:nosearch:

.. _op_ai_onnx_DequantizeLinear-19:

DequantizeLinear - version 19
=============================

This page documents version **19** of operator **DequantizeLinear**. See :doc:`DequantizeLinear` for the latest version (since version 25).

- **Domain**: ``ai.onnx``
- **Since version**: 19

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).
``zero-point`` is usually not used in the case of float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz quantization,
but the dequantization formula remains the same for consistency and 'x_scale' still determines the output type.

**Inputs**

- **x** (*T1*): N-D quantized input tensor to be de-quantized.
- **x_scale** (*T2*): 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** (*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** (*T2*): N-D full precision output tensor. It has same shape as input 'x'.

**Type Constraints**

- **T1**: Constrain 'x_zero_point' and 'x' to 8-bit integer or float, or /32-bit integer tensor.
  Allowed types: tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int32), tensor(int8), tensor(uint8).
- **T2**: 'x_scale' determines the output type.
  Allowed types: tensor(bfloat16), tensor(float), tensor(float16).

Differences with previous version (13)
--------------------------------------

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

* old version: 13
* new version: 19
* breaking: **yes**

**Breaking reasons:**

* input 'x' (changed): type_str changed 'T' -> 'T1'
* input 'x_scale' (changed): type_str changed 'tensor(float)' -> 'T2'
* input 'x_zero_point' (changed): type_str changed 'T' -> 'T1'
* output 'y' (changed): type_str changed 'tensor(float)' -> 'T2'
* type constraint 'T' (removed): entire constraint removed

**Inputs:**

* [BREAKING] changed 'x': type_str changed 'T' -> 'T1'
* [BREAKING] changed 'x_scale': type_str changed 'tensor(float)' -> 'T2'
* [BREAKING] changed 'x_zero_point': type_str changed 'T' -> 'T1'

**Outputs:**

* [BREAKING] changed 'y': type_str changed 'tensor(float)' -> 'T2'

**Type constraints:**

* [BREAKING] removed 'T': entire constraint removed
* added 'T1': added types: ['tensor(float8e4m3fn)', 'tensor(float8e4m3fnuz)', 'tensor(float8e5m2)', 'tensor(float8e5m2fnuz)', 'tensor(int32)', 'tensor(int8)', 'tensor(uint8)']
* added 'T2': added types: ['tensor(bfloat16)', 'tensor(float)', 'tensor(float16)']

**Documentation:**

* line similarity: 0.86 (+2/-0 lines)

.. code-block:: diff

    --- DequantizeLinear v13
    +++ DequantizeLinear v19
    @@ -4,3 +4,5 @@
     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).
    +`zero-point` is usually not used in the case of float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz quantization,
    +but the dequantization formula remains the same for consistency and 'x_scale' still determines the output type.