com.microsoft.BiasAdd

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

Description

Adds a 1-D bias (broadcast over the channel dimension) to input X, then adds the residual tensor skip elementwise. All three tensors share the same channel count C; X and skip have shape (N, S, C).

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

Inputs

Name Logical dtype Rank Shape Description Presence
X T 3 Input tensor of shape (N, S, C): batch size N, spatial size S, and C channels. required
bias T 1 1-D bias vector of length C, broadcast-added along the channel dimension. required
skip T 3 Residual tensor with the same (N, S, C) shape as X, added after the bias. required

Outputs

Name Logical dtype Rank Shape Description Presence
Y T 3 same as X Output tensor of shape (N, S, C): the elementwise sum X + bias + skip. required

Type constraints

Variable Allowed dtypes
T float32, float16

Files

Use with @huggingface/kernels

npm install --save-exact @huggingface/[email protected]

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.BiasAdd", { version: 1 });
const { Y } = await kernel({
  X: { data: XData, shape: [1, 2, 4] },
  bias: { data: biasData, shape: [4] },
  skip: { data: skipData, shape: [1, 2, 4] },
});
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Requires WebGPU support. See the compatibility table.