QConfig#

class QConfig(weight_observer, act_observer, weight_fake_quant, act_fake_quant)[source]#

A config class indicating how to do quantize toward QATModule ‘s activation and weight. See set_qconfig for detail usage.

Parameters:

  • weight_observer – interface to instantiate an Observer indicating how to collect scales and zero_point of wegiht.

  • act_observer – similar to weight_observer but toward activation.

  • weight_fake_quant – interface to instantiate a FakeQuantize indicating how to do fake_quant calculation.

  • act_observer – similar to weight_fake_quant but toward activation.

Examples

# Default EMA QConfig for QAT.
ema_fakequant_qconfig = QConfig(
    weight_observer=partial(MinMaxObserver, dtype="qint8_narrow"),
    act_observer=partial(ExponentialMovingAverageObserver, dtype="qint8"),
    weight_fake_quant=partial(FakeQuantize, dtype="qint8_narrow"),
    act_fake_quant=partial(FakeQuantize, dtype="qint8"),
)

Each parameter is a class rather than an instance. And we recommand using functools.partial to add initialization parameters of the class, so that don’t need to provide parameters in set_qconfig.

Usually we choose narrow version dtype (like qint8_narrow) for weight related paramters and normal version for activation related ones. For the result of multiplication and addition as a * b + c * d, if four variables are all -128 of dtype qint8, then the result will be 2^15 and cause overflow. Weights are commonly calculated in this way, so need to narrow qmin to -127.