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
|
# Owner(s): ["oncall: profiler"]
# if tqdm is not shutdown properly, it will leave the monitor thread alive.
# This causes an issue in the multithreading test because we check all events
# in that test with their tids. The events that correspond to these lingering
# threads all have TID of (uint64_t)(-1) which is invalid.
# The work around is turnning off monitoring thread when tqdm is loaded.
# Since these are unit tests, it is safe to turn off monitor thread.
try:
import tqdm
tqdm.tqdm.monitor_interval = 0
except ImportError:
None
from typing import Any, Dict
import torch
import torch.optim
import torch.utils.data
import torch.utils.data.datapipes as dp
from torch.autograd import (
_record_function_with_args_enter,
_record_function_with_args_exit,
)
from torch.autograd.profiler import profile as _profile
from torch.profiler import kineto_available, record_function
from torch.testing._internal.common_utils import run_tests, TestCase
Json = Dict[str, Any]
class TestRecordFunction(TestCase):
def _record_function_with_param(self):
u = torch.randn(3, 4, 5, requires_grad=True)
with _profile(
with_stack=True, use_kineto=kineto_available(), record_shapes=True
) as prof:
with record_function("## TEST 1 ##", "1, 2, 3"):
rf_handle = _record_function_with_args_enter(
"## TEST 2 ##", 1, False, 2.5, [u, u], "hello", u
)
_record_function_with_args_exit(rf_handle)
with record_function("## TEST 3 ##"):
rf_handle = _record_function_with_args_enter("## TEST 4 ##")
_record_function_with_args_exit(rf_handle)
return prof
def test_record_function(self):
prof_result = self._record_function_with_param()
found_test_1 = False
found_test_2 = False
found_test_3 = False
found_test_4 = False
for e in prof_result.function_events:
if "## TEST 1 ##" == e.name:
found_test_1 = True
self.assertTrue(e.input_shapes == [[]])
elif "## TEST 2 ##" == e.name:
found_test_2 = True
self.assertTrue(e.input_shapes == [[], [], [], [], [], [3, 4, 5]])
elif "## TEST 3 ##" == e.name:
found_test_3 = True
self.assertTrue(e.input_shapes == [])
elif "## TEST 4 ##" == e.name:
found_test_4 = True
self.assertTrue(e.input_shapes == [])
self.assertTrue(found_test_1)
self.assertTrue(found_test_2)
self.assertTrue(found_test_3)
self.assertTrue(found_test_4)
def test_datapipe_with_record_function(self):
with _profile(
with_stack=True, use_kineto=kineto_available(), record_shapes=True
) as prof:
input_dp1 = dp.iter.IterableWrapper(range(4))
input_dp2 = dp.iter.IterableWrapper(range(4, 8))
input_dp3 = dp.iter.IterableWrapper(range(8, 12))
output_dp = input_dp1.mux(input_dp2, input_dp3)
output = list(output_dp)
has_iter = False
has_mux = False
for e in prof.function_events:
if has_iter and has_mux:
break
if not has_iter and "IterableWrapper" in e.name:
has_iter = True
if not has_mux and "Multiplexer" in e.name:
has_mux = True
self.assertTrue(has_iter)
self.assertTrue(has_mux)
def test_datapipe_delegation_with_profiler(self):
class IDPIterator(torch.utils.data.IterDataPipe):
def __init__(self) -> None:
self.data = list(range(10))
self._idx = 0
def __iter__(self):
return self
def __next__(self):
if self._idx >= 10:
self._idx = 0
raise StopIteration
self._idx += 1
return self.data[self._idx - 1]
def get_value(self, idx):
return self.data[idx]
dp1 = IDPIterator() # The object itself is an iterator
self.assertEqual(5, dp1.get_value(5))
it_dp1 = iter(dp1) # This creates the 1st iterator
self.assertEqual(5, it_dp1.get_value(5)) # type: ignore[attr-defined]
self.assertEqual(list(range(10)), list(it_dp1))
class IDPDelegator(torch.utils.data.IterDataPipe):
def __init__(self, datapipe):
self.datapipe = datapipe
def __iter__(self):
return iter(self.datapipe)
dp2 = IDPDelegator(dp1)
it_dp2 = iter(dp2)
self.assertEqual(5, it_dp2.get_value(5))
self.assertEqual(list(range(10)), list(it_dp2))
def test_datapipe_with_record_function_fork(self):
with _profile(
with_stack=True, use_kineto=kineto_available(), record_shapes=True
) as prof:
input_dp = dp.iter.IterableWrapper(range(10))
dp1, dp2, dp3 = input_dp.fork(num_instances=3)
output1 = list(dp1)
has_iter = False
has_child = False
for e in prof.function_events:
if has_iter and has_child:
break
if not has_iter and "IterableWrapper" in e.name:
has_iter = True
if not has_child and "_ChildDataPipe" in e.name:
has_child = True
self.assertTrue(has_iter)
self.assertTrue(has_child)
if __name__ == "__main__":
run_tests()
|
