ai.onnx.Elu
ai.onnx · standard ONNX operator · ONNX opset ≥ 6
Description
Applies the Exponential Linear Unit activation elementwise: f(x) = x for x >= 0, and f(x) = alpha * (exp(x) - 1) for x < 0. The alpha coefficient controls the saturation value for negative inputs.
See the ONNX Elu spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
X |
x |
T |
— | — | Values transformed elementwise by the ELU activation. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
Y |
y |
T |
same as X |
same as X |
Output tensor with ELU applied elementwise; same shape as X. | required |
Attributes
Default values (overridable per request):
| Attribute | Default | Description |
|---|---|---|
alpha |
1 |
Scaling coefficient applied to the exponential term for negative inputs; defaults to 1.0. |
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.Elu", { version: 1 });
const { y } = await kernel({ x: { data: xData, shape: [3, 4, 5] } });
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Requires WebGPU support. See the compatibility table.