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# Owner(s): ["oncall: distributed"]
import sys
from enum import Enum
import torch
import torch.nn as nn
import torch.optim as optim
from torch import distributed as dist
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.nn.parallel import DistributedDataParallel
from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
from torch.testing._internal.common_fsdp import (
FSDPTest,
get_full_params,
)
from torch.testing._internal.common_utils import (
TEST_WITH_DEV_DBG_ASAN,
instantiate_parametrized_tests,
parametrize,
run_tests,
)
if not dist.is_available():
print("Distributed not available, skipping tests", file=sys.stderr)
sys.exit(0)
if TEST_WITH_DEV_DBG_ASAN:
print(
"Skip dev-asan as torch + multiprocessing spawn have known issues",
file=sys.stderr,
)
sys.exit(0)
class Model(nn.Module):
def __init__(self, with_fsdp, freeze_after_wrap_fsdp):
super().__init__()
self.trunk = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3),
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d(output_size=(1, 1)),
nn.Flatten(),
)
self.head = nn.Linear(64, 10)
if with_fsdp and freeze_after_wrap_fsdp:
self.fsdp_wrap()
def fsdp_wrap(self):
self.trunk = FSDP(self.trunk)
self.head = FSDP(self.head)
def forward(self, x):
return self.head(self.trunk(x))
class NestedTrunkModel(nn.Module):
def __init__(self, with_fsdp, freeze_after_wrap_fsdp):
super().__init__()
self.trunk = nn.Sequential(
self._create_block(3, 64, with_fsdp, freeze_after_wrap_fsdp),
self._create_block(64, 64, with_fsdp, freeze_after_wrap_fsdp),
)
self.head = nn.Sequential(
nn.AdaptiveAvgPool2d(output_size=(1, 1)),
nn.Flatten(),
nn.Linear(64, 10),
)
if with_fsdp and freeze_after_wrap_fsdp:
self.fsdp_wrap()
def fsdp_wrap(self):
for name, child in self.trunk.named_children():
wrapped_child = FSDP(child)
setattr(self.trunk, name, wrapped_child)
self.trunk = FSDP(self.trunk)
self.head = FSDP(self.head)
def forward(self, x):
return self.head(self.trunk(x))
def _create_block(
self, in_channels, out_channels, with_fsdp, freeze_after_wrap_fsdp
):
block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3),
nn.ReLU(inplace=True),
)
return block
class FreezingMethod(str, Enum):
GradToNone = "grad_to_none"
RequiresGrad = "requires_grad"
class TestFreezingWeights(FSDPTest):
def _create_model(self, with_fsdp, with_nested_trunk, freeze_after_wrap_fsdp):
if with_nested_trunk:
model = NestedTrunkModel(with_fsdp, freeze_after_wrap_fsdp)
else:
model = Model(with_fsdp, freeze_after_wrap_fsdp)
return model
def _dist_train(
self, with_nested_trunk, freezing_method, freeze_after_wrap_fsdp, with_fsdp
):
torch.manual_seed(0)
batch = torch.randn(size=(2, 3, 224, 224)).cuda()
model = self._create_model(with_fsdp, with_nested_trunk, freeze_after_wrap_fsdp)
model = model.cuda()
# freezing the trunk using requires_grad.
if freezing_method == FreezingMethod.RequiresGrad:
for param in model.trunk.parameters():
param.requires_grad = False
if with_fsdp:
if not freeze_after_wrap_fsdp:
model.fsdp_wrap()
model = FSDP(model)
else:
model = DistributedDataParallel(model, device_ids=[self.rank])
target = torch.tensor([0, 1], dtype=torch.long).cuda()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
for iteration in range(3):
out = model(batch)
fake_loss = criterion(out, target)
optimizer.zero_grad()
fake_loss.backward()
if freezing_method == FreezingMethod.GradToNone:
for param in model.module.trunk.parameters():
param.grad = None
optimizer.step()
if with_fsdp:
return get_full_params(model)
return list(model.parameters())
@skip_if_lt_x_gpu(2)
@parametrize("with_nested_trunk", [True, False])
@parametrize(
"freezing_method", [FreezingMethod.RequiresGrad, FreezingMethod.GradToNone]
)
@parametrize("freeze_after_wrap_fsdp", [True, False])
def test_freezing_weights(
self, with_nested_trunk, freezing_method, freeze_after_wrap_fsdp
):
# DDP
ddp_state = self._dist_train(
with_nested_trunk, freezing_method, freeze_after_wrap_fsdp, with_fsdp=False
)
# FSDP
fsdp_state = self._dist_train(
with_nested_trunk, freezing_method, freeze_after_wrap_fsdp, with_fsdp=True
)
self.assertEqual(
ddp_state,
fsdp_state,
exact_device=True,
msg="FullyShardedDataParallel states didn't match PyTorch DDP states",
)
instantiate_parametrized_tests(TestFreezingWeights)
if __name__ == "__main__":
run_tests()
|
