def test_auto_wrap_preset_force_leaf_custom(self, wrap_method):
"""
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}),
)
sequential = nn.Sequential(nn.Linear(10, 10),
nn.ModuleList([nn.Linear(10, 10)]))
if wrap_method == WrapMethod.WRAP_API:
with enable_wrap(
auto_wrap_policy=my_auto_wrap_policy,
wrapper_cls=FSDP,
process_group=self.process_group,
):
model = auto_wrap(sequential)
else:
assert wrap_method == WrapMethod.FSDP_CTOR
model = FSDP(sequential,
process_group=self.process_group,
fsdp_auto_wrap_policy=my_auto_wrap_policy)
# Model was wrapped in FSDP as no inner modules were wrapped.
self.assertTrue(isinstance(model, FSDP))
self.assertTrue(isinstance(model.module[0], nn.Linear))
self.assertTrue(isinstance(model.module[1], nn.ModuleList))
def test_auto_wrap_preset_exclude_wrap(self, wrap_method):
"""
Test to ensure excluded modules are not wrapped, regardless if the total param size is greater than the
min_num_params. the default_auto_wrap_policy excludes wrapping for {nn.ModuleList, nn.ModuleDict}
"""
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)
if wrap_method == WrapMethod.WRAP_API:
with enable_wrap(wrapper_cls=FSDP,
process_group=self.process_group):
model = auto_wrap(sequential,
auto_wrap_policy=my_auto_wrap_policy)
else:
assert wrap_method == WrapMethod.FSDP_CTOR
model = FSDP(sequential,
process_group=self.process_group,
fsdp_auto_wrap_policy=my_auto_wrap_policy)
# Note that outermost module will be FSDP instance for FSDP_CTOR
# approach, because we need to call the FSDP ctor so the returned obj
# will be an FSDP instance. If we don't want to shard the outermost
# module based on policy, we can apply the policy manually to the
# outermost instance and skip the sharding.
if wrap_method == WrapMethod.WRAP_API:
self.assertTrue(isinstance(model, nn.ModuleList))
else:
self.assertTrue(isinstance(model, FSDP))
self.assertTrue(isinstance(model[0], nn.Linear))
self.assertTrue(isinstance(model[1], nn.Linear))
def test_auto_wrap_smoke_test(self, fsdp_init_mode, cpu_offload):
# CPU offload and CUDA after don't work together as expected.
if (
cpu_offload.offload_params and fsdp_init_mode == FSDPInitMode.CUDA_AFTER
):
return
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(find_free_port())
torch.distributed.init_process_group(backend="nccl", rank=0, world_size=1)
# NOTE: We move model to CUDA after init with FSDP to simulate real use
# cases where full model cannot be loaded onto GPU, but their shards can.
cuda_after_init = fsdp_init_mode == FSDPInitMode.CUDA_AFTER
try:
sequential = TestFSDPWrap.NestedSequentialModel.get_model(cuda=(not cuda_after_init))
my_auto_wrap_policy = functools.partial(
default_auto_wrap_policy, min_num_params=40
)
model = FSDP(sequential, cpu_offload=cpu_offload, fsdp_auto_wrap_policy=my_auto_wrap_policy)
TestFSDPWrap.NestedSequentialModel.verify_model(self, model)
if cuda_after_init:
model = model.cuda()
input = torch.rand((1, 5), dtype=torch.float).to(device)
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 test_main_wrap_api(self, cpu_offload, fsdp_init_mode):
if fsdp_init_mode == FSDPInitMode.CUDA_AFTER and cpu_offload.offload_params:
# they don't work together, expected
return
move_to_cuda = fsdp_init_mode == FSDPInitMode.CUDA_BEFORE
class Nested(nn.Module):
def __init__(self):
super().__init__()
self.nested_lin = _maybe_cuda(nn.Linear(1, 1, bias=False), move_to_cuda)
def forward(self, input):
return self.nested_lin(input)
class MyModel(nn.Module):
def __init__(self):
super().__init__()
self.lin1 = _maybe_cuda(nn.Linear(1, 1, bias=False), move_to_cuda)
self.lin2 = _maybe_cuda(nn.Linear(1, 1, bias=False), move_to_cuda)
self.lin3 = _maybe_cuda(nn.Linear(1, 1, bias=False), move_to_cuda)
self.lin4 = Nested()
def forward(self, input):
return self.lin4(self.lin3(self.lin2(self.lin1(input))))
model = MyModel()
wrapped_model = FSDP(
model,
fsdp_auto_wrap_policy=functools.partial(
default_auto_wrap_policy,
min_num_params=0, # wrap all modules
),
cpu_offload=cpu_offload,
)
if fsdp_init_mode == FSDPInitMode.CUDA_AFTER:
wrapped_model = wrapped_model.cuda()
modules = [
wrapped_model,
wrapped_model.module.lin1,
wrapped_model.module.lin2,
wrapped_model.module.lin3,
wrapped_model.module.lin4,
# Nested FSDP
wrapped_model.module.lin4.module.nested_lin,
]
for module in modules:
self.assertTrue(isinstance(module, FSDP))
self._check_cpu_offload(module, cpu_offload)
# Run model a few times for sanity check.
optim = torch.optim.SGD(wrapped_model.parameters(), lr=1e-2, momentum=0.9)
inp = torch.ones(1).cuda()
for _ in range(6):
optim.zero_grad()
loss = wrapped_model(inp).sum()
loss.backward()
optim.step()
def __init__(self, nested):
super().__init__()
# TODO: test the various init modes.
move_to_cuda = fsdp_init_mode == FSDPInitMode.CUDA_BEFORE
# if nested=True, the FSDP module will be nested one layer deep
# and we should pick that up.
if nested:
self.lin1 = nn.Sequential(
_maybe_cuda(fn_self._get_linear(1, 1), move_to_cuda),
FSDP(_maybe_cuda(fn_self._get_linear(1, 1), move_to_cuda)),
)
else:
self.lin1 = FSDP(
_maybe_cuda(fn_self._get_linear(1, 1), move_to_cuda)
)
self.lin2 = FSDP(_maybe_cuda(fn_self._get_linear(1, 1), move_to_cuda))
self.lin3 = FSDP(_maybe_cuda(fn_self._get_linear(1, 1), move_to_cuda))
def test_error_already_wrapped(self, nested, fsdp_init_mode):
"""
Test that an error is raised if we attempt to wrap when submodules are
already FSDP.
"""
wrapped_fsdp = self._get_already_wrapped_fsdp(nested=nested, fsdp_init_mode=fsdp_init_mode)
if fsdp_init_mode == FSDPInitMode.CUDA_AFTER:
wrapped_fsdp = wrapped_fsdp.cuda()
with self.assertRaisesRegex(ValueError, "to NOT be FullyShardedDataParallel"):
mod = FSDP(wrapped_fsdp, fsdp_auto_wrap_policy=default_auto_wrap_policy)
def test_auto_wrap_api(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.
"""
sequential = TestFSDPWrap.NestedSequentialModel.get_model(cuda=False)
my_auto_wrap_policy = functools.partial(default_auto_wrap_policy,
min_num_params=40)
model = FSDP(sequential,
process_group=self.process_group,
fsdp_auto_wrap_policy=my_auto_wrap_policy)
TestFSDPWrap.NestedSequentialModel.verify_model(self, model)
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
"""
sequential = nn.ModuleList([nn.Linear(10, 10)])
my_auto_wrap_policy = functools.partial(
default_auto_wrap_policy, min_num_params=40
)
model = FSDP(sequential, process_group=self.process_group, fsdp_auto_wrap_policy=my_auto_wrap_policy)
self.assertTrue(isinstance(model, FSDP))
self.assertTrue(isinstance(model[0], FSDP))
def test_wrap(self, wrap_method):
if wrap_method == WrapMethod.WRAP_API:
with enable_wrap(wrapper_cls=FSDP, process_group=self.process_group):
layer = wrap(nn.Linear(5, 5))
else:
assert wrap_method == WrapMethod.FSDP_CTOR
layer = FSDP(
nn.Linear(5, 5),
process_group=self.process_group,
fsdp_auto_wrap_policy=functools.partial(default_auto_wrap_policy, min_num_params=1)
)
self.assertTrue(isinstance(layer, FSDP))
self.assertEqual(layer.rank, self.process_group.rank())
self.assertEqual(layer.world_size, self.process_group.size())
def test_auto_wrap_preset_force_leaf(self):
"""
Test to ensure force-leaf modules are not wrapped, and children are not wrapped. The
default_auto_wrap_policy forces leaf modules of type {nn.MultiheadAttention} to not be wrapped
"""
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 = FSDP(sequential, process_group=self.process_group, fsdp_auto_wrap_policy=my_auto_wrap_policy)
self.assertTrue(isinstance(model.module[0], FSDP))
# Assert children of multihead attention are not wrapped
self.assertTrue(isinstance(model.module[1], nn.MultiheadAttention))
self.assertTrue(isinstance(model.module[1].out_proj, nn.Linear))
def test_basic_checkpoint_end_to_end(self, cpu_offload):
seq = TestFSDPCheckpoint.SequentialModule().to(torch.cuda.current_device())
# Runs FSDP with no checkpointing
fsdp_only_seq = FSDP(deepcopy(seq), cpu_offload=cpu_offload)
# Runs checkpoint-wrapped FSDP
checkpointed_fsdp = checkpoint_wrapper(FSDP(deepcopy(seq), cpu_offload=cpu_offload))
# Runs FSDP-wrapped checkpointed module
fsdp_wrapped_checkpoint = FSDP(checkpoint_wrapper(deepcopy(seq)), cpu_offload=cpu_offload)
# Runs FSDP with manual calls to checkpoint.
fsdp_call_checkpoint = FSDP(deepcopy(seq), cpu_offload=cpu_offload)
# note that reentrant-based checkpointing requires inputs to have grad
# flag set.
inp = torch.randn(10, 3, device=torch.cuda.current_device(), requires_grad=True)
models = [
fsdp_only_seq,
checkpointed_fsdp,
fsdp_wrapped_checkpoint,
fsdp_call_checkpoint
]
for _ in range(6):
losses = []
outputs = []
for m in models:
if m == fsdp_call_checkpoint:
out = checkpoint(m, inp)
else:
out = m(inp)
loss = out.sum()
loss.backward()
losses.append(loss)
outputs.append(out)
self._verify_parity(losses, outputs, models)
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. the default_auto_wrap_policy excludes wrapping for {nn.ModuleList, nn.ModuleDict}
"""
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 = FSDP(sequential,
process_group=self.process_group,
fsdp_auto_wrap_policy=my_auto_wrap_policy)
self.assertTrue(isinstance(model, FSDP))
self.assertTrue(isinstance(model[0], nn.Linear))
self.assertTrue(isinstance(model[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}
),
)
sequential = nn.Sequential(
nn.Linear(10, 10), nn.ModuleList([nn.Linear(10, 10)])
)
model = FSDP(sequential, process_group=self.process_group, fsdp_auto_wrap_policy=my_auto_wrap_policy)
# Model was wrapped in FSDP as no inner modules were wrapped.
self.assertTrue(isinstance(model, FSDP))
self.assertTrue(isinstance(model.module[0], nn.Linear))
self.assertTrue(isinstance(model.module[1], nn.ModuleList))
def test_auto_wrap_foo(self, wrap_method):
"""
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.
"""
sequential = TestFSDPWrap.NestedSequentialModel.get_model(cuda=False)
my_auto_wrap_policy = functools.partial(default_auto_wrap_policy,
min_num_params=40)
if wrap_method == WrapMethod.WRAP_API:
with enable_wrap(wrapper_cls=FSDP,
process_group=self.process_group):
model = auto_wrap(sequential,
auto_wrap_policy=my_auto_wrap_policy)
else:
assert wrap_method == WrapMethod.FSDP_CTOR
model = FSDP(sequential,
process_group=self.process_group,
fsdp_auto_wrap_policy=my_auto_wrap_policy)
TestFSDPWrap.NestedSequentialModel.verify_model(self, model)
def test_auto_wrap_preset_force_leaf(self, wrap_method):
"""
Test to ensure force-leaf modules are not wrapped, and children are not wrapped. The
default_auto_wrap_policy forces leaf modules of type {nn.MultiheadAttention} to not be wrapped
"""
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)
if wrap_method == WrapMethod.WRAP_API:
with enable_wrap(wrapper_cls=FSDP,
process_group=self.process_group):
model = auto_wrap(sequential,
auto_wrap_policy=my_auto_wrap_policy)
else:
assert wrap_method == WrapMethod.FSDP_CTOR
model = FSDP(sequential,
process_group=self.process_group,
fsdp_auto_wrap_policy=my_auto_wrap_policy)
self.assertTrue(isinstance(model.module[0], FSDP))
# Assert children of multihead attention are not wrapped
self.assertTrue(isinstance(model.module[1], nn.MultiheadAttention))
self.assertTrue(isinstance(model.module[1].out_proj, nn.Linear))
def test_auto_wrap_preset_exclude_wrap_include_children(self, wrap_method):
"""
Test to ensure excluded modules are not wrapped, but children are if param size is greater than
min_num_params
"""
sequential = nn.ModuleList([nn.Linear(10, 10)])
my_auto_wrap_policy = functools.partial(default_auto_wrap_policy,
min_num_params=40)
if wrap_method == WrapMethod.WRAP_API:
with enable_wrap(wrapper_cls=FSDP,
process_group=self.process_group):
model = auto_wrap(sequential,
auto_wrap_policy=my_auto_wrap_policy)
else:
assert wrap_method == WrapMethod.FSDP_CTOR
model = FSDP(sequential,
process_group=self.process_group,
fsdp_auto_wrap_policy=my_auto_wrap_policy)
if wrap_method == WrapMethod.WRAP_API:
self.assertTrue(isinstance(model, nn.ModuleList))
else:
self.assertTrue(isinstance(model, FSDP))
self.assertTrue(isinstance(model[0], FSDP))
def test_basic_checkpoint_end_to_end(self, cpu_offload, offload_activations):
seq = TestFSDPCheckpoint.SequentialModule().to(torch.cuda.current_device())
# Runs FSDP with no checkpointing
fsdp_only_seq = FSDP(deepcopy(seq), cpu_offload=cpu_offload)
# Runs checkpoint-wrapped FSDP
checkpointed_fsdp = checkpoint_wrapper(
FSDP(deepcopy(seq), cpu_offload=cpu_offload),
offload_to_cpu=offload_activations,
)
# Runs FSDP-wrapped checkpointed module
fsdp_wrapped_checkpoint = FSDP(
checkpoint_wrapper(deepcopy(seq), offload_to_cpu=offload_activations),
cpu_offload=cpu_offload,
)
# Runs FSDP with manual calls to checkpoint.
fsdp_call_checkpoint = FSDP(deepcopy(seq), cpu_offload=cpu_offload)
# note that reentrant-based checkpointing requires inputs to have grad
# flag set.
inp = torch.randn(10, 3, device=torch.cuda.current_device(), requires_grad=True)
models = [
fsdp_only_seq,
checkpointed_fsdp,
fsdp_wrapped_checkpoint,
fsdp_call_checkpoint,
]
offload_to_cpu_event = "Memcpy DtoH"
for i in range(6):
losses = []
outputs = []
for m in models:
check_offload = m != fsdp_only_seq and i == 0 and offload_activations
profiler_ctx = (
torch.profiler.profile(use_cuda=True)
if check_offload
else contextlib.suppress()
)
with profiler_ctx as prof:
if m == fsdp_call_checkpoint:
offload_ctx = (
torch.autograd.graph.save_on_cpu(pin_memory=True)
if offload_activations
else contextlib.suppress()
)
with offload_ctx:
out = checkpoint(m, inp)
else:
out = m(inp)
if check_offload:
event_names = [event.name for event in prof.events()]
offload_occured = any(
offload_to_cpu_event in name for name in event_names
)
self.assertTrue(offload_occured)
loss = out.sum()
loss.backward()
losses.append(loss)
outputs.append(out)
self._verify_parity(losses, outputs, models)