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
raise AssertionError(
"Version number: " + str(version) +
"in get_default_qat_qconfig is not supported. Version number must be 0 or 1"
)
return qconfig
"""
Default symmetric QAT qconfig for qnnpack. And its per channel weight variant.
"""
default_symmetric_qnnpack_qat_qconfig = QConfig(
activation=FusedMovingAvgObsFakeQuantize.with_args(
observer=MovingAverageMinMaxObserver,
quant_min=-128,
quant_max=127,
dtype=torch.qint8,
reduce_range=False,
eps=2**-12),
weight=fused_wt_fake_quant_range_neg_127_to_127)
default_per_channel_symmetric_qnnpack_qat_qconfig = QConfig(
activation=FusedMovingAvgObsFakeQuantize.with_args(
observer=MovingAverageMinMaxObserver,
quant_min=-128,
quant_max=127,
dtype=torch.qint8,
reduce_range=False,
eps=2**-12),
weight=fused_per_channel_wt_fake_quant_range_neg_127_to_127)
