§ קg&ãó—ddlZddlZddlmZddlmZmZmZmZddl m Z m Z m Z ddl mZmZmZmZddlmZgd¢Zed¬ ¦«Gd „d ¦«¦«ZGd „d ej¦«ZGd„dej¦«ZdS)éN)Ú dataclass)ÚAnyÚListÚOptionalÚUnion)ÚMetadataÚ MetadataIndexÚ StorageMeta)ÚLoadPlanÚ LoadPlannerÚSavePlanÚ SavePlanner)ÚFuture)Ú WriteResultÚ StorageWriterÚ StorageReaderT)Úfrozencó.—eZdZUeed<eed<eed<dS)rÚindexÚ size_in_bytesÚ storage_dataN)Ú__name__Ú __module__Ú __qualname__r Ú__annotations__Úintr©óú`/var/www/html/ai-engine/env/lib/python3.11/site-packages/torch/distributed/checkpoint/storage.pyrrs6€€€€€€à ÐÐÑàÐÐÑØÐÐÑÐÐrrc óè—eZdZdZejddeeej dfddfd„¦«Z ejde ddfd„¦«Z ejde de fd „¦«Zejd ee dee fd „¦«Zejde d edeeefd „¦«Zejdedeeeddfd„¦«Zeejdeeej fde fd„¦«¦«Zdeefd„ZdS)ra8 Interface used by ``save_state_dict`` to write to storage. One StorageWriter instance acts as both the coordinator and the follower in a distributed checkpoint. As part of initialization, each instance is told its role. A subclass should expect the following sequence of calls. 0) (all ranks) set checkpoint_id if users pass a valid checkpoint_id. 1) (all ranks) set_up_storage_writer() 2) (all ranks) prepare_local_plan() 3) (coordinator) prepare_global_plan() 4) (all ranks) write_data() 5) (coordinator) finish() NÚ checkpoint_idÚreturncó—dS)a© Calls to indicates a brand new checkpoint write is going to happen. A checkpoint_id may be present if users set the checkpoint_id for this checkpoint write. The meaning of the checkpiont_id is storage-dependent. It can be a path to a folder/file or a key for a key-value storage. Args: 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``) Nr©Úselfr!s rÚresetzStorageWriter.reset-ó €ð ˆrÚis_coordinatorcó—dS)z± Initialize this instance. Args: is_coordinator (bool): Whether this instance is responsible for coordinating the checkpoint. Nr)r%r(s rÚset_up_storage_writerz#StorageWriter.set_up_storage_writer?ó€€€rÚplancó—dS)a‰ Perform storage-specific local planning. While this method can produce a completely different plan, the recommended way is to store storage specific data in SavePlan::storage_data. Args: plan (SavePlan): The local plan from the ``SavePlanner`` in use. Returns: A transformed ``SavePlan`` after storage local planning Nr©r%r,s rÚprepare_local_planz StorageWriter.prepare_local_planIr+rÚplanscó—dS)aÌ Perform centralized planning of storage. This method is only called on the coordinator instance. While this method can produce a completely different plan, the preferred way is to store storage specific data in SavePlan::storage_data. Args: plans: A list of ``SavePlan`` instances, one for each rank. Returns: A list of transformed ``SavePlan`` after storage global planning Nr©r%r0s rÚprepare_global_planz!StorageWriter.prepare_global_planXr+rÚplannercó—dS)a Write all items from ``plan`` using ``planner`` to resolve the data. A subclass should call ``SavePlanner::resolve_data`` on each item from the plan to get access to the underlying object to write. Subclasses should lazily call `resolve_data` as it can allocate memory. In case of tensors, make following assumptions: - They might be on any device, including not matching the one on ``WriteItem::tensor_data`` - They might be views or not contiguous. Only the projection needs to be saved. Args: plan (SavePlan): The save plan to execute. planner (SavePlanner): Planner object to be used to resolve items to data. Returns: A future that completes to a list of WriteResult Nr©r%r,r4s rÚ write_datazStorageWriter.write_datair+rÚmetadataÚresultscó—dS)a½ Write the metadata and marks the current checkpoint as successful. The actual format/schema used for serializing `metadata` is an implementation detail. The only requirement is that it's recoverable in to the same object graph. Args: metadata (Metadata): metadata for the new checkpoint results: A list of WriteResults from all ranks. Returns: None Nr)r%r8r9s rÚfinishzStorageWriter.finishr+rcó—dS©zŒ Check if the given checkpoint_id is supported by the stroage. This allow us to enable automatic storage selection. Nr©Úclsr!s rÚvalidate_checkpoint_idz$StorageWriter.validate_checkpoint_id’ó €ð ˆrcó—dS)a= Return the storage-specific metadata. This is used to store additional information in a checkpoint that can be useful for providing request-level observability. StorageMeta is passed to the ``SavePlanner`` during save calls. Returns None by default. TODO: provide an example Nr©r%s rÚ storage_metazStorageWriter.storage_meta›s €ðˆtr©N)rrrÚ__doc__ÚabcÚabstractmethodrÚstrÚosÚPathLiker&Úboolr*r r/rr3rrrr7rr;Ú classmethodr@rr rDrrrrrsò€€€€€ððð" Ôð ð  5¨¨b¬k¸4Ð)?Ô#@ð ÈDð ð ð ñÔð ð" Ôð °Dð ¸Tð ð ð ñÔð ð Ôð   xð  °Hð  ð  ð  ñÔð  ð Ô𠨨h¬ð ¸DÀ¼Nð ð ð ñÔð ð  Ôð Øð Ø'2ð à [Ô!Ô "ð ð ð ñÔð ð. Ôð ˜xð °$°t¸KÔ7HÔ2Ið Èdð ð ð ñÔð ð ØÔð °5¸¸b¼kÐ9IÔ3Jð Ètð ð ð ñÔñ„[ð ð˜h {Ô3ððððððrrcó¨—eZdZdZejddeeej dfddfd„¦«Z ejde fd„¦«Z ejde de ddfd „¦«Zejd edefd „¦«Zejd eedeefd „¦«Zejd edededfd„¦«Zeejdeeej fde fd„¦«¦«ZdS)raV Interface used by ``load_state_dict`` to read from storage. One StorageReader instance acts as both the coordinator and the follower in a distributed checkpoint. As part of initialization, each instance is told its role. A subclass should expected the following sequence of calls by ``load_state_dict``: 0) (all ranks) set checkpoint_id if users pass a valid checkpoint_id. 1) (all ranks) read_metadata() 2) (all ranks) set_up_storage_reader() 3) (all ranks) prepare_local_plan() 4) (coordinator) prepare_global_plan() 5) (all ranks) read_data() Nr!r"có—dS)a± Calls to indicates a brand new checkpoint read is going to happen. A checkpoint_id may be present if users set the checkpoint_id for this checkpoint read. The meaning of the checkpiont_id is storage-dependent. It can be a path to a folder/file or a key for a key-value storage. Args: 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 more like a key-value store. (Default: ``None``) Nrr$s rr&zStorageReader.reset¸r'rcó—dS)z Read the checkpoint metadata. Returns: The metadata object associated with the checkpoint being loaded. NrrCs rÚ read_metadatazStorageReader.read_metadataÊr+rr8r(có—dS)zî Initialize this instance. Args: metadata (Metadata): The metadata schema to use. is_coordinator (bool): Whether this instance is responsible for coordinating the checkpoint. Nr)r%r8r(s rÚset_up_storage_readerz#StorageReader.set_up_storage_readerÔr+rr,có—dS)a† Perform storage-specific local planning. While this method can produce a completely different plan, the recommended way is to store storage specific data in LoadPlan::storage_data. Args: plan (LoadPlan): The local plan from the ``LoadPlan`` in use. Returns: A transformed ``LoadPlan`` after storage local planning Nrr.s rr/z StorageReader.prepare_local_planßr+rr0có—dS)aÔ Perform centralized planning of storage loading. This method is only called on the coordinator instance. While this method can produce a completely different plan, the preferred way is to store storage specific data in LoadPlan::storage_data. Args: plans: A list of ``LoadPlan`` instances, one for each rank. Returns: A list of transformed ``LoadPlan`` after storage global planning Nrr2s rr3z!StorageReader.prepare_global_planîr+rr4có—dS)a« Read all items from ``plan`` using ``planner`` to resolve the data. A subclass should call ``LoadPlanner::load_bytes`` to deserialize a BytesIO object into the right place. A subclass should call ``LoadPlanner::resolve_tensor`` to get access to the tensors that in should load data into. It's the StorageLayer responsibility to properly schedule any cross device copies required. Args: plan (LoadPlan): The local plan to execute on planner (LoadPlanner): The planner object to use to resolve items. Returns: A future that completes once all reads are finished. Nrr6s rÚ read_datazStorageReader.read_dataÿr+rcó—dSr=rr>s rr@z$StorageReader.validate_checkpoint_idrArrE)rrrrFrGrHrrIrJrKr&rrQrLrSr r/rr3r rrWrMr@rrrrr¦s¹€€€€€ððð" Ôð ð  5¨¨b¬k¸4Ð)?Ô#@ð ÈDð ð ð ñÔð ð" Ôð ˜xð ð ð ñÔð ð Ôð ¨hð Èð ÐQUð ð ð ñÔð ð Ôð   xð  °Hð  ð  ð  ñÔð  ð Ô𠨨h¬ð ¸DÀ¼Nð ð ð ñÔð ð  Ôð ˜hð °ð ÀÈÄð ð ð ñÔð ð*ØÔð °5¸¸b¼kÐ9IÔ3Jð Ètð ð ð ñÔñ„[ð ð ð rr)rGrJÚ dataclassesrÚtypingrrrrÚ%torch.distributed.checkpoint.metadatarr r Ú$torch.distributed.checkpoint.plannerr r r rÚ torch.futuresrÚ__all__rÚABCrrrrrúr`sðØ € € € Ø € € € Ø!Ð!Ð!Ð!Ð!Ð!Ø-Ð-Ð-Ð-Ð-Ð-Ð-Ð-Ð-Ð-Ð-Ð-àVÐVÐVÐVÐVÐVÐVÐVÐVÐVððððððððððððð !Ð Ð Ð Ð Ð ð <Ð ;Ð ;€ð €$ÐÑÔðððððñôñÔððHðHðHðHðHC”GñHôHðHðVv ðv ðv ðv ðv C”Gñv ôv ðv ðv ðv r