U Jºcã @s~ddlmZmZmZddlmZddlmZddl m Z ddl m Z ddl mZd eeefeeejeee dd œd d „ZdS) é)ÚAnyÚDictÚOptionalNé)Ú StorageReader)Ú LoadPlanner)ÚDefaultLoadPlanner)Ú _DistWrapperF)Ú state_dictÚstorage_readerÚ process_groupÚcoordinator_rankÚno_distÚplannerÚreturnc sjt|| |ƒ‰ˆdkrtƒ‰‡‡‡‡fdd„}‡‡fdd„}ˆ d||¡‰‡‡‡fdd„}ˆ d |¡} dS) aÑ Load a distributed state_dict in SPMD style. Each rank will try to read the least amount of data necessary to fullfill the requested `state_dict`. When loading ShardedTensor instances, each rank only reads data for their local shards. All tensors in ``state_dict`` must be allocated on their destination device prior to calling this function. All non-tensor data is loaded using `torch.load()` and modified in place on state_dict. Users must call `load_state_dict` on the root module to ensure load pos-processing and non-tensor data properly propagates. This function can be used for local inference and load a checkpoint produced by ``save_state_dict`` without having a process group initialized by passing ``no_dist=True`` and by using Tensors instead of ShardedTensors. Args: state_dict (Dict[str, Any]) : The state_dict to load. Note that this state dict will updated in places. storage_reader (StorageReader): StorageReader used to load data from. process_group (ProcessGroup): ProcessGroup to be used for cross-rank synchronization coordinator_rank (int): Rank to use to coordinate the checkpoint, rank0 is used by default no_dist (bool): Don't attempt to load in SPMD style. Default to False Returns: None. Examples >>> # xdoctest: +SKIP >>> my_model = MyModule() >>> optimizer = Adagrad(my_model.parameters()) >>> model_state_dict = my_model.state_dict() >>> fs_storage_loader = torch.distributed._shard.checkpoint.FileSystemLoader("/checkpoint/1") >>> torch.distributed._shard.checkpoint.load_state_dict( >>> state_dict=model_state_dict, >>> storage_reader=fs_storage_loader, >>> ) >>> # module.load_state_dict() function might have customized steps >>> # to flush the state_dict, must call it to >>> # ensure correct behavior. >>> my_model.load_state_dict(model_state_dict) .. note:: load_state_dict uses collectives to coordinate reads across ranks. For NCCL-based process groups, internal tensor representations of objects must be moved to the GPU device before communication takes place. In this case, the device used is given by ``torch.cuda.current_device()`` and it is the user's responsibility to ensure that this is set so that each rank has an individual GPU, via ``torch.cuda.set_device()`` NcsHˆdk s t‚ˆ ¡}ˆ ˆ|ˆj¡ˆ |ˆj¡ˆ ¡}ˆ |¡}|S©N)ÚAssertionErrorZ read_metadataÚinitZis_coordinatorZcreate_local_planZprepare_local_plan)ÚmetadataZ local_plan)ÚdistWrr r ©úY/tmp/pip-unpacked-wheel-gikjz4vx/torch/distributed/_shard/checkpoint/state_dict_loader.pyÚ local_stepSs  z#load_state_dict..local_stepcs$ˆdk s t‚ˆ |¡}ˆ |¡}|Sr)rZcreate_global_planZprepare_global_plan)Zall_local_plans)rr rrÚ global_step]s   z$load_state_dict..global_stepZplancs.ˆdk s t‚ˆ ˆ¡}ˆ |ˆ¡}| ¡dSr)rZ finish_planÚ read_dataÚwait)Zfinal_local_planZ all_reads)Ú central_planrr rrres    z"load_state_dict..read_dataÚread)r rZreduce_scatterZ all_gather) r r r r rrrrrÚ_r)rrrr r rÚload_state_dict sA r)NrFN)ÚtypingrrrZtorch.distributedZ distributedÚdistZstoragerrrZdefault_plannerrÚutilsr ÚstrZ ProcessGroupÚintÚboolrrrrrÚs$     ú ù