library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
ai.onnx.Upsample
ai.onnx · standard ONNX operator · ONNX opset ≥ 9
Description
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 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
Default values (overridable per request):
| Attribute | Default | Description |
|---|---|---|
mode |
"nearest" |
Interpolation algorithm to use when mapping output coordinates back to input values; either "nearest" or "linear". |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16, int32, int8, uint8 |
S |
float32 |
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesresize-coord-transform.wgsl.jinjaresize-generic.wgsl.jinjaresize-linear-2x-stencil.wgsl.jinjaresize-nearest-integer-scale.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:
y
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.
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" } },
});