def test_histogram_observer(self, qdtype, qscheme, reduce_range):
myobs = HistogramObserver(bins=3,
dtype=qdtype,
qscheme=qscheme,
reduce_range=reduce_range)
# Calculate qparams should work for empty observers
qparams = myobs.calculate_qparams()
x = torch.tensor([2.0, 3.0, 4.0, 5.0], requires_grad=True)
y = torch.tensor([5.0, 6.0, 7.0, 8.0])
out_x = myobs(x)
self.assertTrue(out_x.requires_grad)
myobs(y)
self.assertEqual(myobs.min_val, 2.0)
self.assertEqual(myobs.max_val, 8.0)
self.assertEqual(myobs.histogram, [2., 3., 3.])
qparams = myobs.calculate_qparams()
if reduce_range:
if qscheme == torch.per_tensor_symmetric:
ref_scale = 0.0470588 * 255 / 127
ref_zero_point = 0 if qdtype is torch.qint8 else 128
else:
ref_scale = 0.0235294 * 255 / 127
ref_zero_point = -64 if qdtype is torch.qint8 else 0
else:
if qscheme == torch.per_tensor_symmetric:
ref_scale = 0.0470588
ref_zero_point = 0 if qdtype is torch.qint8 else 128
else:
ref_scale = 0.0235294
ref_zero_point = -128 if qdtype is torch.qint8 else 0
self.assertEqual(qparams[1].item(), ref_zero_point)
self.assertEqual(qparams[0].item(), ref_scale, atol=1e-5, rtol=0)
# 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 = HistogramObserver(bins=3,
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.histogram, loaded_obs.histogram)
self.assertEqual(myobs.bins, loaded_obs.bins)
self.assertEqual(myobs.calculate_qparams(),
loaded_obs.calculate_qparams())
def test_histogram_observer(self, qdtype, qscheme, reduce_range):
myobs = HistogramObserver(bins=3, dtype=qdtype, qscheme=qscheme, reduce_range=reduce_range)
x = torch.tensor([2.0, 3.0, 4.0, 5.0])
y = torch.tensor([5.0, 6.0, 7.0, 8.0])
myobs(x)
myobs(y)
self.assertEqual(myobs.min_val, 2.0)
self.assertEqual(myobs.max_val, 8.0)
self.assertEqual(myobs.histogram, [2., 3., 3.])
qparams = myobs.calculate_qparams()
if reduce_range:
if qscheme == torch.per_tensor_symmetric:
ref_scale = 0.0470588 * 255 / 127
ref_zero_point = 0 if qdtype is torch.qint8 else 128
else:
ref_scale = 0.0235294 * 255 / 127
ref_zero_point = -64 if qdtype is torch.qint8 else 0
else:
if qscheme == torch.per_tensor_symmetric:
ref_scale = 0.0470588
ref_zero_point = 0 if qdtype is torch.qint8 else 128
else:
ref_scale = 0.0235294
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)
def test_histogram_observer_one_sided(self):
myobs = HistogramObserver(bins=8, dtype=torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=True)
x = torch.tensor([0.0, 0.3, 1.2, 1.7])
y = torch.tensor([0.1, 1.3, 2.0, 2.7])
myobs(x)
myobs(y)
self.assertEqual(myobs.min_val, 0)
qparams = myobs.calculate_qparams()
self.assertEqual(qparams[1].item(), 0)
def test_histogram_observer_against_reference(self, N, bins, dtype, qscheme, reduce_range):
ref_obs = _ReferenceHistogramObserver(bins=bins, dtype=dtype, qscheme=qscheme, reduce_range=reduce_range)
my_obs = HistogramObserver(bins=bins, dtype=dtype, qscheme=qscheme, reduce_range=reduce_range)
for _ in range(10):
X = torch.randn(N)
my_obs(X)
ref_obs(X)
ref_qparams = ref_obs.calculate_qparams()
my_qparams = my_obs.calculate_qparams()
self.assertEqual(ref_qparams, my_qparams)
def test_histogram_observer_same_inputs(self):
myobs = HistogramObserver(bins=3, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, reduce_range=False)
w = torch.ones(4, requires_grad=True)
x = torch.zeros(4, requires_grad=True)
y = torch.tensor([2.0, 3.0, 4.0, 5.0], requires_grad=True)
z = torch.tensor([5.0, 6.0, 7.0, 8.0])
myobs(w)
myobs(x)
myobs(x)
myobs(y)
myobs(z)
qparams = myobs.calculate_qparams()
self.assertEqual(myobs.min_val, 2.0)
self.assertEqual(myobs.max_val, 8.0)
self.assertEqual(myobs.histogram, [2., 3., 3.])