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library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
---
# ai.onnx.DequantizeLinear
`ai.onnx` · standard ONNX operator · ONNX opset ≥ 25
## Description
Dequantizes a quantized tensor back to full precision using the formula `y = (x - x_zero_point) * x_scale`. Scale and zero point determine quantization granularity: scalar for per-tensor, 1-D for per-axis, or same rank as input for blocked quantization. The output type matches `x_scale` unless overridden by `output_dtype`.
See the [ONNX `DequantizeLinear` spec](https://onnx.ai/onnx/operators/onnx__DequantizeLinear.html) for the reference semantics.
## Inputs
| Name | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- |
| `x` | `TQ` | — | — | N-D quantized input tensor to be dequantized. | required |
| `x_scale` | `TF` | — | — | Scale for `x`; scalar for per-tensor, 1-D for per-axis, or same-rank tensor for blocked dequantization. | required |
| `x_zero_point` | `TQ` | — | — | Zero point for `x` with shape matching `x_scale`; defaults to zero when absent. | optional |
## Outputs
| Name | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- |
| `y` | `TF` | same as `x` | same as `x` | N-D full-precision output with the same shape as `x`, typed according to `x_scale` or `output_dtype`. | required |
## Attributes
Default values (overridable per request):
| Attribute | Default | Description |
| --- | --- | --- |
| `axis` | `1` | The axis of the dequantizing dimension, used for per-axis and blocked quantization; negative values index from the end. |
| `block_size` | `0` | Number of elements along `axis` that share each scale value for blocked quantization; 0 means not blocked. |
| `output_dtype` | `0` | ONNX TensorProto element-type code for `y`; `0` uses the data type of `x_scale`. The supported float16/float32 routes require it to agree with `x_scale`. |
## Type constraints
| Variable | Allowed dtypes |
| --- | --- |
| `TQ` | `uint8`, `int8`, `int16`, `int32` |
| `TF` | `float32`, `float16` |
## Files
- [`metadata.json`](build/webgpu/metadata.json) — kernel metadata (id, digests, per-variant templates, 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
- [`quant-linear-blocked-axis.wgsl.jinja`](build/webgpu/quant-linear-blocked-axis.wgsl.jinja)
- [`quant-linear-scalar.wgsl.jinja`](build/webgpu/quant-linear-scalar.wgsl.jinja)
- [`quant-linear-vec4.wgsl.jinja`](build/webgpu/quant-linear-vec4.wgsl.jinja)
## Use with `@huggingface/kernels`
```sh
npm install --save-exact @huggingface/kernels@0.0.1-preview.2
```
Required output shapes and logical data types are inferred from the supplied inputs and attributes; result tensors are allocated automatically.
The `version: 1` option selects the published kernel contract; it is independent of any operator opset, contrib `since_version`, or model version.
It follows the `v1` branch as fixes land. To pin exact artifact bytes, pass a 40-character commit `revision` instead of `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.DequantizeLinear", { version: 1 });
const { y } = await kernel({
x: { data: xData, shape: [4] },
x_scale: { data: x_scaleData, shape: [] },
});
```
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