def test_per_channel_minmax_observer(self, qdtype, qscheme, ch_axis, reduce_range):
# reduce_range cannot be true for symmetric quantization with uint8
if qdtype == torch.quint8 and qscheme == torch.per_channel_symmetric:
reduce_range = False
myobs = PerChannelMinMaxObserver(reduce_range=reduce_range, ch_axis=ch_axis, dtype=qdtype, qscheme=qscheme)
x = torch.tensor(
[
[[[1.0, 2.0], [2.0, 2.5]], [[3.0, 4.0], [4.5, 6.0]]],
[[[-4.0, -3.0], [5.0, 5.0]], [[6.0, 3.0], [7.0, 8.0]]],
]
)
result = myobs(x)
self.assertEqual(result, x)
qparams = myobs.calculate_qparams()
ref_min_vals = [[1.0, -4.0], [-4.0, 3.0], [-4.0, 2.0], [-4.0, -3.0]]
ref_max_vals = [[6.0, 8.0], [5.0, 8.0], [6.0, 8.0], [7.0, 8.0]]
per_channel_symmetric_ref_scales = [
[0.04705882, 0.06274509],
[0.03921569, 0.0627451],
[0.04705882, 0.0627451],
[0.05490196, 0.0627451],
]
per_channel_affine_ref_scales = [
[0.02352941, 0.04705882],
[0.03529412, 0.03137255],
[0.03921569, 0.03137255],
[0.04313726, 0.04313726],
]
per_channel_affine_qint8_zp = [
[-128, -43],
[-15, -128],
[-26, -128],
[-35, -58],
]
per_channel_affine_quint8_zp = [[0, 85], [113, 0], [102, 0], [93, 70]]
self.assertEqual(myobs.min_vals, ref_min_vals[ch_axis])
self.assertEqual(myobs.max_vals, ref_max_vals[ch_axis])
if qscheme == torch.per_channel_symmetric:
ref_scales = per_channel_symmetric_ref_scales[ch_axis]
ref_zero_points = [0, 0] if qdtype is torch.qint8 else [128, 128]
else:
ref_scales = per_channel_affine_ref_scales[ch_axis]
ref_zero_points = (
per_channel_affine_qint8_zp[ch_axis]
if qdtype is torch.qint8
else per_channel_affine_quint8_zp[ch_axis]
)
if reduce_range:
ref_scales = [s * 255 / 127 for s in ref_scales]
ref_zero_points = [math.floor(z / 2) for z in ref_zero_points]
self.assertTrue(torch.allclose(qparams[0], torch.tensor(ref_scales, dtype=qparams[0].dtype)))
self.assertTrue(torch.allclose(qparams[1], torch.tensor(ref_zero_points, dtype=qparams[1].dtype)))
# 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 = PerChannelMinMaxObserver(reduce_range=reduce_range, ch_axis=ch_axis, dtype=qdtype, qscheme=qscheme)
loaded_obs.load_state_dict(loaded_dict)
loaded_qparams = loaded_obs.calculate_qparams()
self.assertEqual(myobs.min_vals, loaded_obs.min_vals)
self.assertEqual(myobs.max_vals, loaded_obs.max_vals)
self.assertEqual(myobs.calculate_qparams(), loaded_obs.calculate_qparams())
def test_per_channel_observers(self, qdtype, qscheme, ch_axis,
reduce_range):
# reduce_range cannot be true for symmetric quantization with uint8
if qscheme == torch.per_channel_affine_float_qparams:
reduce_range = False
if qdtype == torch.quint8 and qscheme == torch.per_channel_symmetric:
reduce_range = False
ObserverList = [
PerChannelMinMaxObserver(reduce_range=reduce_range,
ch_axis=ch_axis,
dtype=qdtype,
qscheme=qscheme),
MovingAveragePerChannelMinMaxObserver(averaging_constant=0.5,
reduce_range=reduce_range,
ch_axis=ch_axis,
dtype=qdtype,
qscheme=qscheme)
]
for myobs in ObserverList:
# Calculate qparams should work for empty observers
qparams = myobs.calculate_qparams()
x = torch.tensor([
[[[1.0, 2.0], [2.0, 2.5]], [[3.0, 4.0], [4.5, 6.0]]],
[[[-4.0, -3.0], [5.0, 5.0]], [[6.0, 3.0], [7.0, 8.0]]],
])
if type(myobs) == MovingAveragePerChannelMinMaxObserver:
# Scaling the input tensor to model change in min/max values
# across batches
result = myobs(0.5 * x)
result = myobs(1.5 * x)
self.assertEqual(result, 1.5 * x)
else:
result = myobs(x)
self.assertEqual(result, x)
qparams = myobs.calculate_qparams()
ref_min_vals = [[1.0, -4.0], [-4.0, 3.0], [-4.0, 2.0],
[-4.0, -3.0]]
ref_max_vals = [[6.0, 8.0], [5.0, 8.0], [6.0, 8.0], [7.0, 8.0]]
per_channel_symmetric_ref_scales = [
[0.04705882, 0.06274509],
[0.03921569, 0.0627451],
[0.04705882, 0.0627451],
[0.05490196, 0.0627451],
]
per_channel_affine_ref_scales = [
[0.02352941, 0.04705882],
[0.03529412, 0.03137255],
[0.03921569, 0.03137255],
[0.04313726, 0.04313726],
]
per_channel_affine_qint8_zp = [
[-128, -43],
[-15, -128],
[-26, -128],
[-35, -58],
]
per_channel_affine_float_qparams_ref_scales = [
[0.0196, 0.0471],
[0.0353, 0.0196],
[0.0392, 0.0235],
[0.0431, 0.0431],
]
per_channel_affine_quint8_zp = [[0, 85], [113, 0], [102, 0],
[93, 70]]
self.assertEqual(myobs.min_val, ref_min_vals[ch_axis])
self.assertEqual(myobs.max_val, ref_max_vals[ch_axis])
if qscheme == torch.per_channel_symmetric:
ref_scales = per_channel_symmetric_ref_scales[ch_axis]
ref_zero_points = [0, 0
] if qdtype is torch.qint8 else [128, 128]
elif qscheme == torch.per_channel_affine_float_qparams:
ref_scales = per_channel_affine_float_qparams_ref_scales[
ch_axis]
ref_zero_points = [
-1 * ref_min_vals[ch_axis][i] / ref_scales[i]
for i in range(len(ref_scales))
]
else:
ref_scales = per_channel_affine_ref_scales[ch_axis]
ref_zero_points = (per_channel_affine_qint8_zp[ch_axis]
if qdtype is torch.qint8 else
per_channel_affine_quint8_zp[ch_axis])
if reduce_range:
ref_scales = [s * 255 / 127 for s in ref_scales]
ref_zero_points = [math.floor(z / 2) for z in ref_zero_points]
self.assertTrue(
torch.allclose(qparams[0],
torch.tensor(ref_scales,
dtype=qparams[0].dtype),
atol=0.0001))
if qscheme == torch.per_channel_affine_float_qparams:
self.assertTrue(
torch.allclose(qparams[1],
torch.tensor(ref_zero_points,
dtype=qparams[1].dtype),
atol=1))
else:
self.assertTrue(
torch.allclose(
qparams[1],
torch.tensor(ref_zero_points, dtype=qparams[1].dtype)))
# 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 = PerChannelMinMaxObserver(reduce_range=reduce_range,
ch_axis=ch_axis,
dtype=qdtype,
qscheme=qscheme)
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())