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
|
# Owner(s): ["oncall: jit"]
import os
import sys
from itertools import product
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.testing import FileCheck
import unittest
try:
import torchvision
HAS_TORCHVISION = True
except ImportError:
HAS_TORCHVISION = False
skipIfNoTorchVision = unittest.skipIf(not HAS_TORCHVISION, "no torchvision")
# Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)
from torch.testing._internal.jit_utils import JitTestCase
if __name__ == '__main__':
raise RuntimeError("This test file is not meant to be run directly, use:\n\n"
"\tpython test/test_jit.py TESTNAME\n\n"
"instead.")
activations = [
F.celu,
F.elu,
F.hardsigmoid,
F.hardswish,
F.hardtanh,
F.leaky_relu,
F.relu,
F.relu6,
F.rrelu,
F.selu,
F.silu,
]
class TestFunctionalToInplaceActivation(JitTestCase):
def test_check_no_type_promotion(self):
dtypes = [
torch.bool,
torch.int8,
torch.int16,
torch.int32,
torch.int64,
torch.float32,
torch.float64,
]
# restore_mutation.h contains a mapping from activation operators
# to whether they allow type conversion. Use this checking to
# guard the mapping, and if any later change breaks the assumption
# we need to update the mapping correspondingly.
for activation, dtype in product(activations, dtypes):
inp = torch.normal(0, 5, size=(4, 4)).to(dtype)
try:
out = activation(inp)
self.assertEqual(dtype, out.dtype)
except RuntimeError:
# Skip the not implemented error
pass
def test_functional_to_inplace_activation(self):
for activation in activations:
def test_basic(x):
y = x + 1
z = activation(y)
return z
fn = torch.jit.script(test_basic)
self.run_pass("inline", fn.graph)
self.run_pass("constant_propagation", fn.graph)
FileCheck().check(f"aten::{activation.__name__}(").run(fn.graph)
self.run_pass('functional_to_inplace_activation', fn.graph)
FileCheck().check_not(f"aten::{activation.__name__}(").run(fn.graph)
FileCheck().check(f"aten::{activation.__name__}_").run(fn.graph)
inp = torch.rand([2, 2])
self.assertEqual(fn(inp), test_basic(inp))
def test_no_functional_to_inplace(self):
# inplace conversion should not happen because sigmoid may
# perform type conversion
def test1():
y = torch.ones([2, 2])
z = torch.sigmoid(y)
return z
fn = torch.jit.script(test1)
self.run_pass('functional_to_inplace_activation', fn.graph)
FileCheck().check_not("aten::sigmoid_").run(fn.graph)
# inplace conversion should not happen because y is alias
# the input x
def test2(x):
y = x[0]
z = torch.relu(y)
return z
fn = torch.jit.script(test2)
self.run_pass('functional_to_inplace_activation', fn.graph)
FileCheck().check_not("aten::relu_").run(fn.graph)
# inplace conversion should not happen because self.x is
# at the global scope
class Test3(nn.Module):
def __init__(self, x):
super(Test3, self).__init__()
self.x = x
def forward(self):
y = torch.relu(self.x)
return y
fn = torch.jit.script(Test3(torch.rand([2, 2])).eval())
self.run_pass('functional_to_inplace_activation', fn.graph)
FileCheck().check_not("aten::relu_").run(fn.graph)
@skipIfNoTorchVision
def test_resnet18_correctness(self):
model = torchvision.models.resnet18()
frozen_model = torch.jit.freeze(torch.jit.script(model.eval()))
N, C, H, W, = 10, 3, 224, 224
inp = torch.randn(N, C, H, W)
self.run_pass('functional_to_inplace_activation', frozen_model.graph)
self.assertEqual(model(inp), frozen_model(inp))
class TestInplaceToFunctionalActivation(JitTestCase):
def test_inplace_to_functional_activation(self):
for activation in activations:
def test_basic(x):
y = x + 1
activation(y, inplace=True)
return y
fn = torch.jit.script(test_basic)
self.run_pass("inline", fn.graph)
self.run_pass("constant_propagation", fn.graph)
FileCheck().check(f"aten::{activation.__name__}_").run(fn.graph)
self.run_pass('inplace_to_functional_activation', fn.graph)
FileCheck().check_not(f"aten::{activation.__name__}_").run(fn.graph)
FileCheck().check(f"aten::{activation.__name__}(").run(fn.graph)
for activation in [
torch.relu_,
torch.sigmoid_,
torch.tanh_,
]:
def test_basic(x):
y = x + 1
activation(y)
return y
fn = torch.jit.script(test_basic)
self.run_pass("inline", fn.graph)
self.run_pass("constant_propagation", fn.graph)
FileCheck().check(f"aten::{activation.__name__}").run(fn.graph)
self.run_pass('inplace_to_functional_activation', fn.graph)
FileCheck().check_not(f"aten::{activation.__name__}").run(fn.graph)
FileCheck().check(f"aten::{activation.__name__[:-1]}(").run(fn.graph)
inp = torch.rand([2, 2])
self.assertEqual(fn(inp), test_basic(inp))
@skipIfNoTorchVision
def test_resnet18_correctness(self):
model = torchvision.models.resnet18()
frozen_model = torch.jit.freeze(torch.jit.script(model.eval()))
N, C, H, W, = 10, 3, 224, 224
inp = torch.randn(N, C, H, W)
self.run_pass('inplace_to_functional_activation', frozen_model.graph)
self.assertEqual(model(inp), frozen_model(inp))
|
