com.microsoft.FusedMatMul

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

Description

Matrix product of two N-dimensional tensors A and B, following NumPy-style matrix-multiplication broadcasting. Supports optional transposition of either operand's last two dimensions, optional batch-dimension transposition, and a scalar alpha multiplier. Float32 and float16 are supported; double and bfloat16 are not.

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

Inputs

Outputs

Attributes

Default values (overridable per request):

Type constraints

Device requirements

Some implementation variants require subgroup-matrix and subgroups. These are route-specific capabilities, not package-wide requirements; availability also depends on the request shape and dtype.

Files

• metadata.json — kernel metadata (id, digests, provenance)
• manifest.json — the op contract (source of truth)
• test.json — correctness cases
• bench.json — benchmark + tuning cases
• fused-matmul-subgroup-matrix.wgsl.jinja
• matmul-subgroup-matrix-ext.wgsl.jinja
• matmul-tiled-general-reg.wgsl.jinja
• matmul-tiled-general.wgsl.jinja
• matmul-vector-matrix-vec4.wgsl.jinja
• reduce-axis0-splitk-combine.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.FusedMatMul", { version: 1 });
const { Y } = await kernel({ A: { data: AData, shape: [3] }, B: { data: BData, shape: [3] } });