| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.CastLike |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 25 |
|
|
| ## Description |
|
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| Casts every element of `input` to the same dtype as `target_type`, producing an output with the same shape as `input`. The `target_type` tensor itself is used only for its dtype and is not read elementwise. |
|
|
| See the [ONNX `CastLike` spec](https://onnx.ai/onnx/operators/onnx__CastLike.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `x` | `input` | `T1` | — | — | Input tensor whose elements are to be cast. | required | |
| | `target` | `target_type` | `T2` | — | — | Tensor whose element type defines the destination dtype; its values are not used. | required | |
|
|
| ## Outputs |
|
|
| | Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `y` | `output` | `T2` | same as `x` | same as `x` | Output tensor with the same shape as `input` and the element type of `target_type`. | required | |
|
|
| ## Attributes |
|
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| Default values (overridable per request): |
|
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| | Attribute | Default | Description | |
| | --- | --- | --- | |
| | `round_mode` | `"up"` | Rounding direction used only when casting to float8e8m0. The implemented non-float8 subset accepts the ONNX default `"up"`. | |
| | `saturate` | `1` | Whether casts to float8 saturate at the finite range. The implemented non-float8 subset accepts the ONNX default `1`. | |
|
|
| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T1` | `float32`, `float16`, `uint32`, `int32`, `uint8`, `int8`, `bool` | |
| | `T2` | `float32`, `float16`, `uint32`, `int32`, `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 + tuning cases |
| - [`cast-scalar-x4.wgsl.jinja`](build/webgpu/cast-scalar-x4.wgsl.jinja) |
| - [`unary-scalar.wgsl.jinja`](build/webgpu/unary-scalar.wgsl.jinja) |
| - [`unary-vec4.wgsl.jinja`](build/webgpu/unary-vec4.wgsl.jinja) |
|
|
| ## Use with `@huggingface/kernels` |
|
|
| ```sh |
| npm install --save-exact @huggingface/kernels@0.0.1-preview.2 |
| ``` |
|
|
| Required output shapes and logical data types are inferred from the supplied inputs and attributes; result tensors are allocated automatically. |
|
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| 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`. |
|
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| 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.CastLike", { version: 1 }); |
| const { y } = await kernel({ x: { data: xData, shape: [] }, target: { data: targetData, shape: [3] } }); |
| ``` |
|
|
