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