Source code for torch.distributed.checkpoint.state_dict
# mypy: allow-untyped-defs
import contextlib
import functools
import gc
import warnings
from dataclasses import asdict, dataclass, field
from itertools import chain
from typing import (
Any,
Callable,
cast,
Dict,
Generator,
Iterable,
List,
no_type_check,
Optional,
Set,
Tuple,
Union,
)
import torch
import torch.distributed as dist
import torch.nn as nn
from torch.distributed._shard.sharded_tensor import ShardedTensor
from torch.distributed._state_dict_utils import (
_broadcast_state_dict,
_distribute_state_dict,
_flatten_state_dict,
_gather_state_dict,
_offload_state_dict_to_cpu,
_unflatten_state_dict,
)
from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
_CHECKPOINT_PREFIX,
)
from torch.distributed.fsdp import (
FullOptimStateDictConfig,
FullStateDictConfig,
FullyShardedDataParallel as FSDP,
OptimStateDictConfig,
ShardedOptimStateDictConfig,
ShardedStateDictConfig,
StateDictConfig,
StateDictType,
)
from torch.distributed.fsdp._common_utils import (
_get_module_fsdp_state_if_fully_sharded_module,
FSDP_WRAPPED_MODULE,
)
from torch.distributed.tensor import DTensor
from torch.nn.modules.module import _IncompatibleKeys
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils._pytree import tree_map_only
__all__ = [
"FQNS_T",
"PrimitiveType",
"ValueType",
"DictValueType",
"ListDictValueType",
"OptimizerStateType",
"StateDictOptions",
"get_model_state_dict",
"get_optimizer_state_dict",
"get_state_dict",
"set_model_state_dict",
"set_optimizer_state_dict",
"set_state_dict",
]
_FLAT_PARAM = "_flat_param"
_PG = "param_groups"
_PARAMS = "params"
_STATE = "state"
FQNS_T = Set[str]
PrimitiveType = Union[DTensor, ShardedTensor, torch.Tensor, int, float, str]
ValueType = Union[
PrimitiveType, List[PrimitiveType], Tuple[PrimitiveType], Dict[str, "ValueType"]
]
DictValueType = Dict[str, ValueType]
ListDictValueType = List[DictValueType]
OptimizerStateType = Dict[str, Union[DictValueType, ListDictValueType]]
_patched_state_dict: Set[Callable] = set()
@contextlib.contextmanager
def _gc_context():
is_enabled = gc.isenabled()
gc.disable()
try:
yield
finally:
if is_enabled:
gc.enable()
[docs]@dataclass
class StateDictOptions:
"""
This dataclass specifies how get_state_dict/set_state_dict will work.
- ``full_state_dict``: if this is set to True, all the tensors in the
returned state_dict will be gathered. No ShardedTensor and DTensor
will be in the returned state_dict.
- ``cpu_offload``: offload all the tensors to cpu. To prevent CPU OOM, if
``full_state_dict`` is also true, then only the rank0 will get the
state_dict and all other ranks will get empty state_dict.
- ``ignore_frozen_params``: if the value is True, the returned state_dict
won't contain any frozen parameters -- the ``requires_grad`` is False.
The default value is False.
- ``keep_submodule_prefixes`` (deprecated): when ``submodules`` is not None, this option
indicates whether to keep the submodule prefixes from the state_dict keys.
or example, if the submodule is ``module.pretrain`` and the full FQN of
the parameter is ``pretrain.layer1.weight`` of the param. When this option
is True, the parameter's key in the returned state_dict will be
``pretrain.layer1.weight``. If the options is False, the key will be
``layer1.weight``.
Note that if ``keep_submodule_prefixes`` is False, there may be conflicted
FQNs, hence there should be only one submodule in ``submodules``.
- ``strict``: the ``strict`` option when ``set_state_dict`` calls
model.load_state_dict().
- ``broadcast_from_rank0``: when the option is True, rank0 should receive a
full state_dict and will broadcast the tensors in the state_dict/
optim_state_dict one by one to other ranks. Other ranks will receive
the tensors and shard according to the local shards in the model and
optimizer. ``full_state_dict`` must be set to True when using this option.
This option currently only supports DTensor, not the legacy ShardedTensor.
"""
full_state_dict: bool = False
cpu_offload: bool = False
ignore_frozen_params: bool = False
keep_submodule_prefixes: bool = True
strict: bool = True
broadcast_from_rank0: bool = False
flatten_optimizer_state_dict: bool = False
@dataclass
class _StateDictInfo(StateDictOptions):
fqn_param_mapping: Dict[
Union[str, torch.Tensor], Union[FQNS_T, torch.Tensor]
] = field(default_factory=dict)
shared_params_mapping: Dict[
Union[str, torch.Tensor], Union[FQNS_T, torch.Tensor]
] = field(default_factory=dict)
submodule_prefixes: Set[str] = field(default_factory=set)
handle_model: bool = True
handle_optim: bool = True
fsdp_context: Callable = contextlib.nullcontext
fsdp_modules: List[nn.Module] = field(default_factory=list)
@functools.lru_cache(maxsize=None)
def _get_fqns(
model: nn.Module,
name: str,
skip_ddp_prefix: bool = True,
skip_compiler_prefix: bool = True,
) -> FQNS_T:
"""
This API is used to convert the name of a parameter to the FQNs. For FSDP
without `use_orig_params`, the name of FlatParameter can be mapped to
multiple original parameters. As a result, the return type of this function
is `Set[str]`.
Args:
module (nn.Module): the root model.
name (str): the name
skip_ddp_prefix (bool): whether to skip DDP's `module` prefix
Returns:
The canonical FQNs based on the model traversal.
"""
# Remove the checkpoint prefix, if it exists.
name = name.replace(_CHECKPOINT_PREFIX, "")
if "." not in name:
return {name}
obj_names = name.split(".")
fqn_obj_names = []
curr_obj = model
for i, curr_obj_name in enumerate(obj_names):
if isinstance(curr_obj, DDP):
assert curr_obj_name == "module"
curr_obj = curr_obj.module
if not skip_ddp_prefix:
fqn_obj_names.append(curr_obj_name)
elif isinstance(curr_obj, FSDP):
if i < len(obj_names) - 1 and obj_names[i + 1] == _FLAT_PARAM:
prefix = ".".join(fqn_obj_names)
flat_param = getattr(curr_obj, _FLAT_PARAM)
if prefix:
prefix = f"{prefix}."
return {f"{prefix}{fqn}" for fqn in flat_param._fqns}
curr_obj = getattr(curr_obj, FSDP_WRAPPED_MODULE)
if curr_obj_name != FSDP_WRAPPED_MODULE:
fqn_obj_names.append(curr_obj_name)
curr_obj = getattr(curr_obj, curr_obj_name)
elif isinstance(curr_obj, torch._dynamo.eval_frame.OptimizedModule):
assert curr_obj_name == "_orig_mod"
curr_obj = curr_obj._orig_mod
if not skip_compiler_prefix:
fqn_obj_names.append(curr_obj_name)
else:
fqn_obj_names.append(curr_obj_name)
if curr_obj_name == nn.modules.module._EXTRA_STATE_KEY_SUFFIX:
if i != len(obj_names) - 1:
raise RuntimeError("Expect `_extra_state` to be the last obj name")
else:
curr_obj = getattr(curr_obj, curr_obj_name)
return {".".join(fqn_obj_names).replace(_CHECKPOINT_PREFIX, "")}
class _EXTRA_STATE:
pass
def _iterate_valid_model_state(model):
visited_modules: Set[nn.Module] = set()
def recurse(module: nn.Module, curr_fqn: str) -> Generator:
visited_modules.add(module)
curr_fqn = f"{curr_fqn}." if curr_fqn else ""
for name, submodule in module.named_children():
if submodule in visited_modules:
continue
new_fqn = f"{curr_fqn}{name}"
yield from recurse(submodule, new_fqn)
for name, obj in chain(
module.named_buffers(recurse=False), module.named_parameters(recurse=False)
):
if name in module._non_persistent_buffers_set:
continue
new_fqn = f"{curr_fqn}{name}"
yield new_fqn, obj
if (
getattr(module.__class__, "get_extra_state", nn.Module.get_extra_state)
!= nn.Module.get_extra_state
):
new_fqn = f"{curr_fqn}{nn.modules.module._EXTRA_STATE_KEY_SUFFIX}"
yield new_fqn, _EXTRA_STATE()
yield from recurse(model, "")
def _verify_options(
model: nn.Module,
optims: Tuple[torch.optim.Optimizer, ...],
optim_only: bool,
*,
submodules: Optional[Set[nn.Module]] = None,
options: Optional[StateDictOptions] = None,
) -> _StateDictInfo:
"""
Verify the model and options passed by the user and generates _StateDictInfo.
"""
if submodules:
warnings.warn(
"Getting submodules only model/optim state_dict is deprecated and "
"will be removed in 2.5. This feature can be achieved by manually "
"filtering out the state_dict returned from get_state_dict.",
FutureWarning,
)
if optim_only and not optims:
raise RuntimeError(
"Optimizers are not passed in but optim_only is set to True."
)
options = options or StateDictOptions()
fqn_param_mapping: Dict[
Union[str, torch.Tensor], Union[Set[str], torch.Tensor]
] = {}
shared_params_mapping: Dict[
Union[str, torch.Tensor], Union[Set[str], torch.Tensor]
] = {}
for name, param in _iterate_valid_model_state(model):
if isinstance(param, _EXTRA_STATE):
continue
fqns = _get_fqns(model, name)
fqn = fqn_param_mapping.get(param, None)
if fqn is not None:
cast(Set[str], fqn_param_mapping[param]).update(fqns)
shared_params_mapping[param] = fqn_param_mapping[param]
else:
# We need to do copy as _get_fqns is lru_cached
fqn_param_mapping[param] = fqns.copy()
for fqn in fqns:
if not isinstance(param, _EXTRA_STATE):
fqn_param_mapping[fqn] = param
for param_, fqns_ in list(shared_params_mapping.items()):
for fqn in fqns_:
shared_params_mapping[fqn] = cast(torch.Tensor, param_)
submodule_prefixes: Set[str] = set()
if submodules:
submodules = set(submodules)
for name, module in model.named_modules():
if module not in submodules:
continue
fqns = _get_fqns(model, name)
assert len(fqns) == 1, "Submodule FQN should only have 1 instance"
submodule_prefixes.update(f"{fqn}." for fqn in fqns)
if options.broadcast_from_rank0 and not options.full_state_dict:
raise ValueError(
"full_state_dict must be True when broadcast_from_rank0 is True."
)
fsdp_modules = FSDP.fsdp_modules(model)
state_dict_config: StateDictConfig
optim_state_dict_config: OptimStateDictConfig
fsdp_context: Callable
if fsdp_modules:
# FSDP API only work if at least one FSDP instance exists.
if options.full_state_dict:
state_dict_config = FullStateDictConfig(
offload_to_cpu=options.cpu_offload, rank0_only=options.cpu_offload
)
optim_state_dict_config = FullOptimStateDictConfig(
offload_to_cpu=options.cpu_offload,
rank0_only=(options.cpu_offload or options.broadcast_from_rank0),
)
state_dict_type = StateDictType.FULL_STATE_DICT
else:
state_dict_config = ShardedStateDictConfig(
offload_to_cpu=options.cpu_offload,
)
optim_state_dict_config = ShardedOptimStateDictConfig(
offload_to_cpu=options.cpu_offload,
)
state_dict_type = StateDictType.SHARDED_STATE_DICT
@contextlib.contextmanager
def fsdp_state_dict_type_without_warning(
module,
state_dict_type,
state_dict_config,
optim_state_dict_config,
):
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message="FSDP.state_dict_type", category=FutureWarning
)
with FSDP.state_dict_type(
module=module,
state_dict_type=state_dict_type,
state_dict_config=state_dict_config,
optim_state_dict_config=optim_state_dict_config,
):
yield
fsdp_context = functools.partial(
fsdp_state_dict_type_without_warning,
module=model,
state_dict_type=state_dict_type,
state_dict_config=state_dict_config,
optim_state_dict_config=optim_state_dict_config,
)
else:
fsdp_context = contextlib.nullcontext
return _StateDictInfo(
**asdict(options),
fqn_param_mapping=fqn_param_mapping,
shared_params_mapping=shared_params_mapping,
submodule_prefixes=submodule_prefixes,
fsdp_context=fsdp_context,
fsdp_modules=cast(List[nn.Module], fsdp_modules),
handle_model=not optim_only,
handle_optim=(len(optims) > 0),
)
def _verify_state_dict(
model_state_dict: Dict[str, ValueType],
optim_state_dict: OptimizerStateType,
info: _StateDictInfo,
) -> None:
for module in info.fsdp_modules:
fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
assert fsdp_state is not None, "Expected a fsdp_state with a fsdp module."
# Verify if the model_state_dict and optim_state_dict are valid. This API
# should give the users an explicit error message to debug or report.
if (
info.handle_model
and not model_state_dict
and not info.submodule_prefixes
and not info.ignore_frozen_params
and not (info.cpu_offload and info.full_state_dict)
and info.strict
and not info.broadcast_from_rank0
):
raise RuntimeError(
"The option indicates that model state_dict is required to save "
"or load, but model state_dict is empty."
f"rank = {dist.get_rank()=}."
)
if info.handle_optim:
if (
not optim_state_dict
and not (info.cpu_offload and info.full_state_dict)
and (not info.broadcast_from_rank0)
):
raise RuntimeError(
"The option indicates that model state_dict is required to save, "
f"or load but optim state_dict is empty. {optim_state_dict}"
)
for key in model_state_dict.keys():
if _FLAT_PARAM in key:
raise RuntimeError(
f"{key} contains {_FLAT_PARAM}. This can happen if the model "
"is not the root module."
)
def _state_dict_fn(obj: Union[nn.Module, torch.optim.Optimizer], api: str) -> Callable:
call = getattr(obj, api)
if call in _patched_state_dict:
call = functools.partial(getattr(obj.__class__, api), self=obj)
return call
def _maybe_full_or_cpu_state_dict(
state_dict: Dict[str, Any], info: _StateDictInfo
) -> Dict[str, Any]:
if info.full_state_dict:
ranks_only = (
()
if (not info.cpu_offload or not torch.distributed.is_initialized())
else (0,)
)
return _gather_state_dict(
state_dict, cpu_offload=info.cpu_offload, ranks_only=ranks_only
)
elif info.cpu_offload:
return _offload_state_dict_to_cpu(state_dict)
else:
return state_dict
@torch.no_grad()
def _get_model_state_dict(
model: nn.Module, info: _StateDictInfo
) -> Dict[str, ValueType]:
if not info.handle_model:
return {}
with info.fsdp_context():
state_dict = _state_dict_fn(model, "state_dict")()
for key in list(state_dict.keys()):
fqns = _get_fqns(model, key)
assert len(fqns) == 1, (key, fqns)
fqn = next(iter(fqns))
if fqn != key:
# As we only support FSDP, DDP, and TP, the only cases are
# wrapper-based DDP and compiler. Verify if the assumption
# is correct.
def verify(key, fqn) -> bool:
if len(fqn) >= len(key):
return False
fqn_split = fqn.split(".")
key_split = key.split(".")
fqn_idx = 0
for key_idx, key_name in enumerate(key_split):
if key_name == fqn_split[fqn_idx]:
fqn_idx += 1
if fqn_idx == len(fqn_split):
return key_idx == len(key_split) - 1
elif key_name in ("module", "_orig_mod"):
continue
else:
return False
return True
if not verify(key, fqn):
raise RuntimeError(f"An unexpected key, {key}, exists. FQN is {fqn}")
state_dict[fqn] = state_dict.pop(key)
if info.submodule_prefixes:
new_state_dict: Dict[str, ValueType] = {}
# TODO: make this faster.
for fqn in state_dict.keys():
for prefix in info.submodule_prefixes:
if not fqn.startswith(prefix):
continue
if info.keep_submodule_prefixes:
new_state_dict[fqn] = state_dict[fqn]
else:
new_fqn = fqn[len(prefix) :]
new_state_dict[new_fqn] = state_dict[fqn]
state_dict = new_state_dict
if info.ignore_frozen_params:
for key, param in model.named_parameters():
if param.requires_grad:
continue
fqns = _get_fqns(model, key)
for fqn in fqns:
state_dict.pop(fqn)
for key, p in list(state_dict.items()):
if torch.is_tensor(p) and p.is_meta:
state_dict.pop(key)
return _maybe_full_or_cpu_state_dict(state_dict, info)
@torch.no_grad()
def _load_model_state_dict(
model: nn.Module,
state_dict: Dict[str, ValueType],
info: _StateDictInfo,
) -> _IncompatibleKeys:
if not info.handle_model or (not state_dict and not info.broadcast_from_rank0):
return _IncompatibleKeys({}, {})
local_state_dict = {}
for key, value in _iterate_valid_model_state(model):
fqns = _get_fqns(model, key)
fqns_with_prefix = _get_fqns(
model, key, skip_ddp_prefix=False, skip_compiler_prefix=False
)
for fqn, fqn_with_prefix in zip(fqns, fqns_with_prefix):
if (
not info.broadcast_from_rank0 or dist.get_rank() == 0
) and fqn != fqn_with_prefix:
state_dict[fqn_with_prefix] = state_dict.pop(fqn)
local_state_dict[fqn_with_prefix] = value
assign = False
if info.broadcast_from_rank0 or info.full_state_dict:
device = None
for key, value in local_state_dict.items():
if torch.is_tensor(value) and value.dim() > 0:
if device is None:
device = value.device
else:
assert device == value.device
assert device is not None
if device == torch.device("meta"):
device = dist.distributed_c10d._get_pg_default_device()
assign = True
if info.broadcast_from_rank0:
_broadcast_state_dict(
state_dict, local_state_dict, device=device, strict=info.strict
)
elif info.full_state_dict:
_distribute_state_dict(state_dict, local_state_dict, device=device)
for fqn, local_state in local_state_dict.items():
state_dict[fqn] = local_state
with info.fsdp_context():
return cast(
_IncompatibleKeys,
_state_dict_fn(model, "load_state_dict")(
state_dict=state_dict, strict=info.strict, assign=assign
),
)
def _init_optim_state(optim: torch.optim.Optimizer) -> None:
"""
Initialize optim states by calling the step() with zero grads.
"""
if optim.state:
# The optimizer state is initialized.
return
# There are some stateless optimizers like SGD. These optimizer will
# not return in the above condition. So if gradients exist, we should also
# return. If gradients do not exist, the following initialization should
# not disturb SGD because the gradients and lr are both zero.
for param_group in optim.param_groups:
for param in param_group[_PARAMS]:
if param.grad is not None:
return
for param_group in optim.param_groups:
for param in param_group[_PARAMS]:
if param.requires_grad:
param.grad = torch.zeros_like(param)
# Some optimizers will update parameters regardless of grads due to lr, so
# make lr to zero when calling `step()`.
lrs = []
for param_group in optim.param_groups:
if "lr" in param_group:
lrs.append(param_group["lr"])
param_group["lr"] = 0.0
optim.step(closure=None)
# Whether to recover the "lr" should not matter too much as we will
# restore checkpointing later.
for param_group in optim.param_groups:
if "lr" in param_group:
param_group["lr"] = lrs.pop(0)
optim.zero_grad(set_to_none=True)
def _flatten_optim_state_dict(state_dict: OptimizerStateType) -> Dict[str, ValueType]:
"""
This API flattens the optimizer state_dict to support optimizer resharding for
MPMD, e.g., pipeline parallelism.
Without the API, the original optimizer state_dict looks like:
{
"state": {
"layer1.weight": {
"step": 10, "exp_avg": SomeTensor, "exp_avg_sq": SomeTensor
},
"layer2.weight": {
"step": 10, "exp_avg": SomeTensor, "exp_avg_sq": SomeTensor
},
},
"param_group": [
{
"lr": 0.0,
"betas": (0.9, 0.95), ...,
"params": ["layer1.weight", "layer2.weight"]
}
]
}
With this API, the optimizer state_dict looks like:
{
"state.layer1.weight.step": 10,
"state.layer2.weight.step": 10,
"state.layer1.weight.exp_avg": SomeTensor,
"state.layer2.weight.exp_avg": SomeTensor,
"state.layer1.weight.exp_avg_sq": SomeTensor,
"state.layer2.weight.exp_avg_sq": SomeTensor,
"param_group.layer1.weight.lr" : 0.1,
"param_group.layer2.weight.lr" : 0.1,
"param_group.layer1.weight.betas" : (0.9, 0.95),
"param_group.layer2.weight.betas" : (0.9, 0.95),
}
Note that if any of the value is a container, like the betas in the example,
this API won't flattent it.
"""
def _raise_if_type_not_supported(v):
if not isinstance(v, (torch.Tensor, int, float)):
raise NotImplementedError(
"Flattening optimizer state_dict only supports "
"tensor, int, float states now. "
f"Type is {type(v)}."
)
ret: Dict[str, ValueType] = {}
for fqn, state in cast(DictValueType, state_dict[_STATE]).items():
for k, v in cast(DictValueType, state).items():
_raise_if_type_not_supported(v)
ret[f"{_STATE}.{fqn}.{k}"] = v
for param_group in cast(ListDictValueType, state_dict[_PG]):
fqns = param_group.pop(_PARAMS)
for fqn in cast(List[str], fqns):
for k, v in param_group.items():
ret[f"{_PG}.{fqn}.{k}"] = v
return ret
def _unflatten_optim_state_dict(
optim: torch.optim.Optimizer,
state_dict: Dict[str, ValueType],
info: _StateDictInfo,
) -> OptimizerStateType:
"""
This API unflattens the state_dict generated by _flatten_optim_state_dict().
See the docstring of _flatten_optim_state_dict() for more detail.
"""
state: DictValueType = {}
pg_state: ListDictValueType = []
return_osd: OptimizerStateType = {_STATE: state, _PG: pg_state}
for param_group in optim.param_groups:
pg_state.append({_PARAMS: []})
for param in param_group[_PARAMS]:
for fqn in info.fqn_param_mapping[param]:
params = pg_state[-1][_PARAMS]
assert isinstance(params, list) # typing
params.append(fqn)
if not param.requires_grad:
continue
state[fqn] = {}
for state_name in optim.state[param].keys():
cast(DictValueType, state[fqn])[state_name] = state_dict[
f"{_STATE}.{fqn}.{state_name}"
]
first_param_fqn = cast(List[str], pg_state[-1][_PARAMS])[0]
for k in param_group.keys():
if k == _PARAMS:
continue
value = state_dict[f"{_PG}.{first_param_fqn}.{k}"]
if k not in pg_state[-1]:
pg_state[-1][k] = value
elif pg_state[-1][k] != value:
raise RuntimeError(
"All the parameters in the same parameter group should have "
f"the same saved param_group value. But {first_param_fqn}.{k} "
f"is {value} while other(s) is {pg_state[-1][k]}."
)
return return_osd
@torch.no_grad()
def _get_optim_state_dict(
model: nn.Module,
optimizers: Tuple[torch.optim.Optimizer, ...],
info: _StateDictInfo,
) -> OptimizerStateType:
if not info.handle_optim:
return {}
optim_state_dict: OptimizerStateType = {_STATE: {}, _PG: []}
for optim in optimizers:
_init_optim_state(optim)
osd = _state_dict_fn(optim, "state_dict")()
if info.fsdp_modules:
with info.fsdp_context():
osd = FSDP.optim_state_dict(model, optim, osd)
# We need to specially handle FlatParameter FSDP as
# FlatParameter FSDP converts the FQNs.
# There are no easy ways to do this conversion systematically.
# We can only use a string replacment without correctness check.
if not osd:
continue
for k in list(osd[_STATE].keys()):
if "_orig_mod" in k:
osd[_STATE][k.replace("_orig_mod.", "")] = osd[_STATE].pop(k)
for g in osd[_PG]:
params = [k.replace("_orig_mod.", "") for k in g[_PARAMS]]
g[_PARAMS] = params
else:
params = list(chain.from_iterable(g[_PARAMS] for g in optim.param_groups))
param_pid_mapping = dict(zip(params, range(len(params))))
fqn_pid_mapping = {}
for key, param in model.named_parameters():
fqns = _get_fqns(model, key)
assert len(fqns) == 1
fqn = next(iter(fqns))
if param not in param_pid_mapping:
continue
pid = param_pid_mapping[param]
fqn_pid_mapping[fqn] = pid
fqn_pid_mapping[pid] = fqn
for key in list(osd[_STATE].keys()):
fqn = fqn_pid_mapping[key]
osd[_STATE][fqn] = osd[_STATE].pop(key)
for group in osd[_PG]:
group[_PARAMS] = [fqn_pid_mapping[pid] for pid in group[_PARAMS]]
if not osd:
continue
cast(DictValueType, optim_state_dict[_STATE]).update(osd[_STATE])
cast(ListDictValueType, optim_state_dict[_PG]).extend(osd[_PG])
if info.flatten_optimizer_state_dict:
optim_state_dict = cast(
OptimizerStateType, _flatten_optim_state_dict(optim_state_dict)
)
return _maybe_full_or_cpu_state_dict(optim_state_dict, info)
def _split_optim_state_dict(
model: nn.Module,
optim: torch.optim.Optimizer,
optim_state_dict: OptimizerStateType,
info: _StateDictInfo,
) -> OptimizerStateType:
"""
Extract the corresponding optim state_dict from ``optim_state_dict`` for
``optim`` and return the result optim state_dict.
Args:
model (nn.Module): the root model.
optim (torch.optim.Optimizer): the optimizer.
optim_state_dict (Dict[str, ValueType]): the superset optim state_dict that
contains the optim state_dict of ``optim``.
info (_StateDictInfo): state dict information.
Returns:
The optim state_dict of ``optim``.
"""
state: DictValueType = {}
pg_state: ListDictValueType = []
return_osd: OptimizerStateType = {_STATE: state, _PG: pg_state}
pg_mapping: Dict[int, int] = {}
if all(
isinstance(k, int) for k in cast(DictValueType, optim_state_dict[_STATE]).keys()
):
return optim_state_dict
for param_group in optim.param_groups:
pg_state.append({_PARAMS: []})
for param in param_group[_PARAMS]:
for fqn in info.fqn_param_mapping[param]:
if fqn in info.shared_params_mapping:
in_params = False
for loaded_param_group in cast(
ListDictValueType, optim_state_dict[_PG]
):
if fqn in cast(List[str], loaded_param_group[_PARAMS]):
in_params = True
break
else:
in_params = True
if not in_params:
continue
params = pg_state[-1][_PARAMS]
assert isinstance(params, list)
params.append(fqn)
if param.requires_grad:
state[fqn] = cast(DictValueType, optim_state_dict[_STATE])[fqn]
for loaded_param_group in cast(
ListDictValueType, optim_state_dict[_PG]
):
if fqn in cast(List[str], loaded_param_group[_PARAMS]):
pg_mapping[id(loaded_param_group)] = len(return_osd[_PG]) - 1
for param_group in cast(ListDictValueType, optim_state_dict[_PG]):
idx = pg_mapping.get(id(param_group), -1)
if idx == -1:
continue
for key, value in param_group.items():
if key == _PARAMS:
continue
# TODO: check if value is the same if exists.
pg_state[idx][key] = value
return return_osd
@torch.no_grad()
def _load_optim_state_dict(
model: nn.Module,
optimizers: Tuple[torch.optim.Optimizer, ...],
state_dict: OptimizerStateType,
info: _StateDictInfo,
) -> None:
if not info.handle_optim:
return
for optim in optimizers:
_init_optim_state(optim)
if state_dict:
if _STATE in state_dict:
optim_state_dict = _split_optim_state_dict(
model, optim, state_dict, info
)
else:
optim_state_dict = _unflatten_optim_state_dict(
optim, cast(Dict[str, ValueType], state_dict), info
)
else:
optim_state_dict = {}
if info.fsdp_modules:
# We need to specially handle FlatParameter FSDP as
# FlatParameter FSDP converts the FQNs.
for original_fqn, _ in model.named_parameters():
fqns = _get_fqns(model, original_fqn)
fqns_with_compiler = _get_fqns(
model, original_fqn, skip_compiler_prefix=False
)
if fqns == fqns_with_compiler:
continue
assert len(fqns) == 1
fqn = fqns.pop()
fqn_with_compiler = fqns_with_compiler.pop()
for g in optim_state_dict[_PG]:
val = cast(Dict[str, Any], g)
params = [
key.replace(fqn, fqn_with_compiler) for key in val[_PARAMS]
]
val[_PARAMS] = params
osd_state = cast(DictValueType, optim_state_dict[_STATE])
for k in list(osd_state.keys()):
if fqn in k:
osd_state[k.replace(fqn, fqn_with_compiler)] = osd_state.pop(k)
with info.fsdp_context():
optim_state_dict = FSDP.optim_state_dict_to_load(
model, optim, optim_state_dict
)
elif info.full_state_dict:
info.full_state_dict = False
local_state_dict = _get_optim_state_dict(model, (optim,), info)
info.full_state_dict = True
device = None
def _device(t):
if t.dim() > 0:
nonlocal device
if device is None:
device = t.device
elif device != t.device:
raise ValueError("Device mismatch")
return t
_ = tree_map_only(torch.Tensor, _device, local_state_dict)
assert device is not None
flatten_osd, osd_mapping = _flatten_state_dict(optim_state_dict)
flatten_local_osd, local_osd_mapping = _flatten_state_dict(local_state_dict)
if info.broadcast_from_rank0:
_broadcast_state_dict(flatten_osd, flatten_local_osd, device=device)
else:
_distribute_state_dict(flatten_osd, flatten_local_osd, device=device)
# The modifications listed seek to address the problem where optim might possess
# dissimilar parameters in comparison to optim_state_dict. This is achieved by
# incorporating differential parameters within local, which may result in optim
# having additional parameters ultimately.
for optim_key in flatten_osd.keys():
if optim_key not in flatten_local_osd:
assert optim_key in osd_mapping
flatten_local_osd[optim_key] = flatten_osd[optim_key]
local_osd_mapping[optim_key] = osd_mapping[optim_key]
optim_state_dict = _unflatten_state_dict(
flatten_local_osd, local_osd_mapping
)
# Note that we do not have to convert the FQN back to param id here if
# order in optim.param_groups[idx][_PARAMS] is the same as the one in
# optim_state_dict[_PG][idx][_PARAMS].
_state_dict_fn(optim, "load_state_dict")(state_dict=optim_state_dict)
[docs]def get_model_state_dict(
model: nn.Module,
*,
submodules: Optional[Set[nn.Module]] = None,
options: Optional[StateDictOptions] = None,
) -> Dict[str, ValueType]:
"""
Return the model state_dict of ``model``.
See ``get_state_dict`` for the detail usage.
Args:
model (nn.Module): the nn.Module to the model.
submodules (deprecated): Optional[Set[nn.Module]]: only return the model parameters
that belong to the submodules.
options (StateDictOptions): the options to control how
model state_dict and optimizer state_dict should be returned. See
`StateDictOptions` for the details.
Returns:
The state_dict for ``model``.
:rtype: typing.Dict[str, ValueType]
"""
with _gc_context():
info = _verify_options(
model,
(),
optim_only=False,
submodules=submodules,
options=options,
)
model_state_dict = _get_model_state_dict(model, info)
_verify_state_dict(model_state_dict, {}, info)
return model_state_dict
[docs]def get_optimizer_state_dict(
model: nn.Module,
optimizers: Union[torch.optim.Optimizer, Iterable[torch.optim.Optimizer]],
*,
submodules: Optional[Set[nn.Module]] = None,
options: Optional[StateDictOptions] = None,
) -> OptimizerStateType:
"""
Return the combined state_dict for optimizers.
See ``get_state_dict`` for the detail usage.
Args:
model (nn.Module): the nn.Module to the model.
optimizers (Union[None, Optimizer, Iterable[Optimizer]]):
The optimizers that are used to optimize ``model``.
submodules (deprecated): Optional[Set[nn.Module]]: only return the model parameters
that belong to the submodules.
options (StateDictOptions): the options to control how
model state_dict and optimizer state_dict should be returned. See
`StateDictOptions` for the details.
Returns:
The state_dict for ``optimizers``.
:rtype: OptimizerStateType
"""
with _gc_context():
optimizers = (
(optimizers,)
if isinstance(optimizers, torch.optim.Optimizer)
else tuple(optimizers)
)
info = _verify_options(
model,
optimizers,
optim_only=True,
submodules=submodules,
options=options,
)
optim_state_dict = _get_optim_state_dict(model, optimizers, info)
_verify_state_dict({}, optim_state_dict, info)
return optim_state_dict
[docs]def get_state_dict(
model: nn.Module,
optimizers: Union[torch.optim.Optimizer, Iterable[torch.optim.Optimizer]],
*,
submodules: Optional[Set[nn.Module]] = None,
options: Optional[StateDictOptions] = None,
) -> Tuple[Dict[str, ValueType], OptimizerStateType]:
"""
Return the model state_dict and optimizers state_dict.
``get_state_dict`` can process any module that is parallelized by PyTorch
FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any
combination of these parallelisms. The main functions of ``get_state_dict``
are: 1.) returning a model and optimizer state_dict that can be resharded
with a different number of trainers and/or different parallelisms.
2.) hiding the parallelism-specific state_dict APIs. Users don't have to call
these APIs.
3.) sanity checking the result state_dict.
The keys of the result state dictionary are the canonical FQNs (Fully
Qualified Names). A canonical FQN refers to the FQN based on a parameter's
position in an nn.Module hierarchy. More specifically, a canonical FQN to a
parameter is the FQN returned by ``module.named_parameters()`` or
``module.named_buffers()`` when the module is not distributed by any
parallelisms. Since the optimizer internally uses parameter IDs to represent
a parameter, there will be a conversion from the parameter IDs to the
canonical FQNs when calling this API.
``get_state_dict`` can also process a module that is not parallelized. In
such a case, ``get_state_dict`` only performs one function -- converting the
optimizer parameter IDs to the canonical FQNs.
Example:
>>> # xdoctest: +SKIP
>>> import torch
>>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
>>> from torch.nn.parallel import DistributedDataParallel as DDP
>>> from torch.distributed.checkpoint.state_dict import get_state_dict
>>> fsdp_model = FSDP(copy.deepcopy(model))
>>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
>>> ddp_model = DDP(copy.deepcopy(model))
>>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
>>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim)
>>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict(fsdp_model, fsdp_optim)
>>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(),
>>> # the asserts will fail.
>>> assert ddp_state_dict == fsdp_state_dict
>>> assert ddp_optim_state == fsdp_optim_state_dict
Args:
model (nn.Module): the nn.Module to the model.
optimizers (Union[None, Optimizer, Iterable[Optimizer]]):
The optimizers that are used to optimize ``model``.
submodules (deprecated): Optional[Set[nn.Module]]: only return the model parameters
that belong to the submodules.
options (StateDictOptions): the options to control how
model state_dict and optimizer state_dict should be returned. See
`StateDictOptions` for the details.
Returns:
``Tuple`` that contain model state_dict and optimizer state_dict.
:rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType]
"""
with _gc_context():
optimizers = (
(optimizers,)
if isinstance(optimizers, torch.optim.Optimizer)
else tuple(optimizers)
)
info = _verify_options(
model,
optimizers,
optim_only=False,
submodules=submodules,
options=options,
)
model_state_dict = _get_model_state_dict(model, info)
optim_state_dict = _get_optim_state_dict(model, optimizers, info)
_verify_state_dict(model_state_dict, optim_state_dict, info)
return model_state_dict, optim_state_dict
def _unflatten_model_state_dict(
model: nn.Module,
state_dict: Union[Dict[nn.Module, Dict[str, ValueType]], Dict[str, ValueType]],
) -> Dict[str, ValueType]:
if not state_dict:
return {}
if isinstance(next(iter(state_dict.keys())), nn.Module):
warnings.warn(
"Passing model_state_dict as a ``Dict[nn.Module, Dict[str, Any]]``"
"is deprecated and will be removed in 2.5. If you need this "
"feature, please preprocessing the model_state_dict to achieve the "
"same functionality.",
FutureWarning,
)
cast_state_dict = cast(Dict[nn.Module, Dict[str, ValueType]], state_dict)
new_state_dict: Dict[str, ValueType] = {}
for submodule, sub_state_dict in cast_state_dict.items():
for name, m in model.named_modules():
if m != submodule:
continue
fqns = _get_fqns(model, name)
assert len(fqns) == 1, "FQNs for a submodule should only have 1 element"
prefix = f"{next(iter(fqns))}."
new_state_dict.update(
{prefix + subfqn: value for subfqn, value in sub_state_dict.items()}
)
return new_state_dict
else:
return cast(Dict[str, ValueType], state_dict)
[docs]def set_model_state_dict(
model: nn.Module,
model_state_dict: Dict[str, ValueType],
*,
options: Optional[StateDictOptions] = None,
) -> _IncompatibleKeys:
"""Load the model state_dict.
The counterpart of ``get_model_state_dict`` to set the state_dict to the
model. See ``set_state_dict`` for the detail usage.
Args:
model (nn.Module): the nn.Module to the model.
model_state_dict: (Dict[str, ValueType]):
the model state_dict to load. If the key of the ``model_state_dict``
is nn.Module, the key is a submodule of ``model`` and the value should
be the state_dict of the submodule. When loading the state_dict,
the prefix of the submodule will be append to the state_dict.
options (StateDictOptions): the options to control how
model state_dict and optimizer state_dict should be loaded. See
`StateDictOptions` for the details.
Returns:
``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:
* **missing_keys** is a list of str containing the missing keys
* **unexpected_keys** is a list of str containing the unexpected keys
:type model_state_dict: typing.Dict[str, ValueType]
"""
model_state_dict: Dict[str, ValueType] = _unflatten_model_state_dict(
model, model_state_dict
)
with _gc_context():
info = _verify_options(model, (), optim_only=False, options=options)
_verify_state_dict(model_state_dict, {}, info)
return _load_model_state_dict(model, model_state_dict, info)
[docs]def set_optimizer_state_dict(
model: nn.Module,
optimizers: Union[torch.optim.Optimizer, Iterable[torch.optim.Optimizer]],
optim_state_dict: OptimizerStateType,
*,
options: Optional[StateDictOptions] = None,
) -> None:
"""Load the optimizers state_dict.
The counterpart of ``get_optimizer_state_dict`` to set the state_dict to the
optimizers. See ``set_state_dict`` for the detail usage.
Args:
model (nn.Module): the nn.Module to the model.
optimizers (Union[Optimizer, Iterable[Optimizer]]):
The optimizers that are used to optimize ``model``.
optim_state_dict: OptimizerStateType:
the optimizer state_dict to load.
options (StateDictOptions): the options to control how
model state_dict and optimizer state_dict should be loaded. See
`StateDictOptions` for the details.
Returns:
None
:type optim_state_dict: typing.OptimizerStateType
"""
with _gc_context():
optimizers = (
(optimizers,)
if isinstance(optimizers, torch.optim.Optimizer)
else tuple(optimizers)
)
info = _verify_options(model, optimizers, optim_only=True, options=options)
_verify_state_dict({}, optim_state_dict, info)
_load_optim_state_dict(model, optimizers, optim_state_dict, info)
[docs]def set_state_dict(
model: nn.Module,
optimizers: Union[torch.optim.Optimizer, Iterable[torch.optim.Optimizer]],
*,
model_state_dict: Dict[str, ValueType],
optim_state_dict: OptimizerStateType,
options: Optional[StateDictOptions] = None,
) -> _IncompatibleKeys:
"""Load the model state_dict and optimizers state_dict.
The counterpart of ``get_state_dict`` to set the state_dict to the model and
optimizers. The given ``model_state_dict`` and ``optim_state_dict`` do not
have to be returned by ``get_state_dict`` but must meet the following
requirements: 1) all FQNs are canonical FQNs as defined in ``get_state_dict``,
2) if a tensor is sharded, it must be either a ShardedTensor or DTensor,
3) optimizer state_dict cannot contain the parameter IDs; the keys should be
the canonical FQNs.
Args:
model (nn.Module): the nn.Module to the model.
optimizers (Union[Optimizer, Iterable[Optimizer]]):
The optimizers that are used to optimize ``model``.
model_state_dict: (Union[Dict[nn.Module, Dict[str, ValueType]], Dict[str, ValueType]]):
the model state_dict to load. If the key of the ``model_state_dict``
is nn.Module, the key is a submodule of ``model`` and the value should
be the state_dict of the submodule. When loading the state_dict,
the prefix of the submodule will be append to the state_dict.
optim_state_dict: OptimizerStateType:
the optimizer state_dict to load.
options (StateDictOptions): the options to control how
model state_dict and optimizer state_dict should be loaded. See
`StateDictOptions` for the details.
Returns:
``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:
* **missing_keys** is a list of str containing the missing keys of the model state_dict.
* **unexpected_keys** is a list of str containing the unexpected keys of the model state_dict.
:type model_state_dict: typing.Dict[str, ValueType]
:type optim_state_dict: typing.OptimizerStateType
"""
model_state_dict: Dict[str, ValueType] = _unflatten_model_state_dict(
model, model_state_dict
)
with _gc_context():
optimizers = (
(optimizers,)
if isinstance(optimizers, torch.optim.Optimizer)
else tuple(optimizers)
)
info = _verify_options(
model, optimizers, optim_only=not model_state_dict, options=options
)
_verify_state_dict(model_state_dict, optim_state_dict, info)
_load_optim_state_dict(model, optimizers, optim_state_dict, info)
return _load_model_state_dict(model, model_state_dict, info)
# TODO: correct the state_dict function signature.
# TODO: this API is not yet fully tested. Make it private
@no_type_check
def _patch_model_state_dict(
model: nn.Module,
*,
options: Optional[StateDictOptions] = None,
) -> None:
"""Patch the ``state_dict`` and ``load_state_dict`` attributes of ``model``.
Patch the ``state_dict`` and ``load_state_dict`` attributes of ``model`` to
be a partial function to call ``get_state_dict`` and ``set_state_dict``.
Example:
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.checkpoint.state_dict import patch_model_state_dict
model = fsdp(model)
patch_model_state_dict(model)
Args:
model (nn.Module): the nn.Module to the model.
options (StateDictOptions): the options to control how
model state_dict and optimizer state_dict should be loaded. See
`StateDictOptions` for the details.
Returns:
None
"""
_state_dict_call = functools.partial(
get_model_state_dict,
model=model,
options=options,
)
def state_dict_call():
return _state_dict_call()
model.state_dict = state_dict_call
_load_state_dict_call = functools.partial(
set_model_state_dict,
model=model,
options=options,
)
def load_state_dict_call(state_dict: Dict[str, Any]):
_load_state_dict_call(model_state_dict=state_dict)
model.load_state_dict = load_state_dict_call
_patched_state_dict.add(state_dict_call)
_patched_state_dict.add(load_state_dict_call)
# TODO: correct the load_state_dict function signature.
# TODO: this API is not yet fully tested. Make it private
@no_type_check
def _patch_optimizer_state_dict(
model: nn.Module,
*,
optimizers: Tuple[torch.optim.Optimizer, ...],
options: Optional[StateDictOptions] = None,
) -> None:
"""Patch the ``state_dict`` and ``load_state_dict`` attributes of ``optimizers``.
Patch the ``state_dict`` and ``load_state_dict`` attributes of ``optimizers`` to
be a partial function to call ``get_state_dict`` and ``set_state_dict``.
Note that if there are multiple optimizers, all of the optimizers will be patched.
So users only need to call one of the state_dict() to get the full result.
Example:
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.checkpoint.state_dict import patch_model_state_dict
model = fsdp(model)
patch_model_state_dict(model)
Args:
model (nn.Module): the nn.Module to the model.
options (StateDictOptions): the options to control how
model state_dict and optimizer state_dict should be loaded. See
`StateDictOptions` for the details.
Returns:
None
"""
_state_dict_call = functools.partial(
get_optimizer_state_dict,
model=model,
optimizers=optimizers,
options=options,
)
def state_dict_call():
return _state_dict_call()
_load_state_dict_call = functools.partial(
set_optimizer_state_dict,
model=model,
optimizers=optimizers,
options=options,
)
def load_state_dict_call(state_dict: Dict[str, Any]):
_load_state_dict_call(optim_state_dict=state_dict)
_patched_state_dict.add(state_dict_call)
_patched_state_dict.add(load_state_dict_call)
optimizers = (
(optimizers,)
if isinstance(optimizers, torch.optim.Optimizer)
else tuple(optimizers)
)
for optim in optimizers:
optim.state_dict = state_dict_call
optim.load_state_dict = load_state_dict_call
