1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
|
# Owner(s): ["oncall: quantization"]
import torch
from torch.testing._internal.common_utils import TestCase
from torch.ao.quantization.utils import get_fqn_to_example_inputs
from torch.nn.quantized.modules.utils import _quantize_weight
from torch.ao.quantization import MovingAverageMinMaxObserver, MovingAveragePerChannelMinMaxObserver
class TestUtils(TestCase):
def _test_get_fqn_to_example_inputs(self, M, example_inputs, expected_fqn_to_dim):
m = M().eval()
fqn_to_example_inputs = get_fqn_to_example_inputs(m, example_inputs)
for fqn, expected_dims in expected_fqn_to_dim.items():
assert fqn in expected_fqn_to_dim
example_inputs = fqn_to_example_inputs[fqn]
for example_input, expected_dim in zip(example_inputs, expected_dims):
assert example_input.dim() == expected_dim
def test_get_fqn_to_example_inputs_simple(self):
class Sub(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear1 = torch.nn.Linear(5, 5)
self.linear2 = torch.nn.Linear(5, 5)
def forward(self, x):
x = self.linear1(x)
x = self.linear2(x)
return x
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear1 = torch.nn.Linear(5, 5)
self.linear2 = torch.nn.Linear(5, 5)
self.sub = Sub()
def forward(self, x):
x = self.linear1(x)
x = self.linear2(x)
x = self.sub(x)
return x
expected_fqn_to_dim = {
"": (2,),
"linear1": (2,),
"linear2": (2,),
"sub": (2,),
"sub.linear1": (2,),
"sub.linear2": (2,)
}
example_inputs = (torch.rand(1, 5),)
self._test_get_fqn_to_example_inputs(M, example_inputs, expected_fqn_to_dim)
def test_get_fqn_to_example_inputs_default_kwargs(self):
""" Test that we can get example inputs for functions with default keyword arguments
"""
class Sub(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear1 = torch.nn.Linear(5, 5)
self.linear2 = torch.nn.Linear(5, 5)
def forward(self, x, key1=torch.rand(1), key2=torch.rand(1)):
x = self.linear1(x)
x = self.linear2(x)
return x
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear1 = torch.nn.Linear(5, 5)
self.linear2 = torch.nn.Linear(5, 5)
self.sub = Sub()
def forward(self, x):
x = self.linear1(x)
x = self.linear2(x)
# only override `key2`, `key1` will use default
x = self.sub(x, key2=torch.rand(1, 2))
return x
expected_fqn_to_dim = {
"": (2,),
"linear1": (2,),
"linear2": (2,),
# second arg is `key1`, which is using default argument
# third arg is `key2`, override by callsite
"sub": (2, 1, 2),
"sub.linear1": (2,),
"sub.linear2": (2,)
}
example_inputs = (torch.rand(1, 5),)
self._test_get_fqn_to_example_inputs(M, example_inputs, expected_fqn_to_dim)
def test_get_fqn_to_example_inputs_complex_args(self):
""" Test that we can record complex example inputs such as lists and dicts
"""
class Sub(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear1 = torch.nn.Linear(5, 5)
self.linear2 = torch.nn.Linear(5, 5)
def forward(self, x, list_arg, dict_arg):
x = self.linear1(x)
x = self.linear2(x)
return x
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear1 = torch.nn.Linear(5, 5)
self.linear2 = torch.nn.Linear(5, 5)
self.sub = Sub()
def forward(self, x):
x = self.linear1(x)
x = self.linear2(x)
x = self.sub(x, [x], {"3": x})
return x
example_inputs = (torch.rand(1, 5),)
m = M().eval()
fqn_to_example_inputs = get_fqn_to_example_inputs(m, example_inputs)
assert "sub" in fqn_to_example_inputs
assert isinstance(fqn_to_example_inputs["sub"][1], list)
assert isinstance(fqn_to_example_inputs["sub"][2], dict) and \
"3" in fqn_to_example_inputs["sub"][2]
def test_quantize_weight_clamping_per_tensor(self):
""" Test quant_{min, max} from per tensor observer is honored by `_quantize_weight` method
"""
fp_min, fp_max = -1000.0, 1000.0
q8_min, q8_max = -10, 10
float_tensor = torch.tensor([fp_min, fp_max])
observer = MovingAverageMinMaxObserver(
averaging_constant=1.0,
dtype=torch.qint8,
quant_min=q8_min,
quant_max=q8_max,
qscheme=torch.per_tensor_symmetric,
)
observer(float_tensor)
assert observer.min_val == fp_min
assert observer.max_val == fp_max
quantized_tensor = _quantize_weight(float_tensor, observer)
assert quantized_tensor.int_repr().max().item() == q8_max
assert quantized_tensor.int_repr().min().item() == q8_min
# Actual weight values can be outside than observer [min_val, max_val] for the moving average observer
float_tensor *= 1.2
quantized_tensor = _quantize_weight(float_tensor, observer)
assert quantized_tensor.int_repr().max().item() == q8_max
assert quantized_tensor.int_repr().min().item() == q8_min
def test_quantize_weight_clamping_per_channel(self):
""" Test quant_{min, max} from per channel observer is honored by `_quantize_weight` method
"""
fp_min, fp_max = -1000.0, 1000.0
q8_min, q8_max = -10, 10
float_tensor = torch.tensor([[fp_min, fp_max]])
observer = MovingAveragePerChannelMinMaxObserver(
averaging_constant=1.0,
dtype=torch.qint8,
quant_min=q8_min,
quant_max=q8_max,
qscheme=torch.per_channel_symmetric,
ch_axis=0,
)
observer(float_tensor)
assert observer.min_val == fp_min
assert observer.max_val == fp_max
quantized_tensor = _quantize_weight(float_tensor, observer)
assert quantized_tensor.int_repr().max().item() == q8_max
assert quantized_tensor.int_repr().min().item() == q8_min
# Actual weight values can be outside than observer [min_val, max_val] for the moving average observer
float_tensor *= 1.2
quantized_tensor = _quantize_weight(float_tensor, observer)
assert quantized_tensor.int_repr().max().item() == q8_max
assert quantized_tensor.int_repr().min().item() == q8_min
|
