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
194
195
|
# Owner(s): ["oncall: jit"]
import io
import math
import unittest
import torch
from torch.nn import init
from torch.testing._internal.common_utils import skipIfLegacyJitExecutor
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."
)
class TestGenerator(JitTestCase):
# torch.jit.trace does not properly capture the generator manual seed
# and thus is non deterministic even if the generator is manually seeded
@skipIfLegacyJitExecutor("legacy JIT executor does not support Generator type")
@unittest.expectedFailure
def test_trace(self):
def f():
generator = torch.Generator()
generator.seed()
generator.manual_seed(2023)
generator.initial_seed()
tensor = torch.empty(2, 2)
tensor.uniform_(0, 1, generator=generator)
return tensor
traced_f = torch.jit.trace(f, ())
# Run this 3 times to ensure that the generator is being manually seeded
# each time the traced function is run
for i in range(3):
torch.manual_seed(1)
eager_tensor = f()
# Change the seed of the default generator to
# check that we're using the generator from the
# trace
torch.manual_seed(2)
traced_tensor = traced_f()
self.assertEqual(eager_tensor, traced_tensor)
def test_script(self):
def f():
generator = torch.Generator()
generator.seed()
generator.manual_seed(2023)
generator.initial_seed()
tensor = torch.empty(2, 2)
tensor.normal_(-1.0, 1.0, generator=generator)
return tensor
script_f = torch.jit.script(f, ())
# Run this 3 times to ensure that the generator is being manually seeded
# each time the traced function is run
for i in range(3):
torch.manual_seed(1)
eager_tensor = f()
# Change the seed of the default generator to
# check that we're using the generator from the
# trace
torch.manual_seed(2)
script_tensor = script_f()
self.assertEqual(eager_tensor, script_tensor)
def test_default_generator(self):
def f():
# check that calling manual seed for the default generator works
torch.manual_seed(2023)
tensor = torch.empty(2, 2)
tensor.normal_(-1.0, 1.0)
return tensor
torch.manual_seed(1)
eager_tensor = f()
torch.manual_seed(2)
script_f = torch.jit.script(f, ())
script_tensor = script_f()
self.assertEqual(eager_tensor, script_tensor)
def test_generator_arg(self):
def f(generator: torch.Generator):
tensor = torch.empty(2, 2)
tensor.normal_(-1.0, 1.0, generator=generator)
return tensor
generator = torch.Generator()
generator.manual_seed(2023)
script_f = torch.jit.script(f, (generator,))
for i in range(3):
generator = torch.Generator()
generator.manual_seed(2023 + i)
torch.manual_seed(1 + i)
eager_tensor = f(generator)
generator = torch.Generator()
generator.manual_seed(2023 + i)
torch.manual_seed(1 + i)
script_tensor = script_f(generator)
self.assertEqual(eager_tensor, script_tensor)
def test_save_load(self):
class Foo(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.foo = torch.nn.Linear(2, 2, bias=False)
self.bar = torch.nn.Linear(2, 2, bias=False)
self.reset_parameters()
def reset_linear(self, module, generator):
init.kaiming_uniform_(
module.weight, a=math.sqrt(5), generator=generator
)
def reset_parameters(self):
generator = torch.Generator()
generator.manual_seed(1)
self.reset_linear(self.foo, generator)
generator = torch.Generator()
generator.manual_seed(2)
self.reset_linear(self.bar, generator)
def forward(self, x):
x = self.foo(x)
x = self.bar(x)
generator = torch.Generator()
generator.manual_seed(3)
r = torch.empty_like(x)
r.normal_(0.0, 1.0, generator=generator)
return x, r
eager_foo = Foo()
script_module = torch.jit.script(Foo())
saved_module = io.BytesIO()
torch.jit.save(script_module, saved_module)
saved_module.seek(0)
loaded_module = torch.jit.load(saved_module)
self.assertEqual(eager_foo.foo.weight, loaded_module.foo.weight)
self.assertEqual(eager_foo.bar.weight, loaded_module.bar.weight)
try:
# Run this 3 times so make sure that the generator seed is being set
# every time forward is called
for i in range(3):
x = torch.ones(2, 2)
out1, r1 = eager_foo(x)
out2, r2 = loaded_module(x)
try:
self.assertEqual(out1, out2)
except: # noqa: B001, E722
print(f"Iteration {i}:\n{out1=}\n{out2=}")
raise
try:
self.assertEqual(r1, r2)
except: # noqa: B001, E722
print(f"Iteration {i}:\n{r1=}\n{r2=}")
raise
except: # noqa: B001, E722
print(loaded_module.forward.code)
raise
|
