concrete/docs/user/howto/use_quantization.md

Using Quantization in Concrete Framework

In this section we detail some usage of quantization as implemented in Concrete.

Quantization Basics

The very basic purpose of the quantization is to convert floating point values to integers. We can apply such conversion using QuantizedArray available in concrete.quantization.

QuantizedArray takes 2 arguments:

• n_bits that defines the precision of the quantization. Currently, n_bits is limited to 7, due to some ConcreteLib limits.
• values that will be converted to integers

from concrete.quantization import QuantizedArray
import numpy
numpy.random.seed(0)
A = numpy.random.uniform(-2, 2, 10)
# array([ 0.19525402,  0.86075747,  0.4110535,  0.17953273, -0.3053808,
#         0.58357645, -0.24965115,  1.567092 ,  1.85465104, -0.46623392])
q_A = QuantizedArray(7, A)
q_A.qvalues
# array([ 37,          73,          48,         36,          9,  
#         58,          12,          112,        127,         0])
# the quantized integers values from A.
q_A.scale
# 0.018274684777173276, the scale S.
q_A.zero_point 
# 26, the zero point Z.
q_A.dequant()
# array([ 0.20102153,  0.85891018,  0.40204307,  0.18274685, -0.31066964,
#         0.58478991, -0.25584559,  1.57162289,  1.84574316, -0.4751418 ])
# Dequantized values.

Operations in the Quantized Realm

Why are the scale and zero point not a problem ?

Compilation

Future releases