# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
Quantization aware training
User can use quantization aware to train a model. MindSpore supports quantization aware training,
which models quantization errors in both the forward and backward passes using fake-quantization
operations. Note that the entire computation is carried out in floating point. At the end of quantization
aware training, MindSpore provides conversion functions to convert the trained model into lower precision.
"""
import re
import mindspore.context as context
from ... import nn, ops
from ..._checkparam import Validator, Rel
from ...nn.layer import quant
from ...ops import functional as F
from ..common import QuantDtype
from .quantizer import Quantizer, OptimizeOption
__all__ = ["QuantizationAwareTraining", "create_quant_config"]
[docs]def create_quant_config(quant_observer=(nn.FakeQuantWithMinMaxObserver, nn.FakeQuantWithMinMaxObserver),
quant_delay=(0, 0),
quant_dtype=(QuantDtype.INT8, QuantDtype.INT8),
per_channel=(False, False),
symmetric=(False, False),
narrow_range=(False, False)):
r"""
Configs the observer type of weights and data flow with quant params.
Args:
quant_observer (Observer, list or tuple): The observer type to do quantization. The first element represent
weights and second element represent data flow.
Default: (nn.FakeQuantWithMinMaxObserver, nn.FakeQuantWithMinMaxObserver)
quant_delay (int, list or tuple): Number of steps after which weights and activations are quantized during
eval. The first element represent weights and second element represent data flow. Default: (0, 0)
quant_dtype (QuantDtype, list or tuple): Datatype to use for quantize weights and activations. The first
element represent weights and second element represent data flow.
Default: (QuantDtype.INT8, QuantDtype.INT8)
per_channel (bool, list or tuple): Quantization granularity based on layer or on channel. If `True`
then base on per channel otherwise base on per layer. The first element represent weights
and second element represent data flow. Default: (False, False)
symmetric (bool, list or tuple): Whether the quantization algorithm is symmetric or not. If `True` then base on
symmetric otherwise base on asymmetric. The first element represent weights and second
element represent data flow. Default: (False, False)
narrow_range (bool, list or tuple): Whether the quantization algorithm uses narrow range or not.
The first element represents weights and the second element represents data flow. Default: (False, False)
Returns:
QuantConfig, Contains the observer type of weight and activation.
"""
weight_observer = quant_observer[0].partial_init(quant_delay=quant_delay[0], quant_dtype=quant_dtype[0],
per_channel=per_channel[0], symmetric=symmetric[0],
narrow_range=narrow_range[0])
act_observer = quant_observer[-1].partial_init(quant_delay=quant_delay[-1], quant_dtype=quant_dtype[-1],
per_channel=per_channel[-1], symmetric=symmetric[-1],
narrow_range=narrow_range[-1])
return quant.QuantConfig(weight=weight_observer, activation=act_observer)
class _AddFakeQuantInput(nn.Cell):
"""
Add FakeQuant OP at input of the network. Only support one input case.
"""
def __init__(self, network, quant_delay=0):
super(_AddFakeQuantInput, self).__init__(auto_prefix=False)
self.fake_quant_input = quant.FakeQuantWithMinMaxObserver(min_init=-6, max_init=6,
quant_delay=quant_delay, ema=True)
self.fake_quant_input.update_parameters_name('fake_quant_input.')
self.network = network
def construct(self, data):
data = self.fake_quant_input(data)
output = self.network(data)
return output
class _AddFakeQuantAfterSubCell(nn.Cell):
"""
Add FakeQuant OP after of the sub Cell.
"""
def __init__(self, subcell, **kwargs):
super(_AddFakeQuantAfterSubCell, self).__init__(auto_prefix=False)
self.subcell = subcell
self.fake_quant_act = quant.FakeQuantWithMinMaxObserver(min_init=-6,
max_init=6,
ema=True,
quant_dtype=kwargs["quant_dtype"],
quant_delay=kwargs["quant_delay"],
per_channel=kwargs["per_channel"],
symmetric=kwargs["symmetric"],
narrow_range=kwargs["narrow_range"])
def construct(self, *data):
output = self.subcell(*data)
output = self.fake_quant_act(output)
return output
[docs]class QuantizationAwareTraining(Quantizer):
r"""
Quantizer for quantization aware training.
Args:
bn_fold (bool): Flag to used bn fold ops for simulation inference operation. Default: True.
freeze_bn (int): Number of steps after which BatchNorm OP parameters used total mean and variance. Default: 1e7.
quant_delay (int, list or tuple): Number of steps after which weights and activations are quantized during
eval. The first element represent weights and second element represent data flow. Default: (0, 0)
quant_dtype (QuantDtype, list or tuple): Datatype to use for quantize weights and activations. The first
element represent weights and second element represent data flow.
Default: (QuantDtype.INT8, QuantDtype.INT8)
per_channel (bool, list or tuple): Quantization granularity based on layer or on channel. If `True`
then base on per channel otherwise base on per layer. The first element represent weights
and second element represent data flow. Default: (False, False)
symmetric (bool, list or tuple): Whether the quantization algorithm is symmetric or not. If `True` then base on
symmetric otherwise base on asymmetric. The first element represent weights and second
element represent data flow. Default: (False, False)
narrow_range (bool, list or tuple): Whether the quantization algorithm uses narrow range or not.
The first element represents weights and the second element represents data flow. Default: (False, False)
optimize_option (OptimizeOption, list or tuple): Specifies the quant algorithm and options, currently only
support QAT. Default: OptimizeOption.QAT
one_conv_fold (bool): Flag to used one conv bn fold ops for simulation inference operation. Default: True.
Examples:
>>> class LeNet5(nn.Cell):
... def __init__(self, num_class=10, channel=1):
... super(LeNet5, self).__init__()
... self.type = "fusion"
... self.num_class = num_class
...
... # change `nn.Conv2d` to `nn.Conv2dBnAct`
... self.conv1 = nn.Conv2dBnAct(channel, 6, 5, pad_mode='valid', activation='relu')
... self.conv2 = nn.Conv2dBnAct(6, 16, 5, pad_mode='valid', activation='relu')
... # change `nn.Dense` to `nn.DenseBnAct`
... self.fc1 = nn.DenseBnAct(16 * 5 * 5, 120, activation='relu')
... self.fc2 = nn.DenseBnAct(120, 84, activation='relu')
... self.fc3 = nn.DenseBnAct(84, self.num_class)
...
... self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
... self.flatten = nn.Flatten()
...
... def construct(self, x):
... x = self.conv1(x)
... x = self.max_pool2d(x)
... x = self.conv2(x)
... x = self.max_pool2d(x)
... x = self.flatten(x)
... x = self.fc1(x)
... x = self.fc2(x)
... x = self.fc3(x)
... return x
...
>>> net = LeNet5()
>>> quantizer = QuantizationAwareTraining(bn_fold=False, per_channel=[True, False], symmetric=[True, False])
>>> net_qat = quantizer.quantize(net)
"""
__quant_op_name__ = ["Add", "Sub", "Mul", "RealDiv"]
def __init__(self,
bn_fold=True,
freeze_bn=10000000,
quant_delay=(0, 0),
quant_dtype=(QuantDtype.INT8, QuantDtype.INT8),
per_channel=(False, False),
symmetric=(False, False),
narrow_range=(False, False),
optimize_option=OptimizeOption.QAT,
one_conv_fold=True):
"""Init for QuantizationAwareTraining quantizer"""
super(QuantizationAwareTraining, self).__init__(optimize_option=optimize_option)
def convert2list(name, value):
if not isinstance(value, list) and not isinstance(value, tuple):
value = [value]
elif len(value) > 2:
raise ValueError("input `{}` len should less then 2".format(name))
return value
quant_delay = convert2list("quant delay", quant_delay)
quant_dtype = convert2list("quant dtype", quant_dtype)
per_channel = convert2list("per channel", per_channel)
symmetric = convert2list("symmetric", symmetric)
narrow_range = convert2list("narrow range", narrow_range)
self.weight_qdelay = Validator.check_non_negative_int(quant_delay[0], "quant delay")
self.act_qdelay = Validator.check_int(quant_delay[-1], 0, Rel.GE, "quant delay")
self.bn_fold = Validator.check_bool(bn_fold, "bn fold")
self.freeze_bn = Validator.check_non_negative_int(freeze_bn, "freeze bn")
self.weight_dtype = Validator.check_isinstance("weights dtype", quant_dtype[0], QuantDtype)
self.act_dtype = Validator.check_isinstance("activations dtype", quant_dtype[-1], QuantDtype)
self.weight_channel = Validator.check_bool(per_channel[0], "per channel")
self.act_channel = Validator.check_bool(per_channel[-1], "per channel")
self.weight_symmetric = Validator.check_bool(symmetric[0], "symmetric")
self.act_symmetric = Validator.check_bool(symmetric[-1], "symmetric")
self.weight_range = Validator.check_bool(narrow_range[0], "narrow range")
self.act_range = Validator.check_bool(narrow_range[-1], "narrow range")
self.one_conv_fold = Validator.check_bool(one_conv_fold, "one conv fold")
self._convert_method_map = {nn.Conv2dBnAct: self._convert_conv,
nn.DenseBnAct: self._convert_dense}
self.quant_config = create_quant_config(quant_delay=quant_delay,
quant_dtype=quant_dtype,
per_channel=per_channel,
symmetric=symmetric,
narrow_range=narrow_range)
def _convert_op_name(self, name):
pattern = re.compile(r'([A-Z]{1})')
name_new = re.sub(pattern, r'_\1', name).lower()
if name_new[0] == '_':
name_new = name_new[1:]
return name_new
[docs] def quantize(self, network):
"""
Quant API to convert input network to a quantization aware training network
Args:
network (Cell): network to be quantized.
Examples:
>>> net = Net()
>>> quantizer = QuantizationAwareTraining()
>>> net_qat = quantizer.quantize(net)
"""
support_device = ["Ascend", "GPU"]
if context.get_context('device_target') not in support_device:
raise KeyError("Unsupported {} device target.".format(context.get_context('device_target')))
if OptimizeOption.QAT in self.optimize_option:
network.update_cell_prefix()
network = self._convert_subcells2quant(network)
network.update_cell_type("quant")
return network
def _convert_subcells2quant(self, network):
"""
convert sub cell like `Conv2dBnAct` and `DenseBnAct` to quant cell
"""
cells = network.name_cells()
change = False
for name in cells:
subcell = cells[name]
if subcell == network:
continue
elif isinstance(subcell, (nn.Conv2dBnAct, nn.DenseBnAct)):
prefix = subcell.param_prefix
new_subcell = self._convert_method_map[type(subcell)](subcell)
new_subcell.update_parameters_name(prefix + '.')
network.insert_child_to_cell(name, new_subcell)
change = True
else:
self._convert_subcells2quant(subcell)
if isinstance(network, nn.SequentialCell) and change:
network.cell_list = list(network.cells())
# add FakeQuant OP after OP in while list
add_list = []
for name in network.__dict__:
if name[0] == '_':
continue
attr = network.__dict__[name]
if isinstance(attr, ops.Primitive) and attr.name in self.__quant_op_name__:
add_list.append((name, attr))
for name, prim_op in add_list:
prefix = name
add_quant = _AddFakeQuantAfterSubCell(prim_op,
quant_dtype=self.act_dtype,
quant_delay=self.act_qdelay,
per_channel=self.act_channel,
symmetric=self.act_symmetric,
narrow_range=self.act_range)
prefix = self._convert_op_name(prim_op.name)
if network.param_prefix:
prefix = '.'.join([network.param_prefix, self._convert_op_name(prim_op.name)])
add_quant.update_parameters_name(prefix + '.')
del network.__dict__[name]
network.insert_child_to_cell(name, add_quant)
return network
def _convert_conv(self, subcell):
"""
convert Conv2d cell to quant cell
"""
conv_inner = subcell.conv
if subcell.has_bn:
if self.bn_fold:
bn_inner = subcell.batchnorm
if self.one_conv_fold:
conv_inner = quant.Conv2dBnFoldQuantOneConv(conv_inner.in_channels,
conv_inner.out_channels,
kernel_size=conv_inner.kernel_size,
stride=conv_inner.stride,
pad_mode=conv_inner.pad_mode,
padding=conv_inner.padding,
dilation=conv_inner.dilation,
group=conv_inner.group,
eps=bn_inner.eps,
momentum=bn_inner.momentum,
has_bias=conv_inner.has_bias,
bias_init=conv_inner.bias_init,
quant_config=self.quant_config,
quant_dtype=self.weight_dtype,
fake=True)
else:
conv_inner = quant.Conv2dBnFoldQuant(conv_inner.in_channels,
conv_inner.out_channels,
kernel_size=conv_inner.kernel_size,
stride=conv_inner.stride,
pad_mode=conv_inner.pad_mode,
padding=conv_inner.padding,
dilation=conv_inner.dilation,
group=conv_inner.group,
eps=bn_inner.eps,
momentum=bn_inner.momentum,
has_bias=conv_inner.has_bias,
bias_init=conv_inner.bias_init,
freeze_bn=self.freeze_bn,
quant_config=self.quant_config,
quant_dtype=self.weight_dtype,
fake=True)
# change original network BatchNormal OP parameters to quant network
conv_inner.gamma = subcell.batchnorm.gamma
conv_inner.beta = subcell.batchnorm.beta
conv_inner.moving_mean = subcell.batchnorm.moving_mean
conv_inner.moving_variance = subcell.batchnorm.moving_variance
del subcell.batchnorm
subcell.batchnorm = None
subcell.has_bn = False
else:
bn_inner = subcell.batchnorm
conv_inner = quant.Conv2dBnWithoutFoldQuant(conv_inner.in_channels,
conv_inner.out_channels,
kernel_size=conv_inner.kernel_size,
stride=conv_inner.stride,
pad_mode=conv_inner.pad_mode,
padding=conv_inner.padding,
dilation=conv_inner.dilation,
group=conv_inner.group,
eps=bn_inner.eps,
momentum=bn_inner.momentum,
has_bias=conv_inner.has_bias,
bias_init=conv_inner.bias_init,
quant_config=self.quant_config,
quant_dtype=self.weight_dtype)
# change original network BatchNormal OP parameters to quant network
conv_inner.batchnorm.gamma = subcell.batchnorm.gamma
conv_inner.batchnorm.beta = subcell.batchnorm.beta
conv_inner.batchnorm.moving_mean = subcell.batchnorm.moving_mean
conv_inner.batchnorm.moving_variance = subcell.batchnorm.moving_variance
del subcell.batchnorm
subcell.batchnorm = None
subcell.has_bn = False
else:
conv_inner = quant.Conv2dQuant(conv_inner.in_channels,
conv_inner.out_channels,
kernel_size=conv_inner.kernel_size,
stride=conv_inner.stride,
pad_mode=conv_inner.pad_mode,
padding=conv_inner.padding,
dilation=conv_inner.dilation,
group=conv_inner.group,
has_bias=conv_inner.has_bias,
quant_config=self.quant_config,
quant_dtype=self.weight_dtype)
# change original network Conv2D OP parameters to quant network
conv_inner.weight = subcell.conv.weight
if subcell.conv.has_bias:
conv_inner.bias = subcell.conv.bias
subcell.conv = conv_inner
if subcell.has_act and subcell.activation is not None:
subcell.activation = self._convert_activation(subcell.activation)
elif subcell.after_fake:
subcell.has_act = True
subcell.activation = _AddFakeQuantAfterSubCell(F.identity,
quant_dtype=self.act_dtype,
quant_delay=self.act_qdelay,
per_channel=self.act_channel,
symmetric=self.act_symmetric,
narrow_range=self.act_range)
return subcell
def _convert_dense(self, subcell):
"""
convert dense cell to quant cell
"""
dense_inner = subcell.dense
dense_inner = quant.DenseQuant(dense_inner.in_channels,
dense_inner.out_channels,
has_bias=dense_inner.has_bias,
quant_config=self.quant_config,
quant_dtype=self.weight_dtype)
# change original network Dense OP parameters to quant network
dense_inner.weight = subcell.dense.weight
if subcell.dense.has_bias:
dense_inner.bias = subcell.dense.bias
subcell.dense = dense_inner
if subcell.has_act and subcell.activation is not None:
subcell.activation = self._convert_activation(subcell.activation)
elif subcell.after_fake:
subcell.has_act = True
subcell.activation = _AddFakeQuantAfterSubCell(F.identity,
quant_dtype=self.act_dtype,
quant_delay=self.act_qdelay,
per_channel=self.act_channel,
symmetric=self.act_symmetric,
narrow_range=self.act_range)
return subcell
def _convert_activation(self, activation):
"""
convert activation cell to quant cell
"""
act_class = activation.__class__
act_list = [nn.ReLU, nn.ReLU6, nn.Sigmoid]
act_list_with_fake_before = [nn.LeakyReLU, nn.HSigmoid, nn.HSwish]
if act_class in act_list:
return quant.ActQuant(activation=activation,
quant_config=self.quant_config,
quant_dtype=self.act_dtype)
if act_class in act_list_with_fake_before:
return quant.ActQuant(activation=activation,
ema=True,
fake_before=True,
quant_config=self.quant_config,
quant_dtype=self.act_dtype)
raise ValueError("Unsupported activation in auto quant: ", act_class)