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PyTorch quantization observer

文章目录

  • PyTorch quantization observer
    • basic class
    • standard observer
    • substandard observer

PyTorch quantization observer

basic class

nameinheritdescribe
ObserverBaseABC, nn.ModuleBase observer Module
UniformQuantizationObserverBaseObserverBase

standard observer

nameinheritdescribe
MinMaxObserverUniformQuantizationObserverBasecomputing the quantization parameters based on the running min and max values
MovingAverageMinMaxObserverMinMaxObservercomputing the quantization parameters based on the moving average of the min and max values
PerChannelMinMaxObserverUniformQuantizationObserverBasecomputing the quantization parameters based on the running per channel min and max values
MovingAveragePerChannelMinMaxObserverPerChannelMinMaxObservercomputing the quantization parameters based on the running per channel min and max values
HistogramObserverUniformQuantizationObserverBaserecords the running histogram of tensor values along with min/max values.
PlaceholderObserverObserverBasedoesn’t do anything and just passes its configuration to the quantized module’s .from_float().
RecordingObserverObserverBasemainly for debug and records the tensor values during runtime.
NoopObserverObserverBasedoesn’t do anything and just passes its configuration to the quantized module’s .from_float().
FixedQParamsObserverObserverBase
ReuseInputObserverObserverBase

substandard observer

nameinheritdescribe
default_observerMinMaxObserverquant_min=0,
quant_max=127
default_placeholder_observerPlaceholderObserverDefault placeholder observer, usually used for quantization to torch.float16.
default_debug_observerRecordingObserverDefault debug-only observer.
default_weight_observerMinMaxObserverdtype=torch.qint8,
qscheme=torch.per_tensor_symmetric
default_histogram_observerHistogramObserverquant_min=0,
quant_max=127
default_per_channel_weight_observerPerChannelMinMaxObserverdtype=torch.qint8,
qscheme=torch.per_channel_symmetric
default_dynamic_quant_observerPlaceholderObserverdtype=torch.float,
compute_dtype=torch.quint8
default_float_qparams_observerPerChannelMinMaxObserverdtype=torch.quint8,
qscheme=torch.per_channel_affine_float_qparams,
ch_axis=0
weight_observer_range_neg_127_to_127MinMaxObserverdtype=torch.qint8,
qscheme=torch.per_tensor_symmetric,
quant_min=-127,
quant_max=127,
eps=2 ** -12
per_channel_weight_observer_range_neg_127_to_127MinMaxObserverdtype=torch.qint8,
qscheme=torch.per_channel_symmetric,
quant_min=-127,
quant_max=127,
eps=2 ** -12
default_float_qparams_observer_4bitPerChannelMinMaxObserverdtype=torch.quint4x2, qscheme=torch.per_channel_affine_float_qparams,
ch_axis=0
default_fixed_qparams_range_neg1to1_observerFixedQParamsObserverscale=2.0 / 256.0,
zero_point=128,
dtype=torch.quint8,
quant_min=0,
quant_max=255
default_fixed_qparams_range_0to1_observerFixedQParamsObserverscale=1.0 / 256.0,
zero_point=0,
dtype=torch.quint8,
quant_min=0,
quant_max=255
default_symmetric_fixed_qparams_observerdefault_fixed_qparams_range_neg1to1_observer
default_affine_fixed_qparams_observerdefault_fixed_qparams_range_0to1_observer
default_reuse_input_observerReuseInputObserver
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