def get_default_qat_qconfig(backend='fbgemm', version=1):
# Histogram observer is too slow for quantization aware training
if version is None:
if backend == 'fbgemm':
qconfig = QConfig(activation=FakeQuantize.with_args(observer=MovingAverageMinMaxObserver,
quant_min=0,
quant_max=255,
reduce_range=True),
weight=default_per_channel_weight_fake_quant)
elif backend == 'qnnpack':
qconfig = QConfig(activation=FakeQuantize.with_args(observer=MovingAverageMinMaxObserver,
quant_min=0,
quant_max=255,
reduce_range=False),
weight=default_weight_fake_quant)
else:
qconfig = default_qat_qconfig
# Use the fused observer + fake_quant modules for doing QAT.
if version == 1:
if backend == 'fbgemm':
qconfig = QConfig(activation=FusedMovingAvgObsFakeQuantize.with_args(observer=MovingAverageMinMaxObserver,
quant_min=0,
quant_max=255,
reduce_range=True),
weight=default_fused_per_channel_wt_fake_quant)
elif backend == 'qnnpack':
qconfig = QConfig(activation=FusedMovingAvgObsFakeQuantize.with_args(observer=MovingAverageMinMaxObserver,
quant_min=0,
quant_max=255,
reduce_range=False),
weight=default_fused_wt_fake_quant)
else:
qconfig = default_qat_qconfig_v2
return qconfig
def get_default_qat_qconfig(backend='fbgemm', version=1):
"""
Returns the default QAT qconfig for the specified backend.
Args:
* `backend`: a string representing the target backend. Currently supports `fbgemm`,
`qnnpack` and `onednn`.
* `version`: version, for backwards compatibility. Can be `None` or `1`.
Return:
qconfig
"""
# Histogram observer is too slow for quantization aware training
if version == 0:
if backend == 'fbgemm':
qconfig = QConfig(activation=FakeQuantize.with_args(observer=MovingAverageMinMaxObserver,
quant_min=0,
quant_max=255,
reduce_range=True),
weight=default_per_channel_weight_fake_quant)
elif backend == 'qnnpack':
qconfig = QConfig(activation=FakeQuantize.with_args(observer=MovingAverageMinMaxObserver,
quant_min=0,
quant_max=255,
reduce_range=False),
weight=default_weight_fake_quant)
elif backend == 'onednn':
qconfig = QConfig(activation=FakeQuantize.with_args(observer=MovingAverageMinMaxObserver,
quant_min=0,
quant_max=255),
weight=default_per_channel_weight_fake_quant)
else:
qconfig = default_qat_qconfig
# Use the fused observe + fake_quant modules for doing QAT.
elif version == 1:
if backend == 'fbgemm':
qconfig = QConfig(activation=FusedMovingAvgObsFakeQuantize.with_args(observer=MovingAverageMinMaxObserver,
quant_min=0,
quant_max=255,
reduce_range=True),
weight=default_fused_per_channel_wt_fake_quant)
elif backend == 'qnnpack':
qconfig = QConfig(activation=FusedMovingAvgObsFakeQuantize.with_args(observer=MovingAverageMinMaxObserver,
quant_min=0,
quant_max=255,
reduce_range=False),
weight=default_fused_wt_fake_quant)
elif backend == 'onednn':
qconfig = QConfig(activation=FusedMovingAvgObsFakeQuantize.with_args(observer=MovingAverageMinMaxObserver,
quant_min=0,
quant_max=255),
weight=default_fused_per_channel_wt_fake_quant)
else:
qconfig = default_qat_qconfig_v2
else:
raise AssertionError("Version number: " + str(version) +
"in get_default_qat_qconfig is not supported. Version number must be 0 or 1")
return qconfig
import torch
from torch.ao.quantization.qconfig import QConfig
from torch.ao.quantization import MinMaxObserver
from torch.ao.quantization.fake_quantize import FakeQuantize
from torch.ao.quantization.experimental.fake_quantize import APoTFakeQuantize
"""
Default symmetric fake_quant for activations.
"""
default_symmetric_fake_quant = FakeQuantize.with_args(
observer=MinMaxObserver,
qscheme=torch.per_tensor_symmetric,
dtype=torch.quint8)
"""
Default symmetric fake_quant for weights.
"""
default_weight_symmetric_fake_quant = FakeQuantize.with_args(
observer=MinMaxObserver,
qscheme=torch.per_tensor_symmetric,
dtype=torch.qint8)
# uniform activation and weight, b=8 k=2
uniform_qconfig_8bit = QConfig(
activation=default_symmetric_fake_quant,
weight=default_weight_symmetric_fake_quant.with_args)
# uniform activation, APoT weight, b=8 k=2
apot_weight_qconfig_8bit = QConfig(
activation=default_symmetric_fake_quant.with_args,
weight=APoTFakeQuantize.with_args(b=8, k=2, dtype=torch.qint8))
# APoT activation and uniform weight, b=8 k=2
