| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.GridSample |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 20 |
|
|
| ## Description |
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| 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. |
|
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| See the [ONNX `GridSample` spec](https://onnx.ai/onnx/operators/onnx__GridSample.html) for the reference semantics. |
|
|
| ## Inputs |
|
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| | 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 | |
|
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| ## Attributes |
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| Default values (overridable per request): |
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| | 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. | |
|
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| ## Type constraints |
|
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| | 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) |
|
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| ## Use with `@huggingface/kernels` |
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| 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. |
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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. |
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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.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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|
