ai.onnx.Gelu
ai.onnx · standard ONNX operator · ONNX opset ≥ 20
Description
Applies the Gaussian Error Linear Unit activation elementwise: y = 0.5 * x * (1 + erf(x / sqrt(2))). When approximate is "tanh", uses the tanh-based approximation y = 0.5 * x * (1 + tanh(sqrt(2/π) * (x + 0.044715 * x³))) instead.
See the ONNX Gelu spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
X |
x |
T |
— | — | Values transformed elementwise by the selected GELU formulation. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
Y |
y |
T |
same as X |
same as X |
Output tensor; same shape as the input, with GELU applied elementwise. | required |
Attributes
Default values (overridable per request):
| Attribute | Default | Description |
|---|---|---|
approximate |
"none" |
Selects the GELU approximation algorithm: "none" (default) uses the exact erf-based formula; "tanh" uses the faster tanh-based polynomial approximation. |
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.Gelu", { version: 1 });
const { y } = await kernel({ x: { data: xData, shape: [] } });
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Requires WebGPU support. See the compatibility table.