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from typing import Type
from torch import optim
from .functional_adagrad import _FunctionalAdagrad
from .functional_adam import _FunctionalAdam
from .functional_adamw import _FunctionalAdamW
from .functional_sgd import _FunctionalSGD
from .functional_adadelta import _FunctionalAdadelta
from .functional_rmsprop import _FunctionalRMSprop
from .functional_rprop import _FunctionalRprop
from .functional_adamax import _FunctionalAdamax
# dict to map a user passed in optimizer_class to a functional
# optimizer class if we have already defined inside the
# distributed.optim package, this is so that we hide the
# functional optimizer to user and still provide the same API.
functional_optim_map = {
optim.Adagrad: _FunctionalAdagrad,
optim.Adam: _FunctionalAdam,
optim.AdamW: _FunctionalAdamW,
optim.SGD: _FunctionalSGD,
optim.Adadelta: _FunctionalAdadelta,
optim.RMSprop: _FunctionalRMSprop,
optim.Rprop: _FunctionalRprop,
optim.Adamax: _FunctionalAdamax,
}
def register_functional_optim(key, optim):
"""
Interface to insert a new functional optimizer to functional_optim_map
``fn_optim_key`` and ``fn_optimizer`` are user defined. The optimizer and key
need not be of :class:`torch.optim.Optimizer` (e.g. for custom optimizers)
Example::
>>> # import the new functional optimizer
>>> # xdoctest: +SKIP
>>> from xyz import fn_optimizer
>>> from torch.distributed.optim.utils import register_functional_optim
>>> fn_optim_key = "XYZ_optim"
>>> register_functional_optim(fn_optim_key, fn_optimizer)
"""
if key not in functional_optim_map:
functional_optim_map[key] = optim
def as_functional_optim(optim_cls: Type, *args, **kwargs):
try:
functional_cls = functional_optim_map[optim_cls]
except KeyError:
raise ValueError(f"Optimizer {optim_cls} does not have a functional counterpart!")
return _create_functional_optim(functional_cls, *args, **kwargs)
def _create_functional_optim(functional_optim_cls: Type, *args, **kwargs):
return functional_optim_cls(
[],
*args,
**kwargs,
_allow_empty_param_list=True,
)
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