# mypy: allow-untyped-decorators # mypy: allow-untyped-defs """ This module implements observers which are used to collect statistics about the values observed during calibration (PTQ) or training (QAT). """ import re import warnings from abc import ABCMeta, abstractmethod from collections import OrderedDict from functools import partial from typing import Any, Dict, List, Optional, Tuple import torch import torch.nn as nn from torch.ao.quantization.utils import ( calculate_qmin_qmax, check_min_max_valid, is_per_channel, is_per_tensor, validate_qmin_qmax, ) __all__ = [ "default_affine_fixed_qparams_observer", "default_debug_observer", "default_dynamic_quant_observer", "default_fixed_qparams_range_0to1_observer", "default_fixed_qparams_range_neg1to1_observer", "default_float_qparams_observer", "default_float_qparams_observer_4bit", "default_histogram_observer", "default_observer", "default_per_channel_weight_observer", "default_placeholder_observer", "default_reuse_input_observer", "default_symmetric_fixed_qparams_observer", "default_weight_observer", "get_observer_state_dict", "load_observer_state_dict", "per_channel_weight_observer_range_neg_127_to_127", "weight_observer_range_neg_127_to_127", "FixedQParamsObserver", "HistogramObserver", "MinMaxObserver", "MovingAverageMinMaxObserver", "MovingAveragePerChannelMinMaxObserver", "NoopObserver", "ObserverBase", "PerChannelMinMaxObserver", "PlaceholderObserver", "RecordingObserver", "ReuseInputObserver", "UniformQuantizationObserverBase", ] class _PartialWrapper: def __init__(self, p): self.p = p self.callable_args = {} def __call__(self, *args, **keywords): # call each arg in callable_args and add them partial, then run with keywords # skip if arg_name in keywords so its possible to overwrite for arg_name in self.callable_args: if arg_name not in keywords: keywords = {**keywords, arg_name: self.callable_args[arg_name]()} return self.p(*args, **keywords) def __repr__(self): return self.p.__repr__() + self.callable_args.__repr__() def with_args(self, **kwargs): return _with_args(self, **kwargs) def with_callable_args(self, **kwargs): result = _PartialWrapper(p=self.p) result.callable_args = {**self.callable_args, **kwargs} return result def _with_args(cls_or_self, **kwargs): r"""Wrapper that allows creation of class factories. This can be useful when there is a need to create classes with the same constructor arguments, but different instances. Can be used in conjunction with _callable_args Example:: >>> # xdoctest: +SKIP("Undefined vars") >>> Foo.with_args = classmethod(_with_args) >>> foo_builder = Foo.with_args(a=3, b=4).with_args(answer=42) >>> foo_instance1 = foo_builder() >>> foo_instance2 = foo_builder() >>> id(foo_instance1) == id(foo_instance2) False """ r = _PartialWrapper(partial(cls_or_self, **kwargs)) return r def _with_callable_args(cls_or_self, **kwargs): r"""Wrapper that allows creation of class factories args that need to be called at construction time. This can be useful when there is a need to create classes with the same constructor arguments, but different instances and those arguments should only be calculated at construction time. Can be used in conjunction with _with_args Example:: >>> # xdoctest: +SKIP("Undefined vars") >>> Foo.with_callable_args = classmethod(_with_callable_args) >>> Foo.with_args = classmethod(_with_args) >>> foo_builder = Foo.with_callable_args(cur_time=get_time_func).with_args(name="dan") >>> foo_instance1 = foo_builder() >>> # wait 50 >>> foo_instance2 = foo_builder() >>> id(foo_instance1.creation_time) == id(foo_instance2.creation_time) False """ r = _PartialWrapper(partial(cls_or_self)) return r.with_callable_args(**kwargs) ABC: Any = ABCMeta("ABC", (object,), {}) # compatible with Python 2 *and* 3: class ObserverBase(ABC, nn.Module): r"""Base observer Module. Any observer implementation should derive from this class. Concrete observers should follow the same API. In forward, they will update the statistics of the observed Tensor. And they should provide a `calculate_qparams` function that computes the quantization parameters given the collected statistics. Args: dtype: dtype argument to the `quantize` node needed to implement the reference model spec. is_dynamic: indicator for whether the observer is a placeholder for dynamic quantization or static quantization """ def __init__(self, dtype, is_dynamic=False): super().__init__() self.dtype = dtype self.is_dynamic = is_dynamic @abstractmethod def forward(self, x): pass @abstractmethod def calculate_qparams(self, **kwargs): pass with_args = classmethod(_with_args) with_callable_args = classmethod(_with_callable_args) class UniformQuantizationObserverBase(ObserverBase): r"""Common base for all observers using uniform quantization to calculate scale and zero_point. Args: dtype: dtype argument to the `quantize` node needed to implement the reference model spec. qscheme: Quantization scheme to be used. reduce_range: Reduces the range of the quantized data type by 1 bit. This is sometimes required to avoid instruction overflow. quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup. quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup. eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`. .. warning:: :attr:`dtype` can only take ``torch.qint8`` or ``torch.quint8``. or `torch.int8` or `torch.uint8` .. warning:: :attr:`qscheme` can only take one of the following options: - ``torch.per_tensor_affine`` - ``torch.per_tensor_symmetric`` - ``torch.per_channel_affine`` - ``torch.per_channel_symmetric`` """ # Note: the version is shared by all observer types # # Version 1/None # self # # Version 2 (base class only, does not include child class buffers) # self # |--- eps : Tensor # # Version 3 # for HistogramObserver only, changed the shape of uninitialized # min_val and max_val buffers from torch.Size([0]) to torch.Size([]) # for PerChannelObservers, changed the name of the buffers from min_vals # to min_val and from max_vals to max_val. _version = 3 eps: torch.Tensor def __init__( self, dtype=torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=False, quant_min=None, quant_max=None, factory_kwargs=None, eps=torch.finfo(torch.float32).eps, is_dynamic=False, **kwargs, ) -> None: factory_kwargs = torch.nn.factory_kwargs(factory_kwargs) super().__init__(dtype=dtype, is_dynamic=is_dynamic, **kwargs) self.qscheme = qscheme if reduce_range: warnings.warn( "Please use quant_min and quant_max to specify the range for observers. \ reduce_range will be deprecated in a future release of PyTorch." ) self.reduce_range = reduce_range self.register_buffer("eps", torch.tensor([eps], **factory_kwargs)) assert self.qscheme in ( torch.per_tensor_affine, torch.per_tensor_symmetric, torch.per_channel_affine, torch.per_channel_symmetric, torch.per_channel_affine_float_qparams, ), "Default Observer only works for per_tensor_affine, \ per_tensor_symmetric, per_channel_affine, \ per_channel_symmetric and per_channel_float_qparams quantization scheme" _ALLOWED_DTYPES = ( torch.qint8, torch.quint8, torch.quint4x2, torch.qint32, torch.int8, torch.uint8, torch.int16, torch.int32, torch.float8_e5m2, torch.float8_e4m3fn, ) assert ( self.dtype in _ALLOWED_DTYPES ), f"Default Observer only works for {_ALLOWED_DTYPES} data type" self.has_customized_qrange = (quant_min is not None) and (quant_max is not None) if self.has_customized_qrange: validate_qmin_qmax(quant_min, quant_max) self.quant_min, self.quant_max = calculate_qmin_qmax( quant_min, quant_max, self.has_customized_qrange, self.dtype, self.reduce_range, ) def _load_from_state_dict( self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, ): version = local_metadata.get("version", None) if version is None or version == 1: # eps was moved to a buffer in version 2 eps = torch.tensor([torch.finfo(torch.float32).eps]) state_dict[prefix + "eps"] = eps super()._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, ) @torch.jit.export def _validate_qmin_qmax(self, quant_min: int, quant_max: int) -> None: r"""Validates that the user-specified quantization range is properly initialized and within the given bound supported by the observer dtype. To accommodate lower-bit quantization with respect to the existing torch.qint8 and torch.quint8 datatypes, the user can choose to use dynamic quantization range by passing in a tuple of initial qmin and qmax values. One use case is these customized qmin and qmax values are used to calculate static estimates of the scale and zero point for aggressive lower-bit fake quantization. These estimates are compared against parameters learned through backpropagation. The related literatures for scale and zero point via backpropagation are as follows: Learned Step Size Quantization: https://openreview.net/pdf?id=rkgO66VKDS Trained Quantization Thresholds: https://arxiv.org/pdf/1903.08066.pdf """ # The variable names are prefixed with "initial" because their values (qmin and qmax) might be adjusted # based on whether quantization range is reduced and the datatype (signed/unsigned) used by the observer. assert ( quant_min <= 0 <= quant_max ), "Used-specified quantization range must include 0." assert ( quant_min < quant_max ), "qmin must be strictly less than qmax for user-specified quantization range." @torch.jit.export def _calculate_qparams( self, min_val: torch.Tensor, max_val: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor]: r"""Calculates the quantization parameters, given min and max value tensors. Works for both per tensor and per channel cases Args: min_val: Minimum values per channel max_val: Maximum values per channel Returns: scales: Scales tensor of shape (#channels,) zero_points: Zero points tensor of shape (#channels,) """ # Functionally equivalent to 'determine_qparams' in utils.py. Observers must be torchscriptable however and qscheme # as far as I can tell is not allowed to passed as a parameter in torchscript functions. This makes refactoring observer # to use this utility a massive pain and very gross. For now Im opting just to duplicate as this code # seems unlikey to change (last update over 1 year ago) and when torchscript is fully deprecated we can refactor. # TODO(jakeszwe, jerryzh168) if not check_min_max_valid(min_val, max_val): return torch.tensor([1.0], device=min_val.device.type), torch.tensor( [0], device=min_val.device.type ) quant_min, quant_max = self.quant_min, self.quant_max min_val_neg = torch.min(min_val, torch.zeros_like(min_val)) max_val_pos = torch.max(max_val, torch.zeros_like(max_val)) device = min_val_neg.device scale = torch.ones(min_val_neg.size(), dtype=torch.float32, device=device) zero_point = torch.zeros(min_val_neg.size(), dtype=torch.int64, device=device) if ( self.qscheme == torch.per_tensor_symmetric or self.qscheme == torch.per_channel_symmetric ): max_val_pos = torch.max(-min_val_neg, max_val_pos) scale = max_val_pos / (float(quant_max - quant_min) / 2) scale = torch.max(scale, self.eps) if self.dtype in [torch.quint8, torch.uint8]: if self.has_customized_qrange: # When customized quantization range is used, down-rounded midpoint of the range is chosen. zero_point = zero_point.new_full( zero_point.size(), (quant_min + quant_max) // 2 ) else: zero_point = zero_point.new_full(zero_point.size(), 128) elif self.qscheme == torch.per_channel_affine_float_qparams: scale = (max_val - min_val) / float(quant_max - quant_min) scale = torch.where(scale > self.eps, scale, torch.ones_like(scale)) # We use the quantize function # xq = Round(Xf * inv_scale + zero_point), # setting zero_point to (-1 * min *inv_scale) we get # Xq = Round((Xf - min) * inv_scale) zero_point = -1 * min_val / scale else: scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min) scale = torch.max(scale, self.eps) zero_point = quant_min - torch.round(min_val_neg / scale).to(torch.int) zero_point = torch.clamp(zero_point, quant_min, quant_max) # For scalar values, cast them to Tensors of size 1 to keep the shape # consistent with default values in FakeQuantize. if len(scale.shape) == 0: # TODO: switch to scale.item() after adding JIT support scale = torch.tensor([float(scale)], dtype=scale.dtype, device=device) if len(zero_point.shape) == 0: # TODO: switch to zero_point.item() after adding JIT support zero_point = torch.tensor( [int(zero_point)], dtype=zero_point.dtype, device=device ) if self.qscheme == torch.per_channel_affine_float_qparams: zero_point = torch.tensor( [float(zero_point)], dtype=zero_point.dtype, device=device ) return scale, zero_point @torch.jit.export def reset_min_max_vals(self): raise NotImplementedError("Cannot reset min/max values in the given observer.") # Originally, this class was called `_ObserverBase`. Keeping the old name around # for backwards compatibility. # TODO(after v1.13): delete this _ObserverBase = UniformQuantizationObserverBase class MinMaxObserver(UniformQuantizationObserverBase): r"""Observer module for computing the quantization parameters based on the running min and max values. This observer uses the tensor min/max statistics to compute the quantization parameters. The module records the running minimum and maximum of incoming tensors, and uses this statistic to compute the quantization parameters. Args: dtype: dtype argument to the `quantize` node needed to implement the reference model spec. qscheme: Quantization scheme to be used reduce_range: Reduces the range of the quantized data type by 1 bit quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup. quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup. eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`. Given running min/max as :math:`x_\text{min}` and :math:`x_\text{max}`, scale :math:`s` and zero point :math:`z` are computed as: The running minimum/maximum :math:`x_\text{min/max}` is computed as: .. math:: \begin{array}{ll} x_\text{min} &= \begin{cases} \min(X) & \text{if~}x_\text{min} = \text{None} \\ \min\left(x_\text{min}, \min(X)\right) & \text{otherwise} \end{cases}\\ x_\text{max} &= \begin{cases} \max(X) & \text{if~}x_\text{max} = \text{None} \\ \max\left(x_\text{max}, \max(X)\right) & \text{otherwise} \end{cases}\\ \end{array} where :math:`X` is the observed tensor. The scale :math:`s` and zero point :math:`z` are then computed as: .. math:: \begin{aligned} \text{if Symmetric:}&\\ &s = 2 \max(|x_\text{min}|, x_\text{max}) / \left( Q_\text{max} - Q_\text{min} \right) \\ &z = \begin{cases} 0 & \text{if dtype is qint8} \\ 128 & \text{otherwise} \end{cases}\\ \text{Otherwise:}&\\ &s = \left( x_\text{max} - x_\text{min} \right ) / \left( Q_\text{max} - Q_\text{min} \right ) \\ &z = Q_\text{min} - \text{round}(x_\text{min} / s) \end{aligned} where :math:`Q_\text{min}` and :math:`Q_\text{max}` are the minimum and maximum of the quantized data type. .. warning:: :attr:`dtype` can only take ``torch.qint8`` or ``torch.quint8``. .. note:: If the running minimum equals to the running maximum, the scale and zero_point are set to 1.0 and 0. """ min_val: torch.Tensor max_val: torch.Tensor def __init__( self, dtype=torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=False, quant_min=None, quant_max=None, factory_kwargs=None, eps=torch.finfo(torch.float32).eps, is_dynamic=False, **kwargs, ) -> None: if not is_per_tensor(qscheme): raise NotImplementedError( "MinMaxObserver's qscheme only support torch.per_tensor_symmetric \ and torch.per_tensor_affine." ) # TODO: MinMaxObserver by itself doesn't support dynamic quantization, but # if it's inherited by MovingAverageObserver, and averaging_constant is 1, it # supports dynamic quantization, we may need to better error checking here # For x86 quantized kernels, we need to ensure that the vpmaddubsw # instruction does not overflow. We allow for a reduce_range argument to # observers that reduces the quantized range to (0,127) or (-64, 63). # For more details see aten/src/ATen/native/quantized/cpu/qconv.cpp # This is not an optimal choice for non x86 backends as it loses a bit # of precision for activations. super().__init__( dtype=dtype, qscheme=qscheme, reduce_range=reduce_range, quant_min=quant_min, quant_max=quant_max, factory_kwargs=factory_kwargs, eps=eps, is_dynamic=is_dynamic, **kwargs, ) factory_kwargs = torch.nn.factory_kwargs(factory_kwargs) self.register_buffer("min_val", torch.tensor(float("inf"), **factory_kwargs)) self.register_buffer("max_val", torch.tensor(float("-inf"), **factory_kwargs)) if ( self.qscheme == torch.per_tensor_symmetric and self.reduce_range and self.dtype == torch.quint8 ): raise NotImplementedError( "Cannot reduce range for symmetric \ quantization for quint8" ) def forward(self, x_orig): r"""Records the running minimum and maximum of ``x``.""" if x_orig.numel() == 0: return x_orig x = x_orig.detach() # avoid keeping autograd tape x = x.to(self.min_val.dtype) min_val_cur, max_val_cur = torch.aminmax(x) min_val = torch.min(min_val_cur, self.min_val) max_val = torch.max(max_val_cur, self.max_val) self.min_val.copy_(min_val) self.max_val.copy_(max_val) return x_orig @torch.jit.export def calculate_qparams(self): r"""Calculates the quantization parameters.""" return self._calculate_qparams(self.min_val, self.max_val) @torch.jit.export def extra_repr(self): return f"min_val={self.min_val}, max_val={self.max_val}" @torch.jit.export def reset_min_max_vals(self): """Resets the min/max values.""" self.min_val.copy_(torch.tensor(float("inf"))) self.max_val.copy_(torch.tensor(float("-inf"))) class MovingAverageMinMaxObserver(MinMaxObserver): r"""Observer module for computing the quantization parameters based on the moving average of the min and max values. This observer computes the quantization parameters based on the moving averages of minimums and maximums of the incoming tensors. The module records the average minimum and maximum of incoming tensors, and uses this statistic to compute the quantization parameters. Args: averaging_constant: Averaging constant for min/max. dtype: dtype argument to the `quantize` node needed to implement the reference model spec. qscheme: Quantization scheme to be used reduce_range: Reduces the range of the quantized data type by 1 bit quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup. quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup. eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`. The moving average min/max is computed as follows .. math:: \begin{array}{ll} x_\text{min} = \begin{cases} \min(X) & \text{if~}x_\text{min} = \text{None} \\ (1 - c) x_\text{min} + c \min(X) & \text{otherwise} \end{cases}\\ x_\text{max} = \begin{cases} \max(X) & \text{if~}x_\text{max} = \text{None} \\ (1 - c) x_\text{max} + c \max(X) & \text{otherwise} \end{cases}\\ \end{array} where :math:`x_\text{min/max}` is the running average min/max, :math:`X` is is the incoming tensor, and :math:`c` is the ``averaging_constant``. The scale and zero point are then computed as in :class:`~torch.ao.quantization.observer.MinMaxObserver`. .. note:: Only works with ``torch.per_tensor_affine`` quantization scheme. .. note:: If the running minimum equals to the running maximum, the scale and zero_point are set to 1.0 and 0. """ def __init__( self, averaging_constant=0.01, dtype=torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=False, quant_min=None, quant_max=None, eps=torch.finfo(torch.float32).eps, is_dynamic=False, **kwargs, ) -> None: if not is_per_tensor(qscheme): raise NotImplementedError( f"MovingAverageMinMaxObserver's qscheme only support \ torch.per_tensor_symmetric and torch.per_tensor_affine. \ but got: {qscheme}" ) self.averaging_constant = averaging_constant if is_dynamic and self.averaging_constant != 1: raise NotImplementedError( "MovingAverageMinMaxObserver doesn't support dynamic quantization for " f"averaging constant of {self.averaging_constant}" ) super().__init__( dtype=dtype, qscheme=qscheme, reduce_range=reduce_range, quant_min=quant_min, quant_max=quant_max, eps=eps, is_dynamic=is_dynamic, **kwargs, ) def forward(self, x_orig): if x_orig.numel() == 0: return x_orig x = x_orig.detach() # avoid keeping autograd tape x = x.to(self.min_val.dtype) min_val = self.min_val max_val = self.max_val if min_val == float("inf") and max_val == float("-inf"): min_val, max_val = torch.aminmax(x) else: min_val_cur, max_val_cur = torch.aminmax(x) min_val = min_val + self.averaging_constant * (min_val_cur - min_val) max_val = max_val + self.averaging_constant * (max_val_cur - max_val) self.min_val.copy_(min_val) self.max_val.copy_(max_val) return x_orig class PerChannelMinMaxObserver(UniformQuantizationObserverBase): r"""Observer module for computing the quantization parameters based on the running per channel min and max values. This observer uses the tensor min/max statistics to compute the per channel quantization parameters. The module records the running minimum and maximum of incoming tensors, and uses this statistic to compute the quantization parameters. Args: ch_axis: Channel axis dtype: dtype argument to the `quantize` node needed to implement the reference model spec. qscheme: Quantization scheme to be used reduce_range: Reduces the range of the quantized data type by 1 bit quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup. quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup. eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`. The quantization parameters are computed the same way as in :class:`~torch.ao.quantization.observer.MinMaxObserver`, with the difference that the running min/max values are stored per channel. Scales and zero points are thus computed per channel as well. .. note:: If the running minimum equals to the running maximum, the scales and zero_points are set to 1.0 and 0. """ min_val: torch.Tensor max_val: torch.Tensor def __init__( self, ch_axis=0, dtype=torch.quint8, qscheme=torch.per_channel_affine, reduce_range=False, quant_min=None, quant_max=None, factory_kwargs=None, eps=torch.finfo(torch.float32).eps, is_dynamic=False, **kwargs, ) -> None: if not is_per_channel(qscheme): raise NotImplementedError( "PerChannelMinMaxObserver's qscheme only support \ torch.per_channel_symmetric, torch.per_channel_affine and torch.per_channel_affine_float_qparams." ) if is_dynamic: raise NotImplementedError( "PerChannelMinMaxObserver doesn't support dynamic quantization" ) super().__init__( dtype=dtype, qscheme=qscheme, reduce_range=reduce_range, quant_min=quant_min, quant_max=quant_max, factory_kwargs=factory_kwargs, eps=eps, is_dynamic=is_dynamic, **kwargs, ) factory_kwargs = torch.nn.factory_kwargs(factory_kwargs) self.ch_axis = ch_axis self.register_buffer("min_val", torch.tensor([], **factory_kwargs)) self.register_buffer("max_val", torch.tensor([], **factory_kwargs)) if ( self.qscheme == torch.per_channel_symmetric and self.reduce_range and self.dtype == torch.quint8 ): raise NotImplementedError( "Cannot reduce range for symmetric quantization for quint8" ) def forward(self, x_orig): return self._forward(x_orig) def _forward(self, x_orig): if x_orig.numel() == 0: return x_orig x = x_orig.detach() # avoid keeping autograd tape min_val = self.min_val max_val = self.max_val x_dim = x.size() new_axis_list = [i for i in range(len(x_dim))] # noqa: C416 new_axis_list[self.ch_axis] = 0 new_axis_list[0] = self.ch_axis y = x.permute(new_axis_list) # Need to match dtype of min/max because the updates to buffers # are done in place and types need to match for comparisons y = y.to(self.min_val.dtype) y = torch.flatten(y, start_dim=1) if min_val.numel() == 0 or max_val.numel() == 0: min_val, max_val = torch.aminmax(y, dim=1) else: min_val_cur, max_val_cur = torch.aminmax(y, dim=1) min_val = torch.min(min_val_cur, min_val) max_val = torch.max(max_val_cur, max_val) self.min_val.resize_(min_val.shape) self.max_val.resize_(max_val.shape) self.min_val.copy_(min_val) self.max_val.copy_(max_val) return x_orig @torch.jit.export def calculate_qparams(self): return self._calculate_qparams(self.min_val, self.max_val) def extra_repr(self): return f"min_val={self.min_val}, max_val={self.max_val}" def _load_from_state_dict( self, state_dict: Dict[str, Any], prefix: str, local_metadata: Dict[str, torch.Tensor], strict: bool, missing_keys: List[str], unexpected_keys: List[str], error_msgs: List[str], ): version = local_metadata.get("version", None) if version is not None and version < 3: local_state = ["min_vals", "max_vals"] expected_min_name = "min_vals" expected_max_name = "max_vals" else: local_state = ["min_val", "max_val"] expected_min_name = "min_val" expected_max_name = "max_val" for name in local_state: key = prefix + name if key in state_dict: val = state_dict[key] # Custom handling to allow loading min_val or max_val # 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. if name == expected_min_name: self.min_val.resize_(val.shape) elif name == expected_max_name: self.max_val.resize_(val.shape) else: warnings.warn( f"Observer load_from_state_dict got unexpected name {name}" ) # For torchscript module we need to update the attributes here since we do not # call the `_load_from_state_dict` function defined module.py if torch.jit.is_scripting(): if name == expected_min_name: self.min_val.copy_(val) elif name == expected_max_name: self.max_val.copy_(val) else: warnings.warn( f"Observer load_from_state_dict got unexpected name {name}" ) elif strict: missing_keys.append(key) if not torch.jit.is_scripting(): super()._load_from_state_dict( state_dict, prefix, local_metadata, False, missing_keys, unexpected_keys, error_msgs, ) def _load_from_state_dict_script( self, state_dict: Dict[str, Any], prefix: str, local_metadata: Dict[str, torch.Tensor], strict: bool, missing_keys: List[str], unexpected_keys: List[str], error_msgs: List[str], ): self._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, ) @torch.jit.export def reset_min_max_vals(self): """Resets the min/max values.""" # This used to be torch.ones but that does not work because # JIT compiler can optimize it via common subexpression elimination # in which case both min_val and max_val point to the same tensor. self.min_val = torch.rand( 0, ) self.max_val = torch.rand( 0, ) class MovingAveragePerChannelMinMaxObserver(PerChannelMinMaxObserver): r"""Observer module for computing the quantization parameters based on the running per channel min and max values. This observer uses the tensor min/max statistics to compute the per channel quantization parameters. The module records the running minimum and maximum of incoming tensors, and uses this statistic to compute the quantization parameters. Args: averaging_constant: Averaging constant for min/max. ch_axis: Channel axis dtype: Quantized data type qscheme: Quantization scheme to be used reduce_range: Reduces the range of the quantized data type by 1 bit quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup. quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup. eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`. The quantization parameters are computed the same way as in :class:`~torch.ao.quantization.observer.MovingAverageMinMaxObserver`, with the difference that the running min/max values are stored per channel. Scales and zero points are thus computed per channel as well. .. note:: If the running minimum equals to the running maximum, the scales and zero_points are set to 1.0 and 0. """ def __init__( self, averaging_constant=0.01, ch_axis=0, dtype=torch.quint8, qscheme=torch.per_channel_affine, reduce_range=False, quant_min=None, quant_max=None, eps=torch.finfo(torch.float32).eps, is_dynamic=False, **kwargs, ) -> None: if not is_per_channel(qscheme): raise NotImplementedError( "MovingAveragePerChannelMinMaxObserver's qscheme only support \ torch.per_channel_symmetric, torch.per_channel_affine and torch.per_channel_affine_float_qparams." ) if is_dynamic: raise NotImplementedError( "MovingAveragePerChannelMinMaxObserver doesn't support dynamic quantization" ) super().__init__( ch_axis=ch_axis, dtype=dtype, qscheme=qscheme, reduce_range=reduce_range, quant_min=quant_min, quant_max=quant_max, eps=eps, is_dynamic=is_dynamic, **kwargs, ) self.averaging_constant = averaging_constant def forward(self, x_orig): if x_orig.numel() == 0: return x_orig x = x_orig.detach() # avoid keeping autograd tape x = x.to(self.min_val.dtype) min_val = self.min_val max_val = self.max_val x_dim = x.size() new_axis_list = [i for i in range(len(x_dim))] # noqa: C416 new_axis_list[self.ch_axis] = 0 new_axis_list[0] = self.ch_axis y = x.permute(new_axis_list) y = torch.flatten(y, start_dim=1) if min_val.numel() == 0 or max_val.numel() == 0: min_val, max_val = torch.aminmax(y, dim=1) else: min_val_cur, max_val_cur = torch.aminmax(y, dim=1) min_val = min_val + self.averaging_constant * (min_val_cur - min_val) max_val = max_val + self.averaging_constant * (max_val_cur - max_val) self.min_val.resize_(min_val.shape) self.max_val.resize_(max_val.shape) self.min_val.copy_(min_val) self.max_val.copy_(max_val) return x_orig class HistogramObserver(UniformQuantizationObserverBase): r""" The module records the running histogram of tensor values along with min/max values. ``calculate_qparams`` will calculate scale and zero_point. Args: bins: Number of bins to use for the histogram dtype: dtype argument to the `quantize` node needed to implement the reference model spec qscheme: Quantization scheme to be used reduce_range: Reduces the range of the quantized data type by 1 bit eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`. The scale and zero point are computed as follows: 1. Create the histogram of the incoming inputs. The histogram is computed continuously, and the ranges per bin change with every new tensor observed. 2. Search the distribution in the histogram for optimal min/max values. The search for the min/max values ensures the minimization of the quantization error with respect to the floating point model. 3. Compute the scale and zero point the same way as in the :class:`~torch.ao.quantization.MinMaxObserver` """ histogram: torch.Tensor min_val: torch.Tensor max_val: torch.Tensor def __init__( self, bins: int = 2048, dtype: torch.dtype = torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=False, quant_min=None, quant_max=None, factory_kwargs=None, eps=torch.finfo(torch.float32).eps, is_dynamic=False, **kwargs, ) -> None: if not is_per_tensor(qscheme): raise NotImplementedError( "HistogramObserver's qscheme only support torch.per_tensor_symmetric \ and torch.per_tensor_affine." ) if is_dynamic: raise NotImplementedError( "HistogramObserver doesn't support dynamic quantization" ) # bins: The number of bins used for histogram calculation. super().__init__( dtype=dtype, qscheme=qscheme, reduce_range=reduce_range, quant_min=quant_min, quant_max=quant_max, factory_kwargs=factory_kwargs, eps=eps, is_dynamic=is_dynamic, **kwargs, ) factory_kwargs = torch.nn.factory_kwargs(factory_kwargs) self.bins = bins self.register_buffer("histogram", torch.zeros(self.bins, **factory_kwargs)) self.register_buffer("min_val", torch.tensor(float("inf"), **factory_kwargs)) self.register_buffer("max_val", torch.tensor(float("-inf"), **factory_kwargs)) self.dst_nbins = 2 ** torch.iinfo(self.dtype).bits self.upsample_rate = ( 16 # used to reduce quantization errors when upscaling histogram ) def _get_norm( self, delta_begin: torch.Tensor, delta_end: torch.Tensor, density: torch.Tensor ) -> torch.Tensor: r""" Compute the norm of the values uniformaly distributed between delta_begin and delta_end. Currently only L2 norm is supported. norm = density * (integral_{begin, end} x^2) = density * (end^3 - begin^3) / 3 """ norm = ( delta_end * delta_end * delta_end - delta_begin * delta_begin * delta_begin ) / 3 return density * norm def _compute_quantization_error(self, next_start_bin: int, next_end_bin: int): r""" Compute the quantization error if we use start_bin to end_bin as the min and max to do the quantization. """ bin_width = (self.max_val.item() - self.min_val.item()) / self.bins dst_bin_width = bin_width * (next_end_bin - next_start_bin + 1) / self.dst_nbins if dst_bin_width == 0.0: return 0.0 src_bin = torch.arange(self.bins, device=self.histogram.device) # distances from the beginning of first dst_bin to the beginning and # end of src_bin src_bin_begin = (src_bin - next_start_bin) * bin_width src_bin_end = src_bin_begin + bin_width # which dst_bins the beginning and end of src_bin belong to? dst_bin_of_begin = torch.clamp( torch.div(src_bin_begin, dst_bin_width, rounding_mode="floor"), 0, self.dst_nbins - 1, ) dst_bin_of_begin_center = (dst_bin_of_begin + 0.5) * dst_bin_width dst_bin_of_end = torch.clamp( torch.div(src_bin_end, dst_bin_width, rounding_mode="floor"), 0, self.dst_nbins - 1, ) density = self.histogram / bin_width norm = torch.zeros(self.bins, device=self.histogram.device) delta_begin = src_bin_begin - dst_bin_of_begin_center delta_end = dst_bin_width / 2 norm += self._get_norm( delta_begin, torch.ones(self.bins, device=self.histogram.device) * delta_end, density, ) norm += (dst_bin_of_end - dst_bin_of_begin - 1) * self._get_norm( torch.tensor(-dst_bin_width / 2), torch.tensor(dst_bin_width / 2), density ) dst_bin_of_end_center = dst_bin_of_end * dst_bin_width + dst_bin_width / 2 delta_begin = -dst_bin_width / 2 delta_end = src_bin_end - dst_bin_of_end_center norm += self._get_norm(torch.tensor(delta_begin), delta_end, density) return norm.sum().item() def _non_linear_param_search(self) -> Tuple[torch.Tensor, torch.Tensor]: r"""Non-linear parameter search. An approximation for L2 error minimization for selecting min/max. By selecting new min/max, we filter out outliers in input distribution. This follows the implementation of NormMinimization::NonlinearQuantizationParamsSearch in caffe2/quantization/server/norm_minimization.cc """ assert self.histogram.size()[0] == self.bins, "bins mismatch" bin_width = (self.max_val - self.min_val) / self.bins # cumulative sum total = torch.sum(self.histogram).item() cSum = torch.cumsum(self.histogram, dim=0) stepsize = 1e-5 # granularity alpha = 0.0 # lower bound beta = 1.0 # upper bound start_bin = 0 end_bin = self.bins - 1 norm_min = float("inf") while alpha < beta: # Find the next step next_alpha = alpha + stepsize next_beta = beta - stepsize # find the left and right bins between the quantile bounds l = start_bin r = end_bin while l < end_bin and cSum[l] < next_alpha * total: l = l + 1 while r > start_bin and cSum[r] > next_beta * total: r = r - 1 # decide the next move next_start_bin = start_bin next_end_bin = end_bin if (l - start_bin) > (end_bin - r): # move the start bin next_start_bin = l alpha = next_alpha else: # move the end bin next_end_bin = r beta = next_beta if next_start_bin == start_bin and next_end_bin == end_bin: continue # calculate the quantization error using next_start_bin and next_end_bin norm = self._compute_quantization_error(next_start_bin, next_end_bin) if norm > norm_min: break norm_min = norm start_bin = next_start_bin end_bin = next_end_bin new_min = self.min_val + bin_width * start_bin new_max = self.min_val + bin_width * (end_bin + 1) return new_min, new_max def _upscale_histogram( self, histogram: torch.Tensor, orig_min: torch.Tensor, orig_max: torch.Tensor, update_min: torch.Tensor, update_max: torch.Tensor, ): # this turns the histogram into a more fine-coarsed histogram to reduce # bin quantization errors histogram = histogram.repeat_interleave(self.upsample_rate) / self.upsample_rate bin_size = (orig_max - orig_min) / (self.bins * self.upsample_rate) mid_points_histogram = ( torch.linspace( orig_min, orig_max, self.bins * self.upsample_rate + 1, device=orig_min.device, )[:-1].to(histogram.device) + 0.5 * bin_size ) boundaries_new_histogram = torch.linspace( update_min, update_max, self.bins + 1, device=update_min.device ).to(histogram.device) # this maps the mid-poits of the histogram to the new histogram's space bucket_assignments = ( torch.bucketize(mid_points_histogram, boundaries_new_histogram, right=True) - 1 ) # this then maps the histogram mid-points in the new space, weighted by the original histogram's values # this is just the old histogram in the new histogram's space # In case due to numerical issues the values land higher/lower than the maximum/minimum bucket_assignments[bucket_assignments >= self.bins] = self.bins - 1 bucket_assignments[bucket_assignments < 0] = 0 update_histogram = torch.bincount( bucket_assignments, weights=histogram, minlength=self.bins ) return update_histogram def _combine_histograms( self, orig_hist: torch.Tensor, orig_min: torch.Tensor, orig_max: torch.Tensor, update_hist: torch.Tensor, update_min: torch.Tensor, update_max: torch.Tensor, ) -> torch.Tensor: # If the new min and max are the same as the current min and max, # we can just add the new histogram to the original histogram if update_min == orig_min and update_max == orig_max: return orig_hist + update_hist # If the orig hist only has one value (i.e., the min and max are the same) # we can just add it into new histogram if orig_min == orig_max: bin_value = torch.sum(update_hist) transformed_orig_hist = ( torch.histc(orig_min, bins=self.bins, min=update_min, max=update_max) # type: ignore[arg-type] * bin_value ) return transformed_orig_hist + update_hist # We assume the update_hist is already in the target range, we will map the orig_max to it assert update_min <= orig_min assert update_max >= orig_max # Now we need to turn the old_histogram, into the range of the new histogram transformed_orig_hist = self._upscale_histogram( orig_hist, orig_min, orig_max, update_min, update_max, ) return update_hist + transformed_orig_hist def reset_histogram( self, x: torch.Tensor, min_val: torch.Tensor, max_val: torch.Tensor ) -> None: self.min_val.resize_(min_val.shape) self.min_val.copy_(min_val) self.max_val.resize_(max_val.shape) self.max_val.copy_(max_val) assert ( min_val.numel() == 1 and max_val.numel() == 1 ), "histogram min/max values must be scalar." new_histogram = torch.histc(x, self.bins, min=min_val, max=max_val) # type: ignore[arg-type] self.histogram.detach_().resize_(new_histogram.shape) self.histogram.copy_(new_histogram) def forward(self, x_orig: torch.Tensor) -> torch.Tensor: # pyre-ignore[14] if x_orig.numel() == 0: return x_orig x = x_orig.detach() x_min, x_max = torch.aminmax(x) # want to ignore torch.inf since we don't actually # want to make our quantization range infinite # and in practice those values will be clamped if x_min == -torch.inf or x_max == torch.inf: warnings.warn("torch.inf detected in input tensor, ignoring input") x = x[x.abs() != torch.inf] if x.numel() == 0: return x_orig x_min, x_max = torch.aminmax(x) current_min = self.min_val current_max = self.max_val is_uninitialized = self.min_val == float("inf") or self.max_val == float("-inf") if is_uninitialized: self.reset_histogram(x, x_min, x_max) else: update_min, update_max = x_min, x_max new_min = torch.min(current_min, update_min) new_max = torch.max(current_max, update_max) # TODO: For some reason, this is required for it to pass torchscript test # new_min and new_max should already have requires_grad set to False new_min, new_max = new_min.detach(), new_max.detach() update_histogram = torch.histc( x, self.bins, min=new_min, max=new_max # type: ignore[arg-type] ).to(self.histogram.device) if new_min == current_min and new_max == current_max: combined_histogram = self.histogram + update_histogram self.histogram.detach_().resize_(combined_histogram.shape) self.histogram.copy_(combined_histogram) else: combined_histogram = self._combine_histograms( self.histogram, current_min, current_max, update_histogram, new_min, new_max, ) self.histogram.detach_().resize_(combined_histogram.shape) self.histogram.copy_(combined_histogram) self.min_val.detach_().resize_(new_min.shape) self.min_val.copy_(new_min) self.max_val.detach_().resize_(new_max.shape) self.max_val.copy_(new_max) return x_orig @torch.jit.export def calculate_qparams(self): is_uninitialized = self.min_val == float("inf") and self.max_val == float( "-inf" ) if is_uninitialized: warnings.warn( "must run observer before calling calculate_qparams.\ Returning default scale and zero point " ) return torch.tensor([1.0], device=self.min_val.device.type), torch.tensor( [0], device=self.min_val.device.type ) assert self.bins == len(self.histogram), ( "The number of bins in histogram should be equal to the number of bins " "supplied while making this observer" ) new_min, new_max = self._non_linear_param_search() return self._calculate_qparams(new_min, new_max) def _save_to_state_dict(self, destination, prefix, keep_vars): super()._save_to_state_dict(destination, prefix, keep_vars) destination[prefix + "min_val"] = self.min_val destination[prefix + "max_val"] = self.max_val def _load_from_state_dict( self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, ): version = local_metadata.get("version", None) if version is None or version < 3: # if min_val and max_val are not initialized, update their shape # to account for the differences between v2 and v3 min_val_name, max_val_name = prefix + "min_val", prefix + "max_val" if min_val_name in state_dict: if state_dict[min_val_name].shape == torch.Size([0]): state_dict[min_val_name] = torch.tensor(float("inf")) if max_val_name in state_dict: if state_dict[max_val_name].shape == torch.Size([0]): state_dict[max_val_name] = torch.tensor(float("-inf")) local_state = ["min_val", "max_val"] for name in local_state: key = prefix + name if key in state_dict: val = state_dict[key] setattr(self, name, val) elif strict: missing_keys.append(key) super()._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, ) def extra_repr(self): return f"min_val={self.min_val}, max_val={self.max_val}" class FixedQParamsObserver(ObserverBase): r""" Observer that simulates quantize and dequantize with fixed quantization parameters in training time. Only per tensor quantization is supported. Args: `scale` (float): fixed scale for the observer `zero_point` (int): fixed zero point for the observer `dtype`, `qscheme`, `quant_min`, `quant_max` """ scale: torch.Tensor zero_point: torch.Tensor def __init__( self, scale, zero_point, dtype=torch.quint8, qscheme=torch.per_tensor_affine, quant_min=0, quant_max=255, is_dynamic=False, **kwargs, ): if is_dynamic: raise NotImplementedError( "FixedQParamsObserver doesn't support dynamic quantization" ) super().__init__(dtype=dtype, is_dynamic=is_dynamic, **kwargs) self.quant_min = quant_min self.quant_max = quant_max self.register_buffer("scale", torch.tensor([scale], dtype=torch.float)) self.register_buffer("zero_point", torch.tensor([zero_point], dtype=torch.int)) self.dtype = dtype self.qscheme = qscheme def forward(self, X): return X @torch.jit.export def calculate_qparams(self): return self.scale, self.zero_point class PlaceholderObserver(ObserverBase): r""" Observer that doesn't do anything and just passes its configuration to the quantized module's ``.from_float()``. Can be used for quantization to float16 which doesn't require determining ranges. Args: dtype: dtype argument to the `quantize` node needed to implement the reference model spec. quant_min: minimum value in quantized domain (TODO: align behavior with other observers) quant_max: maximum value in quantized domain custom_op_name: (temporary) specify this observer for an operator that doesn't require any observation (Can be used in Graph Mode Passes for special case ops). compute_dtype (deprecated): if set, marks the future quantize function to use dynamic quantization instead of static quantization. This field is deprecated, use `is_dynamic=True` instead. is_dynamic: if True, the `quantize` function in the reference model representation taking stats from this observer instance will use dynamic quantization. """ def __init__( self, dtype=torch.float32, custom_op_name="", compute_dtype=None, quant_min=None, quant_max=None, qscheme=None, eps=None, is_dynamic=False, ) -> None: super().__init__(dtype=dtype, is_dynamic=is_dynamic) if qscheme is None: qscheme = torch.per_tensor_affine if eps is None: eps = torch.finfo(torch.float32).eps # dtype of input of the target operator, e.g. for dynamic quantization # ops, the dtype will be float32 self.dtype = dtype self.qscheme = qscheme self.quant_min = quant_min self.quant_max = quant_max self.eps = eps self.custom_op = custom_op_name # used for configuration of computation type for dynamic quantization if compute_dtype: is_dynamic = True warnings.warn( "Please use `is_dynamic` instead of `compute_dtype`. \ `compute_dtype` will be deprecated in a future release \ of PyTorch." ) def forward(self, x): return x @torch.jit.export def extra_repr(self): return f"dtype={self.dtype}, is_dynamic={self.is_dynamic}" @torch.jit.export def calculate_qparams(self): raise Exception( # noqa: TRY002 "calculate_qparams should not be called for PlaceholderObserver" ) class RecordingObserver(ObserverBase): r""" The module is mainly for debug and records the tensor values during runtime. Args: dtype: Quantized data type qscheme: Quantization scheme to be used reduce_range: Reduces the range of the quantized data type by 1 bit """ __annotations__ = {"tensor_val": List[Optional[torch.Tensor]]} def __init__(self, dtype=torch.quint8): super().__init__(dtype=dtype, is_dynamic=False) # type: ignore[call-arg] self.tensor_val = [] def forward(self, x): self.tensor_val.append(x.clone()) return x @torch.jit.export def calculate_qparams(self): raise Exception( # noqa: TRY002 "calculate_qparams should not be called for RecordingObserver" ) @torch.jit.export def get_tensor_value(self): return self.tensor_val class NoopObserver(ObserverBase): r""" Observer that doesn't do anything and just passes its configuration to the quantized module's ``.from_float()``. Primarily used for quantization to float16 which doesn't require determining ranges. Args: dtype: Quantized data type custom_op_name: (temporary) specify this observer for an operator that doesn't require any observation (Can be used in Graph Mode Passes for special case ops). """ def __init__(self, dtype=torch.float16, custom_op_name="") -> None: super().__init__(dtype=dtype, is_dynamic=False) self.dtype = dtype self.custom_op = custom_op_name def forward(self, x): return x @torch.jit.export def calculate_qparams(self): raise Exception( # noqa: TRY002 "calculate_qparams should not be called for NoopObserver" ) class ReuseInputObserver(ObserverBase): r"""This observer is used when we want to reuse the observer from the operator that produces the input Tensor, typically used for operators like reshape, e.g. ``` x0 = ... x1 = x0.reshape() ``` if we configure x0 to be observed by some observer, let's say MinMaxObserver, and reshape is configured with ReuseInputObserver, we'll reuse the observer instance for x0 for x1 (output of reshape). If x0 is not observed, we also won't observe x1. Note: this is only enabled in FX Graph Mode Quantization """ def __init__(self) -> None: super().__init__(torch.quint8, is_dynamic=False) def forward(self, x): return x @torch.jit.export def calculate_qparams(self): raise Exception( # noqa: TRY002 "calculate_qparams should not be called for ReuseInputObserver" ) def _is_observer_script_module(mod, obs_type_name): """Returns true if given mod is an instance of Observer script module.""" if isinstance(mod, torch.jit.RecursiveScriptModule): # qualified name looks like '__torch__.torch.ao.quantization.observer.___torch_mangle_2.MinMaxObserver' suffix = mod._c.qualified_name.split(".", 1)[1] name = re.sub(r"\.___torch_mangle_\d+", "", suffix) return obs_type_name in name return False def _is_activation_post_process(module): return isinstance( module, (torch.ao.quantization.ObserverBase, torch.ao.quantization.FakeQuantizeBase), ) or _is_observer_script_module(module, "quantization.observer") def _is_per_channel_script_obs_instance(module): if isinstance(module, torch.jit.RecursiveScriptModule): return _is_observer_script_module( module, "quantization.observer.PerChannelMinMaxObserver" ) or _is_observer_script_module( module, "quantization.observer.MovingAveragePerChannelMinMaxObserver" ) return False def get_observer_state_dict(mod): r""" Returns the state dict corresponding to the observer stats. Traverse the model state_dict and extract out the stats. """ od = OrderedDict() if isinstance(mod, torch.jit.RecursiveScriptModule): for k, v in mod.state_dict().items(): if "observer" in k: od[k] = v else: # path for GraphModule and nn.Module (eager mode) for k, v in mod.state_dict().items(): if "activation_post_process" in k: od[k] = v od._metadata = mod.state_dict()._metadata # type: ignore[attr-defined] return od def load_observer_state_dict(mod, obs_dict): r""" Given input model and a state_dict containing model observer stats, load the stats back into the model. The observer state_dict can be saved using torch.ao.quantization.get_observer_state_dict """ missing_keys: List[str] = [] unexpected_keys: List[str] = [] for name, module in mod.named_modules(): prefix = name + "." if _is_activation_post_process(module): if _is_per_channel_script_obs_instance(module): # For per-channel observers we need to call a custom load_from_state_dict to resize the tensor. # However this is not called when the module is scripted and we end up calling the default one in module.py module._load_from_state_dict_script( obs_dict, prefix, {}, True, missing_keys, unexpected_keys, [] ) else: module._load_from_state_dict( obs_dict, prefix, {}, False, missing_keys, unexpected_keys, [] ) for k in missing_keys: if "observer" in k or "activation_post_process" in k: raise Exception( # noqa: TRY002 f"Missing keys for observer {k} in state_dict" ) for k in unexpected_keys: if "observer" in k or "activation_post_process" in k: raise Exception( # noqa: TRY002 f"Unexpected keys for observer {k} in state_dict" ) # Restrict activations to be in the range (0,127) default_observer = MinMaxObserver.with_args(quant_min=0, quant_max=127) """ Default observer for static quantization, usually used for debugging. """ default_placeholder_observer = PlaceholderObserver """ Default placeholder observer, usually used for quantization to torch.float16. """ default_debug_observer = RecordingObserver """ Default debug-only observer. """ default_weight_observer = MinMaxObserver.with_args( dtype=torch.qint8, qscheme=torch.per_tensor_symmetric ) """ Default weight observer. """ weight_observer_range_neg_127_to_127 = MinMaxObserver.with_args( dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, quant_min=-127, quant_max=127, eps=2**-12, ) """ Symmetric weight observer with the 8-bit values restricted to [-127, +127], excluding -128. """ default_histogram_observer = HistogramObserver.with_args(quant_min=0, quant_max=127) """ Default histogram observer, usually used for PTQ. """ default_per_channel_weight_observer = PerChannelMinMaxObserver.with_args( dtype=torch.qint8, qscheme=torch.per_channel_symmetric ) """ Default per-channel weight observer, usually used on backends where per-channel weight quantization is supported, such as `fbgemm`. """ per_channel_weight_observer_range_neg_127_to_127 = PerChannelMinMaxObserver.with_args( dtype=torch.qint8, qscheme=torch.per_channel_symmetric, quant_min=-127, quant_max=127, eps=2**-12, ) """ Per-channel, symmetric weight observer with the 8-bit values restricted to [-127, +127], excluding -128. """ default_dynamic_quant_observer = PlaceholderObserver.with_args( dtype=torch.quint8, quant_min=0, quant_max=255, is_dynamic=True, ) """ Default observer for dynamic quantization. """ default_float_qparams_observer = PerChannelMinMaxObserver.with_args( dtype=torch.quint8, qscheme=torch.per_channel_affine_float_qparams, ch_axis=0 ) """ Default observer for a floating point zero-point. """ default_float_qparams_observer_4bit = PerChannelMinMaxObserver.with_args( dtype=torch.quint4x2, qscheme=torch.per_channel_affine_float_qparams, ch_axis=0 ) """ Default observer for a floating point zero-point and 4 bit activations. """ # TODO(future PR): remove these defaults and enforce activation functions # to explicitly specify their output range default_fixed_qparams_range_neg1to1_observer = FixedQParamsObserver.with_args( scale=2.0 / 256.0, zero_point=128, dtype=torch.quint8, quant_min=0, quant_max=255 ) default_fixed_qparams_range_0to1_observer = FixedQParamsObserver.with_args( scale=1.0 / 256.0, zero_point=0, dtype=torch.quint8, quant_min=0, quant_max=255 ) # TODO: the following 2 variables are kept for backwards compatibility; remove after a few releases default_symmetric_fixed_qparams_observer = default_fixed_qparams_range_neg1to1_observer default_affine_fixed_qparams_observer = default_fixed_qparams_range_0to1_observer """ Default observers for fixed qparams operations. """ default_reuse_input_observer = ReuseInputObserver """ Default observer for operators like reshape that reuses the observer of input to the operator """