^j) 4ddlZddlZddlmZddlmZddlZddlmZ ddl m Z ddl m Z mZddlmZddlmZmZddlmZmZmZmZmZdd lmZmZmZdd lmZdd l m!Z!dd l"m#Z#dd l$m%Z%ddl&m'Z'gdZ(Gdde%Z)GddeZ*de+ejXzde+ejXzfdZ-de+ejXzde+ejXzfdZ.e/dk(rGddeZ0ejbZ2e2jgde+de0Dcgc]}|jhc}e0jje2jgde+d e2jgd!e+d" e2jmZ7e8d#e7jrd$e7jtd%e7jvd&d'e7jrd(Z>> # 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)rr)selfrrs t/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py__init__z$BroadcastingTorchSaveReader.__init__;s + 0ctiS)zGExtends the default StorageReader to support building the metadata filestate_dict_metadata)r )r"s r# read_metadataz)BroadcastingTorchSaveReader.read_metadataDsB//r%planplannerc tt|}|jrT|j t dt j |jdd}|jrt|\}}nd}|jD]}|jtjk(r9td|jjdt|jd|jrGt j"j%}||jjj'|}n6t j(|j*|jj}t!j,||j.d t1||j2|j4}|j7|j9}|j;|j;k(s:t d |jd |j;d |j;|j=||j?||tA} | jCd| S) z Reads torch save data on the coordinator rank, and broadcast afterwards this incurrs a communication cost, but avoids having to load the entire checkpoint on each rank, hopefully preventing OOM issues Nz-checkpoint_id must be set before reading datacpuF) map_location weights_onlyNon-tensor value identified at . At this time  only supports loading Tensors.)srcasync_opzreq z mismatch sizes, z vs )"rr is_coordinatorrAssertionErrortorchloadritemstyperBYTE_IO RuntimeError storage_indexfqn__name__distdistributed_c10d_get_pg_default_deviceto empty_like state_dict broadcastrrstorage_offsetslengthsresolve_tensordetachsizecopy_ commit_tensorr set_result) r"r*r+torch_state_dict_req pg_devicetensor target_tensorfuts r# read_dataz%BroadcastingTorchSaveReader.read_dataJs )73   !!)$%TUU$zz""U  ))&89I&J# !# :: 6Cxx<///"5c6G6G6K6K5LM$$(J$7$7#88WY "" 11HHJ )#*;*;*?*?@CCIN))'*<*>@M %%'6;;=8$3,,-->$))+,DA    '  ! !#} 53 66h t r%metadatar5c||_|jrNtj|jk(s-t d|jdtj|j t dy)*Implementation of the StorageReader methodz$Coordinator rank mismatch: expected z, got Nz:checkpoint_id must be set before setting up storage reader)r5r@get_rankrr6r)r"rWr5s r#set_up_storage_readerz1BroadcastingTorchSaveReader.set_up_storage_readers|,   ==?d&;&;;$:4;P;P:QR==?+-    % L  &r%c|SrY)r"r*s r#prepare_local_planz.BroadcastingTorchSaveReader.prepare_local_plans r% global_planc|Sr]r^)r"r`s r#prepare_global_planz/BroadcastingTorchSaveReader.prepare_global_plansr%c||_y)rYN)r)r"rs r#resetz!BroadcastingTorchSaveReader.resets *r%c@tjj|Sr])ospathisfile)clsrs r#validate_checkpoint_idz2BroadcastingTorchSaveReader.validate_checkpoint_idsww~~m,,r%)Nrr!)r? __module__ __qualname____doc__strrfPathLikeintr$r r)rrrrVboolr[r_listrbrd classmethodrjr^r%r#rr's*37 !1R[[(4/11  10x0 4h444n h  QU xHtH~$x.+3#4t#;+t+-33D---r%rc @eZdZdZ ddededzdeddffd ZxZS) ra Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, avoiding the need to read metadata from disk. This is useful when reading formats which don't have a metadata file, like Torch Save files. . N.B. Intended to be used with BroadcastingTorchSaveReader .. warning:: Current implementation only supports loading Tensors. >>> # xdoctest: +SKIP("undefined vars") >>> sd = {"mode": model} >>> dcp.load( >>> sd, >>> storage_reader=BroadcastingTorchSaveReader(), >>> planner=DynamicMetaLoadPlanner(), >>> checkpoint_id="path_to_model.pt" >>> ) NrErWr5rc |t||||i}|jjD]z\}}t j |s%t d|dt|jdtt|j|jt|||<|t||_y)zdSetups of the planner, extnding default behavior by creating the Metadata object from the state dictr0r1r2)dtyper'N)superset_up_plannerrEr9r7 is_tensorr<r:r?rrrvrKrr rW)r"rErWr5r(keyrS __class__s r#rxz%DynamicMetaLoadPlanner.set_up_planners z8^D8:??002 KC??6*"5cU;$$(J$7$7#88WY (= v||4 "6*(  $ !5HI r%)NF) r?rkrlrmr r rqrx __classcell__)r{s@r#rrsJ.%)$ J#JT/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)rrr r7save)r}r~sds 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)r/T)storage_writerrN)r7r8rr)r~r}rEs r#rrs-O%@J#34F#GQUr%__main__ceZdZdZdZy) FormatMode torch_to_dcp dcp_to_torchN)r?rkrl TORCH_TO_DCP DCP_TO_TORCHr^r%r#rrs % % r%rmodezConversion mode)r:helpchoicesdefaultr3zPath 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: )Bargparserfenumrtypingrr7torch.distributed distributedr@torch.distributed._shard._utilsrtorch.distributed.checkpointrr)torch.distributed.checkpoint._nested_dictr,torch.distributed.checkpoint.default_plannerr r %torch.distributed.checkpoint.metadatar r r rr$torch.distributed.checkpoint.plannerrrr,torch.distributed.checkpoint.planner_helpersr.torch.distributed.checkpoint.state_dict_loaderr-torch.distributed.checkpoint.state_dict_saverr$torch.distributed.checkpoint.storager torch.futuresr__all__rrrnrorrr?rArgumentParserparser add_argumentvaluer parse_argsargsprintr3rrcheckpoint_missing_warningrgrhrisdir ValueError)ms0r#rsN  BKHUTKKJ>  x--x-v+J/+J\$bkk)$2;;&$22;;&bkk). 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