ai.onnx.QLinearMatMul

ai.onnx · standard ONNX operator · ONNX opset ≥ 21

Description

Quantized matrix multiplication of two N-dimensional tensors a and b, following ONNX MatMul broadcasting semantics. Each operand and the output carry their own scale and zero point; the result is requantized via y = saturate((x / y_scale) + y_zero_point), with scales and zero points that may be per-tensor, per-row, or per-column.

See the ONNX QLinearMatMul 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
• qlinear-matmul-accumulate.wgsl.jinja
• qlinear-matmul-requantize.wgsl.jinja
• quant-dp4a-matmul.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.QLinearMatMul", { version: 1 });
const { y } = await kernel({
  a: { data: aData, shape: [1, 1] },
  a_scale: { data: a_scaleData, shape: [1] },
  a_zero_point: { data: a_zero_pointData, shape: [1] },
  b: { data: bData, shape: [1, 2] },
  b_scale: { data: b_scaleData, shape: [1] },
  b_zero_point: { data: b_zero_pointData, shape: [1] },
  y_scale: { data: y_scaleData, shape: [1] },
  y_zero_point: { data: y_zero_pointData, shape: [1] },
});