-
Notifications
You must be signed in to change notification settings - Fork 24k
/
Copy pathtest_c10d_pypg.py
202 lines (147 loc) · 5.4 KB
/
test_c10d_pypg.py
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
200
201
202
# Owner(s): ["oncall: distributed"]
import weakref
import test_c10d_common
import torch
import torch.distributed as dist
import torch.nn as nn
from torch._C._distributed_c10d import _create_work_from_future
from torch.futures import Future
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.testing._internal.common_distributed import MultiThreadedTestCase
from torch.testing._internal.common_utils import run_tests, TestCase
def create_work(result):
future = Future()
future.set_result(result)
return _create_work_from_future(future)
class MyWork(dist._Work):
def __init__(self, result, pg):
super().__init__()
self.result_ = result
self.future_ = torch.futures.Future()
self.future_.set_result(result)
self.pg_ = weakref.ref(pg)
def wait(self, timeout):
self.pg_().wait_count += 1
return True
def get_future(self):
self.pg_().get_future_count += 1
return self.future_
class LonelyRankProcessGroup(dist.ProcessGroup):
"""
This PG only supports world_size of 1
"""
def __init__(self, rank, world, use_wrapper):
super().__init__(rank, world)
assert rank == 0
assert world == 1
self._rank = rank
self._world = world
self.wait_count = 0
self.get_future_count = 0
self.use_wrapper = use_wrapper
self._work = []
def broadcast(self, tensor_list, opts):
if self.use_wrapper:
return create_work(tensor_list)
res = MyWork(tensor_list, self)
self._work.append(res)
return res
def allgather(self, output_tensors, input_tensor, opts):
for o, i in zip(output_tensors[0], input_tensor):
o.copy_(i)
if self.use_wrapper:
return create_work(output_tensors)
res = MyWork(output_tensors, self)
self._work.append(res)
return res
def allreduce(self, tensors, opts):
if self.use_wrapper:
return create_work(tensors)
res = MyWork(tensors, self)
self._work.append(res)
return res
def getSize(self):
return self._world
def getBackendName(self):
return "lonely-pg"
def __repr__(self):
return f"PLG w:{self._world} r:{self._rank}"
class DummyAttrProcessGroup(dist.ProcessGroup):
def getRank(self):
return 123
def getSize(self):
return 456
def getBackendName(self):
return "dummy-attr"
def setGroupName(self, name) -> None:
self._group_name = "py:" + name
def getGroupName(self) -> str:
return self._group_name
def setGroupDesc(self, group_desc) -> None:
self._group_desc = "py:" + group_desc
def getGroupDesc(self) -> str:
return self._group_desc
# We cannot use parametrize as some tests are defined on the base class and use _get_process_group
class AbstractDDPSingleRank(test_c10d_common.CommonDistributedDataParallelTest):
def setUp(self):
super().setUp()
self._spawn_threads()
@property
def world_size(self):
return 1
def _get_process_group(self):
return LonelyRankProcessGroup(self.rank, self.world_size, self.use_wrapper)
def test_ddp_invoke_work_object(self):
pg = self._get_process_group()
torch.manual_seed(123)
model = nn.Sequential(nn.Linear(2, 2), nn.ReLU())
wrapped_model = model
input_tensor = torch.rand(2)
model = DDP(model, process_group=pg)
model(input_tensor).sum().backward()
ddp_grad = wrapped_model[0].bias.grad.clone()
wrapped_model.zero_grad()
wrapped_model(input_tensor).sum().backward()
self.assertEqual(wrapped_model[0].bias.grad, ddp_grad)
if not self.use_wrapper:
self.assertTrue(pg.wait_count > 0)
self.assertTrue(pg.get_future_count > 0)
def test_ddp_with_pypg(self):
pg = self._get_process_group()
self._test_ddp_with_process_group(pg, [torch.device("cpu")], device_ids=None)
def test_ddp_with_pypg_with_grad_views(self):
pg = self._get_process_group()
self._test_ddp_with_process_group(
pg, [torch.device("cpu")], device_ids=None, gradient_as_bucket_view=True
)
def test_ddp_no_init_sync(self):
pg = self._get_process_group()
model = nn.Sequential(nn.Linear(2, 2), nn.ReLU())
model = DDP(model, process_group=pg, init_sync=False)
self.assertEqual(pg.wait_count, 0)
self.assertEqual(pg.get_future_count, 0)
class TestDDPWithWorkSubclass(AbstractDDPSingleRank, MultiThreadedTestCase):
@property
def use_wrapper(self):
return False
class TestDDPWithWorkWrapper(AbstractDDPSingleRank, MultiThreadedTestCase):
@property
def use_wrapper(self):
return True
class TestPyProcessGroup(TestCase):
def test_attr_overrides(self):
pg = DummyAttrProcessGroup(0, 1)
self.assertEqual(pg.name(), "dummy-attr")
self.assertEqual(pg.rank(), 123)
self.assertEqual(pg.size(), 456)
pg._set_group_name("name")
self.assertEqual(pg.group_name, "py:name")
pg._set_group_desc("desc")
self.assertEqual(pg.group_desc, "py:desc")
def test_abort_shutdown(self) -> None:
# verify this are noops
pg = DummyAttrProcessGroup(0, 1)
pg.abort()
pg.shutdown()
if __name__ == "__main__":
run_tests()