library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
ai.onnx.Shape
ai.onnx · standard ONNX operator · ONNX opset ≥ 15
Description
Returns a 1-D tensor containing the shape of the input tensor. Optional start and end attributes select a slice of the shape axes; negative values count from the back, and axes are clamped to [0, rank].
See the ONNX Shape spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
data |
data |
T |
— | — | The input tensor whose shape is computed. | required |
Outputs
| Name | Bind key | Logical dtype | WebGPU storage | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|---|
shape |
shape |
S |
uint32 |
1 |
derived; see description | Logical int64 1-D tensor of dimension sizes for the selected axes of the input; WebGPU emits bounded uint32 values. | required |
Attributes
Attributes and default values (overridable per request):
| Attribute | Default | Description |
|---|---|---|
start |
0 |
First axis (inclusive) of the shape slice; negative values count from the back, default is 0. |
end |
— | Last axis (exclusive) of the shape slice; negative values count from the back; if omitted, all axes through the last are included. |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16, int32, int16, uint32, int8, uint8, bool |
S |
int64 |
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesshape.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.
import { getKernel } from "@huggingface/kernels";
const kernel = await getKernel("webgpu-kernels/ai.onnx.Shape", { version: 1 });
const { shape } = await kernel({ data: { data: dataData, shape: [1, 2, 2] } });