ai.onnx.QuantizeLinear

ai.onnx · standard ONNX operator · ONNX opset ≥ 25

Description

Linearly quantizes a high-precision tensor to a lower-precision integer type using the formula y = saturate((x / y_scale) + y_zero_point), with rounding to nearest even. Supports per-tensor, per-axis, and blocked quantization granularities determined by the shape of y_scale.

See the ONNX QuantizeLinear spec for the reference semantics.

Inputs

Name	Logical dtype	Rank	Shape	Description	Presence
`x`	`TX`	—	—	N-D full-precision input tensor to be quantized.	required
`y_scale`	`TS`	—	—	Scale factor; scalar for per-tensor, 1-D for per-axis, or same rank as `x` (with one axis blocked) for blocked quantization.	required
`y_zero_point`	`TQ`	—	—	Zero point for quantization; must have the same shape as `y_scale`. Defaults to zero if omitted.	optional

Outputs

Name	Logical dtype	Rank	Shape	Description	Presence
`y`	`TQ`	same as `x`	same as `x`	N-D quantized output tensor with the same shape as `x`.	required

Attributes

Default values (overridable per request):

Attribute	Default	Description
`axis`	`1`	Axis of the quantization dimension in `x`, used for per-axis and blocked quantization; negative values count from the end.
`block_size`	`0`	Number of elements along `axis` that share a single scale value for blocked quantization; 0 means blocked quantization is not used.
`output_dtype`	`0`	ONNX TensorProto element-type code for `y`; 0 infers the type from `y_zero_point`, or uint8 when the zero point is omitted.
`precision`	`0`	ONNX TensorProto element-type code used for `x / y_scale`; `0` uses the dtype of `y_scale`, `1` selects FLOAT, and `10` selects FLOAT16.
`saturate`	`1`	Controls out-of-range conversion for float8 outputs. The implemented int8/uint8 subset accepts the ONNX default `1`.

Type constraints

Variable	Allowed dtypes
`TX`	`float32`, `float16`
`TS`	`float32`, `float16`
`TQ`	`uint8`, `int8`

Files

metadata.json — kernel metadata (id, digests, per-variant templates, 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`

npm install --save-exact @huggingface/kernels@0.0.1-preview.2

Outputs with inferable metadata are allocated automatically. Explicit outputs entries request optional results or provide metadata that cannot be inferred from the supplied inputs and attributes.

This example supplies explicit metadata for:

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.

import { getKernel } from "@huggingface/kernels";

const kernel = await getKernel("webgpu-kernels/ai.onnx.QuantizeLinear", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { y } = await kernel({ x: { data: xData, shape: [] }, y_scale: { data: y_scaleData, shape: [] } }, {
  outputs: { y: { shape: [], dtype: "uint8" } },
});

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kernel

webgpu

wgsl

apache-2.0

WebGPU

Requires WebGPU support. See the compatibility table.