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

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

## Description

Fuses MatMul, an optional bias, and FastGelu: `Y = FastGelu(X @ W + bias)`. `X` has rank at least 2 with shape `(..., K)`, `W` has shape `(K, N)`, and `bias` has shape `(N)`. The activation runs in the float32 accumulator before the output is narrowed, avoiding an intermediate `(..., N)` tensor. Bfloat16 is not implemented.

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

## Inputs

| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `X` | `X` | `T` | — | — | Left operand of rank 2 or greater with shape `(..., K)`; every leading-axis coordinate identifies a row of the product. | required |
| `W` | `W` | `T` | `2` | — | Right operand with shape `(K, N)`. | required |
| `bias` | `bias` | `T` | `1` | — | Optional bias with shape `(N)`, added before the activation. | optional |

## Outputs

| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `Y` | `Y` | `T` | same as `X` | ONNX MatMul result of `X` and `W` | `FastGelu(X @ W + bias)`, with the same rank and leading dimensions as `X` and a trailing `N`. | required |

## Type constraints

| Variable | Allowed dtypes |
| --- | --- |
| `T` | `float32`, `float16` |

## Device requirements

Some implementation variants require `subgroup-matrix` and `subgroups`. These are route-specific capabilities, not package-wide requirements; availability also depends on the request shape and dtype.

## 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
- [`gemm-fast-gelu.wgsl.jinja`](build/webgpu/gemm-fast-gelu.wgsl.jinja)
- [`gemm-subgroup-matrix.wgsl.jinja`](build/webgpu/gemm-subgroup-matrix.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.GemmFastGelu", { version: 1 });
const { Y } = await kernel({ X: { data: XData, shape: [5, 6] }, W: { data: WData, shape: [6, 4] } });
```