyeLike", { version: 1 }); |
| const { output } = await kernel({ input: { data: inputData, shape: [2, 2] } }); |
| ``` |
|
|
