ai.onnx.Identity
ai.onnx · standard ONNX operator · ONNX opset ≥ 16
Description
Copies the input tensor to the output unchanged. The output has the same shape, dtype, and values as the input.
See the ONNX Identity spec for the reference semantics.
Inputs
| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
x |
input |
T |
— | — | Input tensor to be copied. | required |
Outputs
| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
y |
output |
T |
same as x |
same as x |
Output tensor; an exact copy of the input. | required |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16, uint32, int32, int16, uint8, int8, bool |
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 casesdatamove-elementwise-copy.wgsl.jinjadatamove-flat-copy.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.Identity", { version: 1 });
const { y } = await kernel({ x: { data: xData, shape: [] } });
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Requires WebGPU support. See the compatibility table.