DynamicQuantizeLinear

DynamicQuantizeLinear - 11

Version

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.