ai.onnx.ConstantOfShape
ai.onnx · standard ONNX operator · ONNX opset ≥ 9
Description
Generates a tensor filled with a constant value and a caller-specified shape. The shape is provided as a 1-D integer tensor; if empty, the output is a scalar. The fill value and its dtype are taken from the value attribute, defaulting to 0 of type float32.
See the ONNX ConstantOfShape spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | WebGPU storage | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|---|
input |
input |
S |
uint32 |
1 |
— | Logical int64 1-D tensor whose elements define the output shape; all values must be non-negative and use uint32 WebGPU storage. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
output |
output |
T |
derived | — | Output tensor of the shape given by input, filled with the constant value. |
required |
Attributes
Attributes and default values (overridable per request):
| Attribute | Default | Description |
|---|---|---|
value |
— | Optional single-element tensor specifying the fill value and output dtype; defaults to 0 of type float32 if omitted. |
Type constraints
| Variable | Allowed dtypes |
|---|---|
S |
int64 |
T |
float32, float16, int32, int16, uint32, int8, uint8, bool |
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesconstant-of-shape.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:
output
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.ConstantOfShape", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { output } = await kernel({ input: { data: inputData, shape: [2] } }, {
outputs: { output: { shape: [2, 3], dtype: "float32" } },
});
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Requires WebGPU support. See the compatibility table.