def test_optim_state_dict_nested(
self,
state_dict_type: StateDictType,
use_multiple_param_groups: bool,
rank0_only: bool,
use_diff_optim_inputs: bool,
) -> None:
"""
Tests :meth:`full_optim_state_dict` and `sharded_optim_state_dict`
by comparing the returned dict for an FSDP-wrapped model with that of
an equivalent non-wrapped model.
The test checks the equivalence excluding the parameter keys since the
FSDP and normal optimizer state dicts key by names and IDs,
respectively. This means that the test can pass even if parameter keys
are incorrectly mapped to values. Their correct mapping is tested in
other tests that exercise the save/load workflow.
"""
if rank0_only and state_dict_type == StateDictType.SHARDED_STATE_DICT:
return # not supported
NUM_ITERS = 3
model1, optim1, optim_input = self._init_nested_model(
wrap=True, use_multiple_param_groups=use_multiple_param_groups,
use_diff_optim_inputs=use_diff_optim_inputs,
)
losses1 = self._step_model(model1, optim1, num_iters=NUM_ITERS)
if state_dict_type == StateDictType.FULL_STATE_DICT:
fsdp_osd = FSDP.full_optim_state_dict(
model1, optim1, optim_input, rank0_only=rank0_only,
)
else:
fsdp_osd = FSDP.sharded_optim_state_dict(
model1, optim1, optim_input
)
# Non-target ranks get an empty state dict
if rank0_only and self.rank != 0:
self.assertEqual(len(fsdp_osd), 0)
return
model2, optim2, _ = self._init_nested_model(
wrap=False, use_multiple_param_groups=use_multiple_param_groups,
use_diff_optim_inputs=use_diff_optim_inputs,
)
losses2 = self._step_model(model2, optim2, num_iters=NUM_ITERS)
ref_osd = optim2.state_dict()
# Check the losses to eliminate model drift as a source of error
for i, (l1, l2) in enumerate(zip(losses1, losses2)):
assert l1 == l2, f"Losses differ on iter {i}: {l1:.5f} {l2:.5f}"
# Do not check the parameter keys since the full/sharded optimizer state
# dict uses parameter names, while the non-wrapped equivalent uses
# parameter IDs
check_same_param_keys = False
self._check_same_param_groups(
fsdp_osd, ref_osd, check_same_param_keys=check_same_param_keys,
)
self._check_same_state(
fsdp_osd, ref_osd, check_same_param_keys=check_same_param_keys,
)
def test_rekey_optim_state_dict_to_ids(
self,
state_dict_type: StateDictType,
use_multiple_param_groups: bool,
):
"""Tests :meth:`rekey_optim_state_dict` with the new keys being
parameter IDs by checking that a wrapped model (i.e. with FSDP modules)
can rekey its optimizer state dict to match that of an equivalent
non-wrapped model (i.e. without FSDP modules)."""
NUM_ITERS = 3
# Run a wrapped model for a few iterations
model1, optim1, optim_input1 = self._init_nested_model(
wrap=True, use_multiple_param_groups=use_multiple_param_groups,
)
self._step_model(model1, optim1, num_iters=NUM_ITERS)
if state_dict_type == StateDictType.FULL_STATE_DICT:
fsdp_osd = FSDP.full_optim_state_dict(model1, optim1, optim_input1)
# Broadcast instead of `torch.save()`/`torch.load()` so that all ranks
# have the full state dict
fsdp_osd = self._broadcast_full_osd(fsdp_osd)
else:
fsdp_osd = FSDP.sharded_optim_state_dict(model1, optim1, optim_input1)
# Run a non-wrapped model for a few iterations
model2, optim2, optim_input2 = self._init_nested_model(
wrap=False, use_multiple_param_groups=use_multiple_param_groups,
)
self._step_model(model2, optim2, num_iters=NUM_ITERS)
# Re-key the wrapped model's optimizer state dict using parameter IDs
# according to the non-wrapped model
rekeyed_osd = FSDP.rekey_optim_state_dict(
fsdp_osd, OptimStateKeyType.PARAM_ID, model2, optim_input2,
)
# Check that the re-keyed dict and actual dict are the same
osd = optim2.state_dict()
check_same_param_keys = True
self._check_same_param_groups(
rekeyed_osd, osd, check_same_param_keys=check_same_param_keys,
)
self._check_same_state(
rekeyed_osd, osd, check_same_param_keys=check_same_param_keys,
)
# As a sanity check, check that we can load and run a few iterations
if state_dict_type != StateDictType.SHARDED_STATE_DICT:
optim2.load_state_dict(rekeyed_osd)
self._step_model(model2, optim2, num_iters=NUM_ITERS)
def test_shard_full_optim_state_dict_unmanaged_params(
self,
state_dict_type: StateDictType,
add_to_fsdp_module: bool,
):
"""
Tests :meth:`shard_full_optim_state_dict` when there are unmanaged
parameters.
- If ``add_to_fsdp_module=True``, then the unmanaged parameters are
added to a module to be wrapped with FSDP, in which case there should
be an error since we require that all unflattened parameter
comprising a flattened parameter have the same scalar state (e.g.
Adam "step") but the added parameter is missing its entry.
- If ``add_to_fsdp_module=False``, then the unmanaged parameters are
added to a module not to be wrapped with FSDP, in which case there
should be no error (emulating model parallel use cases where some
parameters may be managed externally to FSDP).
We do not separately test unmanaged parameters for
:meth:`scatter_full_optim_state_dict` and `flatten_sharded_optim_state_dict`
to save CI cost since it call into the same subroutine
:meth:`_flatten_optim_state_dict`.
"""
NUM_ITERS = 1
# Create a normal wrapped model
model, optim, optim_input = self._init_nested_model(wrap=True)
self._step_model(model, optim, num_iters=NUM_ITERS)
if state_dict_type == StateDictType.FULL_STATE_DICT:
fsdp_osd = FSDP.full_optim_state_dict(
model, optim, optim_input, rank0_only=False,
) # save on all ranks to avoid having to broadcast from rank 0
else:
fsdp_osd = FSDP.sharded_optim_state_dict(model, optim, optim_input)
# Create a new model with the same structure but additional unmanaged
# parameters, representing the model for which we want to load
device = torch.device("cuda")
model = NestedModel().to(device)
model, unmanaged_params = NestedModel.wrap_with_unmanaged_params(
model, add_to_fsdp_module,
)
optim_input = list(model.parameters())
if add_to_fsdp_module:
# If we add the unmanaged parameters to a module wrapped with FSDP,
# then the flattened parameter will be comprised of some
# unflattened parameters with zero-dimensional tensor state (i.e.
# Adam "step") and others without (i.e. the unmanaged parameters),
# which triggers an error that we have to ensure correctness
error_prefix = "^(All unflattened parameters comprising a " \
"single flattened parameter must have scalar state with the " \
"same value and dtype)"
with self.assertRaisesRegex(ValueError, error_prefix):
if state_dict_type == StateDictType.FULL_STATE_DICT:
FSDP.shard_full_optim_state_dict(
fsdp_osd, model, optim_input,
)
else:
FSDP.flatten_sharded_optim_state_dict(
fsdp_osd, model, optim_input,
)
else:
# If we add the unmanaged parameters to a module not wrapped with
# FSDP, then we simply ignore them without erroring to enable
# model parallelism use cases, where some parameters are managed
# externally to FSDP
if state_dict_type == StateDictType.FULL_STATE_DICT:
flattened_osd = FSDP.shard_full_optim_state_dict(
fsdp_osd, model, optim_input,
)
else:
flattened_osd = FSDP.flatten_sharded_optim_state_dict(
fsdp_osd, model, optim_input,
)
# Add entries for the unmanaged parameters to be able to load
for unmanaged_param in unmanaged_params:
NestedModel.add_unmanaged_param_entry(
flattened_osd, unmanaged_param, NUM_ITERS,
)
# Check that we can load the optimizer state dict
optim = torch.optim.Adam(optim_input, lr=1e-3)
optim.load_state_dict(flattened_osd)
