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# Owner(s): ["oncall: distributed"]
import json
import logging
import os
import re
import sys
from functools import partial, wraps
import torch
import torch.distributed as dist
from torch.distributed.c10d_logger import _c10d_logger, _exception_logger
if not dist.is_available():
print("Distributed not available, skipping tests", file=sys.stderr)
sys.exit(0)
from torch.testing._internal.common_distributed import MultiProcessTestCase, TEST_SKIPS
from torch.testing._internal.common_utils import run_tests, TEST_WITH_DEV_DBG_ASAN
if TEST_WITH_DEV_DBG_ASAN:
print(
"Skip dev-asan as torch + multiprocessing spawn have known issues",
file=sys.stderr,
)
sys.exit(0)
BACKEND = dist.Backend.NCCL
WORLD_SIZE = min(4, max(2, torch.cuda.device_count()))
def with_comms(func=None):
if func is None:
return partial(
with_comms,
)
@wraps(func)
def wrapper(self, *args, **kwargs):
if BACKEND == dist.Backend.NCCL and torch.cuda.device_count() < self.world_size:
sys.exit(TEST_SKIPS[f"multi-gpu-{self.world_size}"].exit_code)
self.dist_init()
func(self)
self.destroy_comms()
return wrapper
class C10dErrorLoggerTest(MultiProcessTestCase):
def setUp(self):
super().setUp()
os.environ["WORLD_SIZE"] = str(self.world_size)
os.environ["BACKEND"] = BACKEND
self._spawn_processes()
@property
def device(self):
return (
torch.device(self.rank)
if BACKEND == dist.Backend.NCCL
else torch.device("cpu")
)
@property
def world_size(self):
return WORLD_SIZE
@property
def process_group(self):
return dist.group.WORLD
def destroy_comms(self):
# Wait for all ranks to reach here before starting shutdown.
dist.barrier()
dist.destroy_process_group()
def dist_init(self):
dist.init_process_group(
backend=BACKEND,
world_size=self.world_size,
rank=self.rank,
init_method=f"file://{self.file_name}",
)
# set device for nccl pg for collectives
if BACKEND == "nccl":
torch.cuda.set_device(self.rank)
def test_get_or_create_logger(self):
self.assertIsNotNone(_c10d_logger)
self.assertEqual(1, len(_c10d_logger.handlers))
self.assertIsInstance(_c10d_logger.handlers[0], logging.NullHandler)
@_exception_logger
def _failed_broadcast_raise_exception(self):
tensor = torch.arange(2, dtype=torch.int64)
dist.broadcast(tensor, self.world_size + 1)
@_exception_logger
def _failed_broadcast_not_raise_exception(self):
try:
tensor = torch.arange(2, dtype=torch.int64)
dist.broadcast(tensor, self.world_size + 1)
except Exception:
pass
@with_comms
def test_exception_logger(self) -> None:
with self.assertRaises(Exception):
self._failed_broadcast_raise_exception()
with self.assertLogs(_c10d_logger, level="DEBUG") as captured:
self._failed_broadcast_not_raise_exception()
error_msg_dict = json.loads(
re.search("({.+})", captured.output[0]).group(0).replace("'", '"')
)
self.assertEqual(len(error_msg_dict), 9)
self.assertIn("pg_name", error_msg_dict.keys())
self.assertEqual("None", error_msg_dict["pg_name"])
self.assertIn("func_name", error_msg_dict.keys())
self.assertEqual("broadcast", error_msg_dict["func_name"])
self.assertIn("backend", error_msg_dict.keys())
self.assertEqual("nccl", error_msg_dict["backend"])
self.assertIn("nccl_version", error_msg_dict.keys())
nccl_ver = torch.cuda.nccl.version()
self.assertEqual(
".".join(str(v) for v in nccl_ver), error_msg_dict["nccl_version"]
)
# In this test case, group_size = world_size, since we don't have multiple processes on one node.
self.assertIn("group_size", error_msg_dict.keys())
self.assertEqual(str(self.world_size), error_msg_dict["group_size"])
self.assertIn("world_size", error_msg_dict.keys())
self.assertEqual(str(self.world_size), error_msg_dict["world_size"])
self.assertIn("global_rank", error_msg_dict.keys())
self.assertIn(str(dist.get_rank()), error_msg_dict["global_rank"])
# In this test case, local_rank = global_rank, since we don't have multiple processes on one node.
self.assertIn("local_rank", error_msg_dict.keys())
self.assertIn(str(dist.get_rank()), error_msg_dict["local_rank"])
if __name__ == "__main__":
run_tests()
|
