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import socket
from dataclasses import asdict
import numpy as np
import pytest
from loky import get_reusable_executor
import xgboost as xgb
from xgboost import RabitTracker, build_info, federated
from xgboost import testing as tm
from xgboost.collective import Config
def run_rabit_worker(rabit_env: dict, world_size: int) -> int:
with xgb.collective.CommunicatorContext(**rabit_env):
assert xgb.collective.get_world_size() == world_size
assert xgb.collective.is_distributed()
assert xgb.collective.get_processor_name() == socket.gethostname()
ret = xgb.collective.broadcast("test1234", 0)
assert str(ret) == "test1234"
reduced = xgb.collective.allreduce(np.asarray([1, 2, 3]), xgb.collective.Op.SUM)
assert np.array_equal(reduced, np.asarray([2, 4, 6]))
return 0
@pytest.mark.skipif(**tm.no_loky())
def test_rabit_communicator() -> None:
world_size = 2
tracker = RabitTracker(host_ip="127.0.0.1", n_workers=world_size)
tracker.start()
workers = []
with get_reusable_executor(max_workers=world_size) as pool:
for _ in range(world_size):
worker = pool.submit(
run_rabit_worker, rabit_env=tracker.worker_args(), world_size=world_size
)
workers.append(worker)
for worker in workers:
assert worker.result() == 0
def run_federated_worker(port: int, world_size: int, rank: int) -> int:
with xgb.collective.CommunicatorContext(
dmlc_communicator="federated",
federated_server_address=f"localhost:{port}",
federated_world_size=world_size,
federated_rank=rank,
):
assert xgb.collective.get_world_size() == world_size
assert xgb.collective.is_distributed()
assert xgb.collective.get_processor_name() == f"rank:{rank}"
bret = xgb.collective.broadcast("test1234", 0)
assert str(bret) == "test1234"
aret = xgb.collective.allreduce(np.asarray([1, 2, 3]), xgb.collective.Op.SUM)
assert np.array_equal(aret, np.asarray([2, 4, 6]))
return 0
@pytest.mark.skipif(**tm.skip_win())
@pytest.mark.skipif(**tm.no_loky())
def test_federated_communicator() -> None:
if not build_info()["USE_FEDERATED"]:
pytest.skip("XGBoost not built with federated learning enabled")
port = 9091
world_size = 2
with get_reusable_executor(max_workers=world_size + 1) as pool:
kwargs = {"port": port, "n_workers": world_size, "blocking": False}
tracker = pool.submit(federated.run_federated_server, **kwargs)
if not tracker.running():
raise RuntimeError("Error starting Federated Learning server")
workers = []
for rank in range(world_size):
worker = pool.submit(
run_federated_worker, port=port, world_size=world_size, rank=rank
)
workers.append(worker)
for worker in workers:
assert worker.result() == 0
def test_config_serialization() -> None:
cfg = Config(retry=1, timeout=2, tracker_host_ip="127.0.0.1", tracker_port=None)
cfg1 = Config(**asdict(cfg))
assert cfg == cfg1
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