o ?߱i1 @sddlZddlZddlZddlmmZddlmZddl m Z ddl m Z ddl mZmZmZddlmZmZddlmZddlmZmZmZd d d d ddd gd f d ed eedefddZddZ  dd efddZddededBfddZdS)N)FileSystemReader)S3StorageReader) instantiate) distributedlogmisc)get_config_moduleoverride)easy_io)DefaultLoadPlannerDistributedCheckpointer ModelWrapperz;cosmos_predict2/_src/predict2/configs/video2world/config.pyFToverride_cacheexperiment_optsskip_teacher_initc Cst|} t| } t| dd|g| } |rd| jjj_|dur/| jjjjr/d| jjj_| | t j |dd| j jjtjj_| j jjtjj_dtjj_tjjj_td|t| jdrt| jjdr| jjjd urd d lm} | jjjj}|rtj|d nd }| ||| jjjj d}||krtd||| jjj_| rt| jdrt| jjdrtdd| jj_!|sd| jj_"t #dt$| j}|%Wd n1swYt&d|t'|| ||||}|| fS)a experiment_name: experiment name s3_checkpoint_dir: s3 path to iteration_model s3_credential_path: s3 credential path, if None, use credential from config config_file: config file path enable_fsdp: enable fsdp load_ema_to_reg: load ema as regular model seed: random seed local_cache_dir: local cache directory, if None, do not cache override_cache: override cache, if True, override cache if local cache exists skip_teacher_init: if True, skip loading teacher checkpoint during inference (faster) z--z experiment=FT)seedby_rankzLoading model from configtext_encoder_configNr)cache_text_encoder_checkpoint text_encoderz../cosmos3_interactive_cache_ckpts/text_encoder)Z s3_ckpt_path cache_dirs3_credential_pathz&Using cached text encoder checkpoint: load_teacher_weightszTSetting load_teacher_weights=False for inference to skip teacher checkpoint downloadzinstantiate modelzLoading checkpoint from )(r importlib import_module make_configr modelremaenabledvalidatefreezerset_random_seedtrainercudnn deterministictorchbackends benchmark allow_tf32cudamatmulrinfohasattrrZ9cosmos_predict2._src.interactive.utils.text_encoder_cacher ckpt_pathospathjoinrrZfsdp_shard_sizetimerron_train_startprint%load_model_state_dict_from_checkpoint)experiment_names3_checkpoint_dir config_fileZ enable_fsdpload_ema_to_regZinstantiate_emarlocal_cache_dirrrr config_modulerrZoriginal_ckpt_pathZtext_encoder_cache_dirZcached_ckpt_pathrr=]/data/cameron/vidgen/cosmos-predict2.5/cosmos_predict2/_src/interactive/utils/model_loader.pyload_model_from_checkpoint#sZ            r?c Cs|s|Stdd|D}tdd|D}|rHtdi}|D]\}}|dr<|ddd}|||<q'|dsE|||<q'|S|rQtd|Std |S) a\ Process .pt checkpoint state dict to handle EMA and non-EMA variants. Consolidated .pt checkpoints can be in different formats: 1. Non-EMA checkpoint: Keys like 'net.xxx' - load directly 2. EMA-only checkpoint: Keys like 'net.xxx' (exported from EMA weights) - load directly 3. Full checkpoint with EMA: Has both 'net.xxx' and 'net_ema.xxx' keys When load_ema_to_reg=True: - If checkpoint has 'net_ema.xxx' keys, map them to 'net.xxx' for loading into regular model - If checkpoint only has 'net.xxx' keys (common for exported EMA checkpoints), load directly Args: state_dict: The loaded state dict from .pt file model: The model instance (used to get expected keys) load_ema_to_reg: Whether to load EMA weights into regular model Returns: Processed state dict ready for loading cs|]}|dVqdS)net_ema.N startswith.0kr=r=r> z1_process_pt_state_dict_for_ema..csr@)net.NrBrDr=r=r>rGrHzSProcessing EMA checkpoint: mapping net_ema.* keys to net.* for load_ema_to_reg=TruerArIrz8Loading EMA-exported checkpoint with net.* keys directlyz/Loading checkpoint without net./net_ema. prefix)anykeysrr-itemsrCreplace) state_dictrr:Zhas_net_ema_keysZ has_net_keysZmapped_state_dictkeyvaluenew_keyr=r=r>_process_pt_state_dict_for_emas(      rRcCs<|durt|}|drdnd}|dr0|dkr|}n|ddr(|}n tj|d}n|}dd lm}d } ||} | rit rat d | t j| d d d} t| ||} |j| d dt j|ddn|dkrt d|t rd|vrtj|dddd} nt j|d d d} d| vr| d} n d| vr| d} n| } t| ||} |j| d dt j|dd|durt rt d| t|| nFt d|t|j|jdd d}||}t||d}|}tj||td dd|||durt d| t|| t j|S)Nz.ptptdcpzs3:/z/modelrr)get_checkpoint_pathTz"Loading model cached locally from cpuF) map_location weights_only)strict)srczLoading .pt checkpoint from s3://s3zcredentials/s3_training.secret)backendr) backend_argsrNzCaching model state dict to zLoading DCP checkpoint from s3 ) callbacksZ disable_async)r:)allow_partial_load)storage_readerplanner)strendswithrCrstripr0r1r23cosmos_predict2._src.imaginaire.utils.checkpoint_dbrVris_rank0rr-r'loadrRload_state_dictsync_model_statesr dumprNr checkpointjobget_storage_readerr rTr r+ empty_cache)rrr8r:r;rcheckpoint_formatZcur_key_ckpt_full_pathrVZload_from_localZlocal_s3_ckpt_fprNZ pt_state_dict model_state checkpointerrbZ_model_wrapper _state_dictr=r=r>r6sv           r6checkpoint_pathcredential_pathcCs(d|vr|rt||d}|St|}|S)Nr\)rvr1)rr)rurvrbr=r=r>ro!s ro)FNF)N) rr0r'torch.distributed.checkpointrrmrT'torch.distributed.checkpoint.filesystemrZ:cosmos_predict2._src.imaginaire.checkpointer.s3_filesystemr+cosmos_predict2._src.imaginaire.lazy_configr%cosmos_predict2._src.imaginaire.utilsrr3cosmos_predict2._src.imaginaire.utils.config_helperrr -cosmos_predict2._src.imaginaire.utils.easy_ior Z1cosmos_predict2._src.interactive.checkpointer.dcpr r r boollistrdr?rRr6ror=r=r=r>sF        ]9 l