mirror of
https://github.com/zebrajr/pytorch.git
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6b50ccc41c
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/46871 Test Plan: Imported from OSS Imported from OSS Reviewed By: vkuzo Differential Revision: D24547180 fbshipit-source-id: d2eb9aa74c6e5436204376b1a2ebcc6188d3562f
290 lines
13 KiB
Python
290 lines
13 KiB
Python
import torch
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from torch.nn import Module
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from .observer import MovingAverageMinMaxObserver, HistogramObserver, MovingAveragePerChannelMinMaxObserver, _with_args
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import re
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from abc import ABC, abstractmethod
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def _is_per_channel(qscheme: 'torch.qscheme') -> bool:
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return qscheme in [torch.per_channel_symmetric, torch.per_channel_affine]
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def _is_per_tensor(qscheme: 'torch.qscheme') -> bool:
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return qscheme in [torch.per_tensor_symmetric, torch.per_tensor_affine]
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class FakeQuantizeBase(ABC, Module):
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r""" Base fake quantize module
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Any fake quantize implementation should derive from this class.
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Concrete fake quantize module should follow the same API. In forward, they will update
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the statistics of the observed Tensor and fake quantize the input. They should also provide a
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`calculate_qparams` function that computes the quantization parameters given
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the collected statistics.
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"""
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fake_quant_enabled: torch.Tensor
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observer_enabled: torch.Tensor
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def __init__(self):
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super().__init__()
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# fake_quant_enabled and observer_enabled are buffers to support their
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# replication in DDP. Data type is uint8 because NCCL does not support
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# bool tensors.
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self.register_buffer('fake_quant_enabled', torch.tensor([1], dtype=torch.uint8))
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self.register_buffer('observer_enabled', torch.tensor([1], dtype=torch.uint8))
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@abstractmethod
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def forward(self, x):
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pass
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@abstractmethod
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def calculate_qparams(self, **kwargs):
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pass
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with_args = classmethod(_with_args)
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# TODO: inherit from FakeQuantizeBase
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class FakeQuantize(Module):
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r""" Simulate the quantize and dequantize operations in training time.
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The output of this module is given by
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x_out = (clamp(round(x/scale + zero_point), quant_min, quant_max)-zero_point)*scale
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* :attr:`scale` defines the scale factor used for quantization.
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* :attr:`zero_point` specifies the quantized value to which 0 in floating point maps to
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* :attr:`quant_min` specifies the minimum allowable quantized value.
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* :attr:`quant_max` specifies the maximum allowable quantized value.
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* :attr:`fake_quant_enable` controls the application of fake quantization on tensors, note that
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statistics can still be updated.
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* :attr:`observer_enable` controls statistics collection on tensors
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* :attr:`dtype` specifies the quantized dtype that is being emulated with fake-quantization,
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allowable values are torch.qint8 and torch.quint8. The values of quant_min and
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quant_max should be chosen to be consistent with the dtype
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Args:
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observer (module): Module for observing statistics on input tensors and calculating scale
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and zero-point.
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quant_min (int): The minimum allowable quantized value.
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quant_max (int): The maximum allowable quantized value.
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observer_kwargs (optional): Arguments for the observer module
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Attributes:
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observer (Module): User provided module that collects statistics on the input tensor and
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provides a method to calculate scale and zero-point.
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"""
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fake_quant_enabled: torch.Tensor
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observer_enabled: torch.Tensor
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scale: torch.Tensor
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zero_point: torch.Tensor
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def __init__(self, observer=MovingAverageMinMaxObserver, quant_min=0, quant_max=255, **observer_kwargs):
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super(FakeQuantize, self).__init__()
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assert quant_min <= quant_max, \
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'quant_min must be less than or equal to quant_max'
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self.quant_min = quant_min
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self.quant_max = quant_max
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# fake_quant_enabled and observer_enabled are buffers to support their
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# replication in DDP. Data type is uint8 because NCCL does not support
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# bool tensors.
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self.register_buffer('fake_quant_enabled', torch.tensor([1], dtype=torch.uint8))
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self.register_buffer('observer_enabled', torch.tensor([1], dtype=torch.uint8))
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self.activation_post_process = observer(**observer_kwargs)
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assert torch.iinfo(self.activation_post_process.dtype).min <= quant_min, 'quant_min out of bound'
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assert quant_max <= torch.iinfo(self.activation_post_process.dtype).max, 'quant_max out of bound'
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self.register_buffer('scale', torch.tensor([1.0]))
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self.register_buffer('zero_point', torch.tensor([0]))
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self.dtype = self.activation_post_process.dtype
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self.qscheme = self.activation_post_process.qscheme
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self.ch_axis = self.activation_post_process.ch_axis \
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if hasattr(self.activation_post_process, 'ch_axis') else -1
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assert _is_per_channel(self.qscheme) or \
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_is_per_tensor(self.qscheme), \
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'Only per channel and per tensor quantization are supported in fake quantize' + \
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' got qscheme: ' + str(self.qscheme)
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self.is_per_channel = _is_per_channel(self.qscheme)
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@torch.jit.export
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def enable_fake_quant(self, enabled=True):
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# type: (bool) -> None
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self.fake_quant_enabled[0] = 1 if enabled else 0
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@torch.jit.export
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def disable_fake_quant(self):
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self.enable_fake_quant(False)
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@torch.jit.export
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def enable_observer(self, enabled=True):
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# type: (bool) -> None
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self.observer_enabled[0] = 1 if enabled else 0
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@torch.jit.export
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def disable_observer(self):
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self.enable_observer(False)
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@torch.jit.export
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def calculate_qparams(self):
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return self.activation_post_process.calculate_qparams()
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def forward(self, X):
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if self.observer_enabled[0] == 1:
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self.activation_post_process(X.detach())
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_scale, _zero_point = self.calculate_qparams()
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_scale, _zero_point = _scale.to(self.scale.device), _zero_point.to(self.zero_point.device)
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self.scale.resize_(_scale.shape)
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self.scale.copy_(_scale)
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self.zero_point.resize_(_zero_point.shape)
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self.zero_point.copy_(_zero_point)
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if self.fake_quant_enabled[0] == 1:
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if self.is_per_channel:
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X = torch.fake_quantize_per_channel_affine(X, self.scale, self.zero_point,
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self.ch_axis, self.quant_min, self.quant_max)
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else:
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X = torch.fake_quantize_per_tensor_affine(X, float(self.scale),
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int(self.zero_point), self.quant_min,
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self.quant_max)
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return X
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with_args = classmethod(_with_args)
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@torch.jit.export
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def extra_repr(self):
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return 'fake_quant_enabled={}, observer_enabled={}, ' \
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'quant_min={}, quant_max={}, dtype={}, qscheme={}, ch_axis={}, ' \
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'scale={}, zero_point={}'.format(
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self.fake_quant_enabled, self.observer_enabled,
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self.quant_min, self.quant_max,
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self.dtype, self.qscheme, self.ch_axis, self.scale, self.zero_point)
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def _save_to_state_dict(self, destination, prefix, keep_vars):
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# We cannot currently register scalar values as buffers, so need to manually
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# specify serialization here.
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super(FakeQuantize, self)._save_to_state_dict(destination, prefix, keep_vars)
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destination[prefix + 'scale'] = self.scale
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destination[prefix + 'zero_point'] = self.zero_point
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def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
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missing_keys, unexpected_keys, error_msgs):
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# Removing this function throws an error that the the size of the loaded tensor does not match the original size
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# i.e., These buffers start out with numel 0 and become numel 1 once they have their first forward pass.
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local_state = ['scale', 'zero_point']
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for name in local_state:
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key = prefix + name
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if key in state_dict:
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val = state_dict[key]
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setattr(self, name, val)
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elif strict:
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missing_keys.append(key)
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super(FakeQuantize, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict,
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missing_keys, unexpected_keys, error_msgs)
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class FixedQParamsFakeQuantize(FakeQuantizeBase):
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""" Simulate quantize and dequantize with fixed quantization
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parameters in training time. Only per tensor quantization
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is supported.
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Args:
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`scale` (float): fixed scale for the fake quantize module
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`zero_point` (int): fixed zero point for the fake quantize module
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`dtype`, `qscheme`, `quant_min`, `quant_max`
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"""
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scale: torch.Tensor
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zero_point: torch.Tensor
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def __init__(self,
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scale,
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zero_point,
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dtype,
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qscheme=torch.per_tensor_affine,
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quant_min=0,
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quant_max=255):
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super().__init__()
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assert quant_min <= quant_max, 'quant_min should be less than or equal to quant_max'
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self.quant_min = quant_min
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self.quant_max = quant_max
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self.register_buffer('scale', torch.tensor([scale]))
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self.register_buffer('zero_point', torch.tensor([zero_point]))
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self.dtype = dtype
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self.qscheme = qscheme
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assert _is_per_tensor(self.qscheme), 'Only per tensor quantization is supported' + \
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' FixedQParamsFakeQuantize module, got qscheme:' + str(self.qscheme)
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def forward(self, X):
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if self.fake_quant_enabled[0] == 1:
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X = torch.fake_quantize_per_tensor_affine(X, float(self.scale),
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int(self.zero_point), self.quant_min,
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self.quant_max)
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return X
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@torch.jit.export
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def calculate_qparams(self):
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return self.scale, self.zero_point
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@torch.jit.export
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def extra_repr(self):
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return 'fake_quant_enabled={}, observer_enabled={}, scale={}, zero_point={}, ' \
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'dtype={}, quant_min={}, quant_max={}, qscheme={}'.format(
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self.fake_quant_enabled, self.observer_enabled,
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self.scale, self.zero_point, self.dtype,
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self.quant_min, self.quant_max, self.qscheme)
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default_fake_quant = FakeQuantize.with_args(observer=MovingAverageMinMaxObserver, quant_min=0, quant_max=255,
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dtype=torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=True)
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default_weight_fake_quant = FakeQuantize.with_args(observer=MovingAverageMinMaxObserver, quant_min=-128, quant_max=127,
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dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, reduce_range=False)
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default_symmetric_fixed_qparams_fake_quant = FixedQParamsFakeQuantize.with_args(
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scale=2.0 / 256.0, zero_point=128, dtype=torch.quint8)
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default_affine_fixed_qparams_fake_quant = FixedQParamsFakeQuantize.with_args(
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scale=1.0 / 256.0, zero_point=0, dtype=torch.quint8, quant_min=-128, quant_max=127)
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default_per_channel_weight_fake_quant = FakeQuantize.with_args(observer=MovingAveragePerChannelMinMaxObserver,
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quant_min=-128,
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quant_max=127,
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dtype=torch.qint8,
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qscheme=torch.per_channel_symmetric,
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reduce_range=False,
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ch_axis=0)
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default_histogram_fake_quant = FakeQuantize.with_args(observer=HistogramObserver,
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quant_min=0,
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quant_max=255,
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dtype=torch.quint8,
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qscheme=torch.per_tensor_affine,
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reduce_range=True)
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def _is_fake_quant_script_module(mod):
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''' Returns true if given mod is an instance of FakeQuantize script module.
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'''
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if isinstance(mod, torch.jit.RecursiveScriptModule):
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# qualified name looks like '__torch__.torch.quantization.fake_quantize.___torch_mangle_2.FakeQuantize'
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suffix = mod._c.qualified_name.split('.', 1)[1]
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name = re.sub(r'\.___torch_mangle_\d+', '', suffix)
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return name == 'torch.quantization.fake_quantize.FakeQuantize'
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return False
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def disable_fake_quant(mod):
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if type(mod) == FakeQuantize or _is_fake_quant_script_module(mod):
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mod.disable_fake_quant()
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def enable_fake_quant(mod):
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if type(mod) == FakeQuantize or _is_fake_quant_script_module(mod):
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mod.enable_fake_quant()
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def disable_observer(mod):
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if type(mod) == FakeQuantize or _is_fake_quant_script_module(mod):
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mod.disable_observer()
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def enable_observer(mod):
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if type(mod) == FakeQuantize or _is_fake_quant_script_module(mod):
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mod.enable_observer()
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