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library_name: kernels
license: apache-2.0
tags:
  - kernel
  - webgpu
  - wgsl

com.microsoft.GatedAdd

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

Description

Adds Y, scaled by a per-row gate, to X: output = X + round_to_T(Y * gate). X and Y have shape (..., C); gate has the same rank with a trailing dimension of 1, so one value covers each row of C channels. Rounding the product to T before the addition preserves the semantics of a separate Mul followed by Add. Bfloat16 is not implemented.

See the ONNX Runtime GatedAdd contrib-operator spec for the reference semantics.

Inputs

Name	Logical dtype	Rank	Shape	Description	Presence
`X`	`T`	—	—	Unscaled input with shape `(..., C)`. Any rank of at least 1 is accepted; only the trailing channel axis is distinguished.	required
`Y`	`T`	—	—	Input scaled by the gate, with the same shape as `X`.	required
`gate`	`T`	—	—	Per-row gate with shape `(..., 1)`: the same rank and leading dimensions as `X`, with a trailing dimension of 1 that broadcasts over the `C` channels.	required

Outputs

Name	Logical dtype	Rank	Shape	Description	Presence
`output`	`T`	same as `X`	same as `X`	Gated sum `X + round_to_T(Y * gate)`, with the same shape as `X`.	required

Type constraints

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

Files

metadata.json — kernel metadata (id, digests, per-variant templates, provenance)
manifest.json — the op contract (source of truth)
test.json — correctness cases
bench.json — benchmark + tuning cases
gated-add.wgsl.jinja

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/com.microsoft.GatedAdd", { version: 1 });
const { output } = await kernel({
  X: { data: XData, shape: [2, 3] },
  Y: { data: YData, shape: [2, 3] },
  gate: { data: gateData, shape: [2, 1] },
});