o ίi@sddlZddlZddlmZddlmZddlmZddlm Z m Z e Z eGdddZ ded ed ed ed ef d dZded eded ed ef ddZded ed ededed ed efddZded ed ededed ed efddZde d efddZdejde d efddZdS)N) dataclass)partial)nn)AdamW lr_schedulerc@seZdZUdZeed<dZeed<dZeed<dZeed<d Z eed <d Z eed <d Z e ed<dZ eed<dZeed<d Zeed<d Zeed<dZeed<dZeed<dZeed<dS) OptimArgsga2U0*3?lrg? weight_decayg:0yE>epsilong?beta1gffffff?beta2?clipcosine scheduleriwarmup lr_min_ratio cycle_length cosine_thetai'annealing_stepdecay_fraction? exp_factorN)__name__ __module__ __qualname__rfloat__annotations__r r r r rrstrrintrrrrrrr r >/home/ubuntu/.local/lib/python3.10/site-packages/core/optim.pyrs             rsteprn_steps min_ratioreturncCsP||kr t||}|S||kr$t||||}||d|}|S|}|S)N)r)r"rr#r$rsr r r! lr_linear"s r(rcCs2||kr t||}|St|||||}|S)N)rmax)r"rrr$rr r r! lr_inv_sqrt-s  r*rthetac Cs|||ddd}||krt||}|S||kr@t||||}|dd||ttj|||d}|S|}|S)Nr&r)rmathcospi) r"rr#rr+r$signrr'r r r! lr_cosine5s  r2rc Cs|t||d}t|||}t||}||kr$t||} | S|||kr.d} | S|||krP||krP|||}d||d|d||} | S|} | S)z UNDERSTANDING WARMUP-STABLE-DECAY LEARNING RATES: A RIVER VALLEY LOSS LANDSCAPE PERSPECTIVE https://arxiv.org/pdf/2410.05192 r&r )rr) r"rr#rrr$ cycle_num curr_n_steps decay_lengthrr r r!lr_wsdJs       r6argscCs|jdkr dd}|S|jdkrtt|j||jd}|S|jdkr.tt|j|j|jd}|S|jdkrCtt|j||j|j |jd }|S|jd kr`|j |jksPJtt |j||j |j|jd }|St d |j) NconstantcSsdS)Nr r )xr r r!nszbuild_lr_fn..linear)rr#r$inv_sqrt)rrr$r)rr#rr+r$wsd)rr#rrr$zUnknown scheduler: ) rrr(rrr*rr2rrrr6NotImplementedError)r7r#lr_fnr r r! build_lr_fnlsL !      r@modelcCsXtdt||j|j|jf|j|jdd}t ||}t ||}td||fS)NzStarting build of optimizer...T)rbetasr epsfusedzDone with build of optimizer.) loggerinfor parametersrr r r r r@rLambdaLR)rAr7r# optimizerr?rr r r!build_optimizers    rJ)loggingr. dataclassesr functoolsrtorchr torch.optimrr getLoggerrErrrr(r*r2r6r@ModulerJr r r r!sV      "&