| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.Max |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 13 |
|
|
| ## Description |
|
|
| Computes the elementwise maximum across one or more input tensors with NumPy-style multidirectional broadcasting. All inputs must share the same data type, and the output has the broadcasted shape. |
|
|
| See the [ONNX `Max` spec](https://onnx.ai/onnx/operators/onnx__Max.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `a` | `A` | `T` | — | — | First input tensor. | required | |
| | `b` | `B` | `T` | — | — | Second input tensor, broadcast-compatible with A. | optional | |
| | `c` | `C` | `T` | — | — | Third input tensor, broadcast-compatible with A and B. | optional | |
| | `d` | `D` | `T` | — | — | Fourth input tensor, broadcast-compatible with all other inputs. | optional | |
| | `e` | `E` | `T` | — | — | Fifth input tensor, broadcast-compatible with all other inputs. | optional | |
|
|
| ## Outputs |
|
|
| | Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `y` | `max` | `T` | derived | derived | Elementwise maximum of all input tensors. | required | |
|
|
| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T` | `float32`, `float16`, `int32`, `uint32`, `int16`, `int8`, `uint8` | |
|
|
| ## 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-elementwise-copy.wgsl.jinja`](build/webgpu/datamove-elementwise-copy.wgsl.jinja) |
| - [`minmax-broadcast.wgsl.jinja`](build/webgpu/minmax-broadcast.wgsl.jinja) |
| - [`minmax-vec4.wgsl.jinja`](build/webgpu/minmax-vec4.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.Max", { version: 1 }); |
| const { y } = await kernel({ a: { data: aData, shape: [1, 3] } }); |
| ``` |
|
|
