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

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

## Description

Computes `softmax(data + bias)` over the flattened suffix beginning at `axis`. The required `is_inner_broadcast` attribute selects how bias rows are reused: consecutive groups for inner broadcast or cyclic groups for outer broadcast. This specializes the `softmax(scores + additive_mask)` pattern used by transformer attention. Float16 and float32 are supported; the schema's double type is not.

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

## Inputs

| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `data` | `data` | `T` | — | — | The input data tensor. | required |
| `bias` | `bias` | `T` | — | — | The bias (or additive mask) tensor. Its element count must be an integral number of flattened softmax rows and that row count must divide the data row count. | required |

## Outputs

| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `output` | `output` | `T` | same as `data` | same as `data` | The output tensor; same shape as data. | required |

## Attributes

Attributes and default values (overridable per request):

| Attribute | Default | Description |
| --- | --- | --- |
| `axis` | `1` | The axis from which softmax is applied; dimensions from `axis` onward are included in the softmax reduction. |
| `is_inner_broadcast` | — | When 1, bias is broadcast across dimensions from `broadcast_axis` to `axis-1`; when 0, bias is broadcast across dimensions 0 to `broadcast_axis-1`. |

## 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
- [`bias-softmax-longrow-normalize.wgsl.jinja`](build/webgpu/bias-softmax-longrow-normalize.wgsl.jinja)
- [`bias-softmax-longrow-stats.wgsl.jinja`](build/webgpu/bias-softmax-longrow-stats.wgsl.jinja)
- [`bias-softmax.wgsl.jinja`](build/webgpu/bias-softmax.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.BiasSoftmax", { version: 1 });
const { output } = await kernel({
  data: { data: dataData, shape: [1, 2, 2] },
  bias: { data: biasData, shape: [1, 2, 2] },
}, {
  attrs: { is_inner_broadcast: 1 },
});
```