megengine.quantization.qconfig 源代码

from collections import namedtuple
from functools import partial

from ..module import Module
from .fake_quant import TQT, FakeQuantize
from .observer import (
    ExponentialMovingAverageObserver,
    HistogramObserver,
    MinMaxObserver,
    PassiveObserver,
    SyncExponentialMovingAverageObserver,
    SyncMinMaxObserver,
)


# use namedtuple to make class immutable, comparable and easy to print
[文档]class QConfig( namedtuple( "QConfig", ["weight_observer", "act_observer", "weight_fake_quant", "act_fake_quant"], ) ): r"""A config class indicating how to do quantize toward :class:`~.QATModule` 's ``activation`` and ``weight``. See :meth:`~.QATModule.set_qconfig` for detail usage. Args: weight_observer: interface to instantiate an :class:`~.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 :class:`~.quantization.fake_quant.FakeQuantize` indicating how to do fake_quant calculation. act_observer: similar to ``weight_fake_quant`` but toward activation. Examples: .. code-block:: # 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 :meth:`~.QATModule.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. """ def __new__(cls, weight_observer, act_observer, weight_fake_quant, act_fake_quant): if isinstance(act_observer, Module) or isinstance(weight_observer, Module): raise ValueError( "QConfig must not receive observer instance, please pass observer" " class generator using `partial(Observer, ...)` instead. Use" " partial(MyObserver, x=1) to override arguments to constructor if needed" ) return super().__new__( cls, weight_observer, act_observer, weight_fake_quant, act_fake_quant )
min_max_fakequant_qconfig = QConfig( weight_observer=partial(MinMaxObserver, dtype="qint8_narrow"), act_observer=partial(MinMaxObserver, dtype="qint8"), weight_fake_quant=partial(FakeQuantize, dtype="qint8_narrow"), act_fake_quant=partial(FakeQuantize, dtype="qint8"), ) 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"), ) sync_ema_fakequant_qconfig = QConfig( weight_observer=partial(SyncMinMaxObserver, dtype="qint8_narrow"), act_observer=partial(SyncExponentialMovingAverageObserver, dtype="qint8"), weight_fake_quant=partial(FakeQuantize, dtype="qint8_narrow"), act_fake_quant=partial(FakeQuantize, dtype="qint8"), ) ema_lowbit_fakequant_qconfig = QConfig( weight_observer=partial(MinMaxObserver, dtype="qint4"), act_observer=partial(ExponentialMovingAverageObserver, dtype="qint4"), weight_fake_quant=partial(FakeQuantize, dtype="qint4"), act_fake_quant=partial(FakeQuantize, dtype="qint4"), ) calibration_qconfig = QConfig( weight_observer=partial(MinMaxObserver, dtype="qint8_narrow"), act_observer=partial(HistogramObserver, dtype="qint8"), weight_fake_quant=None, act_fake_quant=None, ) tqt_qconfig = QConfig( weight_observer=None, act_observer=None, weight_fake_quant=partial(TQT, dtype="qint8_narrow"), act_fake_quant=partial(TQT, dtype="qint8"), ) passive_qconfig = QConfig( weight_observer=partial(PassiveObserver, dtype="qint8_narrow"), act_observer=partial(PassiveObserver, dtype="qint8"), weight_fake_quant=partial(FakeQuantize, dtype="qint8_narrow"), act_fake_quant=partial(FakeQuantize, dtype="qint8"), ) easyquant_qconfig = passive_qconfig