oiPSSKJr SSKrSSKJr SSKJrJrJr SSK r SSK J r SSK J r SSK Jr SSKJr S S KJr S S KJr S S KJr S S KJrJr SSjrSSjr"SS\\ RR85rg)) annotationsN)contextmanager)AnyOptionalUnion)remove_hook_from_submodules)nn)PushToHubMixin)DUMMY_MODEL_CONFIG) PeftConfig) PeftModel) MixedModel) _set_adapter_set_trainablecJ[USS5(dU$[USS5(d}[USS5(dj[USS5(dW[US5(aUR5 g [US5(a#S nUR5R U5 g g g g g ) z< Prepares the model for gradient checkpointing if necessary is_gradient_checkpointingTis_loaded_in_8bitFis_loaded_in_4bit is_quantizedenable_input_require_gradsget_input_embeddingsc&URS5 g)NT)requires_grad_)moduleinputoutputs J/home/james-whalen/.local/lib/python3.13/site-packages/peft/mixed_model.pymake_inputs_require_gradK_prepare_model_for_gradient_checkpointing..make_inputs_require_grad3s%%d+N)getattrhasattrrrregister_forward_hook)modelrs r)_prepare_model_for_gradient_checkpointingr&"s 55t < <  *E22 5-u 5 5 5.% 0 0 56 7 7  , , . U2 3 3 ,  & & ( > >?W X 4 1 6 3r!czSSKJn URU;a%[SURRSU35eg)Nr )COMPATIBLE_TUNER_TYPESzThe provided `peft_type` 'zE' is not compatible with the `PeftMixedModel`. Compatible types are: ) tuners.mixedr( peft_type ValueErrorvalue) peft_configr(s r_check_config_compatibler.9sK4$::()>)>)D)D(EF%%;$< >  ;r!c^\rSrSrSrSSU4Sjjjr\SSj5r\SSj5r\S Sj5r Sr Sr S!U4S jjr S"S jr S"S jr\S 5rS#S$S jjrS%SjrS#S&SjjrS'SjrS"SjrS"SjrSrSr\S(Sj5rS)SjrS*SjrS+SjrS,S-Sjjr\S.S/Sjj5rSr U=r!$)0PeftMixedModelCa PeftMixedModel for loading mixing different types of adapters for inference. This class does not support loading/saving, and it shouldn't usually be initialized directly. Instead, use `get_peft_model` with the argument `mixed=True`. > [!TIP] > Read the [Mixed adapter types](https://huggingface.co/docs/peft/en/developer_guides/mixed_models) guide to learn > more about using different adapter types. Example: ```py >>> base_model = ... # load the base model, e.g. from transformers >>> peft_model = PeftMixedModel.from_pretrained(base_model, path_to_adapter1, "adapter1").eval() >>> peft_model.load_adapter(path_to_adapter2, "adapter2") >>> peft_model.set_adapter(["adapter1", "adapter2"]) # activate both adapters >>> peft_model(data) # forward pass using both adapters ``` Args: model (`torch.nn.Module`): The model to be tuned. config (`PeftConfig`): The config of the model to be tuned. The adapter type must be compatible. adapter_name (`str`, `optional`, defaults to `"default"`): The name of the first adapter. low_cpu_mem_usage (`bool`, `optional`, defaults to `False`): Create empty adapter weights on meta device. Useful to speed up the loading process. c>[TU]5 [U5 [U5 SUl[ XU0U5UlURX#5 [US[5Ul [UR S5(aB[UR RS5(aSUR Rl ggg)Nconfigpretraining_tpr ) super__init__r.r&modules_to_saver base_modelset_modules_to_saver"r r3r#r4)selfr%r- adapter_name __class__s rr6PeftMixedModel.__init__bs  -1%8#$U;,GV   ;eX/AB 4??H - -'$//:P:PRb2c2c45DOO " " 13d -r!c.URR$N)r8r-r:s rr-PeftMixedModel.peft_configrs***r!c.URR$r?)r8active_adapterr@s rrCPeftMixedModel.active_adaptervs---r!c.URR$r?)r8active_adaptersr@s rrFPeftMixedModel.active_adapterszs...r!cSnSnUR5Hrup4UR5nUS:Xa[US5(a URnURR S:XaUS-nX%- nUR (dMnX- nMt X4$)zW Returns the number of trainable parameters and number of all parameters in the model. rds_numel Params4bit)named_parametersnumelr#rIr<__name__ requires_grad)r:trainable_params all_param_param num_paramss rget_nb_trainable_parameters*PeftMixedModel.get_nb_trainable_parameters~s  --/HAJQ75*#=#="^^ ''<7'!^  #I""" . 0  **r!c bUR5up[SUSSUSSSU-U- S35 g)a Prints the number of trainable parameters in the model. Note: print_trainable_parameters() uses get_nb_trainable_parameters() which is different from num_parameters(only_trainable=True) from huggingface/transformers. get_nb_trainable_parameters() returns (trainable parameters, all parameters) of the Peft Model which includes modified backbone transformer model. For techniques like LoRA, the backbone transformer model is modified in place with LoRA modules. However, for prompt tuning, the backbone transformer model is unmodified. num_parameters(only_trainable=True) returns number of trainable parameters of the backbone transformer model which can be different. ztrainable params: z,dz || all params: z || trainable%: dz.4fN)rUprint)r:rPrQs rprint_trainable_parameters)PeftMixedModel.print_trainable_parameterssS'+&F&F&H#  !1" 56$R.)!11I=cB D r!c~>[TU]U5$![a US:Xae[URU5s$f=f)z1Forward missing attributes to the wrapped module.r8)r5 __getattr__AttributeErrorr"r8)r:namer<s rr]PeftMixedModel.__getattr__sC 27&t, , 2|#4??D1 1 2s '<<c&UR"U0UD6$)z Forward pass of the model. )r8r:argskwargss rforwardPeftMixedModel.forwards///r!c:URR"U0UD6$)z Generate output. )r8generaterbs rrhPeftMixedModel.generates''888r!c## URR5 Sv URR5 g!URR5 f=f7f)z Disables the adapter module. N)r8disable_adapter_layersenable_adapter_layersr@s rdisable_adapterPeftMixedModel.disable_adapters=  4 OO 2 2 4  OO 1 1 3DOO 1 1 3sA=AAAc[U5 X RU'URRXUS9 UR X!5 g![a XR;a URU ef=f)a^ Add an adapter to the model based on the passed configuration. This adapter is not trained. To load a trained adapter, check out [`PeftModel.load_adapter`]. The name for the new adapter should be unique. The new adapter is not automatically set as the active adapter. Use [`PeftModel.set_adapter`] to set the active adapter. Args: adapter_name (`str`): The name of the adapter to be added. peft_config ([`PeftConfig`]): The configuration of the adapter to be added. low_cpu_mem_usage (`bool`, `optional`, defaults to `False`): Create empty adapter weights on meta device. Useful to speed up the process when loading saved adapters. > [!TIP] > Don't use `low_cpu_mem_usage=True` when creating a new PEFT adapter for training (training is untested > and discouraged for PeftMixedModel in general). )low_cpu_mem_usageN)r.r-r8inject_adapter Exceptionr9)r:r;r-rps r add_adapterPeftMixedModel.add_adaptersu. !- -8  \ * OO * *4Qb * c   ;  ///$$\2  s (A(A/c [USS5=ncgURc[U5UlOURRU5 [ UU[USS5UR S9 g)Nr7) module_namesinference_mode)r"r7setupdaterrw)r:r-r;r7s rr9"PeftMixedModel.set_modules_to_savesk&{4EtL LO U     '#&#7D  ' ' 8   .?F&55  r!c \[U[5(aU/n[U5[URR 55- nU(a;[ S[ U5S[ URR 5535eURRXS9 [XUS9 g)a2 Sets the active adapter(s) for the model. Note that the order in which the adapters are applied during the forward pass may not be the same as the order in which they are passed to this function. Instead, the order during the forward pass is determined by the order in which the adapters were loaded into the model. The active adapters only determine which adapters are active during the forward pass, but not the order in which they are applied. Additionally, this function will set the specified adapter to trainable (i.e., requires_grad=True) unless inference_mode is True. Args: adapter_name (str, list[str]): The name(s) of the adapter(s) to set as active inference_mode (bool, optional): Whether the activated adapter should be frozen (i.e. `requires_grad=False`). Default is False. Adapter(s) not found, available adapters: )rwN) isinstancestrrxr-keysr+sortedr8 set_adapterr)r:r;rw mismatcheds rrPeftMixedModel.set_adapters$ lC ( ((>L&T-=-=-B-B-D)EE fZ011QRXY]YiYiYnYnYpRqQrs  ##L#PTGr!c L[U[5(aU/n[U5[URR 55- nU(a;[ S[ U5S[ URR 5535eURRU5 g)Nr|r}) r~rrxr-rr+rr8delete_adapter)r:r;rs rrPeftMixedModel.delete_adapters lC ( ((>L&T-=-=-B-B-D)EE fZ011QRXY]YiYiYnYnYpRqQrs  &&|4r!c:URR"U0UD6$)a9 This method merges the adapter layers into the base model. This is needed if someone wants to use the base model as a standalone model. Args: progressbar (`bool`): whether to show a progressbar indicating the unload and merge process safe_merge (`bool`): whether to activate the safe merging check to check if there is any potential Nan in the adapter weights adapter_names (`List[str]`, *optional*): The list of adapter names that should be merged. If None, all active adapters will be merged. Defaults to `None`. )r8merge_and_unloadrbs rrPeftMixedModel.merge_and_unload's//@@@r!c:URR"U0UD6$)zx Gets back the base model by removing all the adapter modules without merging. This gives back the original base model. )r8unloadrbs rrPeftMixedModel.unload8s %%t6v66r!cH[SURRS35e)Nz&get_layer_status is not supported for . TypeErrorr<rNr@s rget_layer_statusPeftMixedModel.get_layer_status?#@AXAX@YYZ[\\r!cH[SURRS35e)Nz&get_model_status is not supported for rrr@s rget_model_statusPeftMixedModel.get_model_statusBrr!c.[R"U5$r?)r _split_kwargs)clsrds rrPeftMixedModel._split_kwargsEs&&v..r!c,[R"XUS9$)N) is_trainable)r_check_new_adapter_config)r:r-rs rr(PeftMixedModel._check_new_adapter_configIs224S_``r!cr[R"XU/UQ70UD6nURUR5 U$)a Load a trained adapter into the model. The name for the new adapter should be unique. The new adapter is not automatically set as the active adapter. Use [`PeftModel.set_adapter`] to set the active adapter. Args: adapter_name (`str`): The name of the adapter to be added. peft_config ([`PeftConfig`]): The configuration of the adapter to be added. is_trainable (`bool`, *optional*, defaults to `False`): Whether the adapter should be trainable or not. If `False`, the adapter will be frozen and can only be used for inference. torch_device (`str`, *optional*, defaults to None): The device to load the adapter on. If `None`, the device will be inferred. autocast_adapter_dtype (`bool`, *optional*, defaults to `True`): Whether to autocast the adapter dtype. Defaults to `True`. Right now, this will only cast adapter weights using float16 and bfloat16 to float32, as this is typically required for stable training, and only affect select PEFT tuners. ephemeral_gpu_offload (`bool`, *optional*, defaults to `False`): Whether to use ephemeral GPU offloading for partially loaded modules. Defaults to `False`. low_cpu_mem_usage (`bool`, `optional`, defaults to `False`): Create empty adapter weights on meta device before loading the saved weights. Useful to speed up the process. kwargs: (`optional`): Additional arguments to modify the way the adapter is loaded, e.g. the token for Hugging Face Hub. )r load_adapterrrF)r:model_idr;rcrdrs rrPeftMixedModel.load_adapterLs:@'' VtVvV --. r!cH[SURRS35e)Nz)Model card creation is not supported for  (yet).NotImplementedErrorr<rN)r: output_dirs rcreate_or_update_model_card*PeftMixedModel.create_or_update_model_cardqs#!$MdnnNeNeMffm"noor!c H[SURRS35e)NzSaving is not supported for rr)r:save_directorysafe_serializationselected_adaptersrds rsave_pretrainedPeftMixedModel.save_pretrainedts%"$@AXAX@YY`"abbr!c PSSKJnJn UcURSS5URSS5URSS5URSS5S.n URS S5=n (aXS 'U[R "U40U D6R "U40UD6nO:[U[5(a U(+UlO[S UR35eURU;a[S URS 35e[US S5bL[[URR!55R#SS155S:a [%U5 UR&(aU(a [S5eU(+UlU"XU5nUR("X#4SU0UD6 U$)a Instantiate a PEFT mixed model from a pretrained model and loaded PEFT weights. Note that the passed `model` may be modified inplace. Args: model (`nn.Module`): The model to be adapted. model_id (`str` or `os.PathLike`): The name of the PEFT configuration to use. Can be either: - A string, the `model id` of a PEFT configuration hosted inside a model repo on the Hugging Face Hub. - A path to a directory containing a PEFT configuration file saved using the `save_pretrained` method (`./my_peft_config_directory/`). adapter_name (`str`, *optional*, defaults to `"default"`): The name of the adapter to be loaded. This is useful for loading multiple adapters. is_trainable (`bool`, *optional*, defaults to `False`): Whether the adapter should be trainable or not. If `False`, the adapter will be frozen and use for inference config ([`~peft.PeftConfig`], *optional*): The configuration object to use instead of an automatically loaded configuration. This configuration object is mutually exclusive with `model_id` and `kwargs`. This is useful when configuration is already loaded before calling `from_pretrained`. low_cpu_mem_usage (`bool`, `optional`, defaults to `False`): Create empty adapter weights on meta device before loading the saved weights. Useful to speed up the process. kwargs: (`optional`): Additional keyword arguments passed along to the specific PEFT configuration class. r )PEFT_TYPE_TO_CONFIG_MAPPING PEFT_TYPE_TO_MIXED_MODEL_MAPPINGN subfolderrevision cache_dirtoken)rrrruse_auth_tokenz+The input config must be a PeftConfig, got zAdapter of type z# is not supported for mixed models. hf_device_mapcpudiskrzRCannot set a prompt learning adapter to trainable when loading pretrained adapter.r)mappingrrgetr _get_peft_typefrom_pretrainedr~rwr+r<r*r"lenrxrvalues intersectionris_prompt_learningr) rr%rr;rr3rdrr hf_kwargsrs rrPeftMixedModel.from_pretrained}sP [ >#ZZ T:"JJz48#ZZ T:GT2 I "(,rsT# %'' 8-3!/Y. I^UXX__Ir!