B 3c1 @s8dZddlZddlmZddlmZmZddlmZddl m Z e e Z Gdd d eZd#eed d d Zd$eeedddZd%eeeedddZd&eeeeedddZd'eeeeedddZd(eeeeeedddZejeejeejeejeejeejeiZd)eeefeeeeeeeed d!d"ZdS)*z)Paddle optimization for diffusion models.N)Enum)OptionalUnion) LambdaDecay)loggingc@s$eZdZdZdZdZdZdZdZdS) SchedulerTypelinearcosineZcosine_with_restarts polynomialconstantconstant_with_warmupN) __name__ __module__ __qualname__LINEARCOSINECOSINE_WITH_RESTARTS POLYNOMIALCONSTANTCONSTANT_WITH_WARMUPrr*/home/aistudio/ppdiffusers/optimization.pyrs r) learning_rate last_epochcCst|dd|dS)a Create a schedule with a constant learning rate, using the learning rate set in optimizer. Args: learning_rate (`float`): The base learning rate. It is a python float number. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `paddle.optimizer.lr.LambdaDecay` with the appropriate schedule. cSsdS)Nrr)_rrr2z'get_constant_schedule..)r)r)rrrrrget_constant_schedule%s r)rnum_warmup_stepsrcs tdfdd }t|||dS)aQ Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate increases linearly between 0 and the initial lr set in the optimizer. Args: learning_rate (`float`): The base learning rate. It is a python float number. num_warmup_steps (`int`): The number of steps for the warmup phase. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `paddle.optimizer.lr.LambdaDecay` with the appropriate schedule. ) current_stepcs"|krt|ttdSdS)Ng?)floatmax)r!)r rr lr_lambdaFsz4get_constant_schedule_with_warmup..lr_lambda)r)intr)rr rr$r)r r!get_constant_schedule_with_warmup5sr&)rr num_training_stepsrcs tdfdd }t|||S)a Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. Args: learning_rate (`float`): The base learning rate. It is a python float number. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `paddle.optimizer.lr.LambdaDecay` with the appropriate schedule. )r!csB|krt|ttdStdt|ttdS)Nrg)r"r#)r!)r'r rrr$csz2get_linear_schedule_with_warmup..lr_lambda)r%r)rr r'rr$r)r'r rget_linear_schedule_with_warmupNsr(?)rr r' num_cyclesrcsfdd}t|||S)a Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. Args: learning_rate (`float`): The base learning rate. It is a python float number. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. num_cycles (`float`, *optional*, defaults to 0.5): The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0 following a half-cosine). last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `paddle.optimizer.lr.LambdaDecay` with the appropriate schedule. c sf|krt|ttdSt|ttd}tdddttjtd|S)Nrgg?g?g@)r"r#mathcospi)r!progress)r*r'r rrr$sz2get_cosine_schedule_with_warmup..lr_lambda)r)rr r'r*rr$r)r*r'r rget_cosine_schedule_with_warmupmsr/csfdd}t|||S)a{ Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. Args: learning_rate (`float`): The base learning rate. It is a python float number. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. num_cycles (`int`, *optional*, defaults to 1): The number of hard restarts to use. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `paddle.optimizer.lr.LambdaDecay` with the appropriate schedule. c sr|krt|ttdSt|ttd}|dkrHdStdddttjt|dS)Nrg?gg?)r"r#r+r,r-)r!r.)r*r'r rrr$s zEget_cosine_with_hard_restarts_schedule_with_warmup..lr_lambda)r)rr r'r*rr$r)r*r'r r2get_cosine_with_hard_restarts_schedule_with_warmupsr0Hz>?)rr r'lr_endpowerrcsH|ks"tdddtdfdd }t|||S)a Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. Args: learning_rate (`float`): The base learning rate. It is a python float number. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. lr_end (`float`, *optional*, defaults to 1e-7): The end LR. power (`float`, *optional*, defaults to 1.0): Power factor. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Note: *power* defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT implementation at https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37 Return: `paddle.optimizer.lr.LambdaDecay` with the appropriate schedule. zlr_end (z&) must be be smaller than initial lr ())r!csj|krt|ttdS|kr.S}}d||}||}|SdS)Nr)r"r#)r!lr_range decay_steps pct_remainingdecay)r3lr_initr'r r4rrr$sz.lr_lambda) ValueErrorr%r)rr r'r3r4rr$r)r3r:r'r r4r)get_polynomial_decay_schedule_with_warmups $ r<皙?)namerr r'r*r4rcCst|}t|}|tjkr&|||dS|dkrrr r'r*r4r schedule_funcrrr get_schedulers:     rA)r)r)r)r)r)rr)r1r2r)r=NNrr2r) __doc__r+enumrtypingrrpaddle.optimizer.lrrutilsr get_loggerrloggerrr"r%rr&r(r/r0r<rrrrrrr?strrArrrrs6     "&2