| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.Upsample |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 9 |
|
|
| ## Description |
|
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| Upsamples the input by applying a per-dimension scale factor; each output dimension equals `floor(input_dimension * scale)`. Deprecated in favor of Resize; supports `nearest` and `linear` interpolation modes. |
|
|
| See the [ONNX `Upsample` spec](https://onnx.ai/onnx/operators/onnx__Upsample.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Bind key | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `X` | `x` | `T` | — | — | Input tensor to upsample. | required | |
| | `scales` | `scales` | `S` | `1` | — | Per-dimension scale factors, one value per input dimension. | required | |
|
|
| ## Outputs |
|
|
| | Name | Bind key | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `Y` | `y` | `T` | same as `X` | — | Upsampled output tensor; each dimension is `floor(input_dimension * scale)`. | required | |
|
|
| ## Attributes |
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| Default values (overridable per request): |
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| | Attribute | Default | Description | |
| | --- | --- | --- | |
| | `mode` | `"nearest"` | Interpolation algorithm to use when mapping output coordinates back to input values; either `"nearest"` or `"linear"`. | |
|
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| ## Type constraints |
|
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| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T` | `float32`, `float16`, `int32`, `int8`, `uint8` | |
| | `S` | `float32` | |
|
|
| ## 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 |
| - [`resize-coord-transform.wgsl.jinja`](build/webgpu/resize-coord-transform.wgsl.jinja) |
| - [`resize-generic.wgsl.jinja`](build/webgpu/resize-generic.wgsl.jinja) |
| - [`resize-linear-2x-stencil.wgsl.jinja`](build/webgpu/resize-linear-2x-stencil.wgsl.jinja) |
| - [`resize-nearest-integer-scale.wgsl.jinja`](build/webgpu/resize-nearest-integer-scale.wgsl.jinja) |
|
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| ## Use with `@huggingface/kernels` |
|
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| The loader automatically allocates outputs whose metadata it can derive from the manifest contract and this call. |
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| The explicit `outputs` entries provide shape and logical dtype metadata for the results listed below: |
|
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| - `y` |
|
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| Each entry either requests an optional result or supplies metadata that cannot be inferred from the inputs. |
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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.Upsample", { version: 1 }); |
| // Explicit destinations request optional results or supply metadata that cannot be inferred. |
| const { y } = await kernel({ |
| x: { data: xData, shape: [1, 1, 1, 2] }, |
| scales: { data: scalesData, shape: [4] }, |
| }, { |
| outputs: { y: { shape: [1, 1, 1, 4], dtype: "float32" } }, |
| }); |
| ``` |
|
|
