o ?߱i;a@sxUddlmZddlZddlZddlmZmZmZmZddl Z ddl m Z ddl m Z mZmZmZmZer=ddlmZmZedde jd dd DZd ed <ed kreddlmZddlmZmZnedkree jdree jdrddl mZddl mZmZGdddZ!Gdddeddee"fdee"fdeee"ee#ee#ffgZ$Gddde!Z%d$d"d#Z&dS)%) annotationsN) TYPE_CHECKINGList NamedTupleTuple)ImaginaireModel)callback distributedlogmisc object_store)CheckpointConfig JobConfigccs|]}t|VqdSN)int).0xr\/data/cameron/vidgen/cosmos-predict2.5/cosmos_predict2/_src/imaginaire/utils/checkpointer.py sr.zTuple[int, ...] TORCH_VERSION) ) quantization)FakeQuantizeBase ObserverBaserrrc@seZdZdZd5dd Zd6ddZedd7d8dd Zed! d7d8d"d#Z ed$ % % %d9d:d)d*Z d;d,d-Z dd3d4Zd%S)? Checkpointerz^The checkpointer class. Supports checkpoint saving/loading to both local disk or object store.config_checkpointr config_jobr callbackscallback.CallBackGroupcCs||_|jd|_|jd|_|jj|_|jj|_|j|_|j p#d|_ |j |_ |j |_ d|_ |jr:t |j|_|jrFt |j|_dSdS)zConstructor of the checkpointer. Args: config_checkpoint (CheckpointConfig): The config object for the checkpointer. z /checkpointsN)r# path_localcheckpoint_dir_localpathcheckpoint_dir_object_storesave_to_object_storeenabledload_from_object_store strict_resume load_pathload_training_stateonly_load_scheduler_state save_threadr ObjectStoreobject_store_saverobject_store_loader)selfr!r"r#rrr__init__.s   zCheckpointer.__init__modelr optimizertorch.optim.Optimizer scheduler$torch.optim.lr_scheduler.LRScheduler grad_scalertorch.amp.GradScaler iterationrreturnNonecCs|j||d|dd}tdkrVt|||||d}tj|dd}|jj||d|j r<|j t j |j rD|jn|jd ||tfd |_ |j |jjd |d d S) Save network weights, optimizer parameters, scheduler parameters to a checkpoint. Args: model (ImaginaireModel): The PyTorch model. optimizer (torch.optim.Optimizer): The model optimizer. scheduler (torch.optim.lr_scheduler.LRScheduler): The optimization scheduler. grad_scaler (torch.amp.GradScaler): The gradient scaler (for mixed precision training). iteration (int): Current iteration number. iter_09.ptrr6r7r9r;r=cpudevice state_dictFtargetdaemonargsN)r6r=)r#on_save_checkpoint_startr get_rankdictrIr toon_save_checkpointr0join threadingThreadr)_save_worker_object_store_save_worker_localstarton_save_checkpoint_end)r4r6r7r9r;r=checkpoint_filerIrrrsaveFs*    zCheckpointer.savezcheckpoint saving (local)rrIdict[str, torch.Tensor]rZstrrankc Cstj|j|}tj|jddz-t|||dkr ||t d|t | dd dd}|j j |dWd StyX}ztd |WYd }~d Sd }~ww) a`Worker to save checkpoint to local disk, spawned with a child thread (runs in parallel with the training). Args: state_dict (dict[str, torch.Tensor]): The state dict of the model/optimizer/scheduler. checkpoint_file (str): The file name of the model checkpoint. rank (int): GPU device (default: 0). T)exist_okrzSaved checkpoint (local): rArCr=z#Checkpoint failed to save (local): N)osr'rSr&makedirstorchr[_write_latest_checkpoint_filer successrreplacer#on_save_checkpoint_success Exception exceptionr4rIrZr^checkpoint_pathr=errrrWts   zCheckpointer._save_worker_localz checkpoint saving (object store)c Cstj|j|}z0|jj||dd|dkr||td|t | dd dd}|j j |dWd St yS}ztd |WYd }~d Sd }~ww) a_Worker to upload checkpoint to object store, spawned with a child thread (in parallel with the training). Args: state_dict (dict[str, torch.Tensor]): The state dict of the model/optimizer/scheduler. checkpoint_file (str): The file name of the model checkpoint. rank (int): GPU device (default: 0). rdkeytyperz!Saved checkpoint (object store): rAr`rCraz,Checkpoint failed to upload (object store): N)rbr'rSr(r2 save_objectrer rfrrgr#rhrirjrkrrrrVs  z&Checkpointer._save_worker_object_storecheckpoint loadingNtorch.optim.Optimizer | None+torch.optim.lr_scheduler.LRScheduler | Nonetorch.amp.GradScaler | Nonec Cs|j||}|dur#|jr|jn|j}tj||}d}d} n|j r0|j }|j }|j } nd}d}d} |dur| ||jr[t d||jj|dd} t d|nt d|tj|d d dd } t d ||jj|| d t d|j| d|jd|s| r| d} |sJt d|| d| |_nd} |r|sJt d|| dt d|| dt d| dn t dnd} t dtj|jj|| |d| S)SLoad network weights and optimizer states from a checkpoint in a single process. The priority of the checkpoint loading logic is: 1. Attempt to resume training if possible by looking for latest_checkpoint.txt under the same name. 2. If no latest checkpoint were found, it loads the model weights specified by config_checkpoint.path. - This is typically used for inference mode. - If config_checkpoint.load_optimizer_state is True, then also load the optimizer and scheduler states. 3. If none of the above, randomly initialize the model parameters and train from scratch. Args: model (ImaginaireModel): The PyTorch model. optimizer (torch.optim.Optimizer | None): The model optimizer (default: None). scheduler (torch.optim.lr_scheduler.LRScheduler | None): The optimization scheduler (default: None). grad_scaler (torch.amp.GradScaler | None): The gradient scaler (for mixed precision training). Returns: iteration (int): the iteration number to start/resume from. NTF#Loading checkpoint (object store): rdrn,Complete loading checkpoint (object store): Loading checkpoint (local): cS|Srrstoragelocrrrz#Checkpointer.load..) map_location weights_only%Complete loading checkpoint (local): rH- Loading the model...r6strictr=- Loading the scheduler...r9r- Loading the optimizer...r7 - Loading the gradient scaler...r;,Done with loading the checkpoint (iteration ).!Done with loading the checkpoint.Training from scratch.)r=rl)r#on_load_checkpoint_start_read_latest_checkpoint_filer+r(r&rbr'rSr-r.r/_check_checkpoint_existsr infor3 load_objectrfrdloadon_load_checkpointload_state_dictr, last_epochcuda empty_cacheon_load_checkpoint_end) r4r6r7r9r;latest_checkpoint_filecheckpoint_dirrlresumeZonly_resume_schedulerrIr=rrrrs^         zCheckpointer.load str | NonecCspd}|jr tj|jd}|jj|dr|jj|dd}|Stj|j d}tj |r6t | }|S)zGet the file name of the latest saved checkpoint. If it doesn't exist, return None. Returns: checkpoint_file (str | None): file name of the latest saved checkpoint. Nlatest_checkpoint.txtrotextrn) r+rbr'rSr(r3 object_existsrstripr&isfileopenread)r4rZ latest_pathrrrrs z)Checkpointer._read_latest_checkpoint_filecCs|d}|jrtj|jd}|jj||dddStj|jd}t|d}| |WddS1s9wYdS)zTrack the file name of the latest saved checkpoint. Args: checkpoint_file (str): file name of the latest saved checkpoint.  rrrnwN) r)rbr'rSr(r2rqr&rwrite)r4rZcontentrfilerrrre s   "z*Checkpointer._write_latest_checkpoint_filerlcCsD|jr|jj|dstd|dStj|s td|dS)zIf the file checkpoint_path does not exist, raise an error. Args: checkpoint_path (str): full path to the checkpoint. rzFile not found (object store): zFile not found (local): N)r+r3rFileNotFoundErrorrbr'exists)r4rlrrrrs z%Checkpointer._check_checkpoint_existscCs|jr |jdSdS)zFinalize the checkpointer.N)r0rS)r4rrrfinalize&szCheckpointer.finalize)r!r r"rr#r$ r6rr7r8r9r:r;r<r=rr>r?)r)rIr\rZr]r^rr>r?NNN r6rr7rsr9rtr;rur>r)r>r)rZr]r>r?)rlr]r>r?)r>r?)__name__ __module__ __qualname____doc__r5r[r timerrWrVrrrerrrrrrr +s$  . Y   r c@s eZdZdS)_IncompatibleKeysN)rrrrrrrr,s rIncompatibleKeys missing_keysunexpected_keysincorrect_shapesc@s2eZdZdd dZed   ddddZdS)MultiRankCheckpointerr6rr7r8r9r:r;r<r=rr>r?c Cs|\}}}d|d|d} tt|} | D]J} t| krbt|||||d} tj| dd} |j j || d|j rH|j t j|jrP|jn|jd| | tfd |_ |j qd S) r@rArBrCrDrErFrHFrJN)get_ckpt_postfixlistranger rOrPrIr rQr#rRr0rSrTrUr)rVrWrX) r4r6r7r9r;r=postfix_ total_ema_numrZZ save_ranks_rankrIrrrr[:s0     zMultiRankCheckpointer.saverrNrsrtrucCs|}|dur+|\}}}|d|d}|jr|jn|j} tj| |} d} n|j rE|j } |\}}}| d|d} |j } nd} d} | dur| | |jrnt d| |jj| dd} t d| nt d | tj| d d d } t d | |jj|| dt dt |j| d|jd| r| d} |r|sJt d|| dt d|| d| |_t d|| dt d| dnd} t dnd} t dtj| S)rvNrCTFrwrdrnrxrycSrzrrr{rrrr~rz,MultiRankCheckpointer.load..)rrrHrr6rr=rr7rr9rr;rrrrr)rrrgr+r(r&rbr'rSr-r.rr rr3rrfrdrr#rcriticalrr,rrr)r4r6r7r9r;rrrrrrlrrIr=rrrresX         zMultiRankCheckpointer.loadrrr)rrrr[r rrrrrrr9s +rr6torch.nn.Modulecheckpoint_state_dictrPr>c CsV|}g}t|D]}||vrd|vr td|dq ||}tdkr1t|tjj j r1q t|tj sKt d|dt |dt ||dt|j}t||j}||krtdkohttd ohttd }|rddd} ttf} | ||} t| | rq ||||f||q |j|dd} dd| jD} dd| jD}t| ||dS)N _extra_statez Skipping key z; introduced by TransformerEngine for FP8 in the checkpoint.rzFind non-tensor parameter z in the model. type:  z2, please check if this key is safe to skip or not.rrr6rror]r>cSs.|ddd}|}|D]}t||}q |S)Nr)splitgetattr)r6ro key_parts cur_moduleZkey_partrrr_get_module_for_keys  z2non_strict_load_model.._get_module_for_keyFrcSg|]}d|vr|qSrrrkrrr z)non_strict_load_model..cSrrrrrrrrr)rrr)r6rror]r>r)rIrkeysr warningr isinstancerdnn parameterUninitializedParameterTensor ValueErrorrptupleshapehasattrrrrappendpoprrrr)r6rmodel_state_dictrr model_param shape_modelZshape_checkpointZhas_observer_base_classesrZ cls_to_skipZ target_moduleZ incompatiblerrrrrnon_strict_load_modelsR "      r)r6rrrPr>r)' __future__rrbrTtypingrrrrrd%cosmos_predict2._src.imaginaire.modelr%cosmos_predict2._src.imaginaire.utilsrr r r r &cosmos_predict2._src.imaginaire.configr rr __version__rr__annotations__Ztorch.aortorch.ao.quantizationrrrZtorch.quantizationr r]rrrrrrrrsF *