---
library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
---
# 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](https://onnx.ai/onnx/operators/onnx__DynamicQuantizeLinear.html) for the reference semantics.

## Inputs

| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `x` | `x` | `T` | — | — | Float32 input tensor to quantize. | required |

## Outputs

| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `y` | `y` | `TQ` | same as `x` | same as `x` | Quantized output tensor; same shape as the input. | required |
| `y_scale` | `y_scale` | `T` | `0` | `[]` | Per-tensor scale factor derived from the input min/max range; scalar. | required |
| `y_zero_point` | `y_zero_point` | `TQ` | `0` | `[]` | Per-tensor zero point for the quantization; scalar. | required |

## Type constraints

| Variable | Allowed dtypes |
| --- | --- |
| `T` | `float32` |
| `TQ` | `uint8` |

## 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
- [`dynamic-quantize-linear-quantize.wgsl.jinja`](build/webgpu/dynamic-quantize-linear-quantize.wgsl.jinja)
- [`dynamic-quantize-linear-reduce.wgsl.jinja`](build/webgpu/dynamic-quantize-linear-reduce.wgsl.jinja)
- [`dynamic-quantize-linear.wgsl.jinja`](build/webgpu/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.

```js
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] } });
```