| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.Split |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 18 |
|
|
| ## Description |
|
|
| Splits a tensor into a list of tensors along the specified `axis`. The sizes of each output slice are given by the optional `split` input; if omitted, the tensor is divided into equal parts (the last chunk may be smaller if the axis dimension is not evenly divisible). |
|
|
| See the [ONNX `Split` spec](https://onnx.ai/onnx/operators/onnx__Split.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Logical dtype | WebGPU storage | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `input` | `T` | runtime-selected; narrow integers and bool use 32-bit slots | — | — | The tensor to split. | required | |
| | `split` | `S` | `uint32` | `1` | — | Optional logical int64 1-D tensor specifying the size of each output along the split axis; values must be non-negative, sum to the axis dimension, and use uint32 WebGPU storage. | optional | |
|
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| ## Outputs |
|
|
| | Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `y0` | `Y0` | `T` | same as `input` | — | First output slice after splitting. | required | |
| | `y1` | `Y1` | `T` | same as `input` | — | Second output slice after splitting (optional). | optional | |
| | `y2` | `Y2` | `T` | same as `input` | — | Third output slice after splitting. | optional | |
| | `y3` | `Y3` | `T` | same as `input` | — | Fourth output slice after splitting. | optional | |
| | `y4` | `Y4` | `T` | same as `input` | — | Fifth output slice after splitting. | optional | |
|
|
| ## Attributes |
|
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| Attributes and default values (overridable per request): |
|
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| | Attribute | Default | Description | |
| | --- | --- | --- | |
| | `axis` | `0` | The axis along which to split. Negative values count from the end; accepted range is `[-rank, rank-1]`. | |
| | `num_outputs` | — | Optional number of outputs when the `split` input is omitted. The final output may be smaller when the axis dimension is not evenly divisible. | |
|
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| ## Type constraints |
|
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| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T` | `float32`, `float16`, `uint32`, `int32`, `int16`, `uint8`, `int8`, `bool`, `int64` | |
| | `S` | `int64` | |
|
|
| ## 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 |
| - [`datamove-flat-copy.wgsl.jinja`](build/webgpu/datamove-flat-copy.wgsl.jinja) |
| - [`datamove-split-block.wgsl.jinja`](build/webgpu/datamove-split-block.wgsl.jinja) |
| - [`split-n.wgsl.jinja`](build/webgpu/split-n.wgsl.jinja) |
|
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| ## Use with `@huggingface/kernels` |
|
|
| ```sh |
| npm install --save-exact @huggingface/kernels@0.0.1-preview.3 |
| ``` |
|
|
| 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. |
|
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| This example supplies explicit metadata for: |
|
|
| - `y0` |
| - `y1` |
| - `y2` |
|
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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.Split", { version: 1 }); |
| // Explicit destinations request optional results or supply metadata that cannot be inferred. |
| const { y0, y1, y2 } = await kernel({ input: { data: inputData, shape: [6] } }, { |
| outputs: { |
| y0: { shape: [2], dtype: "float32" }, |
| y1: { shape: [2], dtype: "float32" }, |
| y2: { shape: [2], dtype: "float32" }, |
| }, |
| }); |
| ``` |
|
|
