o
    ZhH                     @   s   U d dl mZ d dlmZmZ d dlmZ d dlm  m	  m
Z d dlm  m	  mZ d dlmZmZ d dlmZmZmZmZ g Zee ed< G dd dZdS )	    )Future)AnyOptionalN)MetadataSTATE_DICT_TYPE)LoadPlannerSavePlannerStorageReaderStorageWriter__all__c                   @   s   e Zd ZdZdddddddededeej d	e	d
e
dee dee fddZdedefddZdedefddZdeeef ddfddZdS )_Checkpointera  This 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.

    Nr   F)process_groupcoordinator_rankno_distload_plannersave_plannerstorage_writerstorage_readerr   r   r   r   r   c                C   s.   || _ || _|| _|| _|| _|| _|| _dS )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)r   r   r   r   r   r   r   )selfr   r   r   r   r   r   r    r   Y/var/www/auris/lib/python3.10/site-packages/torch/distributed/checkpoint/_checkpointer.py__init__    s   
z_Checkpointer.__init__
state_dictreturnc                 C   s    t j|| j| j| j| j| jdS )ziCalls :py:meth: `torch.distributed.state_dict_saver.save`. Utilizing values passed during initialization.)r   r   r   planner)saversaver   r   r   r   r   r   r   r   r   r   r   >   s   z_Checkpointer.savec                 C   s   t j|| j| j| jdS )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`.
        )r   r   r   )r   
async_saver   r   r   r   r   r   r   r   L   s   
z_Checkpointer.async_savec                 C   s   t j|| j| j| jd dS )zjCalls :py:meth: `torch.distributed.state_dict_loader.load`. Utilizing values passed during initialization.)r   r   r   N)loaderloadr   r   r   r   r   r   r   r    ]   s   
z_Checkpointer.load)__name__
__module____qualname____doc__r
   r	   r   distZProcessGroupintboolr   r   r   r   r   r   r   r   dictstrr   r    r   r   r   r   r      sB    	


r   )concurrent.futuresr   typingr   r   Ztorch.distributeddistributedr%   Z.torch.distributed.checkpoint.state_dict_loader
checkpointZstate_dict_loaderr   Z-torch.distributed.checkpoint.state_dict_saverZstate_dict_saverr   Z%torch.distributed.checkpoint.metadatar   r   Z$torch.distributed.checkpoint.storager   r   r	   r
   r   listr)   __annotations__r   r   r   r   r   <module>   s    