def test_per_tensor_observers(self, qdtype, qscheme, reduce_range):
# reduce_range cannot be true for symmetric quantization with uint8
if qdtype == torch.quint8 and qscheme == torch.per_tensor_symmetric:
reduce_range = False
ObserverList = [MinMaxObserver(dtype=qdtype, qscheme=qscheme, reduce_range=reduce_range),
MovingAverageMinMaxObserver(averaging_constant=0.5,
dtype=qdtype,
qscheme=qscheme,
reduce_range=reduce_range)]
for myobs in ObserverList:
# Calculate Qparams should return with a warning for observers with no data
qparams = myobs.calculate_qparams()
if type(myobs) == MinMaxObserver:
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])
else:
# Moving average of min/max for x and y matches that of
# extreme values for x/y used for minmax observer
x = torch.tensor([0.0, 2.0, 2.0, 3.0, 4.0, 5.0, 6.0])
y = torch.tensor([2.0, 5.0, 5.0, 6.0, 7.0, 10.0])
result = myobs(x)
result = myobs(y)
self.assertEqual(result, y)
self.assertEqual(myobs.min_val, 1.0)
self.assertEqual(myobs.max_val, 8.0)
qparams = myobs.calculate_qparams()
if reduce_range:
if qscheme == torch.per_tensor_symmetric:
ref_scale = 0.062745 * 255 / 127
ref_zero_point = 0 if qdtype is torch.qint8 else 128
else:
ref_scale = 0.0313725 * 255 / 127
ref_zero_point = -64 if qdtype is torch.qint8 else 0
else:
if qscheme == torch.per_tensor_symmetric:
ref_scale = 0.062745
ref_zero_point = 0 if qdtype is torch.qint8 else 128
else:
ref_scale = 0.0313725
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)
state_dict = myobs.state_dict()
b = io.BytesIO()
torch.save(state_dict, b)
b.seek(0)
loaded_dict = torch.load(b)
for key in state_dict:
self.assertEqual(state_dict[key], loaded_dict[key])
loaded_obs = MinMaxObserver(dtype=qdtype, qscheme=qscheme, reduce_range=reduce_range)
loaded_obs.load_state_dict(loaded_dict)
loaded_qparams = loaded_obs.calculate_qparams()
self.assertEqual(myobs.min_val, loaded_obs.min_val)
self.assertEqual(myobs.max_val, loaded_obs.max_val)
self.assertEqual(myobs.calculate_qparams(), loaded_obs.calculate_qparams())
def test_minmax_observer(self, qdtype, qscheme, reduce_range):
# reduce_range cannot be true for symmetric quantization with uint8
if qdtype == torch.quint8 and qscheme == torch.per_tensor_symmetric:
reduce_range = False
myobs = MinMaxObserver(dtype=qdtype,
qscheme=qscheme,
reduce_range=reduce_range)
# Calculate Qparams should return with a warning for observers with no data
qparams = myobs.calculate_qparams()
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])
result = myobs(x)
result = myobs(y)
self.assertEqual(result, y)
self.assertEqual(myobs.min_val, 1.0)
self.assertEqual(myobs.max_val, 8.0)
qparams = myobs.calculate_qparams()
if reduce_range:
if qscheme == torch.per_tensor_symmetric:
ref_scale = 0.062745 * 255 / 127
ref_zero_point = 0 if qdtype is torch.qint8 else 128
else:
ref_scale = 0.0313725 * 255 / 127
ref_zero_point = -64 if qdtype is torch.qint8 else 0
else:
if qscheme == torch.per_tensor_symmetric:
ref_scale = 0.062745
ref_zero_point = 0 if qdtype is torch.qint8 else 128
else:
ref_scale = 0.0313725
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)
# Test for serializability
state_dict = myobs.state_dict()
b = io.BytesIO()
torch.save(state_dict, b)
b.seek(0)
loaded_dict = torch.load(b)
for key in state_dict:
self.assertEqual(state_dict[key], loaded_dict[key])
loaded_obs = MinMaxObserver(dtype=qdtype,
qscheme=qscheme,
reduce_range=reduce_range)
loaded_obs.load_state_dict(loaded_dict)
loaded_qparams = loaded_obs.calculate_qparams()
self.assertEqual(myobs.min_val, loaded_obs.min_val)
self.assertEqual(myobs.max_val, loaded_obs.max_val)
self.assertEqual(myobs.calculate_qparams(),
loaded_obs.calculate_qparams())
