o ߗiT@sUddlmZddlmZmZmZmZddlmZ ddl mm m Z ddlmm mZddlmZmZddlmZmZmZmZgZeeed<GdddZdS) )Future)AnyDictListOptionalN)MetadataSTATE_DICT_TYPE) LoadPlanner SavePlanner StorageReader StorageWriter__all__c@seZdZdZdddddddededeejd e d e d ee d ee fd dZ dedefddZdedefddZdeeefddfddZdS) _CheckpointeraThis base class specefies a high level API for saving and loading distributed `state_dict` 's. It provides an abstraction over the low-level APIs provided by :py:mod:`torch.distributed.checkpoint.storage`, essentially calling :py:meth: `torch.distributed.state_dict_saver.save` and :py:meth: `torch.distributed.state_dict_loader.load` with the provided storage readers and writers. .. warning:: This feature is experimental and subject to removal/change. NrF) process_groupcoordinator_rankno_dist load_planner save_plannerstorage_writerstorage_readerrrrrrcCs.||_||_||_||_||_||_||_dS)a{Initializes the Checkpointer instance. Args: storage_writer: Instance of StorageWrite use to perform writes. storage_reader: StorageReader used to load data from. process_group: ProcessGroup to be used for cross-rank synchronization. coordinator_rank: Rank to use to coordinate the checkpoint. rank0 is used by default. no_dist: If ``True``, distributed checkpoint will not load in SPMD style. (Default: ``False``) loader_planner: Instance of LoadPlanner to use when loading. save_planner: Instance of SavePlanner to use when saving. N)rrrrrrr)selfrrrrrrrrh/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_checkpointer.py__init__ s z_Checkpointer.__init__ state_dictreturncCs tj||j|j|j|j|jdS)ziCalls :py:meth: `torch.distributed.state_dict_saver.save`. Utilizing values passed during initialization.)rrrplanner)saversaverrrrrrrrrrr>sz_Checkpointer.savecCstj||j|j|jdS)z Calls :py:meth: `torch.distributed.state_dict_saver._async_save`. Utilizing values passed during initialization. Returns: Future: A future holding the resultant Metadata object from `save`. )rrr)r async_saverrrrrrrr Ls  z_Checkpointer.async_savecCstj||j|j|jddS)zjCalls :py:meth: `torch.distributed.state_dict_loader.load`. Utilizing values passed during initialization.)rrrN)loaderloadrrrrrrrr"]s  z_Checkpointer.load)__name__ __module__ __qualname____doc__r r rdist ProcessGroupintboolr r rrrrrr rstrrr"rrrrrsB   r)concurrent.futuresrtypingrrrrtorch.distributed distributedr'.torch.distributed.checkpoint.state_dict_loader checkpointstate_dict_loaderr!-torch.distributed.checkpoint.state_dict_saverstate_dict_saverr%torch.distributed.checkpoint.metadatarr$torch.distributed.checkpoint.storager r r r r r+__annotations__rrrrrs