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 | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|
data |
T |
— | — | The input tensor whose shape is computed. | required |
Outputs
| Name | Logical dtype | WebGPU storage | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
shape |
S |
uint32 |
1 |
derived | 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 |
|---|---|---|
end |
— | Last axis (exclusive) of the shape slice; negative values count from the back; if omitted, all axes through the last are included. |
start |
0 |
First axis (inclusive) of the shape slice; negative values count from the back, default is 0. |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16, int32, int16, uint32, int8, uint8, 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 casesshape.wgsl.jinja
Use with @huggingface/kernels
npm install --save-exact @huggingface/kernels@0.0.1-preview.2
Required output shapes and logical data types are inferred from the supplied inputs and attributes; result tensors are allocated automatically.
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.Shape", { version: 1 });
const { shape } = await kernel({ data: { data: dataData, shape: [1, 2, 2] } });
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Requires WebGPU support. See the compatibility table.