o vi@Y@sddlmZddlZddlmZmZddlmZddlm Z ddl m Z m Z ddl mZddlmZmZmZmZmZmZmZddlZddlZddlmZdd lmZdd lmZdd lm Z dd l!m"Z"dd l#m$Z$ddl%m&Z&ddl'm(Z(ddl)m*Z*edddZ+Gddde,eZ-d3d4ddZ.e ddGdddeZ/e ddGdd d e/Z0e ddGd!d"d"e0Z1Gd#d$d$ej2Z3Gd%d&d&e3Z4Gd'd(d(e3Z5Gd)d*d*e3Z6Gd+d,d,e3Z7Gd-d.d.ej2eZ8Gd/d0d0e8Z9Gd1d2d2e8Z:dS)5) annotationsN)ABCabstractmethod) defaultdict) nullcontext) dataclassfields)Enum)AnyDictListOptionalTupleTypeVarUnion) ProcessGroup) batch_mul) instantiate)log) broadcast) count_params)disabled_train)easy_ioT BaseCondition)boundc@s"eZdZdZdZdZd ddZdS) DataTypeimagevideomixreturnstrcC|jSN)valueselfr'M/data/cameron/vidgen/cosmos-policy/cosmos_policy/_src/predict2/conditioner.py__str__/szDataType.__str__Nr r!)__name__ __module__ __qualname__IMAGEVIDEOZMIXr)r'r'r'r(r*s r condition process_groupOptional[ProcessGroup]r cCsl|jr|S|jdd}|D]\}}|dur(t|tjr!|}t||||<qd|d<t|di|S)zR Broadcast the condition from the minimum rank in the specified group(s). Fskip_underscoreNT_is_broadcastedr') is_broadcastedto_dictitems isinstancetorchTensorcudartype)r0r1kwargskeyr$r'r'r(broadcast_condition3s  r@T)frozenc@sBeZdZUdZdZded<ddd d Zedd d ZdddZ dS)rz Attributes: _is_broadcasted: Flag indicating if parallel broadcast splitting has been performed. This is an internal implementation detail. Fboolr5Tr4r Dict[str, Any]csfddtDS)zConverts the condition to a dictionary. Returns: Dictionary containing the condition's fields and values. cs,i|]}|jdr s|jt|jqS)_)name startswithgetattr).0fr&r4r'r( Us,z)BaseCondition.to_dict..)rrJr'rJr(r7NszBaseCondition.to_dictcCr"r#)r5r%r'r'r(r6WzBaseCondition.is_broadcastedr1torch.distributed.ProcessGroupcCs|jr|St||S)a]Broadcasts and splits the condition across the checkpoint parallelism group. For most condition, such as Text2WorldCondition, we do not need split. 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This abstract base class initializes and manages a collection of embedders that can dynamically adjust their dropout rates based on conditioning. Attributes: KEY2DIM (dict): A mapping from output keys to dimensions used for concatenation. embedders (nn.ModuleDict): A dictionary containing all embedded models initialized and configured based on the provided configurations. Parameters: emb_models (Union[List, Any]): A dictionary where keys are embedder names and values are configurations for initializing the embedders. Example: See Edify4ConditionerConfig rTr^ emb_modelsUnion[List, Any]c stt|_t|D]P\}\}}t|}t|t s)Jd|j j dt |dd|_ t |dd|_|j sKt|_|D]}d|_qA|td|d |d |||j|<qdS) Nzembedder model z% has to inherit from AbstractEmbModelrwTrzrFzInitialized embedder #-z: )rnronn ModuleDict embedders enumerater8rr9rmrur+rGrwrzrtrain parameters requires_gradevalrinfor)r&rnemb_nameZ emb_configembedderparamrtr'r(ros"      zGeneralConditioner.__init__Nbatchr override_dropout_rateOptional[Dict[str, float]]r r cCst)z?Should be implemented in subclasses to handle conditon datatype)NotImplementedError)r&rrr'r'r(rszGeneralConditioner.forwardc s.tt}dur iD]jvsJdqjD]i\jr+tntj}|Bt j t rH j  d}n$t j ttjjfrbfddj D}n tdjjdWdn1svwY|D] \}}|||qq"fdd|DS) a< Processes the input batch through all configured embedders, applying conditional dropout rates if specified. Output tensors for each key are concatenated along the dimensions specified in KEY2DIM. Parameters: batch (Dict): The input data batch to process. override_dropout_rate (Optional[Dict[str, float]]): Optional dictionary to override default dropout rates per embedder key. Returns: Dict: A dictionary of output tensors concatenated by specified dimensions. Note: In case the network code is sensitive to the order of concatenation, you can either control the order via config file or make sure the embedders return a unique key for each output. Nzinvalid name found c s(g|]}|d|qSr#)rget)rHk)rrrrr'r( sz/GeneralConditioner._forward..z Embedder 'zW' requires an 'input_key' attribute to be defined as either a string or list of stringsc s*i|]\}}|tj|j|ddqS))r)r:catKEY2DIMr)rHrvr%r'r(rKs*z/GeneralConditioner._forward..)rlistkeysrr8rwrr:no_gradr9r|r!rr omegaconf listconfig ListConfigKeyErrorrur+append)r&rroutputZembedding_contextZemb_outrrr')rrrrr&r(_forwards:  zGeneralConditioner._forward data_batchTuple[Any, Any]cCs\ii}}|jD]\}}d||<|jdkrdnd||<q |||d}|||d}||fS)u Processes the provided data batch to generate two sets of outputs: conditioned and unconditioned. This method manipulates the dropout rates of embedders to simulate two scenarios — one where all conditions are applied (conditioned), and one where they are removed or reduced to the minimum (unconditioned). This method first sets the dropout rates to zero for the conditioned scenario to fully apply the embedders' effects. For the unconditioned scenario, it sets the dropout rates to 1 (or to 0 if the initial unconditional dropout rate is insignificant) to minimize the embedders' influences, simulating an unconditioned generation. Parameters: data_batch (Dict): The input data batch that contains all necessary information for embedding processing. The data is expected to match the required format and keys expected by the embedders. Returns: Tuple[Any, Any]: A tuple containing two condition: - The first one contains the outputs with all embedders fully applied (conditioned outputs). - The second one contains the outputs with embedders minimized or not applied (unconditioned outputs). r-C6?rr)rr8rz)r&rcond_dropout_ratesZ dropout_ratesrrr0 un_conditionr'r'r(get_condition_unconditions   z,GeneralConditioner.get_condition_unconditionc Csii}}|jD]\}}d||<t|trd||<q |jdkr#dnd||<q t|}d|vr?t|dtjr?|d|d<|||d}|||d}||fS)zt Similar functionality as get_condition_uncondition But use negative prompts for unconditon rrrZneg_t5_text_embeddingst5_text_embeddingsr) rr8r9rrzcopydeepcopyr:r;) r&rrZuncond_dropout_ratesrrZdata_batch_neg_promptr0rr'r'r("get_condition_with_negative_prompts       z5GeneralConditioner.get_condition_with_negative_prompt)rrr#)rr rrr r )rr rrr r )rr r r) r+r,r-rOrrorrrrrrr'r'rtr(rs 6rc"eZdZ d d fdd ZZS) VideoConditionerNrr rrr rRct||}tdi|SNr')rnrrRr&rrrrtr'r(r/zVideoConditioner.forwardr#)rr rrr rRr+r,r-rrr'r'rtr(r.rcr) GR00TV1Img2VidConditionerNrr rrr rZcrr)rnrrZrrtr'r(r9rz!GR00TV1Img2VidConditioner.forwardr#)rr rrr rZrr'r'rtr(r 8rr r#)r0rr1r2r r); __future__rrabcrr collectionsr contextlibr dataclassesrrenumr typingr r r r rrrrr:torch.nnrtorch.distributedr2cosmos_policy._src.imaginaire.functional.batch_opsr)cosmos_policy._src.imaginaire.lazy_configr#cosmos_policy._src.imaginaire.utilsr4cosmos_policy._src.imaginaire.utils.context_parallelr0cosmos_policy._src.imaginaire.utils.count_paramsrZ2cosmos_policy._src.imaginaire.utils.disabled_trainr+cosmos_policy._src.imaginaire.utils.easy_iorrr!rr@rrRrZModulermrrrrrrr r'r'r'r(sJ    $           %Q5-""