sg( TddlZddlZddlmZddlmZmZmZmZm Z ddl Z ddl m Z ddlmZddlmZmZddlmZddlmZmZddlmZmZmZmZmZdd lmZm Z m!Z!dd l"m#Z#dd l$m%Z%dd l&m'Z'dd l(m)Z)ddl*m+Z+gdZ,Gdde)Z-GddeZ.de e/ej`fde e/ej`ffdZ1de e/ej`fde e/ej`ffdZ2e3dk(rGddeZ4ejjZ6e6jode/de4Dcgc]}|jpc}e4jre6jode/d e6jod!e/d" e6juZ;e>> # xdoctest: +SKIP("undefined vars") >>> sd = {"mode": model} >>> dcp.load( >>> sd, >>> storage_reader=BroadcastingTorchSaveReader(), >>> planner=DynamicMetaLoadPlanner(), >>> checkpoint_id="path_to_model.pt" >>> ) N checkpoint_idcoordinator_rankreturnc ||_||_yN)r!r")selfr!r"s \/var/www/html/venv/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py__init__z$BroadcastingTorchSaveReader.__init__;s + 0ctiS)zGExtends the default StorageReader to support building the metadata filestate_dict_metadata)r)r&s r' read_metadataz)BroadcastingTorchSaveReader.read_metadataCsB//r)planplannerc tt|}|jrK|jJt j |jdd}|j rt |\}}nd}|jD]}|jtjk(r9td|jjdt|jd|jrGtj j#}||jjj%|}n6t j&|j(|jj}tj*||j,dt/||j0|j2}|j5|j7}|j9|j9k(s6Jd |jd |j9d |j9|j;||j=||t?} | jAd| S) z Reads torch save data on the coordinator rank, and broadcast afterwards this incurrs a communication cost, but avoids having to load the entire checkpoint on each rank, hopefully preventing OOM issues NcpuF) map_location weights_onlyNon-tensor value identified at . At this time  only supports loading Tensors.)srcasync_opzreq z mismatch sizes, z vs )!rris_coordinatorr!torchloadr itemstyperBYTE_IO RuntimeError storage_indexfqn__name__distdistributed_c10d_get_pg_default_deviceto empty_like state_dict broadcastr"r storage_offsetslengthsresolve_tensordetachsizecopy_ commit_tensorr set_result) r&r.r/torch_state_dict_req pg_devicetensor target_tensorfuts r' read_dataz%BroadcastingTorchSaveReader.read_dataIs )73   %%1 11$zz""U  ))&89I&J# !# :: 6Cxx<///"5c6G6G6K6K5LM$$(J$7$7#88WY "" 11HHJ )#*;*;*?*?@CCIN))'*<*>@M %%'6;;=8 s(()): %%'(V[[]O= 8    '  ! !#} 5/ 62h t r)metadatar9c||_|jr#tj|jk(sJ|jJy*Implementation of the StorageReader methodN)r9rCget_rankr"r!)r&rZr9s r'set_up_storage_readerz1BroadcastingTorchSaveReader.set_up_storage_reader|s?,   ==?d&;&;; ;;!!---r)c|Sr])r&r.s r'prepare_local_planz.BroadcastingTorchSaveReader.prepare_local_plans r) global_planc|Srarb)r&rds r'prepare_global_planz/BroadcastingTorchSaveReader.prepare_global_plansr)c||_yr\)r!)r&r!s r'resetz!BroadcastingTorchSaveReader.resets *r)c@tjj|Sra)ospathisfile)clsr!s r'validate_checkpoint_idz2BroadcastingTorchSaveReader.validate_checkpoint_idsww~~m,,r))Nrr%)rB __module__ __qualname____doc__rrstrrjPathLikeintr(rr-rrrrYboolr_rcrrfrh classmethodrnrbr)r'rr's *<@ !1c2;;&6 7811  10x0 1h111f.h..QU.xHtH~$x.+5bkk4)?#@+D+-5bkk9I3J-t--r)rc @eZdZdZ ddedeededdffd ZxZ S) ra Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, avoiding the need to read metadata from disk. This is useful when reading formats which don't have a metadata file, like Torch Save files. . N.B. Intended to be used with BroadcastingTorchSaveReader .. warning:: Current implementation only supports loading Tensors. >>> # xdoctest: +SKIP("undefined vars") >>> sd = {"mode": model} >>> dcp.load( >>> sd, >>> storage_reader=BroadcastingTorchSaveReader(), >>> planner=DynamicMetaLoadPlanner(), >>> checkpoint_id="path_to_model.pt" >>> ) NrHrZr9r#c |t||||i}|jjD]z\}}t j |s%t d|dt|jdtt|j|jt|||<|t||_y)zdSetups of the planner, extnding default behavior by creating the Metadata object from the state dictr4r5r6)dtyper+N)superset_up_plannerrHr<r: is_tensorr?r=rBrrryrNrrrZ)r&rHrZr9r,keyrV __class__s r'r{z%DynamicMetaLoadPlanner.set_up_planners z8^D8:??002 KC??6*"5cU;$$(J$7$7#88WY (= v||4 "6*(  $ !5HI r))NF) rBrorprqrrrrur{ __classcell__)r~s@r'rrsK.(,$ J#J8$J J  JJr)rdcp_checkpoint_dirtorch_save_pathcti}t|t|tdtj||y)aq Given a directory containing a DCP checkpoint, this function will convert it into a Torch save file. Args: dcp_checkpoint_dir: Directory containing the DCP checkpoint. torch_save_path: Filename to store the converted Torch save file. .. warning:: To avoid OOM, it's recommended to only run this function on a single rank. T)storage_readerr/no_distN)rr r r:save)rrsds r'rrs6B '(:;*,   JJr?#r)c`tj|d}t|t|dy)aB Given the location of a torch save file, converts it into a DCP checkpoint. Args: torch_save_path: Filename of the Torch save file. dcp_checkpoint_dir: Directory to store the DCP checkpoint. .. warning:: To avoid OOM, it's recommended to only run this function on a single rank. F)r3T)storage_writerrN)r:r;rr )rrrHs r'rrs-O%@J#34F#GQUr)__main__ceZdZdZdZy) FormatMode torch_to_dcp dcp_to_torchN)rBrorp TORCH_TO_DCP DCP_TO_TORCHrbr)r'rrs % % r)rmodezConversion mode)r=helpchoicesdefaultr7zPath to the source model)r=rdstzPath to the destination modelzConverting checkpoint from z to z using method: ''zNo checkpoint found at z. Skipping conversion.zUnknown conversion mode: )Fargparserjenumrtypingrrrrrr:torch.distributed distributedrCtorch.distributed._shard._utilsr torch.distributed.checkpointr r )torch.distributed.checkpoint._nested_dictr ,torch.distributed.checkpoint.default_plannerr r%torch.distributed.checkpoint.metadatarrrrr$torch.distributed.checkpoint.plannerrrr,torch.distributed.checkpoint.planner_helpersr.torch.distributed.checkpoint.state_dict_loaderr-torch.distributed.checkpoint.state_dict_saverr$torch.distributed.checkpoint.storager torch.futuresr__all__rrrrrsrrrBrArgumentParserparser add_argumentvaluer parse_argsargsprintr7rrcheckpoint_missing_warningrkrlrisdir ValueError)ms0r'rse 44 BKHUTKKJ>  l--l-^+J/+J\$c2;;./$3 +,$23 +,c2;;./. z&T& %X $ $ &F   ",-Q-''  C.HI C.MN    D %dhhZtDHH:=Mdii[XYZ "$((+AB yyJ++111 77>>$(( # dhh 1 , - j--33 3 77== " dhh 1 , -4TYYK@AAI.sJ%