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# Owner(s): ["oncall: distributed"]
import io
from copy import deepcopy
import torch
import torch.nn as nn
from torch.distributed._shard.sharded_tensor import ShardedTensor
from torch.distributed._tensor import DTensor, Shard
from torch.distributed.device_mesh import init_device_mesh
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.api import (
ShardedOptimStateDictConfig,
ShardedStateDictConfig,
StateDictType,
)
from torch.testing._internal.common_device_type import instantiate_device_type_tests
from torch.testing._internal.common_fsdp import get_devtype
from torch.testing._internal.common_utils import parametrize, run_tests
from torch.testing._internal.distributed._tensor.common_dtensor import (
DTensorTestBase,
skip_if_lt_x_gpu,
with_comms,
)
device_type = torch.device(get_devtype())
# Simple and boring model to test interface and some corner cases that do not
# require complicated wrapping strategy.
class TestDummyModel(torch.nn.Module):
def __init__(self):
super().__init__()
torch.manual_seed(0)
self.net1 = nn.Sequential(nn.Linear(8, 16), nn.ReLU())
self.net2 = nn.Sequential(nn.Linear(16, 32), nn.ReLU())
self.net3 = nn.Sequential(nn.Linear(32, 64), nn.ReLU())
self.net4 = nn.Sequential(nn.ReLU(), nn.Linear(64, 8))
def forward(self, x):
return self.net4(self.net3(self.net2(self.net1(x))))
def get_input(self):
return torch.rand(8, 8, device=device_type.type)
class TestDummyModelUneven(torch.nn.Module):
def __init__(self):
super().__init__()
torch.manual_seed(0)
self.net1 = nn.Sequential(nn.Linear(5, 10), nn.ReLU())
self.net2 = nn.Sequential(nn.Linear(10, 15), nn.ReLU())
self.net3 = nn.Linear(15, 30)
self.net4 = nn.Sequential(nn.ReLU(), nn.Linear(30, 5))
def forward(self, x):
return self.net4(self.net3(self.net2(self.net1(x))))
def get_input(self):
return torch.rand(5, 5, device=device_type.type)
class TestFSDPWithDeviceMeshAndDTensor(DTensorTestBase):
def _create_model(self, is_even_sharded_model, device_mesh=None):
dummy_model = (
TestDummyModel() if is_even_sharded_model else TestDummyModelUneven()
)
model = FSDP(dummy_model.to(device_type), device_mesh=device_mesh)
optim = torch.optim.Adam(model.parameters(), lr=0.1)
model(model.get_input()).sum().backward()
optim.step()
return model, optim
@with_comms
@skip_if_lt_x_gpu(2)
@parametrize("is_even_sharded_model", [True, False])
def test_fsdp_init_with_device_mesh(self, is_even_sharded_model):
device_mesh = init_device_mesh(device_type.type, (self.world_size,))
model, optim = self._create_model(is_even_sharded_model, device_mesh)
FSDP.set_state_dict_type(
model,
StateDictType.SHARDED_STATE_DICT,
)
state_dict = model.state_dict()
optim_state_dict = FSDP.optim_state_dict(model, optim)
for v in state_dict.values():
self.assertEqual(type(v), DTensor)
self.assertEqual(len(v.placements), 1)
self.assertEqual(v.placements[0], (Shard(dim=0)))
self.assertEqual(v.device_mesh, device_mesh)
for state in optim_state_dict["state"].values():
for k, v in state.items():
if k != "step":
self.assertEqual(type(v), DTensor)
self.assertEqual(len(v.placements), 1)
self.assertEqual(v.placements[0], (Shard(dim=0)))
self.assertEqual(v.device_mesh, device_mesh)
state_dict_type = FSDP.get_state_dict_type(model)
# If device_mesh is used when initializing FSDP, the field _use_dtensor will
# automatically be set to True if StateDictType is set to SHARDED_STATE_DICT.
self.assertEqual(state_dict_type.state_dict_config._use_dtensor, True)
self.assertEqual(state_dict_type.optim_state_dict_config._use_dtensor, True)
@with_comms
@skip_if_lt_x_gpu(2)
@parametrize("offload_to_cpu", [True, False])
@parametrize("is_even_sharded_model", [True, False])
def test_dtensor_sharded_tensor_state_dict_identical(
self, offload_to_cpu, is_even_sharded_model
):
device_mesh = init_device_mesh(device_type.type, (self.world_size,))
model, optim = self._create_model(is_even_sharded_model, device_mesh)
FSDP.set_state_dict_type(
model,
StateDictType.SHARDED_STATE_DICT,
state_dict_config=ShardedStateDictConfig(offload_to_cpu=offload_to_cpu),
optim_state_dict_config=ShardedOptimStateDictConfig(
offload_to_cpu=offload_to_cpu
),
)
dtensor_sd = model.state_dict()
dtensor_osd = FSDP.optim_state_dict(model, optim)
ref_model, ref_optim = self._create_model(is_even_sharded_model)
FSDP.set_state_dict_type(
ref_model,
StateDictType.SHARDED_STATE_DICT,
state_dict_config=ShardedStateDictConfig(offload_to_cpu=offload_to_cpu),
optim_state_dict_config=ShardedOptimStateDictConfig(
offload_to_cpu=offload_to_cpu
),
)
sharded_tensor_sd = ref_model.state_dict()
sharded_tensor_osd = FSDP.optim_state_dict(ref_model, ref_optim)
# Check dtensor and sharded_tensor model state dict values are identical
for dtensor_sd_item, sharded_tensor_sd_item in zip(
dtensor_sd.items(), sharded_tensor_sd.items()
):
k1, v1 = dtensor_sd_item
k2, v2 = sharded_tensor_sd_item
self.assertEqual(k1, k2)
# if the ShardedTensor is an empty shard,
# then the local tensor of DTensor should be local_tensor=tensor([])
if len(v2.local_shards()) == 0:
self.assertEqual(v1.to_local().numel(), 0)
else:
self.assertEqual(type(v1), DTensor)
self.assertEqual(type(v2), ShardedTensor)
# check whether local_tensor are the same
self.assertEqual(v1.to_local(), v2.local_tensor())
# check whether device are the same
self.assertEqual(v1.to_local().device, v2.local_tensor().device)
# Check dtensor and sharde_tensor optim state dict values are identical
for dtensor_osd_state, sharded_tensor_osd_state in zip(
dtensor_osd["state"].items(), sharded_tensor_osd["state"].items()
):
# check FQN are the same
self.assertEqual(dtensor_osd_state[0], sharded_tensor_osd_state[0])
for dtensor_hyper_param, sharded_tensor_hyper_param in zip(
dtensor_osd_state[1].items(),
sharded_tensor_osd_state[1].items(),
):
k1, v1 = dtensor_hyper_param
k2, v2 = sharded_tensor_hyper_param
self.assertEqual(k1, k2)
if k1 != "step":
# if the ShardedTensor is an empty shard,
# then the local tensor of DTensor should be local_tensor=tensor([])
if len(v2.local_shards()) == 0:
self.assertEqual(v1.to_local().numel(), 0)
else:
self.assertEqual(type(v1), DTensor)
self.assertEqual(type(v2), ShardedTensor)
# check whether local_tensor are the same
self.assertEqual(v1.to_local(), v2.local_tensor())
# check whether device are the same
self.assertEqual(v1.to_local().device, v2.local_tensor().device)
else:
self.assertEqual(v1, v2)
@with_comms
@skip_if_lt_x_gpu(2)
@parametrize("offload_to_cpu", [True, False])
@parametrize("is_even_sharded_model", [True, False])
def test_dtensor_sharded_optim_load_state_dict(
self, offload_to_cpu, is_even_sharded_model
):
device_mesh = init_device_mesh(device_type.type, (self.world_size,))
model, optim = self._create_model(is_even_sharded_model, device_mesh)
FSDP.set_state_dict_type(
model,
StateDictType.SHARDED_STATE_DICT,
optim_state_dict_config=ShardedOptimStateDictConfig(
offload_to_cpu=offload_to_cpu
),
)
checkpoint = io.BytesIO()
torch.save(FSDP.optim_state_dict(model, optim), checkpoint)
# Deepcopy to save current optim_state_dict to compare with the optim_state_dict loaded back below.
ref_optim_state_dict = deepcopy(FSDP.optim_state_dict(model, optim))
# Update the parameters so FSDP.optim_state_dict() will be different from ref_optim_state_dict.
model(model.get_input()).sum().backward()
optim.step()
# Load ref_optim_state_dict back.
checkpoint.seek(0)
load_ref_optim_state_dict = torch.load(checkpoint)
optim.load_state_dict(
FSDP.optim_state_dict_to_load(model, optim, load_ref_optim_state_dict)
)
new_optim_state_dict = FSDP.optim_state_dict(model, optim)
# Check whether new_optim_state_dict is the same as ref_optim_state_dict.
for new_optim_state_dict_item, ref_optim_state_dict_item in zip(
new_optim_state_dict["state"].items(),
ref_optim_state_dict["state"].items(),
):
# check FQN are the same
self.assertEqual(new_optim_state_dict_item[0], ref_optim_state_dict_item[0])
for new_optim_hyper_param, ref_optim_hyper_param in zip(
new_optim_state_dict_item[1].items(),
ref_optim_state_dict_item[1].items(),
):
k1, v1 = new_optim_hyper_param
k2, v2 = ref_optim_hyper_param
# check whether keys are the same
self.assertEqual(k1, k2)
# check whether values are the same
self.assertEqual(v1, v2)
if k1 != "step":
self.assertEqual(type(v1), DTensor)
self.assertEqual(type(v2), DTensor)
@with_comms()
@skip_if_lt_x_gpu(2)
@parametrize("offload_to_cpu", [True, False])
@parametrize("is_even_sharded_model", [True, False])
def test_dtensor_sharded_model_load_state_dict(
self, offload_to_cpu, is_even_sharded_model
):
device_mesh = init_device_mesh(device_type.type, (self.world_size,))
model, optim = self._create_model(is_even_sharded_model, device_mesh)
FSDP.set_state_dict_type(
model,
StateDictType.SHARDED_STATE_DICT,
state_dict_config=ShardedStateDictConfig(offload_to_cpu=offload_to_cpu),
)
checkpoint = io.BytesIO()
torch.save(model.state_dict(), checkpoint)
# Deepcopy to save current state_dict to compare with the state_dict loaded back below.
ref_state_dict = deepcopy(model.state_dict())
# Update the parameters so model.state_dict() will be different from ref_dtensor_sd.
model(model.get_input()).sum().backward()
optim.step()
# Load ref_state_dict back.
checkpoint.seek(0)
load_ref_state_dict = torch.load(checkpoint)
model.load_state_dict(load_ref_state_dict)
new_state_dict = model.state_dict()
# Check whether new_state_dict is the same as ref_state_dict.
for (k1, v1), (k2, v2) in zip(ref_state_dict.items(), new_state_dict.items()):
# check whether fqn are the same
self.assertEqual(k1, k2)
self.assertEqual(type(v1), DTensor)
self.assertEqual(type(v2), DTensor)
# check whether DTensor are the same
self.assertEqual(v1, v2)
@with_comms
@skip_if_lt_x_gpu(4)
def test_raises_warning_or_errors(self):
device_mesh = init_device_mesh(device_type.type, (self.world_size,))
model, optim = self._create_model(
is_even_sharded_model=True, device_mesh=device_mesh
)
# initialize optim
model(model.get_input()).sum().backward()
optim.step()
with self.assertRaisesRegex(
RuntimeError, "DeviceMesh is not compatible with LOCAL_STATE_DICT."
):
with FSDP.state_dict_type(model, StateDictType.LOCAL_STATE_DICT):
state_dict = model.state_dict()
with self.assertRaisesRegex(
RuntimeError, "DeviceMesh is not compatible with LOCAL_STATE_DICT."
):
with FSDP.state_dict_type(model, StateDictType.LOCAL_STATE_DICT):
optim_state_dict = FSDP.optim_state_dict(model, optim)
devices = ("cuda", "hpu")
instantiate_device_type_tests(
TestFSDPWithDeviceMeshAndDTensor, globals(), only_for=devices
)
if __name__ == "__main__":
run_tests()
|
