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
196
197
198
199
|
# Owner(s): ["module: dynamo"]
from unittest.mock import patch
import torch
import torch._dynamo.test_case
import torch._dynamo.testing
import torch._dynamo.utils
from torch._dynamo.utils import dynamo_timed
from torch.testing._internal.common_utils import TemporaryFileName
class DynamoProfilerTests(torch._dynamo.test_case.TestCase):
def test_dynamo_timed_profiling_isolated(self):
# dynamo_timed functions should appear in profile traces.
def inner_fn(x):
with dynamo_timed("inner_fn"):
return x.sin()
def outer_fn(x, y):
return inner_fn(x) * y
x, y = (torch.rand((2, 2)) for _ in range(2))
with torch.profiler.profile(with_stack=False) as prof:
outer_fn(x, y)
self.assertTrue(
any("inner_fn (dynamo_timed)" in evt.name for evt in prof.events())
)
def test_dynamo_timed_profiling_backend_compile(self):
# dynamo_timed functions should appear in profile traces.
# this checks whether these actually appear in actual dynamo execution.
# "backend_compile" is just chosen as an example; if it gets renamed
# this test can be replaced or deleted
fn_name = "call_user_compiler"
def fn(x, y):
return x.sin() * y.cos()
x, y = (torch.rand((2, 2)) for _ in range(2))
with torch.profiler.profile(with_stack=False) as prof:
torch.compile(fn, backend="aot_eager")(x, y)
self.assertTrue(
any(f"{fn_name} (dynamo_timed)" in evt.name for evt in prof.events())
)
@patch.object(torch._dynamo.config, "assume_static_by_default", False)
def test_profile_dynamic_shapes_runtime(self):
def fn(x, y, z):
return x @ y + z
opt_fn = torch.compile(fn, backend="aot_eager", dynamic=True, fullgraph=True)
inputs = [
(torch.rand(a, b), torch.rand(b, c), torch.rand(a, c))
for (a, b, c) in [(15, 16, 17), (15, 15, 16), (16, 16, 16)]
]
opt_fn(*inputs[0])
opt_fn(*inputs[1])
with torch.profiler.profile(record_shapes=True):
opt_fn(*inputs[2])
@patch.object(torch._dynamo.config, "assume_static_by_default", False)
def test_profile_dynamic_shapes_compilation(self):
def fn(x, y, z):
return x @ y + z
opt_fn = torch.compile(fn, backend="aot_eager", dynamic=True, fullgraph=True)
inputs = (torch.rand(15, 16), torch.rand(16, 17), torch.rand(15, 17))
with torch.profiler.profile(record_shapes=True):
opt_fn(*inputs)
@patch.object(torch._dynamo.config, "assume_static_by_default", False)
def test_profile_dynamic_shapes_list_compilation(self):
def fn(x, y, z):
return torch.cat([x, y], dim=0) + z
opt_fn = torch.compile(fn, backend="aot_eager", dynamic=True, fullgraph=True)
inputs = (torch.rand(4, 16), torch.rand(12, 16), torch.rand(16, 16))
with torch.profiler.profile(record_shapes=True):
opt_fn(*inputs)
def test_execution_trace_dynamic_shapes(self):
def fn(x, y, z):
return x @ y + z
et = torch.profiler.ExecutionTraceObserver()
opt_fn = torch.compile(fn, dynamic=True, backend="aot_eager")
inputs = [torch.rand((4, 4)) for _ in range(3)]
with TemporaryFileName() as fname:
et.register_callback(fname)
et.start()
out = opt_fn(*inputs)
et.stop()
et.unregister_callback()
def test_profiler_cache_lookup(self):
def fn(x):
y = x**2
y = y + 2
z = y**3
return z
for profiler, get_events in (
(torch.autograd.profiler.profile, lambda prof: prof.function_events),
(torch.profiler.profiler.profile, lambda prof: prof.events()),
):
x = torch.randn((2, 2), requires_grad=True)
ref = fn(x)
opt_fn = torch.compile(fn, backend="aot_eager")
# warmup
opt_fn(x)
with profiler() as prof:
res = opt_fn(x)
events = list(
filter(
lambda event: "TorchDynamo Cache Lookup" in event.name,
get_events(prof),
)
)
self.assertEqual(ref, res)
self.assertTrue(
len(events) == 1,
"Expected one lookup profiler event for one opt_fn run",
)
def test_profiler_cache_lookup_profiler_step(self):
def fn(x, y, z):
return torch.add(torch.sub(x, y), z)
opt_fn = torch.compile(fn, backend="aot_eager")
(
x,
y,
z,
) = (torch.rand(4, 4) for _ in range(3))
prof = torch.profiler.profile(
schedule=torch.profiler.schedule(wait=2, warmup=2, active=2, repeat=1)
)
for _ in range(10):
opt_fn(x, y, z)
prof.step()
self.assertTrue(
any(e.name == "TorchDynamo Cache Lookup" for e in prof.events())
)
def test_profiler_dynamo_compiled_region(self):
def fn(x, y):
r = y.sum(dim=1)
print(r.shape)
return x * r
with torch.profiler.profile() as prof:
fn_c = torch.compile(fn)
fn_c(
torch.randn(10),
torch.randn(10, 10),
)
fn_c(
torch.randn(10),
torch.randn(10, 15),
)
annotations = [e.name for e in prof.events() if "Compiled" in e.name]
self.assertEqual(
annotations,
[
"Torch-Compiled Region: 0/0",
"Torch-Compiled Region: 1/0",
"Torch-Compiled Region: 0/1",
"Torch-Compiled Region: 1/0",
],
)
if __name__ == "__main__":
from torch._dynamo.test_case import run_tests
run_tests()
|
