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library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
---
# ai.onnx.GridSample
`ai.onnx` · standard ONNX operator · ONNX opset ≥ 20
## Description
Samples values from input tensor `X` at positions defined by a flow-field `grid`, producing output `Y` with spatial dimensions taken from `grid`. Grid coordinates are normalized to `[-1, 1]` over the input spatial extent; positions outside this range are handled according to `padding_mode`. Supports spatial `(rank-4, NCHW)` and volumetric `(rank-5, NCDHW)` inputs with `linear`, `nearest`, or `cubic` interpolation.
See the [ONNX `GridSample` spec](https://onnx.ai/onnx/operators/onnx__GridSample.html) for the reference semantics.
## Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `X` | `x` | `T` | — | — | Input tensor of shape `(N, C, D1, ..., Dr)` whose values are sampled. | required |
| `grid` | `grid` | `T` | — | — | Flow-field of shape `(N, D1_out, ..., Dr_out, r)` with normalized sampling coordinates in `[-1, 1]`. | required |
## Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `Y` | `y` | `T` | same as `grid` | derived; see description | Output tensor of shape `(N, C, D1_out, ..., Dr_out)` containing the interpolated samples. | required |
## Attributes
Default values (overridable per request):
| Attribute | Default | Description |
| --- | --- | --- |
| `mode` | `"linear"` | Interpolation method: `linear` (bilinear or trilinear, depending on rank), `nearest`, or `cubic`. Cubic interpolation is supported for rank-4 (2-D spatial) inputs. |
| `padding_mode` | `"zeros"` | How out-of-bound grid positions are handled: `zeros` pads with 0, `border` clamps to the border value, or `reflection` reflects coordinates back into the valid range. |
| `align_corners` | `0` | When 1, extrema values `-1` and `1` map to the center of the corner pixels; when 0 (default) they map to the outer edge of corner pixels, making sampling resolution-agnostic. |
## Type constraints
| Variable | Allowed dtypes |
| --- | --- |
| `T` | `float32`, `float16` |
## 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
- [`grid-sample.wgsl.jinja`](build/webgpu/grid-sample.wgsl.jinja)
- [`grid-sample3d.wgsl.jinja`](build/webgpu/grid-sample3d.wgsl.jinja)
## Use with `@huggingface/kernels`
The loader derives every required output's shape and logical dtype from the manifest contract and this call.
It then allocates the result tensors automatically.
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.GridSample", { version: 1 });
const { y } = await kernel({
x: { data: xData, shape: [1, 1, 2, 2] },
grid: { data: gridData, shape: [1, 1, 3, 2] },
});
```
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