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
|
# Owner(s): ["oncall: mobile"]
import torch
import torch.nn as nn
import torch.ao.nn.quantized as nnq
import torch.utils.bundled_inputs
from torch.ao.quantization import (
default_qconfig,
float_qparams_weight_only_qconfig,
)
# graph mode quantization based on fx
from torch.ao.quantization.quantize_fx import (
prepare_fx,
convert_fx,
)
from torch.testing._internal.common_quantization import NodeSpec as ns
from torch.testing._internal.common_quantization import (
QuantizationLiteTestCase,
LinearModelWithSubmodule,
)
class TestLiteFuseFx(QuantizationLiteTestCase):
# Tests from:
# ./caffe2/test/quantization/fx/test_quantize_fx.py
def test_embedding(self):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.emb = torch.nn.Embedding(num_embeddings=10, embedding_dim=12)
def forward(self, indices):
return self.emb(indices)
model = M().eval()
indices = torch.randint(low=0, high=10, size=(20,))
quantized_node = ns.call_module(nnq.Embedding)
configs = [
(float_qparams_weight_only_qconfig, ns.call_module(nnq.Embedding)),
(None, ns.call_module(nn.Embedding)),
(default_qconfig, ns.call_module(nn.Embedding)),
]
for qconfig, node in configs:
qconfig_dict = {"": qconfig}
m = prepare_fx(model, qconfig_dict)
m = convert_fx(m)
self._compare_script_and_mobile(m, input=indices)
def test_conv2d(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv1 = nn.Conv2d(1, 1, 1)
self.conv2 = nn.Conv2d(1, 1, 1)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
return x
m = M().eval()
qconfig_dict = {"": default_qconfig, "module_name": [("conv1", None)]}
m = prepare_fx(m, qconfig_dict)
data = torch.randn(1, 1, 1, 1)
m = convert_fx(m)
# first conv is quantized, second conv is not quantized
self._compare_script_and_mobile(m, input=data)
def test_submodule(self):
# test quantizing complete module, submodule and linear layer
configs = [
{},
{"module_name": [("subm", None)]},
{"module_name": [("fc", None)]},
]
for config in configs:
model = LinearModelWithSubmodule().eval()
qconfig_dict = {
"": torch.ao.quantization.get_default_qconfig("qnnpack"),
**config,
}
model = prepare_fx(model, qconfig_dict)
quant = convert_fx(model)
x = torch.randn(5, 5)
self._compare_script_and_mobile(quant, input=x)
if __name__ == "__main__":
run_tests()
|
