ai.onnx.MatMulInteger

ai.onnx · standard ONNX operator · ONNX opset ≥ 10

Description

Computes an integer matrix product with 8-bit inputs, int32 accumulation, and independently optional zero points that default to 0. The package implements rank-1 dot products, rank-2 products, rank-2/rank-3 broadcasting, rank-3 products, and rank-4-by-rank-4 products. Scalar zero points are supported throughout; b_zero_point additionally supports [N] for rank-2 B and [batch, 1, N] for non-broadcast rank-3 B. Other standard ONNX matmul rank combinations and N-D per-row/per-column zero-point layouts are not yet implemented.

See the ONNX MatMulInteger spec for the reference semantics.

Inputs

Outputs

Type constraints

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
• matmul-integer-batched.wgsl.jinja
• quant-dp4a-matmul.wgsl.jinja
• quant-matmul-accumulate-rank2.wgsl.jinja
• quant-matmul-accumulate-rank4.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.MatMulInteger", { version: 1 });
const { y } = await kernel({ a: { data: aData, shape: [1, 1] }, b: { data: bData, shape: [1, 1] } });