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# Owner(s): ["oncall: distributed"]
import torch
from torch.distributed._shard.sharded_tensor import (
init_from_local_shards,
Shard,
ShardMetadata,
)
from torch.distributed._shard.sharding_spec import (
ChunkShardingSpec,
EnumerableShardingSpec,
)
from torch.distributed.distributed_c10d import _get_default_group
from torch.distributed.fsdp._shard_utils import (
_create_chunk_sharded_tensor,
_offsets_to_split_sizes,
_reshard_flatten_tensor,
)
from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
from torch.testing._internal.common_fsdp import FSDPTest
from torch.testing._internal.common_utils import TestCase
class TestShardUtils(TestCase):
def test_offsets_to_split_sizes(self):
tensor_numel = 40
def _get_and_check_split_sizes(
world_size,
in_offsets,
out_offsets,
in_split_sizes,
):
for my_rank in range(world_size):
_in_split_sizes = in_split_sizes[my_rank]
_out_split_sizes = [
in_split_sizes[i][my_rank] for i in range(world_size)
]
res_in_split_sizes, res_out_split_sizes = _offsets_to_split_sizes(
in_offsets, out_offsets, tensor_numel, world_size, my_rank
)
self.assertEqual(_in_split_sizes, res_in_split_sizes)
self.assertEqual(_out_split_sizes, res_out_split_sizes)
# The tensor size can be evenly divided by the world size.
world_size = 4
in_offsets = [0, 10, 20, 30]
out_offsets = [0, 10, 20, 30]
in_split_sizes = [
[10, 0, 0, 0],
[0, 10, 0, 0],
[0, 0, 10, 0],
[0, 0, 0, 10],
]
_get_and_check_split_sizes(world_size, in_offsets, out_offsets, in_split_sizes)
world_size = 4
in_offsets = [0, 3, 17, 18]
out_offsets = [0, 10, 20, 30]
in_split_sizes = [
[3, 0, 0, 0],
[7, 7, 0, 0],
[0, 1, 0, 0],
[0, 2, 10, 10],
]
_get_and_check_split_sizes(world_size, in_offsets, out_offsets, in_split_sizes)
world_size = 4
in_offsets = [0, 10, 20, 30]
out_offsets = [0, 3, 17, 18]
in_split_sizes = [
[3, 7, 0, 0],
[0, 7, 1, 2],
[0, 0, 0, 10],
[0, 0, 0, 10],
]
_get_and_check_split_sizes(world_size, in_offsets, out_offsets, in_split_sizes)
world_size = 4
in_offsets = [0, 7, 11, 25]
out_offsets = [0, 10, 17, 18]
in_split_sizes = [
[7, 0, 0, 0],
[3, 1, 0, 0],
[0, 6, 1, 7],
[0, 0, 0, 15],
]
_get_and_check_split_sizes(world_size, in_offsets, out_offsets, in_split_sizes)
# The tensor size cannot be evenly divided by the world size.
world_size = 6
in_offsets = [0, 7, 14, 21, 28, 35]
out_offsets = [0, 7, 14, 21, 28, 35]
in_split_sizes = [
[7, 0, 0, 0, 0, 0],
[0, 7, 0, 0, 0, 0],
[0, 0, 7, 0, 0, 0],
[0, 0, 0, 7, 0, 0],
[0, 0, 0, 0, 7, 0],
[0, 0, 0, 0, 0, 5],
]
_get_and_check_split_sizes(world_size, in_offsets, out_offsets, in_split_sizes)
world_size = 6
in_offsets = [0, 0, 10, 11, 28, 40]
out_offsets = [0, 7, 14, 21, 28, 35]
in_split_sizes = [
[0, 0, 0, 0, 0, 0],
[7, 3, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 3, 7, 7, 0, 0],
[0, 0, 0, 0, 7, 5],
[0, 0, 0, 0, 0, 0],
]
_get_and_check_split_sizes(world_size, in_offsets, out_offsets, in_split_sizes)
class TestShardUtilsDistributed(FSDPTest):
@property
def world_size(self):
return 2
def _create_local_chunk(self, tensor):
chunk = tensor.chunk(2)[self.rank]
offsets = [0] if self.rank == 0 else [tensor.shape[0] - chunk.shape[0]]
shard = Shard.from_tensor_and_offsets(chunk, offsets, self.rank)
return init_from_local_shards([shard], tensor.numel())
def _create_enumerate_spec(self, tensor):
# Since placement is not used, always set placement to rank0 to mimic
# the actual usage.
metadata = [
ShardMetadata([0], [101], placement="rank0/cuda:0"),
ShardMetadata([101], [900], placement="rank0/cuda:0"),
]
return EnumerableShardingSpec(metadata)
def _create_chunk_spec(self):
return ChunkShardingSpec(dim=0, placements=["rank0/cuda:0"])
def _create_tensor(self, *size):
# Keep everything deterministic.
torch.manual_seed(0)
return torch.rand(*size).cuda()
@skip_if_lt_x_gpu(2)
def test_reshard_flatten_tensor(self):
def get_offsets(tensor, shard):
if self.rank == 0:
return [0]
else:
return [tensor.shape[0] - shard.shape[0]]
tensor = self._create_tensor(1001)
shard = _reshard_flatten_tensor(
self._create_local_chunk(tensor),
self._create_enumerate_spec(tensor),
self.world_size,
self.rank,
tensor.device,
_get_default_group(),
)
offsets = [0] if self.rank == 0 else [tensor.shape[0] - shard.shape[0]]
shard = Shard.from_tensor_and_offsets(shard, offsets, self.rank)
uneven_sharded_tensor = init_from_local_shards([shard], tensor.numel())
shard = _reshard_flatten_tensor(
uneven_sharded_tensor,
self._create_chunk_spec(),
self.world_size,
self.rank,
tensor.device,
_get_default_group(),
)
offsets = [0] if self.rank == 0 else [tensor.shape[0] - shard.shape[0]]
shard = Shard.from_tensor_and_offsets(shard, offsets, self.rank)
even_sharded_tensor = init_from_local_shards([shard], tensor.numel())
output = torch.empty(tensor.shape).cuda() if self.rank == 0 else None
even_sharded_tensor.gather(0, output)
if self.rank == 0:
self.assertEqual(tensor, output)
output = torch.empty(tensor.shape).cuda() if self.rank == 0 else None
uneven_sharded_tensor.gather(0, output)
if self.rank == 0:
self.assertEqual(tensor, output)
@skip_if_lt_x_gpu(2)
def test_create_chunk_sharded_tensor(self):
for size in ((1,), (1, 6), (12,), (12, 6), (25,), (25, 6)):
tensor = self._create_tensor(*size)
sharded_tensor = _create_chunk_sharded_tensor(
tensor,
self.rank,
self.world_size,
torch.cuda.device_count(),
_get_default_group(),
)
output = torch.empty(*size).cuda() if self.rank == 0 else None
sharded_tensor.gather(0, output)
if self.rank == 0:
self.assertEqual(tensor, output)
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