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# Owner(s): ["oncall: distributed"]
import io
import sys
from typing import Optional
import torch
import torch.distributed as dist
from torch.distributed._shard.sharded_tensor import (
Shard,
ShardedTensor,
ShardedTensorMetadata,
ShardMetadata,
)
from torch.distributed._shard.sharded_tensor.metadata import TensorProperties
from torch.distributed.c10d_logger import _c10d_logger
from torch.distributed.checkpoint.logger import _dcp_logger
from torch.distributed.checkpoint.metadata import MetadataIndex
from torch.distributed.checkpoint.utils import (
_create_file_view,
_DistWrapper,
find_state_dict_object,
)
from torch.testing._internal.common_utils import (
run_tests,
TEST_WITH_DEV_DBG_ASAN,
TestCase,
)
from torch.testing._internal.distributed._tensor.common_dtensor import (
DTensorTestBase,
skip_if_lt_x_gpu,
with_comms,
)
from torch.testing._internal.distributed.distributed_utils import with_fake_comms
if TEST_WITH_DEV_DBG_ASAN:
print(
"Skip dev-asan as torch + multiprocessing spawn have known issues",
file=sys.stderr,
)
sys.exit(0)
def create_sharded_tensor(rank, world_size, shards_per_rank):
shards_metadata = []
local_shards = []
for idx in range(0, world_size * shards_per_rank):
shard_rank = idx // shards_per_rank
shard_md = ShardMetadata(
shard_offsets=[idx * 8], shard_sizes=[8], placement=f"rank:{shard_rank}/cpu"
)
shards_metadata.append(shard_md)
if shard_rank == rank:
shard = Shard.from_tensor_and_offsets(
torch.rand(*shard_md.shard_sizes),
shard_offsets=shard_md.shard_offsets,
rank=rank,
)
local_shards.append(shard)
sharded_tensor_md = ShardedTensorMetadata(
shards_metadata=shards_metadata,
size=torch.Size([8 * len(shards_metadata)]),
tensor_properties=TensorProperties.create_from_tensor(torch.zeros(1)),
)
return ShardedTensor._init_from_local_shards_and_global_metadata(
local_shards=local_shards, sharded_tensor_metadata=sharded_tensor_md
)
class TestMedatadaIndex(TestCase):
def test_init_convert_offset(self):
a = MetadataIndex("foo", [1, 2])
b = MetadataIndex("foo", torch.Size([1, 2]))
self.assertEqual(a, b)
def test_index_hint_ignored_on_equals(self):
a = MetadataIndex("foo")
b = MetadataIndex("foo", index=99)
self.assertEqual(a, b)
def test_index_hint_ignored_on_hash(self):
a = MetadataIndex("foo")
b = MetadataIndex("foo", index=99)
self.assertEqual(hash(a), hash(b))
def test_flat_data(self):
state_dict = {
"a": torch.rand(10),
"b": [1, 2, 3],
}
a = find_state_dict_object(state_dict, MetadataIndex("a"))
self.assertEqual(a, state_dict["a"])
a = find_state_dict_object(state_dict, MetadataIndex("a", [0]))
self.assertEqual(a, state_dict["a"])
a = find_state_dict_object(state_dict, MetadataIndex("a", index=99))
self.assertEqual(a, state_dict["a"])
b = find_state_dict_object(state_dict, MetadataIndex("b"))
self.assertEqual(b, state_dict["b"])
b = find_state_dict_object(state_dict, MetadataIndex("b", index=1))
self.assertEqual(b, state_dict["b"])
with self.assertRaisesRegex(ValueError, "FQN"):
find_state_dict_object(state_dict, MetadataIndex("c"))
with self.assertRaisesRegex(ValueError, "ShardedTensor"):
find_state_dict_object(state_dict, MetadataIndex("b", [1]))
@with_fake_comms(rank=0, world_size=2)
def test_sharded_tensor_lookup(self):
st = create_sharded_tensor(rank=0, world_size=2, shards_per_rank=3)
state_dict = {"st": st}
obj = find_state_dict_object(state_dict, MetadataIndex("st", [8]))
self.assertEqual(obj, st.local_shards()[1].tensor)
# good hint
obj = find_state_dict_object(state_dict, MetadataIndex("st", [8], index=1))
self.assertEqual(obj, st.local_shards()[1].tensor)
# bad hint
obj = find_state_dict_object(state_dict, MetadataIndex("st", [8], index=2))
self.assertEqual(obj, st.local_shards()[1].tensor)
# broken hint
obj = find_state_dict_object(state_dict, MetadataIndex("st", [8], index=99))
self.assertEqual(obj, st.local_shards()[1].tensor)
with self.assertRaisesRegex(ValueError, "no offset was provided"):
find_state_dict_object(state_dict, MetadataIndex("st"))
with self.assertRaisesRegex(ValueError, "Could not find shard"):
find_state_dict_object(state_dict, MetadataIndex("st", [1]))
def test_dcp_logger(self):
self.assertTrue(_c10d_logger is not _dcp_logger)
self.assertEqual(1, len(_c10d_logger.handlers))
class TestReaderView(TestCase):
def setUp(self):
buffer = io.BytesIO(bytearray(range(ord("A"), ord("Z") + 1)))
self.front_view = _create_file_view(buffer, 0, 5)
buffer = io.BytesIO(bytearray(range(ord("A"), ord("Z") + 1)))
self.middle_view = _create_file_view(buffer, 10, 5)
buffer = io.BytesIO(bytearray(range(ord("A"), ord("Z") + 1)))
self.back_view = _create_file_view(buffer, len(buffer.getbuffer()) - 5, 5)
def testShortRead(self):
self.assertEqual(self.front_view.read(3), b"ABC")
self.assertEqual(self.middle_view.read(3), b"KLM")
self.assertEqual(self.back_view.read(3), b"VWX")
def testLongRead(self):
self.assertEqual(self.front_view.read(10), b"ABCDE")
self.assertEqual(self.middle_view.read(10), b"KLMNO")
self.assertEqual(self.back_view.read(10), b"VWXYZ")
def testAllRead(self):
self.assertEqual(self.front_view.read(-1), b"ABCDE")
self.assertEqual(self.middle_view.read(-1), b"KLMNO")
self.assertEqual(self.back_view.read(-1), b"VWXYZ")
def testShortReadinto(self):
ba = bytearray(3)
self.assertEqual(self.front_view.readinto(ba), 3)
self.assertEqual(ba, b"ABC")
self.assertEqual(self.middle_view.readinto(ba), 3)
self.assertEqual(ba, b"KLM")
self.assertEqual(self.back_view.readinto(ba), 3)
self.assertEqual(ba, b"VWX")
def testLongReadinto(self):
ba = bytearray(8)
self.assertEqual(self.front_view.readinto(ba), 5)
self.assertEqual(ba, b"ABCDE\0\0\0")
self.assertEqual(self.front_view.readinto(ba), 0)
self.assertEqual(ba, b"ABCDE\0\0\0")
self.assertEqual(self.middle_view.readinto(ba), 5)
self.assertEqual(ba, b"KLMNO\0\0\0")
self.assertEqual(self.middle_view.readinto(ba), 0)
self.assertEqual(ba, b"KLMNO\0\0\0")
self.assertEqual(self.back_view.readinto(ba), 5)
self.assertEqual(ba, b"VWXYZ\0\0\0")
self.assertEqual(self.back_view.readinto(ba), 0)
self.assertEqual(ba, b"VWXYZ\0\0\0")
class TestDistWrapper(DTensorTestBase):
@property
def world_size(self):
return min(4, torch.cuda.device_count())
@with_comms
@skip_if_lt_x_gpu(4)
def test_gather_object(self):
mesh_2d = dist.init_device_mesh(self.device_type, (2, self.world_size // 2))
torch.random.manual_seed(dist.get_rank())
dist_wrapper = _DistWrapper(
mesh_2d.get_group(1), use_dist=True, coordinator_rank=0
)
rank = mesh_2d.get_rank()
half_world_size = self.world_size // 2
gathered_objects = dist_wrapper.gather_object(rank)
expected_objects = (
list(range(rank, rank + half_world_size))
if rank % half_world_size == 0
else None
)
assert gathered_objects == expected_objects
@with_comms
@skip_if_lt_x_gpu(4)
def test_scatter_object(self):
mesh_2d = dist.init_device_mesh(self.device_type, (2, self.world_size // 2))
torch.random.manual_seed(dist.get_rank())
dist_wrapper = _DistWrapper(
mesh_2d.get_group(1), use_dist=True, coordinator_rank=0
)
rank = mesh_2d.get_rank()
half_world_size = self.world_size // 2
objects = (
list(range(rank, rank + half_world_size))
if rank % half_world_size == 0
else None
)
scattered_objects = dist_wrapper.scatter_object(objects)
expected_objects = rank
assert scattered_objects == expected_objects
@with_comms
@skip_if_lt_x_gpu(2)
def test_broadcast_object_with_nonzero_coordinator(self):
# Everybody uses WORLD, but src is coordinator_rank=1
dist_wrapper = _DistWrapper(
group=dist.group.WORLD,
use_dist=True,
coordinator_rank=1,
)
rank = dist.get_rank()
# only local rank 1 supplies the payload
payload: Optional[int] = rank if rank == 1 else None
result = dist_wrapper.broadcast_object(payload)
# every rank should receive the value from global rank 1
assert result == 1
@with_comms
@skip_if_lt_x_gpu(4)
def test_broadcast_object_global_local_mismatch(self):
# reproduces issue 152310
mesh_2d = dist.init_device_mesh(self.device_type, (2, self.world_size // 2))
dist_wrapper = _DistWrapper(
group=mesh_2d.get_group(1),
use_dist=True,
coordinator_rank=1, # local coordinator index within the subgroup
)
rank = mesh_2d.get_rank()
# only the local coordinator in each subgroup provides payload
payload: Optional[int] = rank if dist_wrapper.is_coordinator else None
got = dist_wrapper.broadcast_object(payload)
# ensure we broadcast from the *global* coordinator rank,
# not the local index. For rows [0,1] this is global rank 1;
# for rows [2,3] this is global rank 3.
expected = dist_wrapper.global_coordinator_rank
assert got == expected
@with_comms
@skip_if_lt_x_gpu(2)
def test_barrier(self):
mesh_2d = dist.init_device_mesh(self.device_type, (2, self.world_size // 2))
torch.random.manual_seed(dist.get_rank())
dist_wrapper = _DistWrapper(
mesh_2d.get_group(1), use_dist=True, coordinator_rank=0
)
# No exception should be raised.
dist_wrapper.barrier()
if __name__ == "__main__":
run_tests()
|
