piFddlZddlZddlZddlmZddlmZddlmZddl m Z m Z m Z ddl mZddlZddlmZddlmZddlmZdd lmZdd lmZdd lmZdd lmZdd lm Z ddl!m"Z"ddl#m$Z$m%Z%ddl&m'Z'm(Z(m)Z)ddl*m+Z+ddl,m-Z-m.Z.ddl/m0Z0ddl1m2Z2m3Z3m4Z4gdZ5GddeZ6ede7 d2dede-de ejpde9d e:d!e e%d"e"fd#Z;e d$%e2dddddd$d&ded'e eeGd*d+Z?e d$%dddde6jddd$d,ded'e eTlog_exceptions checkpoint_idr2r6r3r5use_collectivesr>r?c ztjjd|xs,tj xstj }|rt jdt5ttt||d}tt||||||cdddS#1swYyxYw)a; Save a distributed model in SPMD style. This function is different from ``torch.save()`` as it handles ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), save will call ``state_dict`` before serialization. .. warning:: There is no guarantees of Backwards Compatibility across PyTorch versions for saved state_dicts. .. warning:: If using the `process_group` argument, make sure that only its ranks call `save_state_dict` and that all data in state_dict belong to it. .. note:: When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of the shard_group should be calling `save_state_dict` and the corresponding process group needs to be passed in. .. note:: If no process group is available, this function assumes the intention is to save the state_dict in the local process. .. note: Rank 0 is assumed to be the coordinator rank. Args: state_dict (Dict[str, Any]): The state_dict to save. checkpoint_id (Union[str, os.PathLike, None]): The ID of this checkpoint instance. The meaning of the checkpoint_id depends on the storage. It can be a path to a folder or to a file. It can also be a key if the storage is a key-value store. (Default: ``None``) storage_writer (Optional[StorageWriter]): Instance of StorageWriter used to perform writes. If this is not specified, DCP will automatically infer the writer based on the checkpoint_id. If checkpoint_id is also None, an exception will be raised. (Default: ``None``) planner (Optional[SavePlanner]): Instance of SavePlanner. If this is not specified, the default planner will be used. (Default: ``None``) process_group (Optional[ProcessGroup]): ProcessGroup to be used for cross-rank synchronization. (Default: ``None``) no_dist (bool): If ``True``, this function will assume the intent is to load a checkpoint on a single rank/process. (Default: ``False``) use_collectives (bool): If ``False``, this function will assume the intent is to save a checkpoint without using cross-rank synchronization. (Default: ``True``) This configuration is experimental and should be used with caution. It will change the format of the saved checkpoint and may not be backward compatible. Returns: Metadata: Metadata object for the saved checkpoint. Example: >>> # xdoctest: +SKIP >>> my_model = MyModule() >>> state_dict = {"model": my_model} >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( ... "/checkpoint/1" ... ) >>> torch.distributed.checkpoint.save( >>> state_dict=state_dict, >>> storage_writer=fs_storage_writer, >>> ) .. note:: save_state_dict uses collectives to coordinate writes 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()``. z!torch.distributed.checkpoint.savezptorch.distributed is disabled, unavailable or uninitialized, assuming the intent is to save in a single process.Freader)r1r2r3r5r6r?N)torch_C_log_api_usage_oncedist is_availableis_initializedwarningswarnrrrrr:_stateful_to_state_dict)r1r>r2r6r3r5r?s r/r r Us~ HH  !DEQd//11Q4;N;N;P7PG ~     >.-PUV  .z:)'+     s 16B11B:c2eZdZUdZeded<eded<y)r#a!This class contains futures for staging and upload completion. It is returned by async_save(). staging_completion is a future that indicates when local copy of state_dict is complete. upload_completion is a future that indicates when a checkpoint completed saving. Nstaging_completionupload_completion)r'r(r)r*r__annotations__r-r.r/r#r#st $d|#r.r#)r>r2r6r3async_checkpointer_type async_stagerr5r?rPrQc tjjdtjrJtj r6|xs t } tjd| jvsJd,|t|tr|nttddddttt||d}t!t#dd t$t&t(t(fffd } | } |t*j,k(r t/n t1} | j3| |||||| } t| t&rh| }t'}|fd t&t(d t&dfd}|j5s|j7|n|j9dt;|| St#dfd}|| S)a Asynchronous version of ``save``. This code first de-stages the state_dict on to the staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. .. warning:: This feature is experimental and subject to change. MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED Args: state_dict (Dict[str, Any]): The state_dict to save. checkpoint_id (Union[str, os.PathLike, None]): The ID of this checkpoint instance. The meaning of the checkpoint_id depends on the storage. It can be a path to a folder or to a file. It can also be a key if the storage is a key-value store. (Default: ``None``) storage_writer (Optional[StorageWriter]): Instance of StorageWriter used to perform 'stage' and 'save'. If this is not specified, DCP will automatically infer the writer based on the checkpoint_id. If checkpoint_id is also None, an exception will be raised. (Default: ``None``) planner (Optional[SavePlanner]): Instance of SavePlanner. If this is not specified, the default planner will be used. (Default: ``None``) process_group (Optional[ProcessGroup]): ProcessGroup to be used for cross-rank synchronization. (Default: ``None``) async_checkpointer_type (AsyncCheckpointerType): whether to do checkpoint in separate thread or process (Default: ``AsyncCheckpointerType.THREAD``) async_stager (AsyncStager): provides staging implementation. If storage_writer implements AsyncStager and async_stager is provided, async_stager will be used for staging no_dist (bool): If ``True``, this function will assume the intent is to save a checkpoint on a single rank/process. (Default: ``False``) use_collectives: If False, Save the checkpoint without rank coordination. (Default: ``True``) This configuration is experimental and should be used with caution. It will change the format of the saved checkpoint and may not be backward compatible. Returns: Future: A future holding the resultant Metadata object from `save`. Example: >>> # xdoctest: +SKIP >>> my_model = MyModule() >>> state_dict = {"model": my_model} >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( ... "/checkpoint/1" ... ) >>> checkpoint_future = torch.distributed.checkpoint.async_save( >>> state_dict=state_dict, >>> storage_writer=fs_storage_writer, >>> ) >>> >>> # ... do some work ... >>> >>> checkpoint_future.result() z'torch.distributed.checkpoint.async_savecpuzfA CPU backend must be enabled for async save; try initializing process group with 'cpu:gloo,cuda:nccl'NFrATr;r7c&jSN)stage)rQr1sr/stage_state_dictz$async_save..stage_state_dictAs!!*--r.r=original_staging_futurereturn_staging_futurec |j|jdy#t$r}|j|Yd}~yd}~wwxYwrU)result set_result Exception set_exception)rXrYes r/callbackzasync_save..callback[sB 7'..0%006 7%33A66 7s!$ AAA)rMrNc@jrjyyrU) should_synchronize_after_executesynchronize_staging)rQsr/maybe_synchronize_stagingz-async_save..maybe_synchronize_stagingps<<002=r.)rCrDrErFrGrHrdevice _device_types isinstancerrrrrrrKrrrr r"r,r r execute_savedoneadd_done_callbackr\r#)r1r>r2r6r3rPrQr5r?pgrWstaging_future_or_state_dictupload_executor upload_futurestaging_futurerYr`rds` ` r/r!r!sT HH  !JK t224  202 LL 2#3#3 3  u 3   %*^[*Q)L( L~nmERN)4Jt,.eF?$;_$LM.-.$4#5  #&;&C&C C -. 0 2 ,88$#%#'9M.75.4h3H 7%+O%< 7#)$< 7""$  , ,X 6 ! , ,T 2!4   4 0 3 1 3 "#r.ci}|jD]*\}}t|tr|jn|||<,|S)z]Creates a shallow copy of `state_dict` where `state_dict` is called for each Stateful object.)itemsrgrr1)r1stateful_state_dictkeyelems r/rKrKysM%%' T!+D(!;DOO  C   r.cNtjjdt|| | t Jdi}t ddx}||d<j |d<td i|fd} td i|fd} drjd| | n| } | | g} | dtd i|fd} td i|fd }rjd | |}|S| }||g}j|S) Nz,torch.distributed.checkpoint.save_state_dictr>r3cFJj}dtjjjvr2t j djjnj|jdtjjjvr)jjjnjjj}j|}|S)N storage_metazThe function definition for SavePlanner.set_up_planner has been updated to include the storage_meta argument. Please update your implementation to include this parameter.)r1rwis_coordinatorkwargs)rankr?) rwinspect signatureset_up_planner parametersrIrJrxset_up_storage_writerrzcreate_local_planprepare_local_plan)rw local_plandistWr6r1r2r?s r/ local_stepz$_save_state_dict..local_steps"""%224 !2!273I3I!J!U!U U MM.   " ":u/C/C D  " "%)$33 #    !E!EFQQ R  0 0$$ZZ / 1   0 01E1E F..0 #66zB r.cZJj|\}j|}|SrU)create_global_planprepare_global_plan)all_local_plansglobal_metadatar6r2s r/ global_stepz%_save_state_dict..global_steps<"""+2+E+Eo+V((<<_Mr.planrcJJj}j|}|j|jSrU) finish_plan write_datawaitvalue)final_local_plan all_writes central_planr6r2s r/rz$_save_state_dict..write_datasX"""'''"..|<#../?I !!r.c6Jj|S)N)metadataresults)finish) all_resultsrr2s r/finish_checkpointz+_save_state_dict..finish_checkpoints(*** Lr.writer-) rCrDrErrgetattrgrouprreduce_scatter all_reducebarrier)r1r2r3r4r5r6r? ckpt_kwargsckpt_idrrr global_planrrr write_resultsrrrs`` `` @@@r/r:r:sc HH  !OP G 5E FE$&   OK>?DAAN'. O$',{{ O$&+&'B&+&'(,L++FJ L )| &1:,&? "1~ &+&"'"&+&' ##GZ9JK O ,6< $m_5  Or.)NrFN)NrFNT)Dr{osrIconcurrent.futuresr dataclassesrenumrtypingrrrtyping_extensionsr rCtorch.distributed distributedrF#torch.distributed._state_dict_utilsr ,torch.distributed.checkpoint._async_executorr 4torch.distributed.checkpoint._async_process_executorr 3torch.distributed.checkpoint._async_thread_executorr +torch.distributed.checkpoint._storage_utilsr,torch.distributed.checkpoint.default_plannerr#torch.distributed.checkpoint.loggerr%torch.distributed.checkpoint.metadatar$torch.distributed.checkpoint.plannerrr$torch.distributed.checkpoint.stagingrrr%torch.distributed.checkpoint.statefulr$torch.distributed.checkpoint.storagerr"torch.distributed.distributed_c10drutilsrrr__all__r" FutureWarning ProcessGroupintboolrstrPathLiker r#r+r!rKr:r-r.r/rsF %!((( ?GKB:F ;KA88 D ! 26%)   ! 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