| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.ArgMax |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 13 |
|
|
| ## Description |
|
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| Returns the index of the maximum value along an axis, choosing the first equal value unless `select_last_index` is enabled. |
|
|
| See the [ONNX `ArgMax` spec](https://onnx.ai/onnx/operators/onnx__ArgMax.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `x` | `data` | `T` | — | — | Values whose maximum index is selected along `axis`. | required | |
|
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| ## Outputs |
|
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| | Name | Upstream name | Logical dtype | WebGPU storage | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | --- | |
| | `y` | `reduced` | `I` | `uint32` | derived | derived | Logical int64 indices of the maximum values along the reduced axis; WebGPU stores these bounded indices as uint32. | required | |
|
|
| ## Attributes |
|
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| Default values (overridable per request): |
|
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| | Attribute | Default | Description | |
| | --- | --- | --- | |
| | `axis` | `0` | Axis to reduce; negative values count from the back. | |
| | `keepdims` | `1` | Retain the reduced dimension with length one when non-zero. | |
| | `select_last_index` | `0` | Choose the last equal maximum instead of the first when non-zero. | |
|
|
| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T` | `float32`, `float16`, `int32`, `uint32`, `int16`, `int8`, `uint8` | |
| | `I` | `int64` | |
|
|
| ## Implementation variants |
|
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| One implementation is selected per call from the device capabilities, the request shapes and the dtypes; these notes say what each one covers. |
|
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| - `axis_split_packed` — Scans four adjacent float32 inner-axis columns per invocation while preserving each column’s axis order and the scalar partial/combine layout. Small output grids retain the scalar route. |
|
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| ## Device requirements |
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| Some implementation variants require `subgroups`. These are route-specific capabilities, not package-wide requirements; availability also depends on the request shape and dtype. |
|
|
| ## 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 |
| - [`reduce-arg-axis-split-combine.wgsl.jinja`](build/webgpu/reduce-arg-axis-split-combine.wgsl.jinja) |
| - [`reduce-arg-axis-split-reduce.wgsl.jinja`](build/webgpu/reduce-arg-axis-split-reduce.wgsl.jinja) |
| - [`reduce-arg-axis-split-tiled.wgsl.jinja`](build/webgpu/reduce-arg-axis-split-tiled.wgsl.jinja) |
| - [`reduce-arg-axis-tiled.wgsl.jinja`](build/webgpu/reduce-arg-axis-tiled.wgsl.jinja) |
| - [`reduce-arg-axis.wgsl.jinja`](build/webgpu/reduce-arg-axis.wgsl.jinja) |
| - [`reduce-arg-row-split.wgsl.jinja`](build/webgpu/reduce-arg-row-split.wgsl.jinja) |
| - [`reduce-arg-row-subgroup.wgsl.jinja`](build/webgpu/reduce-arg-row-subgroup.wgsl.jinja) |
|
|
| ## Use with `@huggingface/kernels` |
|
|
| ```sh |
| npm install --save-exact @huggingface/kernels@0.0.1-preview.3 |
| ``` |
|
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| 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.ArgMax", { version: 1 }); |
| const { y } = await kernel({ x: { data: xData, shape: [2, 2] } }); |
| ``` |
|
|
