# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#'
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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:`~.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