#jdZddlmZddlZddlZddlmZddlmZddl Z ddl m Z ddl m Z mZdd lmZmZdd lmZdd lmZejeZdRd ZdSdTdZdUdZdVdZdSdWdZdXdZdSdZdYdZ dZd[dZ!d\d Z" d]d^d!Z#d_d%Z$ d`d(Z%dd)dad,Z& dbdcd-Z'd.d/ddd1Z dedfd4Z(d.dd5dgd7Z) dhdid8Z*djd?Z+ dkdldBZ,GdCdDZ-GdEdFZ.dUdGZ/ej0eej1e!ej2e#ej3e%ej4eej5eej6e'ej7eej8e(ej9e*ej:e,ej;e/i Z< dmdndLZ=GdMdNe Z>GdOdPe Z?dodQZ@dS)pz$PyTorch optimization for BERT model.) annotationsN)partial)Any) Optimizer)LambdaLRReduceLROnPlateau)LayerWiseDummyOptimizerLayerWiseDummyScheduler) SchedulerType)loggingcdSNr )_s c/lsinfo/ai/hellotax_ai/base_platform/venv/lib/python3.11/site-packages/transformers/optimization.py_get_constant_lambdar#s 1 optimizerr last_epochintc0t|t|S)a Create a schedule with a constant learning rate, using the learning rate set in optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. r)rr)rrs rget_constant_scheduler's I3 K K KKrc t|fi|S)a Create a schedule with a constant learning rate that decreases when a metric has stopped improving. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. kwargs (`dict`, *optional*): Extra parameters to be passed to the scheduler. See `torch.optim.lr_scheduler.ReduceLROnPlateau` for possible parameters. Return: `torch.optim.lr_scheduler.ReduceLROnPlateau` with the appropriate schedule. )rrkwargss rget_reduce_on_plateau_scheduler8s Y 1 1& 1 11r current_stepnum_warmup_stepscl||kr-t|ttd|z SdS)N?floatmax)r r!s r,_get_constant_schedule_with_warmup_lr_lambdar'Js9&&&\""U3s4D+E+E%F%FFF 3rcRtt|}t|||S)ad 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: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. 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: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. r!r)rr'r)rr!r lr_lambdas r!get_constant_schedule_with_warmupr+Ps-"DWghhhI IyZ @ @ @@rnum_training_stepsc ||kr-t|ttd|z Stdt||z ttd||z z S)Nr r$)r r!r,s r*_get_linear_schedule_with_warmup_lr_lambdar/esp&&&\""U3q2B+C+C%D%DDD sE,|;<k>k8l8llH sC3$'E*4E4E*E*Kh*V!W!WWX Y YYrr5cTtt|||}t|||S)a Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. num_cycles (`float`, *optional*, defaults to 0.5): The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0 following a half-cosine). last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. r!r,r3)rr=rrr!r,r3rr*s rget_cosine_schedule_with_warmuprAs822)- I Iy* 5 55rc n||kr-t|ttd|z St||z ttd||z z }|dkrdStdddtjtjt||zdzzzzS)Nr r#r.r5r7r;s r=_get_cosine_with_hard_restarts_schedule_with_warmup_lr_lambdarCs&&&\""U3q2B+C+C%D%DDD\$4455c!EWZjEj>k>k8l8llH3s sC3$'eJ6G6G(6RVY5Y*Z![![[\ ] ]]rcTtt|||}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: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. 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: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. r?)rrCrr@s r2get_cosine_with_hard_restarts_schedule_with_warmuprEs80E)- I Iy* 5 55rlr_endpowerlr_initc||kr-t|ttd|z S||kr||z S||z }||z }d||z |z z }|||zz|z} | |z Srr$) r r!r,rFrGrHlr_range decay_steps pct_remainingdecays r4_get_polynomial_decay_schedule_with_warmup_lr_lambdarNs&&&\""U3q2B+C+C%D%DDD * * *V#(+;; \,<< KK =%//&8wrHz>r#c|jd}||kstd|d|dtt|||||}t |||S)a Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. lr_end (`float`, *optional*, defaults to 1e-7): The end LR. power (`float`, *optional*, defaults to 1.0): Power factor. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Note: *power* defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT implementation at https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37 Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. lrzlr_end (z#) must be smaller than initial lr ())r!r,rFrGrH)defaults ValueErrorrrNr)rr!r,rFrGrrHr*s r)get_polynomial_decay_schedule_with_warmuprUsx> &G f  YFYYwYYYZZZ<)- I Iy* 5 55r) timescalerV int | Nonec||kr-t|ttd|z S||z }dtj||z|z z }|S)Nr r#)r%r&r8sqrt)r r!rVshiftrMs r$_get_inverse_sqrt_schedule_lr_lambdar[ sb&&&\""U3q2B+C+C%D%DDD ( (E $)\E1Y>?? ?E Lrc`||pd}tt||}t|||S)a Create a schedule with an inverse square-root learning rate, from the initial lr set in the optimizer, after a warmup period which increases lr linearly from 0 to the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. timescale (`int`, *optional*, defaults to `num_warmup_steps`): Time scale. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. Ni')r!rVr)rr[r)rr!rVrr*s rget_inverse_sqrt_scheduler](s@.$. k>k8l8llH C$(47U:->->#>#Dx#OPPP QF q; '+ 5F q&>>rmin_lr float | Nonec||td|||jdz }n|tdtt||||}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 min_lr, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. 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. min_lr (`float`, *optional*): The minimum learning rate to reach after the cosine schedule. min_lr_rate (`float`, *optional*): The minimum learning rate as a ratio of the initial learning rate. If set, `min_lr` should not be set. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. N/Only one of min_lr or min_lr_rate should be setrQLOne of min_lr or min_lr_rate should be set through the `lr_scheduler_kwargs`)r!r,r3r^)rTrSrr=r)rr!r,r3rrar^r*s r+get_cosine_with_min_lr_schedule_with_warmuprfQsFk5JKKK  y1$77  ghhh2)- I Iy* 5 55r)r^warmup_lr_ratergct|}t|}t|}||krF||dztd|z St|}|d|z |ztd|dz z zS||z dztd||z z }ddtjtj|zdz|zzz}|d|z z|z}td|S)Nr#r r5r6rr7)r r!r,r3r^rgr<r`s r;_get_cosine_with_min_lr_schedule_with_warmup_lr_rate_lambdaris&&L-..122&&&  ! 3&#c3C*D*DD D">22N!S>%9l$KsSTVfijVjOkOk$ll l//#5#cCUXhCh:i:ijH C$(47Z#7##=#HIII JF q; '+ 5F q&>>rc||td|||jdz }n|tdtt|||||}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 min_lr, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. 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. min_lr (`float`, *optional*): The minimum learning rate to reach after the cosine schedule. min_lr_rate (`float`, *optional*): The minimum learning rate as a ratio of the initial learning rate. If set, `min_lr` should not be set. warmup_lr_rate (`float`, *optional*): The minimum learning rate as a ratio of the start learning rate. If not set, `warmup_lr_rate` will be treated as float(1/num_warmup_steps). Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. NrdrQre)r!r,r3r^rg)rTrSrrir) rr!r,r3rrar^rgr*s r3get_cosine_with_min_lr_schedule_with_warmup_lr_raterksLk5JKKK  y1$77  ghhhC)-% I Iy* 5 55rnum_stable_stepsnum_decay_steps warmup_typestr decay_type min_lr_ratioc||krt|ttd|z }|dkr|} nN|dkr(ddtjtj|zz z} n |dkrdtjd|z z } | d|z z|z} td| S|||zkrdS|||z|zkrt||z |z ttd|z }|dkrd|z } n^|dkr;ddtjtjt|zdz|zzz} n|dkrdtj|z } | d|z z|z} td| S|S) Nr linearcosiner5r#1-sqrtr.r6)r%r&r8r9r:rY) r r!rlrmrnrprqr3r<r`s r_get_wsd_scheduler_lambdarvs&&&&&s16F/G/G)H)HH ( " "FF H $ $C$(47X+=">">>?FF H $ $49S8^444F3-.=3&)9999s&)99OKKK(88;KKLLuUXYZ\kUlUlOmOmm  ! !8^FF 8 # #C$(47U:5F5F+F+Lx+W"X"XXYFF 8 # #49X...F3-.=3 rrsrtc "||td||tjd|dvrtd|d|dvrtd|d|||z |z }tt|||||||} t || | S) a Create a schedule with a learning rate that has three stages: 1. warmup: increase from min_lr_ratio times the initial learning rate to the initial learning rate following a warmup_type. 2. stable: constant learning rate. 3. decay: decrease from the initial learning rate to min_lr_ratio times the initial learning rate following a decay_type. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. num_decay_steps (`int`): The number of steps for the decay phase. num_training_steps (`int`, *optional*): The total number of training steps. This is the sum of the warmup, stable and decay steps. If `num_stable_steps` is not provided, the stable phase will be `num_training_steps - num_warmup_steps - num_decay_steps`. num_stable_steps (`int`, *optional*): The number of steps for the stable phase. Please ensure that `num_warmup_steps + num_stable_steps + num_decay_steps` equals `num_training_steps`, otherwise the other steps will default to the minimum learning rate. warmup_type (`str`, *optional*, defaults to "linear"): The type of warmup to use. Can be 'linear', 'cosine' or '1-sqrt'. decay_type (`str`, *optional*, defaults to "cosine"): The type of decay to use. Can be 'linear', 'cosine' or '1-sqrt'. min_lr_ratio (`float`, *optional*, defaults to 0): The minimum learning rate as a ratio of the initial learning rate. 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: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. Nz@Either num_training_steps or num_stable_steps must be specified.zZBoth num_training_steps and num_stable_steps are specified. num_stable_steps will be used.)rsrtruzUnknown warmup type: z), expected 'linear', 'cosine' or '1-sqrt'zUnknown decay type: )r!rlrmrnrprqr3)rTwarningswarnrrvr) rr!rmr,rlrnrprqr3rr*s rget_wsd_schedulerzsZ!&6&>[\\\%*:*F rsss888gggghhh777e eeefff-0@@?R!))'!   I Iy* 5 55rc2eZdZdZddZdd Zdd Zdd ZdS)StreamingAverageaBRolling window average for smoothing metric values. Maintains a sliding window of values and computes their average, useful for smoothing noisy metric values before making learning rate decisions. Args: window_size (`int`): The maximum number of values to keep in the rolling window. window_sizerreturnNonec0||_g|_d|_dS)Nr.r}valuessum)selfr}s r__init__zStreamingAverage.__init__Ps +#% rvaluer%c|j||xj|z c_t|j|jkr*|jd}|xj|zc_|jt|jz S)z3Add a value and return the current rolling average.r)rappendrlenr}pop)rrremoveds r streamavgzStreamingAverage.streamavgUsz 5!!! E t{  d. . .kooa((G HH HHx#dk****rdict[str, Any]cP|j|j|jdS)Nr)r}rcopyrrs r state_dictzStreamingAverage.state_dict`s-+k&&((8   rrc|d|j|_|dg|_|dd|_dS)Nr}rrr.)getr}rrr)rrs rload_state_dictz StreamingAverage.load_state_dictgsU%>>-9IJJ nnXr227799 >>%--rN)r}rr~r)rr%r~r%r~rrrr~r)__name__ __module__ __qualname____doc__rrrrrrrr|r|Esn + + + +    ......rr|czeZdZdZ d7d8d%Zd9d:d*Zd;d-Zdd2Z d?d4Z d@d6Z d&S)AGreedyLRa Adaptive learning rate scheduler that responds to training metrics. GreedyLR dynamically adjusts the learning rate based on training performance: - Increases LR when metrics improve consistently (divides by factor) - Decreases LR when metrics plateau (multiplies by factor) This differs from traditional schedulers like cosine annealing by responding to actual training dynamics rather than following a predetermined schedule. Reference: `GreedyLR: A Novel Adaptive Learning Rate Scheduler `_ Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. mode (`str`, *optional*, defaults to `"min"`): One of 'min' or 'max'. In 'min' mode, LR will be reduced when the metric has stopped decreasing; in 'max' mode when it has stopped increasing. factor (`float`, *optional*, defaults to 0.95): Factor by which the learning rate will be adjusted. LR is multiplied by factor on plateau and divided by factor on improvement. Must be < 1.0. patience (`int`, *optional*, defaults to 10): Number of epochs with no improvement after which learning rate will be adjusted. threshold (`float`, *optional*, defaults to 1e-06): Threshold for measuring the new optimum. threshold_mode (`str`, *optional*, defaults to `"abs"`): One of 'rel' or 'abs'. cooldown (`int`, *optional*, defaults to 0): Number of epochs to wait before resuming normal operation after LR has been reduced. warmup (`int`, *optional*, defaults to 0): Number of epochs to wait before resuming normal operation after LR has been increased. min_lr (`float` or `list[float]`, *optional*, defaults to 0.001): A lower bound on the learning rate. max_lr (`float` or `list[float]`, *optional*, defaults to 1.0): An upper bound on the learning rate. eps (`float`, *optional*, defaults to 1e-08): Minimal decay applied to lr. verbose (`bool`, *optional*, defaults to `False`): If True, prints a message to stdout for each update. smooth (`bool`, *optional*, defaults to `False`): If True, applies streaming average smoothing to metrics. window_size (`int`, *optional*, defaults to 50): The window size for the streaming average when smooth=True. reset_start (`int`, *optional*, defaults to 500): Number of steps to wait at min_lr before resetting to initial state. Example: ```python >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1) >>> scheduler = GreedyLR(optimizer, mode="min", patience=10) >>> for epoch in range(100): ... train(...) ... val_loss = validate(...) ... scheduler.step(val_loss) ``` minffffff? ư>absrMbP?r#:0yE>F2rrmoderor`r%patiencer thresholdthreshold_modecooldownwarmuprafloat | list[float]max_lrepsverboseboolsmoothr} reset_startr~rc|dkrtdt|ts$tt |jd||_||_||_| |_ ||_ ||_ d|_ d|_ ||_||_||_| |_| |_||_||_||_d|_t| t.t0frnt3| t3|jkr4tdt3|jdt3| t/| |_n| gt3|jz|_t| t.t0frnt3| t3|jkr4tdt3|jdt3| t/| |_n| gt3|jz|_d|jD|_|j|_|d krtAd ntAd |_!d|_"d|_#|d vrtd |d|dvrtd|dd|_$| rtK||_$dSdS)Nr#zFactor should be < 1.0.z is not an Optimizerrz expected z min_lrs, got z max_lrs, got cg|] }|d SrQr.0groups r z%GreedyLR.__init__..sJJJ%%+JJJrrinf-inf)rr&zmode z is unknown!)relrzthreshold mode )&rT isinstancer TypeErrortyperrr`rrrrcooldown_counterwarmup_counterrrrrrr}rreset_start_originalrlisttupler param_groupsmin_lrsmax_lrs _init_lrsr_last_lrr%bestnum_bad_epochsnum_good_epochs_streaming_avgr|)rrrr`rrrrrrarrrrr}rs rrzGreedyLR.__init__s$ S==677 7)Y// OtI7MMMNN N"       ! ", &&$/! ftUm , , B6{{c)"89999 !eS1G-H-H!e!eX[\bXcXc!e!efff<.sNNNtNNNr)r%rrrrrrrr is_betterrr _increase_lrr _reduce_lrrrrr)rrrcurrents rstepz GreedyLR.steps.. ; =4.:)33G<>>!666"e++t~)=!>>>!666rc d}t|jjD]\}}t|d}t ||jz|j|}||z |jkr&||d<|jrtd|d|d|dd|d|j|krd}|r1|xj d zc_ |j d kr| dSdSdS) NTrQEpoch z": reducing learning rate of group  to .4e.Fr r) enumeraterrr%r&r`rrrprintr_reset)rr all_at_mini param_groupold_lrnew_lrs rrzGreedyLR._reduce_lr's '(CDD # #NA{;t,--F$+-t|A??F))$* D!<b`5``A``SY````aaa4 4<?22"      !  1$$   $$rc 4t|jjD]q\}}t|d}t ||jz |j|}||z |jkr&||d<|jrtd|d|d|ddr|j |_ dS)NrQrz$: increasing learning rate of group rrr) rrrr%rr`rrrrrr)rrrrrrs rrzGreedyLR._increase_lr:s'(CDD d dNA{;t,--F$+-t|A??F))$* D!<db5bbabbU[bbbbccc4rct|jjD]\}}|j||d<|jdkrt dnt d|_d|_d|_d|_ d|_ |j |_ |j r |jt|j|_|jrt%ddSdS)NrQrrrrz!Scheduler reset to initial state.)rrrrrr%rrrrrrrrrr|r}rr)rrrs rrzGreedyLR._resetFs'(CDD 2 2NA{ $q 1K  $(I$6$6E%LLLE&MM   !4 ; E4.:"243C"D"DD  < 7 5 6 6 6 6 6 7 7r list[float]c|jS)z8Return last computed learning rate by current scheduler.)rrs r get_last_lrzGreedyLR.get_last_lrWs }rrcid|jd|jd|jd|jd|jd|jd|jd|jd |jd |j d |j d |j d |j d|j d|jd|jd|j|j|j|j|j|j|jd}|jr#|j|j|d<|S)z2Return the state of the scheduler as a dictionary.r`rrrrrrrrrrrrrrrr)rr}rrrrNr)r`rrrrrrrrrrrrrrrrrr}rrrrrr)rstates rrzGreedyLR.state_dict[s^ dk t|  t|    t|     dk   5  d1  DI    d1  DI  d1  t3 48! " $/# $k++$($= /   4 ; G4.:&*&9&D&D&F&FE" # rrc|d|j|_|d|j|_|d|j|_|d|j|_|d|j|_|d|j|_|d|j|_|d|j|_|d |j |_ |d |j |_ |d |j |_ |d |j |_ |d |j |_ |d|j|_|d|j|_|d|j|_|d|j|_|d|j|_|d|j|_|d|j|_|d|j|_|d|j|_|d|j|_d|vr@|jt3|j|_|j|dd|vr*t7|jj|jD] \}}||d< dSdS)zLoad state from a dictionary.r`rrrrrrrrrrrrrrrrrr}rrrrrNrQ)rr`rrrrrrrrrrrrrrrrrr}rrrrrr|rziprr)rrrrQs rrzGreedyLR.load_state_dict|s nnXt{;; !~~i>> !~~i>> "z4=AA !~~i>> "z4=AA  nnXt{;; */A4CX Y Y(nn-=t?RSSNN64955 # T^DD(nn-=t?RSSNN64955 (nn-=t?RSS)~~.?AUVV>>%22$..tGG nnXt{;; %>>-9IJJ%>>-9IJJ$.NN3I4Kd$e$e!"z4=AA # T^DD z ) )"*&6t7G&H&H#   / / ;K0L M M M  # ##&t~'BDM#R#R ' ' R$& D!! $ # ' 'r)rrrrrrrrr#rFFrr) rrrror`r%rrrr%rrorrrrrarrrrr%rrrrr}rrrr~rN)rr%rrWr~r)rr%rr%r~r)rrr~r)r~r)r~rrr) rrrrrrrrrrrrrrrrrrms66v#&*&)!E@E@E@E@E@N,O,O,O,O,O\ 7 7 7 7& 5 5 5 57777"B!'!'!'!'!'!'rrc t|fi|S)a Create an adaptive learning rate scheduler that adjusts LR based on training metrics. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. kwargs (`dict`, *optional*): Extra parameters passed to the scheduler. See [`GreedyLR`] for possible parameters. Return: [`GreedyLR`] with the appropriate schedule. )rrs rget_greedy_schedulers I ( ( ( ((rnamestr | SchedulerTypescheduler_specific_kwargs dict | Nonec t|}t|}|t|trm|j}i |D]}t |||||| |< fd}|D]}|jr||t||j dS|tj kr ||S|i}|tj kr ||fi|S|tj kr ||fi|S|t|d|tjkr |||S|tjkr ||fd|i|S|tjkr ||f||d |S|t|d ||f||d |S) a Unified API to get any scheduler from its name. Args: name (`str` or `SchedulerType`): The name of the scheduler to use. optimizer (`torch.optim.Optimizer`): The optimizer that will be used during training. num_warmup_steps (`int`, *optional*): The number of warmup steps to do. This is not required by all schedulers (hence the argument being optional), the function will raise an error if it's unset and the scheduler type requires it. num_training_steps (`int``, *optional*): The number of training steps to do. This is not required by all schedulers (hence the argument being optional), the function will raise an error if it's unset and the scheduler type requires it. scheduler_specific_kwargs (`dict`, *optional*): Extra parameters for schedulers such as cosine with restarts. Mismatched scheduler types and scheduler parameters will cause the scheduler function to raise a TypeError. N)rr!r,rc<|dSr)r)paramscheduler_dicts rscheduler_hookz%get_scheduler..scheduler_hooks" 5 ! & & ( ( ( ( (rrQ)optimizer_dictrQz; requires `num_warmup_steps`, please provide that argument.r)r!r1z= requires `num_training_steps`, please provide that argument.)r TYPE_TO_SCHEDULER_FUNCTIONrr r get_scheduler requires_grad"register_post_accumulate_grad_hookr rSCONSTANTREDUCE_ON_PLATEAUGREEDYrTCONSTANT_WITH_WARMUP INVERSE_SQRTWARMUP_STABLE_DECAY) rrr!r,r schedule_funcrrrrs @rrrsb2   D.t4MI7N!O!O"1#  E$1(/!1#5*C %%%N5 ! ! ) ) ) ) ) $ I IE" I88HHH&nI[\`Iabbbb }%%%}Y''' ($&! }...}YDD*CDDD }###}YDD*CDDDD]]]^^^ }111}Y9IJJJJ })))}Ygg9IgMfggg }000}  -1  (    !D___``` = )-   $   rceZdZdZ dfd Zed Zed Zed Zed Z e j ddZ xZ S) Adafactora9 AdaFactor pytorch implementation can be used as a drop in replacement for Adam original fairseq code: https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py Paper: *Adafactor: Adaptive Learning Rates with Sublinear Memory Cost* https://huggingface.co/papers/1804.04235 Note that this optimizer internally adjusts the learning rate depending on the `scale_parameter`, `relative_step` and `warmup_init` options. To use a manual (external) learning rate schedule you should set `scale_parameter=False` and `relative_step=False`. Arguments: params (`Iterable[nn.parameter.Parameter]`): Iterable of parameters to optimize or dictionaries defining parameter groups. lr (`float`, *optional*): The external learning rate. eps (`tuple[float, float]`, *optional*, defaults to `(1e-30, 0.001)`): Regularization constants for square gradient and parameter scale respectively clip_threshold (`float`, *optional*, defaults to 1.0): Threshold of root mean square of final gradient update decay_rate (`float`, *optional*, defaults to -0.8): Coefficient used to compute running averages of square beta1 (`float`, *optional*): Coefficient used for computing running averages of gradient weight_decay (`float`, *optional*, defaults to 0.0): Weight decay (L2 penalty) scale_parameter (`bool`, *optional*, defaults to `True`): If True, learning rate is scaled by root mean square relative_step (`bool`, *optional*, defaults to `True`): If True, time-dependent learning rate is computed instead of external learning rate warmup_init (`bool`, *optional*, defaults to `False`): Time-dependent learning rate computation depends on whether warm-up initialization is being used This implementation handles low-precision (FP16, bfloat) values, but we have not thoroughly tested. Recommended T5 finetuning settings (https://discuss.huggingface.co/t/t5-finetuning-tips/684/3): - Training without LR warmup or clip_threshold is not recommended. - use scheduled LR warm-up to fixed LR - use clip_threshold=1.0 (https://huggingface.co/papers/1804.04235) - Disable relative updates - Use scale_parameter=False - Additional optimizer operations like gradient clipping should not be used alongside Adafactor Example: ```python Adafactor(model.parameters(), scale_parameter=False, relative_step=False, warmup_init=False, lr=1e-3) ``` Others reported the following combination to work well: ```python Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None) ``` When using `lr=None` with [`Trainer`] you will most likely need to use [`~optimization.AdafactorSchedule`] scheduler as following: ```python from transformers.optimization import Adafactor, AdafactorSchedule optimizer = Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None) lr_scheduler = AdafactorSchedule(optimizer) trainer = Trainer(..., optimizers=(optimizer, lr_scheduler)) ``` Usage: ```python # replace AdamW with Adafactor optimizer = Adafactor( model.parameters(), lr=1e-3, eps=(1e-30, 1e-3), clip_threshold=1.0, decay_rate=-0.8, beta1=None, weight_decay=0.0, relative_step=False, scale_parameter=False, warmup_init=False, ) ```NgKH9rr#皙r.TFc || rtd| r| std|||||||| | d } t|| dS)Nz;Cannot combine manual `lr` and `relative_step=True` optionsz0`warmup_init=True` requires `relative_step=True`) rQrclip_threshold decay_ratebeta1 weight_decayscale_parameter relative_step warmup_init)rTsuperr) rparamsrQrr r r rrrrrS __class__s rrzAdafactor.__init__vs >m>Z[[ [  Q} QOPP P,$(.*&    *****rc|d}|dr@|dr d|dznd}t|dtj|dz }d}|dr"t|d d |d }||zS) NrQrrrrg{Gz?r#rrr RMS)rr8rYr&)r param_state rel_step_szmin_step param_scales r_get_lrzAdafactor._get_lrs!$'  ' N5@5OYtk&111UYHhdi F8K.L.L(LMMK ( ) Ik%03[5GHHK[((rcDt|dk}|ddu}||fS)Nr )r)r param_shapefactoreduse_first_moments r _get_optionszAdafactor._get_optionss0{##q(&w/t;)))rc\|d|dzz S)Nrr5)normnumel)tensors r_rmszAdafactor._rmss${{1~~3!677rc||ddz d}|d}t j||S)NrT)dimkeepdim)meanrsqrt_ unsqueezersqrttorchmul)exp_avg_sq_rowexp_avg_sq_colr_factorc_factors r_approx_sq_gradzAdafactor._approx_sq_gradsm#^%8%8R%8%N%NNVVXXbbceff!++B//5577y8,,,rc d}| |}|jD]}|dD]}|j |j}|jtjtjhvr|}|jrtd|j |}|j }| ||\}} t|dkrd|d<| rtj ||d<|rptj|dd||d<tj|dd |ddz||d <ntj ||d <d|d <n}| r|d||d<|r=|d||d<|d ||d <n|d ||d <|} |jtjtjhvr| } |dxxd z cc<|| |d <|||} dt%j|d|dz } |dz|ddz} |r|d}|d }|| | dd| z || | d d| z |||} | |n\|d }|| | d| z ||} | || |dz d| | | rC|d}||d| d |dz |} |ddkr!| | |d | z| | |jtjtjhvr|| |S)z Performs a single optimization step Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. Nrz,Adafactor does not support sparse gradients.rrexp_avgrr1r*r2 exp_avg_sqrr r#r rr)r()alphar )rr r)rgraddtyper/float16bfloat16r% is_sparse RuntimeErrorrshaper!r zeros_likezerostor&rr8powmul_add_r+r5r.div_clamp_copy_)rclosurelossrpr:r grad_shaperr  p_data_fp32rQbeta2tupdater1r2r8r7s rrzAdafactor.steps  799D&M )M )E8_L )L )6>v:%-!@@@::<W&'UVVV 1 !Z -1->->uj-Q-Q**u::??$%E&M'B+0+;D+A+Ai(E27+j"o2N2N2Q2QRV2W2W./27+j"oPZ[][^[^P_>_2`2`2c2cdh2i2i.//.3.>t.D.Dl+#$E%LL'E+0+;+>+>t+D+Di(K278H2I2L2LT2R2R./278H2I2L2LT2R2R.//.3L.A.D.DT.J.Jl+ 7u}en==="-"3"3"5"5Kf " #yy55e \\%//txf u\7JKKK'U5\!_4;%*+;%))\)**\* 88\8--\-U]__[[[_[[[[[rrc*eZdZdZdfd ZdZxZS)AdafactorSchedulea8 Since [`~optimization.Adafactor`] performs its own scheduling, if the training loop relies on a scheduler (e.g., for logging), this class creates a proxy object that retrieves the current lr values from the optimizer. It returns `initial_lr` during startup and the actual `lr` during stepping. r.cfd}|jD]}|d<t|||jD]}|d=dS)NcSrr)r initial_lrs rr*z-AdafactorSchedule.__init__..lr_lambdas  rrY)rrr)rrrYr*rrs ` rrzAdafactorSchedule.__init__s     + - -E",E,   I...+ $ $El## $ $rcp|jfdjD}t|dkr|j}|S)Ncg|]B}|ddj|j|ddCS)rr)r:rr)rropts rrz,AdafactorSchedule.get_lr..%sS   Xq!&2 KKsyx);< = =222rr)rrrbase_lrs)rlrsr\s @rget_lrzAdafactorSchedule.get_lr#sRn    )    s88q==-C rr.)rrrrrr_rSrTs@rrVrVsV$$$$$$       rrVc"t||S)aX Get a proxy schedule for [`~optimization.Adafactor`] Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. initial_lr (`float`, *optional*, defaults to 0.0): Initial lr Return: [`~optimization.Adafactor`] proxy schedule object. )rV)rrYs rget_adafactor_schedulerb/s Y 3 33rr)r)rrrr)rr)r rr!r)rrr!rrr)r rr!rr,r)r rr!rr,rr3r%)r5r) rrr!rr,rr3r%rr)r rr!rr,rr3r)r r) rrr!rr,rr3rrr) r rr!rr,rrFr%rGr%rHr)rOr#r)r rr!rrVrW)Nr)rrr!rrVrWrr) r rr!rr,rr3r%r^r%)r5rNN)rrr!rr,rr3r%rrrarbr^rb) r rr!rr,rr3r%r^r%rgrb)r5rNNN)rrr!rr,rr3r%rrrarbr^rbrgrb)r rr!rrlrrmrrnrorprorqr%r3r%)NNrsrtrr5r)rrr!rrmrr,rWrlrWrnrorprorqr%r3r%rr)NNN) rrrrr!rWr,rWrrr`)Ar __future__rr8rx functoolsrtypingrr/ torch.optimrtorch.optim.lr_schedulerrrtrainer_pt_utilsr r trainer_utilsr utilsr get_loggerrloggerrrrr'r+r/r2r=rArCrErNrUr[r]rfrirkrvrzr|rrLINEARCOSINECOSINE_WITH_RESTARTS POLYNOMIALrrrrCOSINE_WITH_MIN_LRCOSINE_WARMUP_WITH_MIN_LRrrrrrrVrbrrrrss+*""""""  !!!!!!@@@@@@@@NNNNNNNN((((((  H % %    LLLLL"2222$ AAAAA*uuuu 66666ZZZZvx66666D^^^^rt66666B,Y[+6+6+6+6\osbdAAAAA>sv $1616161616t#': $#'5656565656p####T&*#'F6F6F6F6F6R%.%.%.%.%.%.%.%.Pp'p'p'p'p'p'p'p'f ) ) ) )"99&(ZG1&(I 9#%C$&Q+-`%'7- &$(%)-1 ^^^^^Bmmmmm mmm`<444444r