def _test_broadcast_helper(self,
group,
group_id,
rank,
cuda=False,
rank_to_GPU=None):
for ttype, value, requires_cuda in [
("torch.FloatTensor", -1e-10, False),
("torch.DoubleTensor", -1e-100, False),
("torch.HalfTensor", -0.1, True),
("torch.CharTensor", -2, False),
("torch.ByteTensor", 129, False),
("torch.IntTensor", -1e5, False),
("torch.LongTensor", -1e15, False),
]:
if requires_cuda and not cuda:
continue
for src in group:
expected_tensor = _build_tensor(src + 1, value).type(ttype)
if cuda:
expected_tensor = expected_tensor.cuda(
rank_to_GPU[rank][0])
if rank == src:
dist.broadcast(expected_tensor, src, group_id)
else:
tensor = _build_tensor(src + 1, -1).type(ttype)
if cuda:
tensor = tensor.cuda(rank_to_GPU[rank][0])
dist.broadcast(tensor, src, group_id)
self.assertEqual(tensor.size(), expected_tensor.size())
self.assertEqual(tensor.ne(expected_tensor).max(), 0)
self._barrier()
def _test_barrier_helper(self, group, group_id, rank):
WAIT_TIME = 0.3 # seconds
for dest in group:
expected_time = torch.DoubleTensor(1).fill_(0.0)
if dest == rank:
expected_time.fill_(time.time() + WAIT_TIME)
dist.broadcast(expected_time, dest, group_id)
time.sleep(WAIT_TIME + 0.1) # sleep a little bit longer
dist.barrier(group_id)
else:
dist.broadcast(expected_time, dest, group_id)
dist.barrier(group_id)
self.assertGreaterEqual(time.time(), expected_time[0])
self._barrier()
def sync_parameters(self):
for param in self.module.parameters():
dist.broadcast(param.data, 0)
