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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/24196 Observer returns output with no changes for post training quant. This unifies observer semantics for QAT and PTQ. ghstack-source-id: 88140887 Differential Revision: D16768277 fbshipit-source-id: fae7c94e3dc0eeda363e9982b3865a15113e11bd
77 lines
3.2 KiB
Python
77 lines
3.2 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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import torch.nn as nn
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import torch
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from functools import partial
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class Observer(nn.Module):
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r"""Default Observer Module
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A default implementation of the observer module, only works for
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`per_tensor_affine` quantization scheme.
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The module will record the running average of max and min value of the
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observed Tensor and calulate_qparams will calculate the scale and zero_point
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Other types of Observers should follow the same API, it can take arbitrary
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number of keyward arguments. In forward, it will update the statistics of
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the observed Tensor. And it should provide a `calculate_qparam` function
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that computes the quantization parameters given the collected statistics.
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TODO: Maybe add an abstract Observer class that enforces these rules?
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"""
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def __init__(self, dtype=torch.quint8, qscheme=torch.per_tensor_affine):
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super(Observer, self).__init__()
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self.dtype = dtype
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self.qscheme = qscheme
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assert self.qscheme in (torch.per_tensor_affine, torch.per_tensor_symmetric), \
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'Default Observer only works for per_tensor_affine and \
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per_tensor_symmetric quantization scheme'
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assert self.dtype in (torch.qint8, torch.quint8), \
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'Default Observer only works for qint8 and quint data type'
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self.min_val = None
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self.max_val = None
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def forward(self, x):
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if self.min_val is None or self.max_val is None:
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self.min_val = torch.min(x)
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self.max_val = torch.max(x)
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else:
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self.min_val = torch.min(torch.min(x), self.min_val)
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self.max_val = torch.max(torch.max(x), self.max_val)
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return x
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def calculate_qparams(self):
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if self.dtype == torch.qint8:
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qmin, qmax = -128, 127
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else:
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qmin, qmax = 0, 255
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n_levels = 255.0
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if self.max_val is None or self.min_val is None:
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raise Exception('must run observer before calling calculate_qparams!')
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max_val, min_val = self.max_val.item(), self.min_val.item()
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if max_val == min_val:
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scale = 1.0
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zero_point = 0
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else:
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if self.qscheme == torch.per_tensor_symmetric:
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max_val = max(-min_val, max_val)
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scale = max_val / 127.0
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scale = max(scale, torch.finfo(torch.float32).eps)
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zero_point = 0 if self.dtype == torch.qint8 else 128
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else:
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scale = (max_val - min_val) / n_levels
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scale = max(scale, torch.finfo(torch.float32).eps)
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zero_point = qmin - round(min_val / scale)
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zero_point = max(qmin, zero_point)
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zero_point = min(qmax, zero_point)
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return torch.tensor([scale, zero_point])
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def observer(observer_cls, **kwargs):
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return partial(observer_cls, **kwargs)
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def default_observer(**kwargs):
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return observer(Observer, **kwargs)
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def default_weight_observer(**kwargs):
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kwargs.setdefault('dtype', torch.qint8)
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kwargs.setdefault('qscheme', torch.per_tensor_symmetric)
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return observer(Observer, **kwargs)
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