---
library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
---
# ai.onnx.MaxUnpool

`ai.onnx`  ·  standard ONNX operator  ·  ONNX opset ≥ 22

## Description

Computes the partial inverse of MaxPool: each pooled value in `X` is scattered back to the position given by its index in `I`, with all other positions set to zero. The optional `output_shape` input disambiguates the output size when multiple input sizes would produce the same pooled result.

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

## Inputs

| Name | Bind key | Logical dtype | WebGPU storage | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- | --- |
| `X` | `x` | `T` | same as logical dtype | — | — | Pooled input tensor to be unpooled, typically the first output of a MaxPool op, with shape `(N x C x D1 x ... x Dn)`. | required |
| `I` | `indices` | `I` | `uint32` | — | — | Logical int64 flat linear indices of the maximal elements corresponding to X, typically the second output of a MaxPool op; same shape as X and stored as uint32 by WebGPU. | required |
| `output_shape` | `output_shape` | `I` | `uint32` | `1` | — | Optional logical int64 1-D tensor specifying the full non-negative output shape, for example `(N, C, H, W)`. It uses uint32 WebGPU storage and, when provided, causes `pads` to be ignored. | optional |

## Outputs

| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `output` | `output` | `T` | same as `X` | — | Unpooled output tensor with pooled values scattered to their original positions and zeros elsewhere. | required |

## Attributes

Attributes and default values (overridable per request):

| Attribute | Default | Description |
| --- | --- | --- |
| `kernel_shape` | — | The size of the pooling kernel along each spatial axis; required and must match the kernel used in the corresponding MaxPool. |
| `strides` | — | Optional stride along each spatial axis. When omitted, output-shape inference uses a stride of 1 along every spatial axis; an explicit list must contain one value per spatial axis. |
| `pads` | — | Optional padding at the beginning and end of each spatial axis in `[x1_begin, x2_begin, ..., x1_end, x2_end]` format. When omitted, output-shape inference uses zero padding; values are ignored when `output_shape` is provided, and an explicit list must contain two values per spatial axis. |

## Type constraints

| Variable | Allowed dtypes |
| --- | --- |
| `T` | `float32`, `float16` |
| `I` | `int64` |

## Files

- [`metadata.json`](build/webgpu/metadata.json) — kernel metadata (id, digests, 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
- [`maxunpool-scatter.wgsl.jinja`](build/webgpu/maxunpool-scatter.wgsl.jinja)
- [`maxunpool-zerofill.wgsl.jinja`](build/webgpu/maxunpool-zerofill.wgsl.jinja)

## Use with `@huggingface/kernels`

The loader automatically allocates outputs whose metadata it can derive from the manifest contract and this call.

The explicit `outputs` entries provide shape and logical dtype metadata for the results listed below:

- `output`

Each entry either requests an optional result or supplies metadata that cannot be inferred from the inputs.

The `version: 1` option selects the published kernel contract; it is independent of any operator opset, contrib `since_version`, or model 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.MaxUnpool", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { output } = await kernel({
  x: { data: xData, shape: [1, 1, 4] },
  indices: { data: indicesData, shape: [1, 1, 4] },
}, {
  attrs: { kernel_shape: [2] },
  outputs: { output: { shape: [1, 1, 5], dtype: "float32" } },
});
```