def _auto_wrap_smoke_test(self, enable_mixed_precision):
device = torch.device("cuda")
torch.cuda.set_device(0)
# Random port in case the next test run quickly, same port would cause conflict.
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = str(random.randint(2000, 3000))
torch.distributed.init_process_group(backend="nccl", rank=0, world_size=1)
try:
with enable_wrap(wrapper_cls=FSDP, mixed_precision=enable_mixed_precision):
sequential = nn.Sequential(
nn.Linear(5, 5), nn.Linear(5, 5), nn.Sequential(nn.Linear(5, 5), nn.Linear(5, 5))
)
my_auto_wrap_policy = functools.partial(default_auto_wrap_policy, min_num_params=40)
model = auto_wrap(sequential, auto_wrap_policy=my_auto_wrap_policy)
model.to(device)
input = torch.rand((1, 5), dtype=torch.float).to(device)
with autocast(enabled=enable_mixed_precision):
output = model(input)
loss = F.mse_loss(input, output)
loss.backward()
finally:
torch.distributed.destroy_process_group()
del os.environ["MASTER_ADDR"]
del os.environ["MASTER_PORT"]
def auto_wrap_big_layers(module: nn.Module, fsdp_config: AttrDict):
"""
Automatically wrap the bigger layer in the module
"""
with enable_wrap(auto_wrap_policy=_BigConvAutoWrapPolicy(
fsdp_config.AUTO_WRAP_THRESHOLD),
wrapper_cls=_FSDP_WRAPPER,
**fsdp_config):
return auto_wrap(module)
def test_auto_wrap_preset_exclude_wrap_include_children(self):
"""
Test to ensure excluded modules are not wrapped, but children are if param size is greater than
min_num_params
"""
with enable_wrap(wrapper_cls=FSDP, process_group=self.process_group, flatten_parameters=False):
sequential = nn.ModuleList([nn.Linear(10, 10)])
my_auto_wrap_policy = functools.partial(default_auto_wrap_policy, min_num_params=40)
model = auto_wrap(sequential, auto_wrap_policy=my_auto_wrap_policy)
assert isinstance(model, nn.ModuleList)
assert isinstance(model[0], FSDP)
def test_auto_wrap_preset_force_leaf(self):
"""
Test to ensure force-leaf modules are not wrapped, and children are not wrapped.
"""
with enable_wrap(wrapper_cls=FSDP, process_group=self.process_group, flatten_parameters=False):
sequential = nn.Sequential(nn.Linear(10, 10), nn.MultiheadAttention(100, 1))
my_auto_wrap_policy = functools.partial(default_auto_wrap_policy, min_num_params=40)
model = auto_wrap(sequential, auto_wrap_policy=my_auto_wrap_policy)
assert isinstance(model.module[0], FSDP)
# Assert children of multihead attention are not wrapped
assert isinstance(model.module[1], nn.MultiheadAttention)
assert isinstance(model.module[1].out_proj, nn.Linear)
def test_auto_wrap_preset_exclude_wrap(self):
"""
Test to ensure excluded modules are not wrapped, regardless if the total param size is greater than the
min_num_params.
"""
with enable_wrap(wrapper_cls=FSDP, process_group=self.process_group, flatten_parameters=False):
sequential = nn.ModuleList([nn.Linear(5, 5), nn.Linear(5, 5)])
my_auto_wrap_policy = functools.partial(default_auto_wrap_policy, min_num_params=40)
model = auto_wrap(sequential, auto_wrap_policy=my_auto_wrap_policy)
assert isinstance(model, nn.ModuleList)
assert isinstance(model[0], nn.Linear)
assert isinstance(model[1], nn.Linear)
def test_auto_wrap_preset_blocklist(self):
"""
Test to ensure blocklisted modules are not wrapped, and children are not wrapped.
"""
with enable_wrap(process_group=self.process_group,
flatten_parameters=False):
sequential = nn.Sequential(nn.Linear(10, 10),
nn.MultiheadAttention(100, 1))
model = auto_wrap(sequential, min_num_params=40)
assert isinstance(model.module[0], FSDP)
# Assert children of multihead attention are not wrapped
assert isinstance(model.module[1], nn.MultiheadAttention)
assert isinstance(model.module[1].out_proj, nn.Linear)
def test_auto_wrap_preset_blocklist_custom(self):
"""
Test to ensure blocklisted modules are not wrapped.
"""
with enable_wrap(module_blocklist=[nn.Linear],
process_group=self.process_group,
flatten_parameters=False):
sequential = nn.Sequential(nn.Linear(10, 10),
nn.ModuleList([nn.Linear(10, 10)]))
model = auto_wrap(sequential, min_num_params=40)
# Model was wrapped in FSDP as no inner modules were wrapped.
assert isinstance(model, FSDP)
assert isinstance(model.module[0], nn.Linear)
assert isinstance(model.module[1], nn.ModuleList)
def test_auto_wrap(self):
"""
Test to ensure with auto wrap, we wrap child modules correctly based on the min_num_params.
``nn.Linear(5, 5)`` does not exceed the bucket size, but combined they do.
"""
with enable_wrap(wrapper_cls=FSDP, process_group=self.process_group, flatten_parameters=False):
sequential = nn.Sequential(
nn.Linear(5, 5), nn.Linear(5, 5), nn.Sequential(nn.Linear(5, 5), nn.Linear(5, 5))
)
my_auto_wrap_policy = functools.partial(default_auto_wrap_policy, min_num_params=40)
model = auto_wrap(sequential, auto_wrap_policy=my_auto_wrap_policy)
assert isinstance(model, FSDP)
assert isinstance(model.module[0], nn.Linear)
assert isinstance(model.module[1], nn.Linear)
assert isinstance(model.module[2], FSDP)
assert isinstance(model.module[2].module[0], nn.Linear)
assert isinstance(model.module[2].module[1], nn.Linear)
def test_auto_wrap_preset_force_leaf_custom(self):
"""
Test to ensure force-leaf modules are not wrapped.
"""
my_auto_wrap_policy = functools.partial(
default_auto_wrap_policy,
min_num_params=40,
force_leaf_modules=default_auto_wrap_policy.FORCE_LEAF_MODULES.union({nn.Linear}),
)
with enable_wrap(
auto_wrap_policy=my_auto_wrap_policy,
wrapper_cls=FSDP,
process_group=self.process_group,
flatten_parameters=False,
):
sequential = nn.Sequential(nn.Linear(10, 10), nn.ModuleList([nn.Linear(10, 10)]))
model = auto_wrap(sequential)
# Model was wrapped in FSDP as no inner modules were wrapped.
assert isinstance(model, FSDP)
assert isinstance(model.module[0], nn.Linear)
assert isinstance(model.module[1], nn.ModuleList)
def _auto_wrap_smoke_test(self, enable_mixed_precision):
from torch.cuda.amp import autocast
device = torch.device("cuda")
torch.cuda.set_device(0)
torch.distributed.init_process_group(backend="nccl",
rank=0,
world_size=1)
with enable_wrap(mixed_precision=enable_mixed_precision):
sequential = nn.Sequential(
nn.Linear(5, 5), nn.Linear(5, 5),
nn.Sequential(nn.Linear(5, 5), nn.Linear(5, 5)))
model = auto_wrap(sequential, min_num_params=40)
model.to(device)
input = torch.rand((1, 5), dtype=torch.float).to(device)
with autocast(enabled=enable_mixed_precision):
output = model(input)
loss = F.mse_loss(input, output)
loss.backward()
torch.distributed.destroy_process_group()