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 | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
input |
x |
T |
— | — | Input tensor to be copied. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
output |
y |
T |
same as input |
same as input |
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, 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
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.Identity", { version: 1 });
const { y } = await kernel({ x: { data: xData, shape: [] } });
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kernel
webgpu
wgsl
apache-2.0
WebGPU
Requires WebGPU support. See the compatibility table.