.. _op_ai_onnx_QuantizeLinear-13:

QuantizeLinear - version 13
===========================

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

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

The linear quantization operator. It consumes a high precision tensor, a scale, and a zero point to compute the low precision / quantized tensor.
The scale factor and 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.
The quantization formula is y = saturate ((x / y_scale) + y_zero_point).
For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8.
For (x / y_scale), it's rounding to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type.

**Inputs**

- **x** (*T1*): N-D full precision Input tensor to be quantized.
- **y_scale** (*tensor(float)*): Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization.
- **y_zero_point** (*T2*): Zero point for doing quantization to get 'y'. Shape must match y_scale. Default is uint8 with zero point of 0 if it's not specified.

**Outputs**

- **y** (*T2*): N-D quantized output tensor. It has same shape as input 'x'.

**Type Constraints**

- **T1**: Constrain 'x' to float or int32 tensor.
  Allowed types: tensor(float), tensor(int32).
- **T2**: Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.
  Allowed types: tensor(int8), tensor(uint8).

Examples
--------

**test_cc_quantizelinear**

.. code-block:: text

    Inputs:
      x: shape=(6,), dtype=float32
        [    0.,     2.,     3.,  1000.,  -254., -1000.]
      y_scale: shape=(), dtype=float32
        2.

    Outputs:
      y: shape=(6,), dtype=uint8
        [  0,   1,   2, 255,   0,   0]

**test_cc_quantizelinear_int8**

.. code-block:: text

    Inputs:
      x: shape=(6,), dtype=float32
        [    0.,     2.,     3.,  1000.,  -254., -1000.]
      y_scale: shape=(), dtype=float32
        2.
      y_zero_point: shape=(), dtype=int8
        -10

    Outputs:
      y: shape=(6,), dtype=int8
        [ -10,   -9,   -8,  127, -128, -128]

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

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

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

**Documentation:**

* line similarity: 0.40 (+4/-2 lines)

.. code-block:: diff

    --- QuantizeLinear v10
    +++ QuantizeLinear v13
    @@ -1,4 +1,6 @@
     
    -The linear per-tensor/layer quantization operator. It consumes a high precision tensor, a scale, a zero point to compute the low precision / quantized tensor.
    -The quantization formula is y = saturate ((x / y_scale) + y_zero_point). For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8.
    +The linear quantization operator. It consumes a high precision tensor, a scale, and a zero point to compute the low precision / quantized tensor.
    +The scale factor and 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.
    +The quantization formula is y = saturate ((x / y_scale) + y_zero_point).
    +For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8.
     For (x / y_scale), it's rounding to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type.