ai.onnx.Mul

ai.onnx · standard ONNX operator · ONNX opset ≥ 14

Description

Performs elementwise binary multiplication of two tensors with multidirectional (NumPy-style) broadcasting. The output has the same element type as the inputs.

See the ONNX Mul spec for the reference semantics.

Inputs

Name Bind key Logical dtype Rank Shape Description Presence
A a T First operand. required
B b T Second operand. required

Outputs

Name Bind key Logical dtype Rank Shape Description Presence
C c T derived broadcast result of A and B Elementwise product; same element type as the inputs. required

Type constraints

Variable Allowed dtypes
T float32, float16, int32, uint32, int8, uint8

Files

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/ai.onnx.Mul", { version: 1 });
const { c } = await kernel({ a: { data: aData, shape: [] }, b: { data: bData, shape: [] } });
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WebGPU

Requires WebGPU support. See the compatibility table.