sgdZddlZddlZddlmZddlmZmZddlmZddl m Z ddl m Z dd l mZdd lmZdd lmZdd lmZdd lmZddlmZddlmZej65ej8dej:deddddeej@drddl!m"Z"ddl#m$Z$ddl%m&Z&gdZ'y#1swY3xYw)aW :mod:`torch.distributed.optim` exposes DistributedOptimizer, which takes a list of remote parameters (:class:`~torch.distributed.rpc.RRef`) and runs the optimizer locally on the workers where the parameters live. The distributed optimizer can use any of the local optimizer :ref:`optimizer-algorithms` to apply the gradients on each worker. N)optim)_apply_optimizer_in_backward_get_in_backward_optimizers)_FunctionalAdadelta)_FunctionalAdagrad)_FunctionalAdam)_FunctionalAdamax)_FunctionalAdamW)_FunctionalRMSprop)_FunctionalRprop)_FunctionalSGD)_NamedOptimizer)as_functional_optimalwaysz`TorchScript` support for functional optimizers is deprecated and will be removed in a future PyTorch release. Consider using the `torch.compile` optimizer instead.) stacklevel _rpc_init)DistributedOptimizer)PostLocalSGDOptimizer)ZeroRedundancyOptimizer)rrrr)(__doc__warningstorchrapply_optimizer_in_backwardrrfunctional_adadeltarfunctional_adagradrfunctional_adamr functional_adamaxr functional_adamwr functional_rmspropr functional_rpropr functional_sgdrnamed_optimizerrutilsrcatch_warnings simplefilterwarnDeprecationWarninghasattr_C optimizerrpost_localSGD_optimizerrzero_redundancy_optimizerr__all__S/var/www/html/venv/lib/python3.12/site-packages/torch/distributed/optim/__init__.pyr3s 52,0.2.*,&XH(#HMM @   588[!/:> 's &(CC