ai.onnx.Expand
ai.onnx · standard ONNX operator · ONNX opset ≥ 13
Description
Broadcasts input to a given shape following NumPy-style rules: dimensions are right-aligned, and corresponding dimensions must be equal or one of them must be 1. The output shape may differ from shape when a requested dimension is 1 or shape has fewer dimensions than input.
See the ONNX Expand spec for the reference semantics.
Inputs
| Name | Logical dtype | WebGPU storage | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
input |
T |
runtime-selected; narrow integers and bool use 32-bit slots | — | — | Input tensor to broadcast. | required |
shape |
S |
uint32 |
1 |
— | Logical int64 1-D tensor specifying the non-negative target shape; WebGPU stores it as uint32. | required |
Outputs
| Name | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|
output |
T |
derived | — | Output tensor with the broadcasted shape. | required |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16, uint32, int32, int16, uint8, int8, bool |
S |
int64 |
Files
metadata.json— kernel metadata (id, digests, per-variant templates, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesexpand-vec4.wgsl.jinjaexpand.wgsl.jinja
Use with @huggingface/kernels
npm install --save-exact @huggingface/kernels@0.0.1-preview.2
Outputs with inferable metadata are allocated automatically. Explicit outputs entries request optional results or provide metadata that cannot be inferred from the supplied inputs and attributes.
This example supplies explicit metadata for:
output
The version: 1 option selects the published kernel contract; it is independent of any operator opset, contrib since_version, or model version.
It follows the v1 branch as fixes land. To pin exact artifact bytes, pass a 40-character commit revision instead of 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.Expand", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { output } = await kernel({
input: { data: inputData, shape: [1, 4] },
shape: { data: shapeData, shape: [2] },
}, {
outputs: { output: { shape: [2, 4], dtype: "int16" } },
});
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Requires WebGPU support. See the compatibility table.