o ?߱i@sTddlmZddlZddlmZmZGdddZGdddZGdd d eZ dS) )OptionalN) distributedlogc@seZdZ         ddeded eed ed eed eed ededededefddZeddZ e j ddZ ddZ ddZ dS)TeroPolySchedulerNQ@?rpoly? total_Mimg batch_sizeref_Mimg ref_batches max_lr_ratio min_lr_ratio rampup_Mimg rampdown_Mimgverbosity_intervalformulapoly_expc Cs`||_|t|_|p||d|_||_||_||_||_||_ | |_ | |_ | |_ d|_ dS)N.A)r rget_world_sizer r r rrrrrrr_model) selfr r r r rrrrrrrra/data/cameron/vidgen/cosmos-predict2.5/cosmos_predict2/_src/imaginaire/functional/lr_scheduler.py__init__s zTeroPolyScheduler.__init__cCs|jSNr)rrrrmodel4szTeroPolyScheduler.modelcCs ||_dSrr)rrrrrr8s cKst|jddd}|jdkrd}n|jdkr#t||jd|j }n td|jd |jdur7t||j}|j durBt||j }|j dkrS||j krS|||j 9}|j dkrj||j |j krj||j ||j 9}|S) NZsample_counterrrconstantrrg:0yE>zInvalid learning rate formula "") getattrrrmaxr r ValueErrorrminrrrr )rnkwargsZcur_Mimglrrrrschedule<s      zTeroPolyScheduler.schedulecK|j|fi|Srr(rr%r&rrr__call__RzTeroPolyScheduler.__call__) NrrNrrrrr ) __name__ __module__ __qualname__intrfloatstrrpropertyrsetterr(r,rrrrrsN       rc@s2eZdZdZd ddZddZddZd d Zd S) LambdaWarmUpCosineSchedulera A learning rate scheduler that combines warm-up with a cosine decay schedule for multiple cycles. It supports different configurations for each cycle, including the number of warm-up steps, minimum and maximum scaling factors for the learning rate. The scheduler is intended to be used with a base learning rate of 1.0, where the actual learning rate at any step is the base learning rate multiplied by the scaling factor computed by the scheduler. Parameters: warm_up_steps (list[int]): List of integers where each element represents the number of warm-up steps for the corresponding cycle. f_min (list[float]): List of the minimum scaling factors for each cycle after warm-up. f_max (list[float]): List of the maximum scaling factors at the start and end of each cosine cycle. f_start (list[float]): List of starting scaling factors for each warm-up phase. cycle_lengths (list[int]): List of the total lengths of each cycle, including warm-up steps. verbosity_interval (int, optional): Interval of training steps at which to print current step and scaling factor information. Set to 0 by default to disable verbosity. Examples: >>> scheduler = LambdaWarmUpCosineScheduler2( warm_up_steps=[10, 10], f_min=[0.1, 0.1], f_max=[1.0, 1.0], f_start=[0.01, 0.01], cycle_lengths=[50, 50], verbosity_interval=10) >>> for step in range(100): >>> lr_multiplier = scheduler(step) >>> print(f"Step {step}: LR Multiplier = {lr_multiplier}") rcCst|t|krt|krt|krt|ks"JJ||_||_||_||_||_tdgt|j|_ d|_ ||_ dS)Nrg) lenlr_warm_up_stepsf_startf_minf_max cycle_lengthsnpcumsumlist cum_cycleslast_fr)r warm_up_stepsr:r;r9r<rrrrrvsD z$LambdaWarmUpCosineScheduler.__init__cCs4d}|jddD]}||kr|S|d7}q dS)Nr)r@)rr%intervalclrrrfind_in_intervals  z,LambdaWarmUpCosineScheduler.find_in_intervalcKs||}||j|}|jdkr'||jdkr'td|d|jd|||j|krI|j||j||j|||j|}||_|S||j||j ||j|}t |d}|j |d|j||j |dt |t j}||_|S)Nrcurrent step: , recent lr-multiplier: , current cycle rr rC)rFr@rrinforAr8r;r9r<r$r:r=cospi)rr%r&cycleftrrrr(s  ," 6z$LambdaWarmUpCosineScheduler.schedulecKr)rr*r+rrrr,r-z$LambdaWarmUpCosineScheduler.__call__N)r)r.r/r0__doc__rrFr(r,rrrrr6Vs    r6c@seZdZdZddZdS)LambdaLinearSchedulerzH Linear instead of cosine decay for the main part of the cycle. cKs||}||j|}|jdkr'||jdkr'td|d|jd|||j|krI|j||j||j|||j|}||_|S|j ||j||j ||j |||j ||j|}||_|S)NrrGrHrI) rFr@rrrJrAr8r;r9r:r<)rr%r&rMrNrrrr(s  ,(zLambdaLinearScheduler.scheduleN)r.r/r0rPr(rrrrrQs rQ) typingrnumpyr=%cosmos_predict2._src.imaginaire.utilsrrrr6rQrrrrs ?G