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
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesbinary-broadcast-vec4.wgsl.jinjabinary-broadcast.wgsl.jinjabinary-vec4.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/ai.onnx.Mul", { version: 1 });
const { c } = await kernel({ a: { data: aData, shape: [] }, b: { data: bData, shape: [] } });
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kernel
webgpu
wgsl
apache-2.0
WebGPU
Requires WebGPU support. See the compatibility table.