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): ["module: dynamo"]
import inspect
import io
import os
import tempfile
from unittest.mock import patch
import torch
from torch._dynamo.test_case import run_tests, TestCase
from torch._dynamo.testing import CompileCounter
class ToyModel(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear = torch.nn.Linear(10, 10)
self.relu = torch.nn.ReLU()
def forward(self, x):
return self.relu(self.linear(x))
class InPlaceCompilationTests(TestCase):
def test_compilation(self):
torch._dynamo.reset()
model = ToyModel()
cnt = CompileCounter()
model.compile(backend=cnt)
x = torch.randn(10, 10)
model(x)
self.assertEqual(cnt.frame_count, 1)
def test_overwrite_call_impl(self):
torch._dynamo.reset()
model = ToyModel()
self.assertTrue(model._compiled_call_impl is None)
model.compile()
self.assertTrue(model._compiled_call_impl is not None)
def test_save(self):
torch._dynamo.reset()
model = ToyModel()
model.compile()
model(torch.randn(1, 10))
with tempfile.TemporaryDirectory() as tmpdirname:
torch.save(model, os.path.join(tmpdirname, "model.pt"))
# weights_only=False as this is a legacy use case that loads a module
loaded_model = torch.load(
os.path.join(tmpdirname, "model.pt"), weights_only=False
)
loaded_model(torch.randn(1, 10))
def test_state_dict_save(self):
torch._dynamo.reset()
model = ToyModel()
model.compile()
model(torch.randn(1, 10))
with tempfile.TemporaryDirectory() as tmpdirname:
torch.save(model.state_dict(), os.path.join(tmpdirname, "model.pt"))
loaded_model = ToyModel()
loaded_model.load_state_dict(
# weights_only=False as this is a legacy use case that loads a module
torch.load(os.path.join(tmpdirname, "model.pt"), weights_only=False)
)
loaded_model(torch.randn(1, 10))
def test_jit_save(self):
torch._dynamo.reset()
model = ToyModel()
model.compile()
model(torch.randn(1, 10))
scripted_model = torch.jit.script(model)
with tempfile.TemporaryDirectory() as tmpdirname:
torch.jit.save(scripted_model, os.path.join(tmpdirname, "model.pt"))
loaded_model = torch.jit.load(os.path.join(tmpdirname, "model.pt"))
loaded_model(torch.randn(1, 10))
def test_compilation_callback(self):
torch._dynamo.reset()
@torch._dynamo.on_compile_start
def start_callback():
print("Compilation started.")
@torch._dynamo.on_compile_end
def end_callback():
print("Compilation ended.")
mod = ToyModel()
x = torch.randn(10, 10)
with patch("sys.stdout", new_callable=io.StringIO) as mock_stdout:
opt_mod = torch.compile(backend="eager", fullgraph=True)(mod)
opt_mod(x)
printed_output = mock_stdout.getvalue().strip()
self.assertEqual(printed_output, "Compilation started.\nCompilation ended.")
def test_compile_eager_options(self):
@torch.compile(backend="eager", options={"foo": 2})
def f(x):
return x + x
f(torch.randn(3))
@torch.compile(backend="aot_eager", options={"foo": 2})
def g(x):
return x + x
g(torch.randn(3))
def test_compilation_callback_with_graph_break(self):
torch._dynamo.reset()
counter = 0
@torch._dynamo.on_compile_start
def start_callback():
nonlocal counter
counter += 1
print(f"Counter = {counter}")
@torch._dynamo.on_compile_end
def end_callback():
nonlocal counter
counter += 1
print(f"Counter = {counter}")
@torch.compile(backend="eager")
def fn(x):
x = x + 1
torch._dynamo.graph_break()
return torch.sin(x)
x = torch.randn(10, 10)
with patch("sys.stdout", new_callable=io.StringIO) as mock_stdout:
fn(x)
printed_output = mock_stdout.getvalue().strip()
self.assertEqual(
printed_output, "Counter = 1\nCounter = 2\nCounter = 3\nCounter = 4"
)
# The private variants of the below functions are extensively tested
# So as long as the signatures match we're good
class PublicTorchCompilerTests(TestCase):
def check_signature(self, public_fn_name, private_fn_name, private_namespace):
public_fn = getattr(torch.compiler, public_fn_name)
private_fn = getattr(private_namespace, private_fn_name)
public_sig = inspect.signature(public_fn)
private_sig = inspect.signature(private_fn)
self.assertEqual(
public_sig,
private_sig,
f"Signatures do not match for function {public_fn_name}() \n Public: {public_sig} \n Private: {private_sig}",
)
def test_dynamo_signatures(self):
function_names = [
"reset",
"allow_in_graph",
"list_backends",
"assume_constant_result",
"disable",
]
for fn_name in function_names:
self.check_signature(fn_name, fn_name, torch._dynamo)
if __name__ == "__main__":
run_tests()
|
