o ?߱iA@sddlmZddlmZmZddlmZmZmZm Z m Z m Z ddl Z ddlZz ddlmZdZWn ey;dZYnwddlmZmZerLdd lmZGd d d Zd!d"ddZGdddejjZGdddeZed#d$ddZed%dd ZdS)&) annotations)contextmanager nullcontext) TYPE_CHECKINGAny GeneratorListOptionalUnionN)parallel_stateTF) distributedlog)ImaginaireModelc@sDeZdZdZddZddd d ZdddZddddZdddZdS) FastEmaModelUpdatera This class is used to update target model~(EMA) given source model~(regular model) and beta. The method interaface mimic :class:`EMAModelTracker` and :class:`PowerEMATracker`. Different from two classes, this class does not maintain the EMA model weights as buffers. It expects the user to have two module with same architecture and weights shape. The class is proposed to work with FSDP model where above two classes are not working as expected. Besides, it is strange to claim model weights as buffers and do unnecessary name changing in :class:`EMAModelTracker` and :class:`PowerEMATracker`. Moeving forward, we should use this class instead of above two classes. cCs d|_dS)NF) is_cached)selfrS/data/cameron/vidgen/cosmos-predict2.5/cosmos_predict2/_src/imaginaire/utils/ema.py__init__-s zFastEmaModelUpdater.__init__H.? src_modeltorch.nn.Module tgt_modelbetafloatreturnNonecCs|g}g}t||D]\}}|jtjks Jd|jd||||jq t||tj||d|ddSNz(EMA model only works in FP32 dtype, got z instead.?)alpha) zip parametersdtypetorchfloat32appenddata _foreach_mul_ _foreach_add_)rrrr target_list source_list tgt_params src_paramsrrrupdate_average1s   z"FastEmaModelUpdater.update_averagecCs.t||D] \}}|j|jq dSN)r r!r&copy_)rrrr+r,rrrcopy_to=szFastEmaModelUpdater.copy_toFr!ris_cpuboolc<|jdus Jd|r dndfdd|D|_d|_dS) Save the current parameters for restoring later. Args: parameters (iterable): Iterable of torch.nn.Parameter to be temporarily stored. F9EMA cache is already taken. Did you forget to restore it?cpucudacg|] }|qSrcloneto.0paramdevicerr Iz-FastEmaModelUpdater.cache..TNrcollected_paramsrr!r1rr?rcacheA  zFastEmaModelUpdater.cachecCN|jsJdt|j|ddD]\}}|j|j|jqg|_d|_dSaRestore the parameters in self.collected_params. Useful to validate the model with EMA parameters without affecting the original optimization process. Store the parameters before copy_to(). After validation (or model saving), use this to restore the former parameters. Args: parameters (iterable): Iterable of torch.nn.Parameter to be updated with the stored parameters. zEMA cache is not taken yet.F)strictNrr rDr&r/type_asrr!Zc_paramr>rrrrestoreL   zFastEmaModelUpdater.restoreN)r)rrrrrrrr)rrrrrrFr!rr1r2rrr!rrr) __name__ __module__ __qualname____doc__rr-r0rFrNrrrrr%s   r param_namestrtorch_compile_buffer_renamingr2rcCs |dd}|r|dd}|S)z This function creates buffer name used by EMA from parameter's name Args: param_name (str): Model's parameter name Returns: buffer_name (str): buffer name to be used for given parameter name .-z _orig_mod-)replace)rWrY buffer_namerrrget_buffer_name^s  r_csjeZdZdZd'd(fd d Zed)d*ddZd+ddZd,d-ddZ d.ddZ e d/d0d%d&Z Z S)1EMAModelTrackeraThis is a class to track the EMA model weights. The EMA weights are registered as buffers, which are extractable as state dicts. The names follow those of the regular weights, except all "." are replaced with "-" (limitation of register_buffer()). This is similar to SDXL's implementation of EMA. There are no optimizable parameters. TODO(snah): multi-EMA weights. Attributes: collected_params (list): temporarily stores the regular weights while in EMA mode. beta (float): EMA decay rate. (default: 0.9999). torch_compile_buffer_renaming (bool): whether to remove '_orig_mod-' from buffer names when torch.compile is used rFmodelrrrrYr2cst||_d|krdkstdtd||_|D]\}}|jr9t||j}||| j q!g|_ d|_ dS)zConstructor of the EMA model weight tracker. Args: model (ImaginaireModel): The PyTorch model. beta (float): EMA decay rate. (default: 0.9999). rzDecay must be between 0 and 1FN)superrrY ValueErrorrnamed_parameters requires_gradr_register_bufferr:detachr&rDr)rrarrYnamer>r^ __class__rrrs   zEMAModelTracker.__init__N iteration Optional[int]rrc Cs~g}g}|}|D]+\}}|jr8t||j}||} | jtjks-Jd| jd|| ||j q t ||j tj ||d|j ddSr) state_dictrerfr_rYr"r#r$r%r&r'rr() rrarlr)r* ema_buffersrir>r^bufferrrrr-s   zEMAModelTracker.update_averagecCsF|}|D]\}}|jr t||j}||}|j|jqdSr.)rnrerfr_rYr&r/)rrarorir>r^rprrrr0s zEMAModelTracker.copy_tor!rr1cr3) r4Fr5r6r7cr8rr9r<r?rrrArBz)EMAModelTracker.cache..TNrCrErr?rrFrGzEMAModelTracker.cachecCrHrIrKrMrrrrNrOzEMAModelTracker.restoreTrrateUnion[float, List[float]]numintenabledOptional[EMAModelTracker]cCs|sdStr tr tjdd}tjd|ddtdnt}tjd|ddt |t r5|n|g}t |t |}||krG||n|d }||krYt d |d ||||S) a= Class method to initialize per rank EMA Model Tracker with different rate. Each rank will have a different rate based on the given configuration, resulting in different EMA weights. Args: model (torch.nn.Module): The neural network model to be tracked. rate (Union[float, List[float]]): The decay rate(s) for the EMA. If a list is provided, it corresponds to rates for different ranks. num (int, optional): The number of leading ranks to consider for different rates. Defaults to 1. enabled (bool, optional): Flag to enable or disable the creation of the tracker. If False, returns None. Defaults to True. Returns: Optional[EMAModelTracker]: An instance of EMAModelTracker if enabled, otherwise None. Example: >>> model = torch.nn.Linear(10, 2) >>> tracker = EMAModelTracker.initialize_ema_from_settings(model, rate=[0.1, 0.2], num=2) >>> print(tracker) Notes: If `rate` is a list and the current rank is less than `num`, the rate for the current rank is used. If the current rank exceeds `num`, the first rate in the list is used by default. NTwith_context_parallel>> model = torch.nn.Linear(10, 2) >>> tracker = PowerEMATracker.initialize_multi_rank_ema(model, num=3, rate=0.99) >>> print(tracker) Notes: The decay rate is modified by dividing it by 2 raised to the power of the rank for each rank less than `num`. If the rank is greater than or equal to `num`, the base rate is used without modification. This approach allows higher ranked processes to have a less aggressive decay, potentially reflecting their delayed synchronization in a distributed training scenario. NTrxrzFr{r}r~rqzPowerEMATracker: rank r) rr rrr rrr rr)rrarrrtrvrdividerrrrr s    z)PowerEMATracker.initialize_multi_rank_ema)rF)rarrrrYr2r.r)T) rarrrrrtrurvr2rr) rSrTrUrr#rr-rrrrrrjrrs  rrarrvcontext str | NoneGenerator[None, None, None]c#sDfdd}| dVWddS1swYdS)aContext manager for switching between regular and EMA model weights. This function is a dispatcher that handles two main cases: 1. If the model has its own `ema_scope` method, it will be used. This allows models to define custom EMA logic (e.g., for FSDP). 2. If not, it falls back to a generic mechanism that expects the model to have a `.ema` attribute containing an EMA tracker object. Args: model (ImaginaireModel): The PyTorch model. enabled (bool): Whether switching to EMA weights is enabled (default: False). context (str | None): A logging context string, passed to the model's ema_scope if used. cs^r,tdo tj}tdotjtttf}|s |s J|r(jdStSt S)N ema_scopeema)r) hasattrcallablerrrrr`rema_scope_genericr)Zhas_custom_scopeZhas_generic_emarrvrarrscope_functionLs    z!ema_scope..scope_functionNr)rarvrrrrrr<s "rc csl|j||j|tdzdVW|j|tddS|j|tdw)zGeneric context manager for switching between regular and EMA model weights. Args: model (ImaginaireModel): The PyTorch model, which must have a `.ema` attribute. zEMA: switched to EMA weights.NzEMA: restored regular weights.)rrFr!r0r inforN)rarrrr_s   rrP)rWrXrYr2rrX)FN)rarrvr2rrrr)rarrr) __future__r contextlibrrtypingrrrrr r numpyrr# megatron.corer r ImportError%cosmos_predict2._src.imaginaire.utilsr r %cosmos_predict2._src.imaginaire.modelrrr_nnModuler`rrrrrrrs.      9G"