:nosearch:

.. _op_ai_onnx_DequantizeLinear-13:

DequantizeLinear - version 13
=============================

This page documents version **13** of operator **DequantizeLinear**. See :doc:`DequantizeLinear` for the latest version (since version 25).

- **Domain**: ``ai.onnx``
- **Since version**: 13

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 same shape, and can be either a scalar
for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization.
``x_zero_point`` and ``x`` must have same type. ``x`` and ``y`` must have same shape. In the case of dequantizing int32,
there's no zero point (zero point is supposed to be 0).

**Inputs**

- **x** (*T*): N-D quantized input tensor to be de-quantized.
- **x_scale** (*tensor(float)*): Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.
- **x_zero_point** (*T*): Zero point for input 'x'. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.

**Outputs**

- **y** (*tensor(float)*): N-D full precision output tensor. It has same shape as input 'x'.

**Type Constraints**

- **T**: Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.
  Allowed types: tensor(int32), tensor(int8), tensor(uint8).

Differences with previous version (10)
--------------------------------------

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

* old version: 10
* new version: 13
* breaking: no

**Documentation:**

* line similarity: 0.36 (+4/-3 lines)

.. code-block:: diff

    --- DequantizeLinear v10
    +++ DequantizeLinear v13
    @@ -1,5 +1,6 @@
     
    -The linear dequantization operator. It consumes a quantized tensor, a scale, 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' are both scalars.
    -'x_zero_point' and 'x' must have same type. 'x' and 'y' must have same shape. In the case of dequantizing int32,
    +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 same shape, and can be either a scalar
    +for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization.
    +`x_zero_point` and `x` must have same type. `x` and `y` must have same shape. In the case of dequantizing int32,
     there's no zero point (zero point is supposed to be 0).