def test_rekey_optim_state_dict_to_names(
self,
use_multiple_param_groups: bool,
):
"""Tests :meth:`rekey_optim_state_dict` with the new keys being
parameter names by checking that a non-wrapped model (i.e. without FSDP
modules) can rekey its optimizer state dict to match the expected
output of :meth:`full_optim_state_dict`, hence be sharded using
:meth:`shard_full_optim_state_dict`, and finally match the per-rank
optimizer state dict of a wrapped model (i.e. with 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)
# 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 non-wrapped model's optimizer state dict using parameter
# names (still according to itself)
osd2 = optim2.state_dict()
rekeyed_osd = FSDP.rekey_optim_state_dict(
osd2,
OptimStateKeyType.PARAM_NAME,
model2,
optim_input2,
)
# Shard the non-wrapped model's re-keyed optimizer state dict, which
# maps back to (flattened) parameter IDs
sharded_osd = FSDP.shard_full_optim_state_dict(
rekeyed_osd,
model1,
optim_input1,
)
# Check that this sharded optimizer state dict matches the wrapped
# model's per-rank optimizer state dict
osd1 = optim1.state_dict()
check_same_param_keys = True
self._check_same_param_groups(
sharded_osd,
osd1,
check_same_param_keys=check_same_param_keys,
)
self._check_same_state(
sharded_osd,
osd1,
check_same_param_keys=check_same_param_keys,
)
# As a sanity check, check that we can load and run a few iterations
optim1.load_state_dict(sharded_osd)
self._step_model(model1, optim1, num_iters=NUM_ITERS)
def test_shard_full_optim_state_dict_unmanaged_params(
self,
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` to save CI cost since it calls
into the same subroutine :meth:`_flatten_full_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)
full_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
# 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):
FSDP.shard_full_optim_state_dict(
full_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
sharded_osd = FSDP.shard_full_optim_state_dict(
full_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(
sharded_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(sharded_osd)
def _test_shard_full_optim_state(
self,
model_class: str,
use_multiple_param_groups: bool,
halve_world_size: bool,
**new_model_kwargs,
):
"""
(1) Runs a model with full world size for K iterations to generate a
full optimizer state dict;
(2) initializes a model with halved world size and possibly different
FSDP wrapping scheme (based on ``new_model_kwargs``);
(3) shards the full optimizer state dict from (1) according to the
halved-world-size model;
(4) runs the halved-world-size model for K iterations; and
(5) checks that the sharded optimizer state dict from (3) matches the
halved-world-size model's local optimizer state dict, meaning that the
former could have equivalently been loaded into the local optimizer.
"""
NUM_ITERS = 3
initializer = self._init_nested_model if model_class == "nested" \
else self._init_transformer_model if model_class == "transformer" \
else None
assert initializer is not None, f"Unsupported model: {model_class}"
# Run a wrapped model with full world size for a few iterations
model1, optim1, optim_input1 = initializer(
wrap=True,
use_multiple_param_groups=use_multiple_param_groups,
)
self._step_model(model1, optim1, num_iters=NUM_ITERS)
full_osd1 = 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
full_osd1 = self._broadcast_full_osd(full_osd1)
if halve_world_size:
# Create a new process group with halved world size
new_group_ranks = [r for r in range(self.world_size) if r % 2 == 0]
new_group = dist.new_group(ranks=new_group_ranks)
if self.rank not in new_group_ranks:
return
else:
new_group = dist.distributed_c10d._get_default_group()
# Run a wrapped model with halved world size (from scratch)
model2, optim2, optim_input2 = initializer(
wrap=True,
group=new_group,
use_multiple_param_groups=use_multiple_param_groups,
**new_model_kwargs, # specify `wrap_alt` to change wrapping
)
self._step_model(model2, optim2, num_iters=NUM_ITERS)
full_osd2 = FSDP.full_optim_state_dict(model2, optim2, optim_input2)
full_osd2 = self._broadcast_full_osd(full_osd2, group=new_group)
# As a sanity check, check that sharding the halved-world-size model's
# full optimizer state dict according to itself is equivalent to its
# local optimizer's state dict
local_osd2 = optim2.state_dict()
sharded_osd2 = FSDP.shard_full_optim_state_dict(
full_osd2,
model2,
optim_input2,
)
check_same_param_keys = True # should all have matching parameter IDs
self._check_same_param_groups(
sharded_osd2,
local_osd2,
check_same_param_keys=check_same_param_keys,
)
self._check_same_state(
sharded_osd2,
local_osd2,
check_same_param_keys=check_same_param_keys,
)
# Check that sharding the full-world-size model's full optimizer state
# dict according to the halved-world-size model is equivalent to the
# halved-world-size model's local optimizer state dict
sharded_osd1 = FSDP.shard_full_optim_state_dict(
full_osd1,
model2,
optim_input2,
)
self._check_same_param_groups(
sharded_osd1,
local_osd2,
check_same_param_keys=check_same_param_keys,
)
self._check_same_state(
sharded_osd1,
local_osd2,
check_same_param_keys=check_same_param_keys,
)
# As a sanity check, check that we can load and run a few iterations
optim2.load_state_dict(sharded_osd1)
self._step_model(model2, optim2, num_iters=NUM_ITERS)
def _test_shard_full_optim_state(
self,
model_class: str,
use_multiple_param_groups: bool,
halve_world_size: bool,
osd_comm_method: _OSDCommMethod,
**new_model_kwargs,
):
"""
(1) Runs a model with full world size for K iterations to generate a
full optimizer state dict;
(2) initializes a model with halved world size and possibly different
FSDP wrapping scheme (based on ``new_model_kwargs``);
(3) shards the full optimizer state dict from (1) according to the
halved-world-size model;
(4) runs the halved-world-size model for K iterations; and
(5) checks that the sharded optimizer state dict from (3) matches the
halved-world-size model's local optimizer state dict, meaning that the
former could have equivalently been loaded into the local optimizer.
"""
NUM_ITERS = 3
initializer = self._init_nested_model if model_class == "nested" \
else self._init_transformer_model if model_class == "transformer" \
else None
assert initializer is not None, f"Unsupported model: {model_class}"
# First, run a wrapped model with full world size for a few iterations
model1, optim1, optim_input1 = initializer(
wrap=True,
use_multiple_param_groups=use_multiple_param_groups,
)
self._step_model(model1, optim1, num_iters=NUM_ITERS)
full_osd1 = FSDP.full_optim_state_dict(model1, optim1, optim_input1)
if halve_world_size:
# Create a new process group with halved world size
new_group_ranks = [r for r in range(self.world_size) if r % 2 == 0]
new_group = dist.new_group(ranks=new_group_ranks)
if self.rank not in new_group_ranks:
return
else:
# Continue using the same group and hence world size
new_group = dist.distributed_c10d._get_default_group()
# Second, run a wrapped model with (possibly) halved world size
model2, optim2, optim_input2 = initializer(
wrap=True,
group=new_group,
use_multiple_param_groups=use_multiple_param_groups,
**new_model_kwargs, # specify `wrap_alt` to change wrapping
)
self._step_model(model2, optim2, num_iters=NUM_ITERS)
full_osd2 = FSDP.full_optim_state_dict(model2, optim2, optim_input2)
# Compute two sharded optim state dicts: (1) for the first model
# according to the second model and (2) for the second model according
# to the second model
if osd_comm_method == _OSDCommMethod.BROADCAST_OBJECT_LIST:
full_osd1 = self._broadcast_full_osd(full_osd1, group=new_group)
sharded_osd1 = FSDP.shard_full_optim_state_dict(
full_osd1,
model2,
optim_input2,
)
full_osd2 = self._broadcast_full_osd(full_osd2, group=new_group)
sharded_osd2 = FSDP.shard_full_optim_state_dict(
full_osd2,
model2,
optim_input2,
)
elif osd_comm_method == _OSDCommMethod.SCATTER_FULL_OSD:
sharded_osd1 = FSDP.scatter_full_optim_state_dict(
full_osd1 if self.rank == 0 else None,
model2,
optim_input2,
group=new_group,
)
sharded_osd2 = FSDP.scatter_full_optim_state_dict(
full_osd2 if self.rank == 0 else None,
model2,
optim_input2,
group=new_group,
)
self._check_state_device(sharded_osd1, on_gpu=True)
self._check_state_device(sharded_osd2, on_gpu=True)
# As a sanity check, check that sharding the second model's full
# optimizer state dict according to itself is equivalent to its local
# optimizer's state dict
local_osd2 = optim2.state_dict()
check_same_param_keys = True # should all have matching parameter IDs
self._check_same_param_groups(
sharded_osd2,
local_osd2,
check_same_param_keys=check_same_param_keys,
)
self._check_same_state(
sharded_osd2,
local_osd2,
check_same_param_keys=check_same_param_keys,
)
# Check that sharding the first model's full optimizer state dict
# according to the second model is equivalent to the second model's
# local optimizer state dict
self._check_same_param_groups(
sharded_osd1,
local_osd2,
check_same_param_keys=check_same_param_keys,
)
self._check_same_state(
sharded_osd1,
local_osd2,
check_same_param_keys=check_same_param_keys,
)
# As a sanity check, check that we can load and run a few iterations
optim2.load_state_dict(sharded_osd1)
self._step_model(model2, optim2, num_iters=NUM_ITERS)
