---
library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
---
# com.microsoft.Gelu

`com.microsoft`  ·  ONNX Runtime contrib operator  ·  contrib since_version 1

## Description

Applies the Gaussian Error Linear Unit (GELU) activation elementwise: `Y = 0.5 * X * (1 + erf(X / sqrt(2)))`. The output has the same shape as the input. Float16 and float32 are supported; the schema's double and bfloat16 types are not.

See the [ONNX Runtime `Gelu` contrib-operator spec](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.Gelu) for the reference semantics.

## Inputs

| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `X` | `X` | `T` | — | — | Values transformed elementwise by the exact GELU activation. | required |

## Outputs

| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `Y` | `Y` | `T` | same as `X` | same as `X` | Output tensor after applying GELU; same shape as the input. | 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
- [`elementwise-bias-gelu.wgsl.jinja`](build/webgpu/elementwise-bias-gelu.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.

```js
import { getKernel } from "@huggingface/kernels";

const kernel = await getKernel("webgpu-kernels/com.microsoft.Gelu", { version: 1 });
const { Y } = await kernel({ X: { data: XData, shape: [] } });
```