U 9%e6@sUddlZddlZddlmZddlmZmZmZmZm Z m Z m Z m Z m Z ddlZddlmZddlmZddlmZddlmZgZe eed<eeZGdd d ejZe eed d d ZdS) N)deepcopy) AnyCallable CollectionDictListMappingOptionalUnionoverload)optim) ShardedTensor)FullyShardedDataParallel__all__c@sJeZdZdZdeeeeje ffe j e e eeefe ejddddZddZeeefdd d Zed ddd d dZeegefed ddZd!e egefe ed ddZeeejefdddZeeefddddZeeefddddZddddZeeefdddZeeefdddZdS)"_NamedOptimizera ``_NamedOptimizer`` takes a dict of parameters and exposes ``state_dict`` by parameter key. We replace the original key (number) in an optim to the fully qualified name (FQN) string. User can initialize the optim as they initialize a PyTorch optim, the only difference is that they also need to pass in the FQN of each parameters. Args: named_parameters (Mapping[str, Union[torch.Tensor, ShardedTensor]]): Mapping from FQN to parameter. optimizer_class (optim.Optimizer): The class of optimizer to instantiate. param_groups (Collection[Mapping[str, Any]]): `param_groups` to pass to optimizer if specified. The key of the inner map needs to be FQNs. Default: None module (nn.Module): the module whose parameters to updated by the optimizer. args: arguments to pass to the optimizer constructor. kwargs: arguments to pass to the optimizer constructor. Example:: >>> # xdoctest: +SKIP("distributed") >>> from torch import optim >>> from torch.distributed.optim import _NamedOptimizer >>> >>> # Define the named optimizer. >>> m = Model(...) >>> named_optim = _NamedOptimizer(m.named_parameters(), optim.SGD) >>> # Forward pass + backward pass. >>> named_optim.step() >>> ... >>> # Call state_dict for the named optimizer returns a FQN state_dict. >>> named_optim.state_dict() Warning: This API is still in development and subject to change. TODO: Add tutorial for _NamedOptimizer. TODO: Add documentation in the docstring for the public attributes like self.param_groups and self.named_parameters. N)named_parametersoptimizer_class param_groupsmodulereturnc Ostjd||_|t||_|dkr6|jn|}||f|||_||_ |dkrlt |j |_ nft ddd|jD}g} |D]8} | dD]*} | |krtd| d| || qq| |_ |jj|_dS)Nz'torch.distributed.optim._NamedOptimizerzvSince we pass in param_groups, we will use param_groups to initialize the optimizer, not all parameters of the module.cSsi|]\}}||qSr.0keyparamrrf/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torch/distributed/optim/named_optimizer.py [sz,_NamedOptimizer.__init__..paramszExpect param name z% found in param group but is missing.)torchZ_CZ_log_api_usage_oncer_param_groups_checkdictrvalues _optimizerrlistkeysordered_param_keyswarningswarnitems ValueErrorappend) selfrrrrargskwargsZparams_for_optimizer param_to_keyr%grouprrrr__init__>s<    z_NamedOptimizer.__init__cCs|jdk r|jD]x}t|ts&tdd|ks6td|d}t|tjrP|g}t|}|D]"}t|tjs\tdt|q\||d<qdS)Nparam group must be a dictrz#param group must contain key paramsz>optimizer can only optimize Tensors, but one of the params is ) r isinstancer AssertionErrorrTensorr# TypeErrortypename)r+ param_grouprrrrrrhs     z#_NamedOptimizer._param_groups_check)rc sj}|d}fdd|dD}g}|D]b}g}|dD]}|j|qDdt|i}|D]\} } | dkrnt| || <qn||q4||dS)z Return the ``state_dict`` of the optimizer. Instead of using number to index parameters, we will use module fully qualified name (FQN) as the key. rcsi|]\}}j||qSr)r%)rZst_key state_valr+rrrsz._NamedOptimizer.state_dict..stater)r:r)r" state_dictr(r*r%sortedr_post_state_dict) r+r;rZ ret_stateZ ret_groupsr/ param_keysrZ ret_groupkvrr9rr;ys       z_NamedOptimizer.state_dict.)closurercCsdSNrr+rArrrstepsz_NamedOptimizer.stepcCsdSrBrrCrrrrDscCs|jj|dS)z Performs a single optimization step. This will call :meth:`torch.optim.Optimizer.step` on the wrapped optimizer. rA)r"rDrCrrrrDscCs|jjSrB)r"r:r9rrrr:sz_NamedOptimizer.state)r;rc Cs|j}||}|d}|d}t|dkr8tdt|jD]l\}}||krZqBt||t||krtdt||d|dt||||D]\}}|||krtd|d|d|||} t |t rnt | t st t| } t| } | | krZ new_group_mapZ new_groupZ group_keyZ src_groupr?rrrrNs    $            z_NamedOptimizer.load_state_dict)r7rcCst|tstd|d}t|tjr2|g|d<n t||d<dd|jD}|dD]$}||krntd|j ||qZ|j ||j j |_ dS)z Add a param group to the :class:`_NamedOptimizer` s `param_groups`. Warning: This API is still in development and subject to change. r1rcSsi|]\}}||qSrrrrrrr!sz3_NamedOptimizer.add_param_group..z%some parameters are not in the moduleN)r2r r3rr4r#rr(r)r%r*r"add_param_groupr)r+r7rr.rrrrrQs     z_NamedOptimizer.add_param_groupcCs>|jD]"}|jr t|}tj||_q |jdddS)z Runs a dummy optimizer step, which allows to initialize optimizer state because we do lazy init for most optimizers. This allows doing in-place loading of optimizer state from a checkpoint. NrE) rr!Z requires_gradrZ zeros_likeZautogradVariableZgradrD)r+rtrrr init_state+s  z_NamedOptimizer.init_statecCs&t|jtr"tj|j|j|ddS|S)NT)Zis_named_optimizer)r2rFSDPZoptim_state_dict_to_loadr"r+r;rrrrH9s z$_NamedOptimizer._pre_load_state_dictcCs"t|jtrt|j|j||SrB)r2rrUZoptim_state_dictr"rVrrrr=Bs z _NamedOptimizer._post_state_dict)NN).)N) __name__ __module__ __qualname____doc__rstrr rr4r r Optimizerr rrnnModuler0rrr;r rDrfloatpropertyr:rNrQrTrHr=rrrrrs0. *" k r)r>rcCsdt|S)zQ Concatenate all param keys as a unique indentifier for one param group. rF)joinr<)r>rrrrMJsrM) loggingr&copyrtypingrrrrrrr r r rZtorch.nnr]r Z'torch.distributed._shard.sharded_tensorr Ztorch.distributed.fsdprrUrr[__annotations__ getLoggerrWloggerr\rrMrrrrs ,     9