| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.Softplus |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 1 |
|
|
| ## Description |
|
|
| Applies the softplus activation elementwise: `y = ln(exp(x) + 1)`. The output has the same shape as the input. |
|
|
| See the [ONNX `Softplus` spec](https://onnx.ai/onnx/operators/onnx__Softplus.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Bind key | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `X` | `x` | `T` | — | — | Values transformed elementwise by the smooth softplus activation. | required | |
|
|
| ## Outputs |
|
|
| | Name | Bind key | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `Y` | `y` | `T` | same as `X` | same as `X` | Output tensor with softplus applied elementwise; same shape as `X`. | required | |
|
|
| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T` | `float32`, `float16` | |
|
|
| ## Files |
|
|
| - [`metadata.json`](build/webgpu/metadata.json) — kernel metadata (id, digests, provenance) |
| - [`manifest.json`](build/webgpu/manifest.json) — the op contract (source of truth) |
| - [`test.json`](build/webgpu/test.json) — correctness cases |
| - [`bench.json`](build/webgpu/bench.json) — benchmark + tuning cases |
| - [`unary-scalar.wgsl.jinja`](build/webgpu/unary-scalar.wgsl.jinja) |
| - [`unary-vec4.wgsl.jinja`](build/webgpu/unary-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. |
|
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| The `version: 1` option selects the published kernel contract; it is independent of any operator opset, contrib `since_version`, or model version. |
|
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| Replace each `*Data` placeholder with a typed array containing the corresponding input data. |
|
|
| ```js |
| import { getKernel } from "@huggingface/kernels"; |
| |
| const kernel = await getKernel("webgpu-kernels/ai.onnx.Softplus", { version: 1 }); |
| const { y } = await kernel({ x: { data: xData, shape: [3] } }); |
| ``` |
|
|
