ai.onnx.Swish

ai.onnx · standard ONNX operator · ONNX opset ≥ 24

Description

Applies the Swish activation elementwise: Y = X * sigmoid(alpha * X). The output has the same shape and dtype as the input.

See the ONNX Swish spec for the reference semantics.

Inputs

Name Upstream name Logical dtype Rank Shape Description Presence
x X T Values transformed elementwise by the Swish activation. required

Outputs

Name Upstream name Logical dtype Rank Shape Description Presence
y Y T same as x same as x Output tensor of the same shape as the input, with Swish applied elementwise. required

Attributes

Default values (overridable per request):

Attribute Default Description
alpha 1 Coefficient multiplied with the input before the sigmoid; defaults to 1.0.

Type constraints

Variable Allowed dtypes
T float32, float16

Files

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.Swish", { version: 1 });
const { y } = await kernel({ x: { data: xData, shape: [3] } });
Downloads last month
-
kernel
webgpu
wgsl
apache-2.0
WebGPU

Requires WebGPU support. See the compatibility table.