:nosearch:

.. _op_ai_onnx_DequantizeLinear-24:

DequantizeLinear - version 24
=============================

This page documents version **24** of operator **DequantizeLinear**. See :doc:`DequantizeLinear` for the latest version (since version 25).

- **Domain**: ``ai.onnx``
- **Since version**: 24

The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the
full-precision tensor. The dequantization formula is ``y = (x - x_zero_point) * x_scale``. ``x_scale`` and ``x_zero_point``
must have the same shape, determining the quantization's granularity: a scalar for per-tensor/per-layer quantization,
a 1-D tensor for per-axis quantization, or have a rank identical to the input for blocked quantization.
See QuantizeLinear for details on quantization granularity.

``x_zero_point`` and ``x`` must have the same type. ``x`` and ``y`` must have the same shape. In the case of dequantizing
``int32``, there's no zero point (zero point is supposed to be 0).
``zero-point`` is usually not used in the case of float8 and 4-bit types quantization, but the dequantization formula remains the same
for consistency. The output type is determined by the attribute ``output_dtype``. If ``output_dtype`` is not supplied then the output type
is the same as ``x_scale``. The output type also determines the precision of the multiplication operation.

**Inputs**

- **x** (*T1*): N-D quantized input tensor to be de-quantized.
- **x_scale** (*T2*): Scale for input ``x``. For per-tensor/layer dequantization the scale is a scalar, for per per-axis dequantization it is a 1-D Tensor and for blocked dequantization it has the same shape as the input, except for one dimension in which blocking is performed.
- **x_zero_point** (*T1*): Zero point for input ``x``. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.

**Outputs**

- **y** (*T3*): N-D full precision output tensor. It has the same shape as input ``x``. The data type is specified by the ``output_dtype`` attribute or, in its absence, the type of ``x_scale``.

**Type Constraints**

- **T1**: The type of the inputs 'x_zero_point' and 'x'.
  Allowed types: tensor(float4e2m1), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int4), tensor(int8), tensor(uint16), tensor(uint4), tensor(uint8).
- **T2**: The type of the input 'x_scale'.
  Allowed types: tensor(bfloat16), tensor(float), tensor(float16), tensor(float8e8m0).
- **T3**: The type of the output 'y'.
  Allowed types: tensor(bfloat16), tensor(float), tensor(float16).

Differences with previous version (23)
--------------------------------------

**SchemaDiff**: ``DequantizeLinear`` (domain ``'ai.onnx'``)

* old version: 23
* new version: 24
* breaking: no

**Type constraints:**

* changed 'T2': added types: ['tensor(float8e8m0)']