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|
# Owner(s): ["oncall: distributed"]
from typing import Dict, Union
import torch
import torch.distributed as dist
import torch.distributed.checkpoint as dist_cp
from torch.distributed._tensor import (
DeviceMesh,
distribute_tensor,
DTensor,
Replicate,
Shard,
zeros,
)
from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed._tensor.common_dtensor import (
DTensorTestBase,
skip_if_lt_x_gpu,
with_comms,
)
from torch.testing._internal.distributed.checkpoint_utils import with_temp_dir
SUBMESH_TENSOR_SIZE = 6
class MyTestModule(torch.nn.Module):
def __init__(
self,
sdt: DTensor,
rdt: DTensor,
submesh_sdt: DTensor,
submesh_rdt: DTensor,
extra_state: int = 1,
extra_state_tensor: torch.Tensor = torch.zeros(1),
) -> None:
super().__init__()
self.sdt = torch.nn.Parameter(sdt)
self.rdt = torch.nn.Parameter(rdt)
self.submesh_sdt = torch.nn.Parameter(submesh_sdt)
self.submesh_rdt = torch.nn.Parameter(submesh_rdt)
self._extra_state = extra_state
self._extra_state_tensor = extra_state_tensor
@property
def extra_state(self) -> int:
return self._extra_state
@extra_state.setter
def extra_state(self, new_extra_state: int) -> None:
self._extra_state = new_extra_state
@property
def extra_state_tensor(self) -> torch.Tensor:
return self._extra_state_tensor
@extra_state_tensor.setter
def extra_state_tensor(self, new_extra_state_tensor: torch.Tensor) -> None:
self._extra_state_tensor = new_extra_state_tensor
def get_extra_state(self) -> Dict[str, Union[int, torch._tensor.Tensor]]:
return {
"extra_state": self._extra_state,
"extra_state_tensor": self._extra_state_tensor,
}
def set_extra_state(
self, state: Dict[str, Union[int, torch._tensor.Tensor]]
) -> None:
self._extra_state = state["extra_state"] # pyre-ignore[8]
self._extra_state_tensor = state["extra_state_tensor"] # pyre-ignore[8]
class DTensorPlanner(DTensorTestBase):
def create_dtensor_model(
self,
tensor_to_shard: torch.tensor,
tensor_to_replicate: torch.tensor,
) -> torch.nn.Module:
mesh = DeviceMesh(
device_type=self.device_type,
mesh=range(dist.get_world_size()),
)
sharded_dt = distribute_tensor(tensor_to_shard, mesh, placements=[Shard(0)])
replicated_dt = distribute_tensor(
tensor_to_replicate, mesh, placements=[Replicate()]
)
# Only even rank will be part of the mesh.
submesh = DeviceMesh(
device_type=self.device_type,
mesh=[i for i in range(dist.get_world_size()) if i % 2 == 0],
)
submesh_tensor_size = [SUBMESH_TENSOR_SIZE]
submesh_sharded_dt = zeros(
submesh_tensor_size,
device_mesh=submesh,
placements=[Shard(0)],
)
submesh_replicated_dt = zeros(
submesh_tensor_size, device_mesh=submesh, placements=[Replicate()]
)
model = MyTestModule(
sharded_dt,
replicated_dt,
submesh_sharded_dt,
submesh_replicated_dt,
).cuda()
return (
model,
sharded_dt,
replicated_dt,
)
@with_comms
@with_temp_dir
@skip_if_lt_x_gpu(2)
def test_distributed_tensor_planner(self) -> None:
CHECKPOINT_DIR = self.temp_dir
local_tensor = torch.arange(0, 4, dtype=torch.float32)
local_tensor_2 = torch.arange(4, 8, dtype=torch.float32)
(model, sharded_dt, replicated_dt) = self.create_dtensor_model(
local_tensor, local_tensor_2
)
state_dict = model.state_dict()
"""
When the model is initialized, the state_dict on rank 0 are as follows when there are 4 GPUs.
rank 0:
OrderedDict(
[
(
'rdt',
DTensor(
local_tensor=tensor([4., 5., 6., 7.], device='cuda:0'),
device_mesh=DeviceMesh:([0, 1, 2, 3]),
placements=[Replicate()]
)
),
(
'sdt',
DTensor(
local_tensor=tensor([0.], device='cuda:0'),
device_mesh=DeviceMesh:([0, 1, 2, 3]),
placements=[Shard(dim=0)])
),
),
(
'submesh_sdt',
DTensor(
local_tensor=tensor([8., 9.], device='cuda:0'),
device_mesh=DeviceMesh:([0, 2]),
placements=[Shard(dim=0)]
),
),
(
'submesh_rdt',
DTensor(
local_tensor=tensor([12., 13., 14., 15.], device='cuda:0'),
device_mesh=DeviceMesh:([0, 2]),
placements=[Replicate()]
)
),
(
'_extra_state',
{'extra_state': 1, 'extra_state_tensor': tensor([0.])}
)
]
)
"""
dist_cp.save(
state_dict=state_dict,
storage_writer=dist_cp.FileSystemWriter(path=CHECKPOINT_DIR),
planner=dist_cp.DefaultSavePlanner(),
)
model, _, _ = self.create_dtensor_model(local_tensor * 10, local_tensor_2 * 10)
state_dict = model.state_dict()
"""
When the model is re-initialized, we have changed the params in state_dict.
The updated values are as follows, when there are 4 GPUs:
rank 0:
OrderedDict(
[
(
'rdt',
DTensor(
local_tensor=tensor([40., 50., 60., 70.], device='cuda:0'),
device_mesh=DeviceMesh:([0, 1, 2, 3]),
placements=[Replicate()],
)
),
(
'sdt',
DTensor(
local_tensor=tensor([0.], device='cuda:0'),
device_mesh=DeviceMesh:([0, 1, 2, 3]),
placements=[Shard(dim=0)],
)
),
(
'submesh_sdt',
DTensor(
local_tensor=tensor([80., 90.], device='cuda:0'),
device_mesh=DeviceMesh:([0, 2]),
placements=[Shard(dim=0)]
)
),
('submesh_rdt',
DTensor(
local_tensor=tensor([120., 130., 140., 150.], device='cuda:0'),
device_mesh=DeviceMesh:([0, 2]),
placements=[Replicate()]
)
),
(
'_extra_state', {'extra_state': 10, 'extra_state_tensor': tensor([10.])}
)
]
)
"""
dist_cp.load(
state_dict=state_dict,
storage_reader=dist_cp.FileSystemReader(CHECKPOINT_DIR),
planner=dist_cp.DefaultLoadPlanner(),
)
"""
After loading the model from the checkpoint, we want to make sure that the values in state_dict
match the values that are originally saved to the checkpoint.
"""
for k, v in state_dict.items():
if k == "sdt":
self.assertEqual(sharded_dt.to_local(), v.to_local())
if k == "rdt":
self.assertEqual(replicated_dt.to_local(), v.to_local())
if k == "submesh_sdt":
if self.rank % 2 == 0:
shard_size = int(SUBMESH_TENSOR_SIZE / v.device_mesh.size())
self.assertEqual(v.to_local().size(), torch.Size([shard_size]))
self.assertEqual(v.to_local(), torch.zeros([shard_size]))
else:
self.assertEqual(v.to_local().size(), torch.Size([0]))
self.assertEqual(v.to_local(), torch.tensor([]))
if k == "submesh_rdt":
if self.rank % 2 == 0:
shard_size = SUBMESH_TENSOR_SIZE
self.assertEqual(v.to_local().size(), torch.Size([shard_size]))
self.assertEqual(v.to_local(), torch.zeros([shard_size]))
else:
self.assertEqual(v.to_local().size(), torch.Size([0]))
self.assertEqual(v.to_local(), torch.tensor([]))
if k == "_extra_state":
self.assertEqual(1, v["extra_state"])
self.assertEqual(torch.tensor([0.0]), v["extra_state_tensor"])
if __name__ == "__main__":
run_tests()
|
