ȅi) DSSKrSSKrSSKJr SSKJr SSKrSSKJr SSK J r SSK J r Jr SSKJr SSKJrJr SSKJrJrJrJrJr SS KJrJrJr SS KJr SS K J!r! SS K"J#r# SS K$J%r% SSK&J'r' /SQr("SS\%5r)"SS\5r*S\+\RX-S\+\RX-4Sjr-S\+\RX-S\+\RX-4Sjr.\/S:XGa"SS\5r0\Rb"5r2\2RgS\+S\0Vs/sHoRhPM sn\0RjS9 \2RgS\+SS 9 \2RgS!\+S"S 9 \2Rm5r7\8"S#\7RrS$\7RtS%\7RvS&35 S'\7RrS(3r<\7Rv\0RjRh:XaQ\RzR}\7Rr5(a\."\7Rr\7Rt5 g\8"\<5 g\7Rv\0R~Rh:XaQ\RzR\7Rr5(a\-"\7Rr\7Rt5 g\8"\<5 g\A"S)\7Rv35egs snf)*N)Enum)cast)narrow_tensor_by_index)FileSystemReaderFileSystemWriter)flatten_state_dict)_EmptyStateDictLoadPlannerDefaultLoadPlanner)MetadataSTATE_DICT_TYPE STORAGE_TYPESTensorPropertiesTensorStorageMetadata) LoadItemTypeLoadPlan LoadPlanner)_create_chunk_list)_load_state_dict)_save_state_dict) StorageReader)Future)dcp_to_torch_savetorch_save_to_dcpBroadcastingTorchSaveReaderDynamicMetaLoadPlannerc2\rSrSrSrSS\\R-S-S\SS4Sjjr S\ 4Sjr S \ S \ S\S4S jrS \ S \SS4SjrS \ S\ 4SjrS\\ S\\ 4SjrSS\\R-S-SS4Sjjr\S\\R-S\4Sj5rSrg)r'a StorageReader for reading a Torch Save file. This reader will read the entire checkpoint on the coordinator rank, and then broadcast and shard each tensor to all ranks. . N.B. Intended to be used with DynamicMetaLoadPlanner .. 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" >>> ) N checkpoint_idcoordinator_rankreturncXlX lgN)rr)selfrrs c/home/james-whalen/.local/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py__init__$BroadcastingTorchSaveReader.__init__;s + 0c[0S9$)zGExtends the default StorageReader to support building the metadata filestate_dict_metadata)r )r#s r$ read_metadata)BroadcastingTorchSaveReader.read_metadataDsB//r'planplannerc [[U5nUR(aWURc [ S5e[ R "URSSS9nUR(a [U5up4OSnURGHnUR[R:Xa9[SURRS[U5RS35eUR(aF[ R"R%5nX5RRR'U5nO7[ R("UR*URR5n[ R,"XpR.SS 9 [1XuR2UR45nUR7U5R95nUR;5UR;5:Xd:[ S URS UR;5S UR;535eUR=U5 UR?XX5 GM [A5n U RCS5 U $) z Reads torch save data on the coordinator rank, and broadcast afterwards this incurrs a communication cost, but avoids having to load the entire checkpoint on each rank, hopefully preventing OOM issues Nz-checkpoint_id must be set before reading datacpuF) map_location weights_onlyNon-tensor value identified at . At this time  only supports loading Tensors.)srcasync_opzreq z mismatch sizes, z vs )"rr is_coordinatorrAssertionErrortorchloadritemstyperBYTE_IO RuntimeError storage_indexfqn__name__distdistributed_c10d_get_pg_default_deviceto empty_like state_dict broadcastrrstorage_offsetslengthsresolve_tensordetachsizecopy_ commit_tensorr set_result) r#r-r.torch_state_dict_req pg_devicetensor target_tensorfuts r$ read_data%BroadcastingTorchSaveReader.read_dataJs )73   !!)$%TUU$zz""U  ))&89I&J# # ::Cxx<///"5c6G6G6K6K5LM$$(J$7$7#88WY "" 11HHJ )*;*;*?*?@CCIN))'*<*>@M %%'6;;=8$3,,-->$))+,DA    '  ! !# 536h t r'metadatar8cX lUR(aQ[R"5UR:Xd.[ SURS[R"535eUR c [ S5eg)*Implementation of the StorageReader methodz$Coordinator rank mismatch: expected z, got Nz:checkpoint_id must be set before setting up storage reader)r8rCget_rankrr9r)r#r[r8s r$set_up_storage_reader1BroadcastingTorchSaveReader.set_up_storage_readersz,   ==?d&;&;;$:4;P;P:QR==?+-    % L  &r'cU$r])r#r-s r$prepare_local_plan.BroadcastingTorchSaveReader.prepare_local_plans r' global_plancU$rbrc)r#rfs r$prepare_global_plan/BroadcastingTorchSaveReader.prepare_global_plansr'cXlg)r]N)r)r#rs r$reset!BroadcastingTorchSaveReader.resets*r'c@[RRU5$rb)ospathisfile)clsrs r$validate_checkpoint_id2BroadcastingTorchSaveReader.validate_checkpoint_idsww~~m,,r')rrr8)Nrr")rB __module__ __qualname____firstlineno____doc__strrnPathLikeintr%r r+rrrrYboolr_rdlistrhrk classmethodrr__static_attributes__rcr'r$rr's*37 !1R[[(4/11  10x0 4h444n h  QU xHtH~$x.+3#4t#;+t+-33D---r'rc N^\rSrSrSrS S\S\S-S\SS4U4SjjjrS r U=r $) ra` Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, avoiding the need to read metadata from disk. This is useful when reading formats which don't have a metadata file, like Torch Save files. . N.B. Intended to be used with BroadcastingTorchSaveReader .. warning:: Current implementation only supports loading Tensors. >>> # xdoctest: +SKIP("undefined vars") >>> sd = {"mode": model} >>> dcp.load( >>> sd, >>> storage_reader=BroadcastingTorchSaveReader(), >>> planner=DynamicMetaLoadPlanner(), >>> checkpoint_id="path_to_model.pt" >>> ) NrHr[r8r c z>[TU]XU5 0nURR5H|upV[R "U5(d%[ SUS[U5RS35e[[URS9UR5[U55XE'M~ [US9Ulg)zdSetups of the planner, extnding default behavior by creating the Metadata object from the state dictr3r4r5)dtyper)N)superset_up_plannerrHr<r: is_tensorr?r=rBrrrrNrr r[)r#rHr[r8r*keyrV __class__s r$r%DynamicMetaLoadPlanner.set_up_planners z^D8:??002KC??6**"5cU;$$(J$7$7#88WY (= v||4 "6*(  $3!5HI r')r[)NF) rBrtrurvrwr r r{rr~ __classcell__)rs@r$rrsJ.%)$ J#JT/J J  JJr'rdcp_checkpoint_dirtorch_save_pathcn0n[U[U5[5SS9 [R"X!5 g)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 r:save)rrsds r$rrs4B '(:;*,   JJr#r'cV[R"USS9n[U[U5SS9 g)a& Given the location of a torch save file, converts it into a DCP checkpoint. Args: torch_save_path: Filename of the Torch save file. dcp_checkpoint_dir: Directory to store the DCP checkpoint. .. warning:: To avoid OOM, it's recommended to only run this function on a single rank. F)r2T)storage_writerrN)r:r;rr)rrrHs r$rrs-O%@J#34F#GQUr'__main__c\rSrSrSrSrSrg) FormatModei torch_to_dcp dcp_to_torchrcN)rBrtrurv TORCH_TO_DCP DCP_TO_TORCHr~rcr'r$rrs % % r'rmodezConversion mode)r=helpchoicesdefaultr6zPath to the source model)r=rdstzPath to the destination modelzConverting checkpoint from z to z using method: ''zNo checkpoint found at z. Skipping conversion.zUnknown conversion mode: )Bargparsernenumrtypingrr:torch.distributed distributedrCtorch.distributed._shard._utilsrtorch.distributed.checkpointrr)torch.distributed.checkpoint._nested_dictr,torch.distributed.checkpoint.default_plannerr r %torch.distributed.checkpoint.metadatar r r rr$torch.distributed.checkpoint.plannerrrr,torch.distributed.checkpoint.planner_helpersr.torch.distributed.checkpoint.state_dict_loaderr-torch.distributed.checkpoint.state_dict_saverr$torch.distributed.checkpoint.storager torch.futuresr__all__rrrxryrrrBrArgumentParserparser add_argumentvaluer parse_argsargsprintr6rrcheckpoint_missing_warningrorprisdir ValueError)ms0r$rsT  BKHUTKKJ>  x--x-v+J/+J\$bkk)$2;;&$22;;&bkk). z&T&  $ $ &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