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 | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
X |
X |
T |
— | — | Unscaled input with shape (..., C). Any rank of at least 1 is accepted; only the trailing channel axis is distinguished. |
required |
Y |
Y |
T |
— | — | Input scaled by the gate, with the same shape as X. |
required |
gate |
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 | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
output |
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, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesgated-add.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.
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] },
});