ai.onnx.Exp
ai.onnx · standard ONNX operator · ONNX opset ≥ 13
Description
Computes exp(x) elementwise for every element of the input tensor. The output has the same shape and type as the input.
See the ONNX Exp spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
input |
x |
T |
— | — | Values used as exponents of e in the elementwise exponential. |
required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
output |
y |
T |
same as input |
same as input |
Output tensor containing the elementwise exponential of the input. | required |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16 |
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesunary-scalar.wgsl.jinjaunary-vec4.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.Exp", { 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.