ai.onnx.DynamicQuantizeLinear

ai.onnx · standard ONNX operator · ONNX opset ≥ 11

Description

Computes a per-tensor scale and zero point from the range of floating-point input x, extending the range to include zero, then quantizes each value to uint8 as saturate(round(x / y_scale) + y_zero_point). Uses round-to-nearest-even and clamps results to [0, 255].

See the ONNX DynamicQuantizeLinear 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
• dynamic-quantize-linear-quantize.wgsl.jinja
• dynamic-quantize-linear-reduce.wgsl.jinja
• dynamic-quantize-linear.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.DynamicQuantizeLinear", { version: 1 });
const { y, y_scale, y_zero_point } = await kernel({ x: { data: xData, shape: [1] } });