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Back out "[quant][observer] Add histogram observer" #26236

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38 changes: 3 additions & 35 deletions test/test_quantization.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
from __future__ import absolute_import, division, print_function, unicode_literals

import unittest
import torch
import torch.nn as nn
Expand All @@ -11,7 +9,7 @@
QConfig_dynamic, default_weight_observer, dump_tensor,\
quantize, prepare, convert, prepare_qat, quantize_qat, fuse_modules, \
quantize_dynamic, default_qconfig, default_debug_qconfig, default_qat_qconfig, \
default_dynamic_qconfig, QuantWrapper, TensorObserver, MinMaxObserver, HistogramObserver
default_dynamic_qconfig, MinMaxObserver, TensorObserver, QuantWrapper

from common_utils import run_tests
from common_quantization import QuantizationTestCase, SingleLayerLinearModel, \
Expand Down Expand Up @@ -777,8 +775,8 @@ def test_minmax_observer(self, qdtype, qscheme, reduce_range):
self.assertEqual(qparams[1].item(), ref_zero_point)
self.assertAlmostEqual(qparams[0].item(), ref_scale, delta=1e-5)

@given(obs=st.sampled_from((torch.quantization.default_observer()(), HistogramObserver(bins=10))))
def test_observer_scriptable(self, obs):
def test_observer_scriptable(self):
obs = torch.quantization.default_observer()()
scripted = torch.jit.script(obs)

x = torch.rand(3, 4)
Expand Down Expand Up @@ -829,35 +827,5 @@ def test_tensor_observer_scriptable(self, qdtype, qscheme):
loaded = torch.jit.load(buf)
self.assertTrue(torch.equal(obs.get_tensor_value()[0], loaded.get_tensor_value()[0]))

@given(qdtype=st.sampled_from((torch.qint8, torch.quint8)),
qscheme=st.sampled_from((torch.per_tensor_affine, torch.per_tensor_symmetric)),
reduce_range=st.booleans())
def test_histogram_observer(self, qdtype, qscheme, reduce_range):
myobs = HistogramObserver(bins=10, dtype=qdtype, qscheme=qscheme, reduce_range=reduce_range)
x = torch.tensor([1.0, 2.0, 2.0, 3.0, 4.0, 5.0, 6.0])
y = torch.tensor([4.0, 5.0, 5.0, 6.0, 7.0, 8.0])
myobs(x)
myobs(y)
self.assertEqual(myobs.min_val, -1.5)
self.assertEqual(myobs.max_val, 8.5)
self.assertEqual(myobs.histogram, [0., 0., 1., 2., 1., 2., 3., 2., 1., 1.])
qparams = myobs.calculate_qparams()
if reduce_range:
if qscheme == torch.per_tensor_symmetric:
ref_scale = 0.066666 * 255 / 127
ref_zero_point = 0 if qdtype is torch.qint8 else 128
else:
ref_scale = 0.0333333 * 255 / 127
ref_zero_point = -64 if qdtype is torch.qint8 else 0
else:
if qscheme == torch.per_tensor_symmetric:
ref_scale = 0.066666
ref_zero_point = 0 if qdtype is torch.qint8 else 128
else:
ref_scale = 0.0333333
ref_zero_point = -128 if qdtype is torch.qint8 else 0
self.assertEqual(qparams[1].item(), ref_zero_point)
self.assertAlmostEqual(qparams[0].item(), ref_scale, delta=1e-5)

if __name__ == '__main__':
run_tests()
100 changes: 11 additions & 89 deletions torch/quantization/observer.py
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Original file line number Diff line number Diff line change
@@ -1,16 +1,14 @@
from __future__ import absolute_import, division, print_function, unicode_literals

import warnings
import torch
import torch.nn as nn
from abc import ABCMeta, abstractmethod
from functools import partial
import warnings

from torch._jit_internal import Optional, List
import torch
import torch.nn as nn


ABC = ABCMeta(str("ABC"), (object,), {}) # compatible with Python 2 *and* 3:

ABC = ABCMeta(str('ABC'), (object,), {}) # compatible with Python 2 *and* 3:

class ObserverBase(ABC, nn.Module):
r"""Observer base Module
Expand All @@ -22,9 +20,7 @@ class ObserverBase(ABC, nn.Module):
the collected statistics.
"""

def __init__(
self, dtype=torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=False
):
def __init__(self, dtype=torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=False):
super(ObserverBase, self).__init__()
self.dtype = dtype
self.qscheme = qscheme
Expand Down Expand Up @@ -56,10 +52,8 @@ def _calculate_qparams(self, min_val, max_val):
"""

if max_val is None or min_val is None:
warnings.warn(
"must run observer before calling calculate_qparams.\
Returning default scale and zero point "
)
warnings.warn("must run observer before calling calculate_qparams.\
Returning default scale and zero point ")
return torch.tensor([1.0]), torch.tensor([0])

assert min_val <= max_val, "min {} should be less than max {}".format(
Expand Down Expand Up @@ -108,10 +102,7 @@ class MinMaxObserver(ObserverBase):
calculate_qparams will calculate scale and zero_point
"""

__annotations__ = {
"min_val": Optional[torch.Tensor],
"max_val": Optional[torch.Tensor],
}
__annotations__ = {'min_val' : Optional[torch.Tensor], 'max_val' : Optional[torch.Tensor]}

def __init__(self, **kwargs):
# For x86 quantized kernels, we need to ensure that the vpmaddubsw instruction
Expand All @@ -124,14 +115,8 @@ def __init__(self, **kwargs):
super(MinMaxObserver, self).__init__(**kwargs)
self.min_val = None
self.max_val = None
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"
)
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):
min_val = self.min_val
Expand All @@ -152,69 +137,7 @@ def calculate_qparams(self):

@torch.jit.export
def extra_repr(self):
return "min_val={}, max_val={}".format(self.min_val, self.max_val)


class HistogramObserver(ObserverBase):
r"""
The module records the running histogram of tensor values along with
min/max values. calculate_qparams will calculate scale and zero_point
"""

__annotations__ = {
"min_val": Optional[torch.Tensor],
"max_val": Optional[torch.Tensor],
"histogram": Optional[torch.Tensor],
}

def __init__(self, bins=2048, **kwargs):
super(HistogramObserver, self).__init__(**kwargs)
self.bins = bins
self.histogram = None
self.min_val = None
self.max_val = None

def forward(self, x):
min_val = self.min_val
max_val = self.max_val
histogram = self.histogram
if min_val is None or max_val is None or histogram is None:
min_val = torch.min(x)
max_val = torch.max(x)
range = max_val - min_val
self.min_val = min_val - 0.5 * range
self.max_val = max_val + 0.5 * range
self.histogram = torch.histc(
x, self.bins, min=min_val - 0.5 * range, max=max_val + 0.5 * range
)
else:
if min_val < torch.min(x) or max_val > torch.max(x):
warnings.warn("Incoming data is outside the min_val/max_val range.")
new_histogram = torch.histc(
x, self.bins, min=min_val, max=max_val
)
self.histogram = new_histogram + histogram

@torch.jit.export
def calculate_qparams(self):
min_val = self.min_val
max_val = self.max_val
histogram = self.histogram

if min_val is None or max_val is None or histogram is None:
return self._calculate_qparams(None, None)
else:
histogram_mask = torch.gt(histogram, 0).to(torch.int8)
c = torch.cumsum(histogram_mask, 0)
# Last non-zero bin
max_bin = torch.argmax(histogram_mask)
# Only one entry is non-zero, find it.
min_bin = torch.argmax(torch.eq(c, 1))
bin_width = (max_val - min_val) / histogram.size()[0]
new_min = min_val + min_bin * bin_width
new_max = min_val + (max_bin + 1) * bin_width
return self._calculate_qparams(new_min, new_max)

return 'min_val={}, max_val={}'.format(self.min_val, self.max_val)


class TensorObserver(ObserverBase):
Expand Down Expand Up @@ -245,7 +168,6 @@ def get_tensor_value(self):
def observer(observer_cls, **kwargs):
return partial(observer_cls, **kwargs)


def default_observer(**kwargs):
# Restrict activations to be in the range (0,127)
kwargs.setdefault("reduce_range", True)
Expand Down