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22 changes: 19 additions & 3 deletions tests/test_optimizer.py
Original file line number Diff line number Diff line change
Expand Up @@ -147,34 +147,50 @@ def test_state_averager(offload_optimizer: bool, reuse_tensors: bool, sync_epoch


@pytest.mark.forked
def test_load_state_from_peers():
@pytest.mark.parametrize("dpu", [True, False])
def test_load_state_from_peers(dpu: bool):
dht1 = hivemind.DHT(start=True)
dht2 = hivemind.DHT(initial_peers=dht1.get_visible_maddrs(), start=True)

model1 = nn.Linear(2, 3)
model2 = nn.Linear(2, 3)

extras1 = (torch.randn(2, 2), -torch.rand(1))
extras2 = (-torch.randn(2, 2), torch.rand(1))

common_kwargs = dict(
optimizer=partial(torch.optim.SGD, lr=0.1),
scheduler=partial(torch.optim.lr_scheduler.LambdaLR, lr_lambda=lambda t: 1.0 / max(1, t)),
offload_optimizer=dpu,
reuse_tensors=dpu,
target_group_size=2,
prefix="my_exp",
)

avgr1 = TrainingStateAverager(
dht=dht1, params=model1.parameters(), allow_state_sharing=False, start=True, **common_kwargs
dht=dht1,
params=model1.parameters(),
allow_state_sharing=False,
start=True,
extra_tensors=extras1,
**common_kwargs,
)

avgr2 = TrainingStateAverager(dht=dht2, params=model2.parameters(), start=True, **common_kwargs)
avgr2 = TrainingStateAverager(
dht=dht2, params=model2.parameters(), start=True, extra_tensors=extras2, **common_kwargs
)

avgr2.local_epoch = 1337
model2.weight.data[...] = 42
extras2[0][:] = 9999
time.sleep(0.1)

avgr1.load_state_from_peers()
assert avgr1.local_epoch == 1337
assert torch.all(model1.weight == 42).item()
assert np.allclose(avgr1.optimizer.param_groups[0]["lr"], 0.1 / 1337)
assert torch.all(extras1[0] == extras2[0]).item() and torch.all(extras1[0] == extras2[0]).item()
assert torch.all(extras1[0] == 9999).item()


@pytest.mark.forked
Expand Down