.. _op_ai_onnx_DynamicQuantizeLinear:

DynamicQuantizeLinear
=====================

- **Domain**: ``ai.onnx``
- **Since version**: 11

A Function to fuse calculation for Scale, Zero Point and FP32->8Bit conversion of FP32 Input data.
Outputs Scale, ZeroPoint and Quantized Input for a given FP32 Input.
Scale is calculated as:

.. code-block::

    y_scale = (maximum(0, max(x)) - minimum(0, min(x))) / (qmax - qmin)


* where qmax and qmin are max and min values for quantization range i.e. [0, 255] in case of uint8
* data range is adjusted to include 0.

Zero point is calculated as:

.. code-block::

    intermediate_zero_point = qmin - min(x)/y_scale
    y_zero_point = cast(round(saturate(intermediate_zero_point)))


* where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8
* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.
* rounding to nearest ties to even.

Data quantization formula is:

.. code-block::

    y = saturate (round (x / y_scale) + y_zero_point)


* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.
* rounding to nearest ties to even.

**Inputs**

- **x** (*T1*): Input tensor

**Outputs**

- **y** (*T2*): Quantized output tensor
- **y_scale** (*tensor(float)*): Output scale. It's a scalar, which means a per-tensor/layer quantization.
- **y_zero_point** (*T2*): Output zero point. It's a scalar, which means a per-tensor/layer quantization.

**Type Constraints**

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

Examples
--------

**test_dynamicquantizelinear**

.. code-block:: text

    Node:
      DynamicQuantizeLinear(x) -> (y, y_scale, y_zero_point)

.. code-block:: text

    Inputs:
      x: shape=(6,), dtype=float32
        [ 0.  ,  2.  , -3.  , -2.5 ,  1.34,  0.5 ]

    Outputs:
      y: shape=(6,), dtype=uint8
        [153, 255,   0,  26, 221, 179]
      y_scale: shape=(), dtype=float32
        0.01960784
      y_zero_point: shape=(), dtype=uint8
        153

**test_dynamicquantizelinear_max_adjusted**

.. code-block:: text

    Node:
      DynamicQuantizeLinear(x) -> (y, y_scale, y_zero_point)

.. code-block:: text

    Inputs:
      x: shape=(6,), dtype=float32
        [-1.  , -2.1 , -1.3 , -2.5 , -3.34, -4.  ]

    Outputs:
      y: shape=(6,), dtype=uint8
        [191, 121, 172,  96,  42,   0]
      y_scale: shape=(), dtype=float32
        0.01568628
      y_zero_point: shape=(), dtype=uint8
        255

**test_dynamicquantizelinear_min_adjusted**

.. code-block:: text

    Node:
      DynamicQuantizeLinear(x) -> (y, y_scale, y_zero_point)

.. code-block:: text

    Inputs:
      x: shape=(3, 4), dtype=float32
        [[1.   , 2.1  , 1.3  , 2.5  ],
         [3.34 , 4.   , 1.5  , 2.6  ],
         [3.9  , 4.   , 3.   , 2.345]]

    Outputs:
      y: shape=(3, 4), dtype=uint8
        [[ 64, 134,  83, 159],
         [213, 255,  96, 166],
         [249, 255, 191, 149]]
      y_scale: shape=(), dtype=float32
        0.01568628
      y_zero_point: shape=(), dtype=uint8
        0