File size: 2,825 Bytes
0877677
ee8ac59
0877677
ee8ac59
 
 
 
0877677
ee8ac59
 
 
 
 
 
 
 
 
 
 
 
e7be652
ee8ac59
e7be652
ee8ac59
 
 
e7be652
ee8ac59
e7be652
ee8ac59
 
 
 
 
 
 
 
 
e7be652
ee8ac59
 
d7d541c
ee8ac59
 
 
 
 
e7be652
d7d541c
e7be652
ee8ac59
e7be652
ee8ac59
e7be652
ee8ac59
e7be652
ee8ac59
 
e7be652
ee8ac59
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
---
library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
---
# ai.onnx.GlobalMaxPool

`ai.onnx`  ·  standard ONNX operator  ·  ONNX opset ≥ 1

## Description

Applies max pooling across all spatial dimensions of `X`, producing one value per channel. Equivalent to MaxPool with kernel size equal to the full spatial extent of the input; output shape is `(N x C x 1 x ... x 1)`.

See the [ONNX `GlobalMaxPool` spec](https://onnx.ai/onnx/operators/onnx__GlobalMaxPool.html) for the reference semantics.

## Inputs

| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `x` | `X` | `T` | — | — | Input tensor of shape `(N x C x D1 x ... x Dn)`, where `N` is the batch size and `C` is the number of channels. | required |

## Outputs

| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `y` | `Y` | `T` | same as `x` | — | Output tensor of shape `(N x C x 1 x ... x 1)`; the maximum value over each spatial region per channel. | required |

## Type constraints

| Variable | Allowed dtypes |
| --- | --- |
| `T` | `float32`, `float16` |

## 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
- [`pool-global-reduction.wgsl.jinja`](build/webgpu/pool-global-reduction.wgsl.jinja)
- [`pool-global-serial.wgsl.jinja`](build/webgpu/pool-global-serial.wgsl.jinja)

## 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.

This example supplies explicit metadata for:

- `y`

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.GlobalMaxPool", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { y } = await kernel({ x: { data: xData, shape: [1, 2, 3] } }, {
  outputs: { y: { shape: [1, 2, 1], dtype: "float32" } },
});
```