File size: 3,918 Bytes
fb9dcfe 746b1e6 fb9dcfe 746b1e6 fb9dcfe 746b1e6 8386dfb 746b1e6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | ---
library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
---
# 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](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.FusedMatMul) for the reference semantics.
## Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `A` | `A` | `T` | — | — | N-dimensional matrix A. | required |
| `B` | `B` | `T` | — | — | N-dimensional matrix B. | required |
## Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `Y` | `Y` | `T` | derived | derived; see description | Matrix-multiplication result whose shape follows NumPy-style rules after applying the requested batch and matrix transpositions. | required |
## Attributes
Default values (overridable per request):
| Attribute | Default | Description |
| --- | --- | --- |
| `alpha` | `1` | Scalar multiplier applied to the product of the input tensors. |
| `transA` | `0` | When non-zero, transposes `A` on its last two dimensions before multiplication. |
| `transB` | `0` | When non-zero, transposes `B` on its last two dimensions before multiplication. |
| `transBatchA` | `0` | When non-zero, transposes `A` on its first dimension and batch dimensions (dim-1 to dim-rank-2) before multiplication. |
| `transBatchB` | `0` | When non-zero, transposes `B` on its first dimension and batch dimensions (dim-1 to dim-rank-2) before multiplication. |
## Type constraints
| Variable | Allowed dtypes |
| --- | --- |
| `T` | `float32`, `float16` |
## 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`](build/webgpu/metadata.json) — kernel metadata (id, digests, provenance)
- [`manifest.json`](build/webgpu/manifest.json) — the op contract (source of truth)
- [`test.json`](build/webgpu/test.json) — correctness cases
- [`bench.json`](build/webgpu/bench.json) — benchmark + tuning cases
- [`fused-matmul-subgroup-matrix.wgsl.jinja`](build/webgpu/fused-matmul-subgroup-matrix.wgsl.jinja)
- [`matmul-subgroup-matrix-ext.wgsl.jinja`](build/webgpu/matmul-subgroup-matrix-ext.wgsl.jinja)
- [`matmul-tiled-general-reg.wgsl.jinja`](build/webgpu/matmul-tiled-general-reg.wgsl.jinja)
- [`matmul-tiled-general.wgsl.jinja`](build/webgpu/matmul-tiled-general.wgsl.jinja)
- [`matmul-vector-matrix-vec4.wgsl.jinja`](build/webgpu/matmul-vector-matrix-vec4.wgsl.jinja)
- [`reduce-axis0-splitk-combine.wgsl.jinja`](build/webgpu/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.
```js
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] } });
```
|