(l-onnx-doc-DynamicQuantizeLinear)=

# DynamicQuantizeLinear


(l-onnx-op-dynamicquantizelinear-11)=

## DynamicQuantizeLinear - 11

### Version

- **name**: [DynamicQuantizeLinear (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#DynamicQuantizeLinear)
- **domain**: `main`
- **since_version**: `11`
- **function**: `True`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 11**.

### Summary

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:
```
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:
```
intermediate_zero_point = qmin - min(x)/y_scale
y_zero_point = cast(round(saturate(itermediate_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:
```
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.

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 11]
>
DynamicQuantizeLinear (x) => (y, y_scale, y_zero_point)
{
   Q_Min = Constant <value: tensor = float {0}> ()
   Q_Max = Constant <value: tensor = float {255}> ()
   X_Min = ReduceMin <keepdims: int = 0> (x)
   X_Min_Adjusted = Min (X_Min, Q_Min)
   X_Max = ReduceMax <keepdims: int = 0> (x)
   X_Max_Adjusted = Max (X_Max, Q_Min)
   X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted)
   Scale = Div (X_Range, Q_Max)
   Min_Scaled = Div (X_Min_Adjusted, Scale)
   Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled)
   Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max)
   Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP)
   Zeropoint = Cast <to: int = 2> (Rounded_ZeroPoint_FP)
   y_scale = Identity (Scale)
   y_zero_point = Identity (Zeropoint)
   y = QuantizeLinear (x, Scale, Zeropoint)
}
```

### Inputs

- **x** (heterogeneous) - **T1**:

  Input tensor

### Outputs

- **y** (heterogeneous) - **T2**:

  Quantized output tensor
- **y_scale** (heterogeneous) - **tensor(float)**:

  Output scale. It's a scalar, which means a per-tensor/layer quantization.
- **y_zero_point** (heterogeneous) - **T2**:

  Output zero point. It's a scalar, which means a per-tensor/layer quantization.

### Type Constraints

* **T1** in ( `tensor(float)` ):

  Constrain 'x' to float tensor.
* **T2** in ( `tensor(uint8)` ):

  Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.