Source code for torch.ao.quantization.fake_quantize
importtorchfromtorch.nnimportModulefromtorch.quantization.observerimport(MovingAverageMinMaxObserver,HistogramObserver,MovingAveragePerChannelMinMaxObserver,_with_args,)importrefromabcimportABC,abstractmethodfromtypingimportAny,Tupledef_is_per_channel(qscheme:'torch.qscheme')->bool:returnqschemein[torch.per_channel_symmetric,torch.per_channel_affine]def_is_per_tensor(qscheme:'torch.qscheme')->bool:returnqschemein[torch.per_tensor_symmetric,torch.per_tensor_affine]def_is_symmetric_quant(qscheme:'torch.qscheme')->bool:returnqschemein[torch.per_tensor_symmetric,torch.per_channel_symmetric]classFakeQuantizeBase(ABC,Module):r""" Base fake quantize module Any fake quantize implementation should derive from this class. Concrete fake quantize module should follow the same API. In forward, they will update the statistics of the observed Tensor and fake quantize the input. They should also provide a `calculate_qparams` function that computes the quantization parameters given the collected statistics. """fake_quant_enabled:torch.Tensorobserver_enabled:torch.Tensordef__init__(self):super().__init__()# fake_quant_enabled and observer_enabled are buffers to support their# replication in DDP. Data type is uint8 because NCCL does not support# bool tensors.self.register_buffer('fake_quant_enabled',torch.tensor([1],dtype=torch.uint8))self.register_buffer('observer_enabled',torch.tensor([1],dtype=torch.uint8))@abstractmethoddefforward(self,x):pass@abstractmethoddefcalculate_qparams(self,**kwargs):pass@torch.jit.exportdefenable_fake_quant(self,enabled:bool=True)->None:self.fake_quant_enabled[0]=1ifenabledelse0@torch.jit.exportdefdisable_fake_quant(self):self.enable_fake_quant(False)@torch.jit.exportdefenable_observer(self,enabled:bool=True)->None:self.observer_enabled[0]=1ifenabledelse0@torch.jit.exportdefdisable_observer(self):self.enable_observer(False)with_args=classmethod(_with_args)
[docs]classFakeQuantize(FakeQuantizeBase):r""" Simulate the quantize and dequantize operations in training time. The output of this module is given by x_out = (clamp(round(x/scale + zero_point), quant_min, quant_max)-zero_point)*scale * :attr:`scale` defines the scale factor used for quantization. * :attr:`zero_point` specifies the quantized value to which 0 in floating point maps to * :attr:`quant_min` specifies the minimum allowable quantized value. * :attr:`quant_max` specifies the maximum allowable quantized value. * :attr:`fake_quant_enable` controls the application of fake quantization on tensors, note that statistics can still be updated. * :attr:`observer_enable` controls statistics collection on tensors * :attr:`dtype` specifies the quantized dtype that is being emulated with fake-quantization, allowable values are torch.qint8 and torch.quint8. The values of quant_min and quant_max should be chosen to be consistent with the dtype Args: observer (module): Module for observing statistics on input tensors and calculating scale and zero-point. quant_min (int): The minimum allowable quantized value. quant_max (int): The maximum allowable quantized value. observer_kwargs (optional): Arguments for the observer module Attributes: observer (Module): User provided module that collects statistics on the input tensor and provides a method to calculate scale and zero-point. """scale:torch.Tensorzero_point:torch.Tensordef__init__(self,observer=MovingAverageMinMaxObserver,quant_min=0,quant_max=255,**observer_kwargs):super().__init__()assertquant_min<=quant_max, \
'quant_min must be less than or equal to quant_max'self.quant_min=quant_minself.quant_max=quant_maxself.activation_post_process=observer(**observer_kwargs)asserttorch.iinfo(self.activation_post_process.dtype).min<=quant_min,'quant_min out of bound'assertquant_max<=torch.iinfo(self.activation_post_process.dtype).max,'quant_max out of bound'self.register_buffer('scale',torch.tensor([1.0],dtype=torch.float))self.register_buffer('zero_point',torch.tensor([0],dtype=torch.int))self.dtype=self.activation_post_process.dtypeself.qscheme=self.activation_post_process.qschemeself.ch_axis=self.activation_post_process.ch_axis \
ifhasattr(self.activation_post_process,'ch_axis')else-1assert_is_per_channel(self.qscheme)or \
_is_per_tensor(self.qscheme), \
'Only per channel and per tensor quantization are supported in fake quantize'+ \
' got qscheme: '+str(self.qscheme)self.is_per_channel=_is_per_channel(self.qscheme)@torch.jit.exportdefcalculate_qparams(self):returnself.activation_post_process.calculate_qparams()defforward(self,X):ifself.observer_enabled[0]==1:self.activation_post_process(X.detach())_scale,_zero_point=self.calculate_qparams()_scale,_zero_point=_scale.to(self.scale.device),_zero_point.to(self.zero_point.device)ifself.scale.shape!=_scale.shape:self.scale.resize_(_scale.shape)self.zero_point.resize_(_zero_point.shape)self.scale.copy_(_scale)self.zero_point.copy_(_zero_point)ifself.fake_quant_enabled[0]==1:ifself.is_per_channel:X=torch.fake_quantize_per_channel_affine(X,self.scale,self.zero_point,self.ch_axis,self.quant_min,self.quant_max)else:X=torch.fake_quantize_per_tensor_affine(X,self.scale,self.zero_point,self.quant_min,self.quant_max)returnX@torch.jit.exportdefextra_repr(self):return'fake_quant_enabled={}, observer_enabled={}, ' \
'quant_min={}, quant_max={}, dtype={}, qscheme={}, ch_axis={}, ' \
'scale={}, zero_point={}'.format(self.fake_quant_enabled,self.observer_enabled,self.quant_min,self.quant_max,self.dtype,self.qscheme,self.ch_axis,self.scale,self.zero_point)def_save_to_state_dict(self,destination,prefix,keep_vars):# We cannot currently register scalar values as buffers, so need to manually# specify serialization here.super(FakeQuantize,self)._save_to_state_dict(destination,prefix,keep_vars)destination[prefix+'scale']=self.scaledestination[prefix+'zero_point']=self.zero_pointdef_load_from_state_dict(self,state_dict,prefix,local_metadata,strict,missing_keys,unexpected_keys,error_msgs):# Removing this function throws an error that the the size of the loaded tensor does not match the original size# i.e., These buffers start out with numel 0 and become numel 1 once they have their first forward pass.local_state=['scale','zero_point']fornameinlocal_state:key=prefix+nameifkeyinstate_dict:val=state_dict[key]# Custom handling to allow loading scale and zero_point# of size N into uninitialized buffers of size 0. The# buffers are resized here, and the values are copied in# the default state_dict loading code of the parent.ifname=='scale':self.scale.resize_(val.shape)else:assertname=='zero_point'self.zero_point.resize_(val.shape)# For torchscript module we need to update the attributes here since we do not# call the `_load_from_state_dict` function defined module.pyiftorch.jit.is_scripting():ifname=='scale':self.scale.copy_(val)else:assertname=='zero_point'self.zero_point.copy_(val)elifstrict:missing_keys.append(key)super(FakeQuantize,self)._load_from_state_dict(state_dict,prefix,local_metadata,strict,missing_keys,unexpected_keys,error_msgs)
classFixedQParamsFakeQuantize(FakeQuantizeBase):""" Simulate quantize and dequantize with fixed quantization parameters in training time. Only per tensor quantization is supported. Args: `scale` (float): fixed scale for the fake quantize module `zero_point` (int): fixed zero point for the fake quantize module `dtype`, `qscheme`, `quant_min`, `quant_max` """scale:torch.Tensorzero_point:torch.Tensordef__init__(self,scale,zero_point,dtype=torch.quint8,qscheme=torch.per_tensor_affine,quant_min=0,quant_max=255):super().__init__()assertquant_min<=quant_max,'quant_min should be less than or equal to quant_max'self.quant_min=quant_minself.quant_max=quant_maxself.register_buffer('scale',torch.tensor([scale],dtype=torch.float))self.register_buffer('zero_point',torch.tensor([zero_point],dtype=torch.int))self.dtype=dtypeself.qscheme=qschemeassert_is_per_tensor(self.qscheme),'Only per tensor quantization is supported'+ \
' FixedQParamsFakeQuantize module, got qscheme:'+str(self.qscheme)defforward(self,X):ifself.fake_quant_enabled[0]==1:X=torch.fake_quantize_per_tensor_affine(X,self.scale,self.zero_point,self.quant_min,self.quant_max)returnX@torch.jit.exportdefcalculate_qparams(self):returnself.scale,self.zero_point@torch.jit.exportdefextra_repr(self):return'fake_quant_enabled={}, observer_enabled={}, scale={}, zero_point={}, ' \
'dtype={}, quant_min={}, quant_max={}, qscheme={}'.format(self.fake_quant_enabled,self.observer_enabled,self.scale,self.zero_point,self.dtype,self.quant_min,self.quant_max,self.qscheme)classFusedMovingAvgObsFakeQuantize(FakeQuantize):r"""Fused module that is used to observe the input tensor (compute min/max), compute scale/zero_point and fake_quantize the tensor. This module uses calculation similar MovingAverageMinMaxObserver for the inputs, to compute the min/max values in order to compute the scale/zero_point. The qscheme input in the observer is used to differentiate between symmetric/affine quantization scheme. The output of this module is given by x_out = (clamp(round(x/scale + zero_point), quant_min, quant_max)-zero_point)*scale Similar to :class:`~torch.quantization.FakeQuantize`, and accepts the same attributes as the base class. """def__init__(self,observer:Any=MovingAverageMinMaxObserver,quant_min:int=0,quant_max:int=255,**observer_kwargs:Any)->None:super().__init__(observer,quant_min,quant_max,**observer_kwargs)assertisinstance(self.activation_post_process,(MovingAverageMinMaxObserver,MovingAveragePerChannelMinMaxObserver)),\
"Fused observer+fake_quant module only works with MovingAverageMinMaxObserver"self.quant_min:int=quant_minself.quant_max:int=quant_maxself.register_buffer("fake_quant_enabled",torch.tensor([1],dtype=torch.long))self.register_buffer("observer_enabled",torch.tensor([1],dtype=torch.long))self.is_symmetric_quant=_is_symmetric_quant(self.activation_post_process.qscheme)self.quant_min,self.quant_max=self.activation_post_process.quant_min,self.activation_post_process.quant_max@torch.jit.exportdefcalculate_qparams(self)->Tuple[torch.Tensor,torch.Tensor]:returnself.activation_post_process.calculate_qparams()@torch.jit.exportdefextra_repr(self)->str:return("fake_quant_enabled={}, observer_enabled={}, scale={}, zero_point={}, ""dtype={}, quant_min={}, quant_max={}, qscheme={}, reduce_range={}".format(self.fake_quant_enabled,self.observer_enabled,self.scale,self.zero_point,self.dtype,self.quant_min,self.quant_max,self.qscheme,self.activation_post_process.reduce_range,))defforward(self,X:torch.Tensor)->torch.Tensor:returntorch.fused_moving_avg_obs_fake_quant(X,self.observer_enabled,self.fake_quant_enabled,self.activation_post_process.min_val,self.activation_post_process.max_val,self.scale,self.zero_point,self.activation_post_process.averaging_constant,self.quant_min,self.quant_max,self.ch_axis,self.is_per_channel,self.is_symmetric_quant,)default_fake_quant=FakeQuantize.with_args(observer=MovingAverageMinMaxObserver,quant_min=0,quant_max=255,dtype=torch.quint8,qscheme=torch.per_tensor_affine,reduce_range=True)default_weight_fake_quant=FakeQuantize.with_args(observer=MovingAverageMinMaxObserver,quant_min=-128,quant_max=127,dtype=torch.qint8,qscheme=torch.per_tensor_symmetric,reduce_range=False)# TODO(future PR): remove these defaults and enforce activation functions# to explicitly specify their output rangedefault_symmetric_fixed_qparams_fake_quant=FixedQParamsFakeQuantize.with_args(scale=2.0/256.0,zero_point=128,dtype=torch.quint8,quant_min=0,quant_max=255)default_affine_fixed_qparams_fake_quant=FixedQParamsFakeQuantize.with_args(scale=1.0/256.0,zero_point=0,dtype=torch.quint8,quant_min=0,quant_max=255)default_per_channel_weight_fake_quant=FakeQuantize.with_args(observer=MovingAveragePerChannelMinMaxObserver,quant_min=-128,quant_max=127,dtype=torch.qint8,qscheme=torch.per_channel_symmetric,reduce_range=False,ch_axis=0)default_histogram_fake_quant=FakeQuantize.with_args(observer=HistogramObserver,quant_min=0,quant_max=255,dtype=torch.quint8,qscheme=torch.per_tensor_affine,reduce_range=True)default_fused_act_fake_quant=FusedMovingAvgObsFakeQuantize.with_args(observer=MovingAverageMinMaxObserver,quant_min=0,quant_max=255,dtype=torch.quint8,)default_fused_wt_fake_quant=FusedMovingAvgObsFakeQuantize.with_args(observer=MovingAverageMinMaxObserver,quant_min=-128,quant_max=127,dtype=torch.qint8,qscheme=torch.per_tensor_symmetric)default_fused_per_channel_wt_fake_quant=FusedMovingAvgObsFakeQuantize.with_args(observer=MovingAveragePerChannelMinMaxObserver,quant_min=-128,quant_max=127,dtype=torch.qint8,qscheme=torch.per_channel_symmetric)def_is_fake_quant_script_module(mod):''' Returns true if given mod is an instance of FakeQuantize script module. '''ifisinstance(mod,torch.jit.RecursiveScriptModule):# qualified name looks like '__torch__.torch.ao.quantization.fake_quantize.___torch_mangle_2.FakeQuantize'suffix=mod._c.qualified_name.split('.',1)[1]name=re.sub(r'\.___torch_mangle_\d+','',suffix)returnname=='torch.ao.quantization.fake_quantize.FakeQuantize'or \
name=='torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize'returnFalsedefdisable_fake_quant(mod):ifisinstance(mod,FakeQuantizeBase)or_is_fake_quant_script_module(mod):mod.disable_fake_quant()defenable_fake_quant(mod):ifisinstance(mod,FakeQuantizeBase)or_is_fake_quant_script_module(mod):mod.enable_fake_quant()defdisable_observer(mod):ifisinstance(mod,FakeQuantizeBase)or_is_fake_quant_script_module(mod):mod.disable_observer()defenable_observer(mod):ifisinstance(mod,FakeQuantizeBase)or_is_fake_quant_script_module(mod):mod.enable_observer()
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