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# Owner(s): ["oncall: distributed"]
import os
import torch
import torch.distributed as dist
from torch.testing._internal.common_utils import (
run_tests,
)
from torch.futures import Future
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel as DDP
import test_c10d_common
import weakref
from torch._C._distributed_c10d import _create_work_from_future
from torch.testing._internal.common_distributed import (
MultiProcessTestCase,
)
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(LonelyRankProcessGroup, self).__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 size(self):
return self._world
def getBackendName(self):
return "lonely-pg"
def __repr__(self):
return f"PLG w:{self._world} r:{self._rank}"
# 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(AbstractDDPSingleRank, self).setUp()
self._spawn_processes()
@property
def world_size(self):
return 1
def tearDown(self):
super(AbstractDDPSingleRank, self).tearDown()
try:
os.remove(self.file_name)
except OSError:
pass
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)
class TestDDPWithWorkSubclass(AbstractDDPSingleRank, MultiProcessTestCase):
@property
def use_wrapper(self):
return False
class TestDDPWithWorkWrapper(AbstractDDPSingleRank, MultiProcessTestCase):
@property
def use_wrapper(self):
return True
if __name__ == '__main__':
run_tests()
|
