o ziX@sddlmZmZddlZddlmZddlmZddlmZm Z ddl m Z ddl m Z dd lmZdd lmZdd lmZmZdd lmZddlZddlZddlZddlZddlZddlZdd lmZddlmZddlmZm Z m!Z!m"Z"ddlm#Z#GdddeeZ$dS))CallableUnionN)Tensor)Module) DataLoaderDataset)vjp)ExponentialMovingAverage)DEVICE)r)ABCabstractmethod)tqdm)datetime) nullcontext)VESDEVPSDETSVESDESDE)load_architecturecs<eZdZddddefdeeefdeeefdeffdd Z de fdd Z e de fd d Z e de fd d ZddZddZddddede fddZeddddddddedededede f ddZegdd fd!ed"efd#d$Zdd%d&d'dd(ed)ded)dd*ddd+d,ddddd-dfd.efd/d0ZZS)1ScoreModelBaseNmodelsdemodel_checkpointc st|dur|durtd|dust|tr-t|||||d\}}|_||n t|dr8||j |durgd| vrIt dd}nd| vrVt dd }nd | vrct d d }nt d t|tr| dvrztd|d| |d<d| vrd|d<| d krt|d|d|dd}nG| d krd| vrd|d<t|d |d|d|dd}n&| dkrd| vrd|d<t|d|d|d|d|d|dd}|jj|d<||_ ||_||_|j|||_||_dS)Nz6Must provide one of 'model' or 'checkpoints_directory')rdevicehyperparametersrrt_starz1Using the Truncated Scaled Variance Exploding SDEtsve sigma_minz Using the Variance Exploding SDEvebeta_minz!Using the Variance Preserving SDEvpz;SDE parameters are missing, please specify which sde to use)rr!rzThe SDE z provided is no supportedrT? sigma_max)rr$r"epsilongh㈵>beta_max)r r&r"r%rbeta)rr$rr'r"r%model_architecture)super__init__ ValueError isinstancestrrloaded_checkpointupdatehasattrrkeysprintKeyErrorlowerrrr __class____name__checkpoints_directoryrtorr)selfrrr7rrr hyperparamsr5E/home/ubuntu/.local/lib/python3.10/site-packages/score_models/base.pyr*sx                   zScoreModelBase.__init__returncGs|j||g|RSN)scorer9txargsr<r<r=forwardaszScoreModelBase.forwardcGdSr?r<rAr<r<r=r@dzScoreModelBase.scorecGrFr?r<)r9rCrDr<r<r=loss_fnhrGzScoreModelBase.loss_fncGsD|j||}|j||}|d|d|j||g|R}|S)N?)rdrift diffusionr@)r9rBrCrDfgf_tilder<r<r= ode_driftls$zScoreModelBase.ode_driftcOs|j|j||g|Ri|Sr?) divergencedrift_fn)r9rBrCrDkwargsr<r<r=hessianrszScoreModelBase.hessianr rademachern_cotangent_vectors noise_typerWcs|j^}}t||gdgt|} t|gt| } |dkr'| } fdd} t| | \} } | | | ddjdd}|S)Nr rUcs|gRSr?r<rCrDrRrBr<r=sz+ScoreModelBase.divergence..r)dim) shapetorchtilelen randn_likesignrflattensum)r9rRrBrCrWrXrDBDsamplesvectorsrM_vjp_funcrQr<rZr=rQus  zScoreModelBase.divergencerEuler)rWrXverbosemethodt0t1 ode_stepsrnrocsD||d|r dnd} |j^} } d} t| gj|}|||}fdd}fdd}tt|| dD]Y}|d krX|||||}| ||||7} ||}q;|d kr|}|||}|||}|d ||||||}| d ||||||||7} ||}q;td | j |  |7} | S) aM A basic implementation of Euler discretisation method of the ODE associated with the marginals of the learned SDE. ode_steps: Number of steps to perform in the ODE hutchinsons_samples: Number of samples to draw to compute the trace of the Jacobian (divergence) Note that this estimator only compute the likelihood for one trajectory. For more precise log likelihood estimation, tile x along the batch dimension and averge the results. You can also increase the number of ode steps and increase the number of cotangent vector for the Hutchinson estimator. Using the instantaneous change of variable formula (Chen et al. 2018,https://arxiv.org/abs/1806.07366) See also Song et al. 2020, https://arxiv.org/abs/2011.13456) rVFTcsj||gRSr?)rPrBrC)rDr9r<r=r[sz/ScoreModelBase.log_likelihood..csjj||gRiSr?)rQrPrrrDrSr9r<r=r[s)disablerkHeunrIz2Invalid method, please select either Euler or Heun) r]r^onesr8rrrangecloneNotImplementedErrorrpriorlog_prob)r9rCrprWrXrlrmrnrorDrtrerflog_prBdtrMdivri previous_xrKnew_xr<rsr=log_likelihoods.       $ zScoreModelBase.log_likelihoodr# conditionlikelihood_score_fnc Cst|ttfstdt||^}}|durdnd}|dur$dd}|j||g|j } |jj |jj  |} t ||j |jj } tt|} D]}} | d|d| d d d |j| d d d | d | | 7} | d |jj kr|S|j| | }|j| | |d|j| | g|R||| | }t | | d}| || }|||} t t | rtd|SqR|S)a An Euler-Maruyama integration of the model SDE shape: Shape of the tensor to sample (including batch size) steps: Number of Euler-Maruyam steps to perform likelihood_score_fn: Add an additional drift to the sampling for posterior sampling. Must have the signature f(t, x) guidance_factor: Multiplicative factor for the likelihood drift z.zSampling from the z | t = rz.1fz | sigma = .1ez | scale ~ rJrIz=Diffusion is not stable, NaN were produced. Stopped sampling.)r,listtupler+typerrzsampler8rr"r%r^rvrrwset_descriptionitemsigmastdrLrKr@raanyisnanr2)r9r]stepsrrguidance_factorrerf sampling_fromrCr}rBpbarrirNrMdwx_meanr<r<r=rs84  8  zScoreModelBase.sampledg-C6?gH.?Finfrq rJ score_modeldatasetc< Cs tjj|j|d}t|j|d}t|||dd}|dur$t|}| dur+|j} |dur3|j }nd}dd}| durLt j | rKt j | d}n|dur[|d td }d}d }| dush|durd }| durut j ||} t j | st | t j | d }t j |st|dC}tjid|d|d|d|d|d|d|d| d| d| d| d| d|d|d|d|d||d d!Wdn1swYt j | d"}t j |s t|d}tj|j|d d!Wdn 1swYtt j | d#} tt j | d$}!d%d&| D}"d'd&| D}#|"r|durd| |"|}$|jtj|$|jjd(|tj|!||k|jd(td)|d*||}n4t|"}%| |%}$|!|%}&|jtj|$|jd(|tj|&|jd(td)|"|%d*||"|%}|durt |t!d+}'g}(d })t""}*d }+d},t#|}-t$t%|}.D]-}/t""|*| d,|+krnt""}0d }1d }2t%|D]}3t""}4zt&|-}5Wnt'yt#|}-t&|-}5Ynwt(|5t)t*fr |5^}6}7n|5}6g}7|dur||6}6|+|j,|6g|7R}8|8-|)| kr?|j.D]}9|t/|)| d-|9d.<q/| d krPtj0j1j2|j| d/|3|4t""|4}:|1|:7}1|2t!|87}2|)d07})q|1t|}1|2t|}2|.5d1|/d0d2d3|2d4d5|(6|2|d6krtd7|/d8|2d9d:|1d9d;n|d0kr|/d0|d krtd7|/d8|2d4t7|2rtd<n"|2d0| |'kr|2}'|}n|d08}t""|*| d,krd },|r|/d0|d ks|d ks|/|d0ks|,r|d07}tt j | d=d>d?}|8|d@|2dAWdn 1s+wY|9t:|j;t j | dB|2dCd |dDdEWdn 1sWwYt:|;t j | dF|2dCd |dDdEtt j | dG} dHd&| D}"dId&| D}#t|"d6|krt<|"};t =t j | dB|#|;dCd |"|;dDdEt =t j | dF|#|;dCd |"|;dDdE|#|;=|"|;=|d krtdJn|,rtdKn |/d krt""|0}+qtdLt""|*d,dMdN|>||(S)OaO Train the model on the provided dataset. Parameters: dataset (torch.utils.data.Dataset): The training dataset. preprocessing_fn (function, optional): A function to preprocess the input data. Default is None. learning_rate (float, optional): The learning rate for optimizer. Default is 1e-4. ema_decay (float, optional): The decay rate for Exponential Moving Average. Default is 0.9999. batch_size (int, optional): The batch size for training. Default is 1. shuffle (bool, optional): Whether to shuffle the dataset during training. Default is False. epochs (int, optional): The number of epochs for training. Default is 100. patience (float, optional): The patience value for early stopping. Default is infinity. tolerance (float, optional): The tolerance value for early stopping. Default is 0. max_time (float, optional): The maximum training time in hours. Default is infinity. warmup (int, optional): The number of warmup iterations for learning rate. Default is 0. clip (float, optional): The gradient clipping value. Default is 0. model_checkpoint (float, optional): If checkpoints_directory is provided, this can be used to restart training from checkpoint. checkpoints_directory (str, optional): The directory to save model checkpoints. Default is None. checkpoints (int, optional): The interval for saving model checkpoints. Default is 10 epochs. models_to_keep (int, optional): The number of best models to keep. Default is 3. seed (int, optional): The random seed for numpy and torch. Default is None. logname (str, optional): The logname for saving checkpoints. Default is None. logdir (str, optional): The path to the directory in which to create the new checkpoint_directory with logname. logname_prefix (str, optional): The prefix for the logname. Default is "score_model". Returns: list: List of loss values during training. )lr)decayF) batch_sizeshuffle drop_lastNcSs|Sr?r<rYr<r<r=r[Arz$ScoreModelBase.fit..riz %y%m%d%H%M%SrTzscript_params.jsonw preprocessing learning_rate ema_decayrrepochspatience tolerancemax_timewarmupclipcheckpoint_directory checkpointsmodels_to_keepseedlognamelogname_prefix)indentzmodel_hparams.jsonzcheckpoint*.ptz optimizer*.ptc S,g|]}ttdtj|ddqSz[0-9]+rintrefindallospathsplit.0rr<r<r= v,z&ScoreModelBase.fit..c Srz([0-9]{1}.[0-9]+e[+-][0-9]{2})rfloatrrrrrrr<r<r=rwr) map_locationzLoaded checkpoint z of rir#r)max_normr zEpoch dz | Cost: rz |rJzepoch z | cost z.2ez | time per step z szModel exploded and returns NaNzscore_sheet.txta)mode  checkpoint_z.4e03dz.pt optimizer_z*.ptc Srrrrr<r<r=rrc Srrrrr<r<r=rrzReached patiencez Out of timezFinished training after z.3fz hours.)?r^optimAdamr parametersr rr`r7r6rrisdirrrnowstrftimejoinmkdirisfileopenjsondumprglobindexload_state_dictloadrr2npargmax manual_seedrtimeiterrrwnext StopIterationr,rr zero_gradrHbackward param_groupsminimumnnutilsclip_grad_norm_stepr/rappendrwriteaverage_parameterssave state_dictargminremovecopy_to)s&