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 for the reference semantics.

Inputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`x`	`x`	`TQ`	—	—	N-D quantized input tensor to be dequantized.	required
`x_scale`	`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`	`x_zero_point`	`TQ`	—	—	Zero point for `x` with shape matching `x_scale`; defaults to zero when absent.	optional

Outputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`y`	`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 dtype of `x_scale`. The current 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 — kernel metadata (id, digests, provenance)
manifest.json — the op contract (source of truth)
test.json — correctness cases
bench.json — benchmark + tuning cases
quant-linear-blocked-axis.wgsl.jinja
quant-linear-scalar.wgsl.jinja
quant-linear-vec4.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.DequantizeLinear", { version: 1 });
const { y } = await kernel({
  x: { data: xData, shape: [4] },
  x_scale: { data: x_scaleData, shape: [] },
});

Downloads last month: -

kernel

webgpu

wgsl

apache-2.0

WebGPU

Requires WebGPU support. See the compatibility table.