big>ddlmZddlZddlZddlmZddlmZmZddl Z ddl m Z ddl m Z mZddlmZmZmZmZdd lmZmZdd lmZdd lmZmZej8gd iZdd ZddZddZ ddZ!ddZ"dddd ddZ# d ddZ$d dZ%ddZ&y)!) annotationsN) attrgetter)LiteralOptional) PeftConfig)PEFT_TYPE_TO_CONFIG_MAPPINGPEFT_TYPE_TO_PREFIX_MAPPING)Conv2dLinear LoraConfig LoraLayer)get_pattern_key infer_device)PeftType)$_insert_adapter_name_into_state_dictload_peft_weights) use_rslora lora_dropout alpha_patternuse_doracd|j||k(ryt|j|tjr|j|j |yt|j|t t fr||j|<ytdt|j|d)z Update the value of the scalings of the LoRA module. Takes into consideration that scalings can be tensors from prepare_model_for_compiled_hotswap. NzSomething went wrong when trying to set the new scale value, expected to find the old value to be of type float or torch.Tensor, got instead.) scaling isinstancetorchTensorfill_floatint ValueErrortype) lora_module adapter_namers M/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/peft/utils/hotswap.py_update_scalingr&$s <(G3+%%l3U\\BL)//8 K'' 5s| D,3 L) **.{/B/B>+?+?;CSCSCYCY+Y d%%&eINN,@,@+ABR R  #I#)..33  r'c |j}|j\}}}}|r|n|}||k(r|S||kDrtd|d|d|rtj|||||j |j } || d|ddddddf<tjj|||j|j|j|jdu|j} ntj|||||j |j } || ddd|ddddf<tjj|||j|j|j|jdu|j} | jj| jk7r0td| jd| jjd |jg| jj|jjk7r:td |jjd| jjd | | j_|j%|jj| j_| S) a Get a new Conv2d layer for LoRA with padded weights according to the target rank. Args: lora_module (nn.Module): The LoRA sub-module (e.g. module.lora_A[adapter_name]). target_rank (int): The desired rank to pad to. is_lora_A (bool): True if this is the LoRA A matrix, False if LoRA B. Returns: nn.Conv2d: A newly created and padded Conv2d layer. If the rank already fit, the original layer is returned. r5r6r7r8N) kernel_sizestridepaddingr:groupszSSomething went wrong when trying to pad the LoRA weights, the new shape should be r;r<zVSomething went wrong when trying to pad the LoRA Conv2d bias, the new shape should be )r-r>r!rr?r)r9r@r rKrLrMr:rNrA) r#rBrCr- out_channels in_channelskhkwrDrGrHs r%_get_padded_conv2drSsw   F(. %L+r2$-L;M #{";K=I6 8  [+r2fmm[a[g[gh)/} }aA%&HHOO  #//%%''!!-%%$ \;Bv}}\b\h\hi(.q,;,1$%HHOO  #//%%''!!-%%$ - a||nE)"2"2"8"8!9:R R  $9>>+?+?;CSCSCYCY+Y d%%&eINN,@,@+ABR R  #I#)..33  r'cd}|jD]}t|trt}nt|trt }n2|j jD]\}}|||d}||j |<!|jjD]\}}|||d}||j|<!d}|S)a\ Pad LoRA weights in a model to a target rank while preserving the original behavior. Args: model (nn.Module): The model containing LoRA modules (with lora_A and lora_B). target_rank (int): The target rank to pad to. Returns: bool: Returns `True` if an appropriate adapter was found, else `False`. FT)rBrC) r*rr rIr rSlora_Ar+lora_B) r.rBr/r0pad_fnr$ lora_A_modulerH lora_B_modules r%_pad_lora_weightsrZsM--/ ff %'F  ''F ,2==+>+>+@ 4 'L-}+QUVI*3FMM, ' 4 ,2==+>+>+@ 4 'L-}+QVWI*3FMM, ' 4 )* r'error)rBconfigcheck_compiledct|dxs t|dd}|r?|dk(r td|dk(rtjdn|dk7rtd |d t |}|t || }nd}|s |s td |sy |y t|tsd|i}|jD]6}||_ |js|jD]}||j|<8y )a Helper function that prepares the model so that it can later be compiled and then used with hot-swapping. It is necessary to call this function on the model for hot-swapping to work if both of these are true: - the different LoRA adapters have different ranks and/or different alpha values (i.e. scalings) - you plan to torch.compile the model and want to avoid re-compilation It is important to call this function *after* the first LoRA adapter has been loaded (i.e. the one that will be swapped out) but *before* the model is compiled. Even with this function, hot-swapping LoRA adapters that target different layers is still not supported. Note: This function modifies the model in-place. If you want to restore the model to its initial state, you will have to reload it. Args: model (`nn.Module`): The model with the loaded adapter, before compilation. target_rank (`int`, *optional*): The target rank to pad the LoRA weights to. Should be the maximum rank among all LoRA adapters that will be hot-swapped. If not specified, the target ranks will not be changed. config (`LoraConfig` or `dict[str, LoraConfig]`, *optional*): Optionally pass the `LoraConfig`s of the LoRA adapters. If passed, the rank in the configs will be updated to `target_rank`. check_compiled (`str`, *optional*, defaults to `"error"`): How to handle the case when the model is already compiled, which should generally be avoided. The options are: - "error" (default): raise an error - "warn": issue a warning - "ignore": do nothing Raises: ValueError If the model is already compiled or if no adpater layer was found, raise an error. Example: ```py base_model = ... model = PeftModel.from_pretrained(base_model, path_adapter_0) # Prepare the model to allow hotswapping even if ranks/scalings of 2nd adapter differ. # You can skip this step if all ranks and scalings are identical. prepare_model_for_compiled_hotswap(model, target_rank=highest_lora_rank) model = torch.compile(model) # do inference with adapter 0 # replace the "default" lora adapter with the new one hotswap_adapter(model, path_adapter_1, adapter_name="default", torch_device=device) # do inference with adapter 1 ``` _orig_mod_compiled_call_implFr[zDCall prepare_model_for_compiled_hotswap *before* compiling the modelwarnzprepare_model_for_compiled_hotswap was called with a model that is already compiled. This will likely result in re-compilation, hurting performance. Call the function before compiling the model.ignorezCcheck_compiles should be one of 'error', 'warn', or 'ignore', got 'z ' instead.N)rBzNo adapter layers found on the model, make sure call `prepare_model_for_compiled_hotswap` after loading the first adapter and before loading the second adapter.dummy) hasattrgetattrr!warningsrar3rZrdictvaluesr rank_pattern) r.rBr\r] is_compiledconversion_found_adapterpadding_found_adapter lora_configr1s r%"prepare_model_for_compiled_hotswapro s&v%-]@UW\1]K W $cd d v % MMo x 'UVdUeeop  ;5A 1%[ Q % $(= G   fd #6"}}< #  # #"// <0; ((- <copy_dimNotImplementedErrorr!)r.rwr$r\parameter_prefixrkis_compiled_inplacek missing_keysunexpected_keysr1new_valold_valmsg module_namer0r_key alpha_keyrankalphars r%hotswap_adapter_from_state_dictrqsN%-Kwu.CTJK$//1e!6F!6KR^bcRcAeLeO#((* % W %joe,G     s 2 3    $ %QRVR[R[\kRlQmmno3  |,hhsyy~cr23 $$[1!*S/%( 3 #((*: Whhsyy~cr23 $$[1  3 3 8 8 :C@#F$8$8$=$=$?E ""&&ufhh7$$((F4E4EF   diio-GdlG\7K"*S/%(**W\\001 #6 # ((': ==GMM ) LL  w|| ,{{}F*)DW[[]OT++ }}Q'--"22 ""1% /w}}Q/066w||Dq!GMM!$44 ""1% Q 2'--"2 22399',,G A#bW[\c\i\i[jkMMm:Gf   " "3 '  p  #"  #s4 P.P.P.#P3 Q3QQ Q#"Q#c d|j|jk7r;d|jjd|jj}t||jtvrIddj tj d|jjd}t|t|j}|j }|j }t}|D]?}|j||}|j||}||k7s-td|d|d|y ) at Check if two configs are compatible for hot-swapping. Only LoRA parameters are checked for now. To hot-swap two adapters, their configs must be compatible. Otherwise, the results could be false. E.g. if they use different alpha values, after hot-swapping, the alphas from the first adapter would still be used with the weights from the 2nd adapter, which would result in incorrect behavior. There is probably a way to swap these values as well, but that's not implemented yet, and we need to be careful not to trigger re-compilation if the model is compiled (so no modification of the dict). zIncompatible PEFT types found: z and zHotswapping only supports rqr;z was passed.zConfigs are incompatible: for z != N) peft_typevaluer!CONFIG_KEYS_TO_CHECKr{rto_dictobjectr)config0config1rconfig_keys_to_checksentinelr1val0val1s r% check_hotswap_configs_compatiblers.G---/0A0A0G0G/HgN_N_NeNeMfgo 44(3G3L3L3N)O(PPU  &&'| 5 o/0A0ABooGooGxH#W{{3){{3) 4<=cU"TF$tfUV V Wr'c | t}ttj||j dd|j dd|j dd|j dd|j dd}|j |fi|}t |j|t|fd|i|}t|j}t||| } t|| ||| y) a3Substitute old adapter data with new adapter data, keeping the rest the same. As of now, only LoRA is supported. This function is useful when you want to replace the loaded adapter with a new adapter. The adapter name will remain the same, but the weights and other parameters will be swapped out. If the adapters are incomptabile, e.g. targeting different layers or having different alpha values, an error will be raised. Example: ```py >>> import torch >>> from transformers import AutoModelForCausalLM >>> from peft import PeftModel >>> from peft.utils.hotswap import hotswap_adapter >>> model_id = ... >>> inputs = ... >>> device = ... >>> model = AutoModelForCausalLM.from_pretrained(model_id).to(device) >>> # load lora 0 >>> model = PeftModel.from_pretrained(model, "path-adapter-0") >>> model = torch.compile(model) # optionally compile the model >>> with torch.inference_mode(): ... output_adapter_0 = model(inputs) >>> # replace the "default" lora adapter with the new one >>> hotswap_adapter(model, "path-adapter-1", adapter_name="default", torch_device=device) >>> with torch.inference_mode(): ... output_adapter_1 = model(inputs).logits ``` Args: model ([`~PeftModel`]): The PEFT model with the loaded adapter. model_name_or_path (`str`): The name or path of the model to load the new adapter from. adapter_name (`str`): The name of the adapter to swap, e.g. `"default"`. The name will stay the same after swapping. torch_device: (`str`, *optional*, defaults to None): The device to load the new adapter onto. **kwargs (`optional`): Additional keyword arguments used for loading the config and weights. N subfolderrevision cache_diruse_auth_tokentoken)rrrrrr))r$r)r.rwr$rr\) rrr_get_peft_typerfrom_pretrainedractive_peft_configrr rrr) r.model_name_or_pathr$ torch_devicekwargs config_clsr\rwrpeft_model_state_dicts r%hotswap_adapterr!sb#~ -!! jjd3ZZ D1jjd3!::&6=**Wd+   J(Z ' '(: Ef EF$U%=%=vF"#5UlUfUJ 363C3CD@@P$(!) r')N)returnrv)r#torch.nn.ModulerBr rCrvrztorch.nn.Linear)r#rrBr rCrvrztorch.nn.Conv2d)r.rrBr rrv) r.rrBz Optional[int]r\z,Optional[LoraConfig | dict[str, LoraConfig]]r]z"Literal['error', 'warn', 'ignore']rNone)lora_) r.rrwzdict[str, torch.Tensor]r$strr\r rr)rrrrrr)' __future__rrrfoperatorrtypingrrr peft.configr peft.mappingrr peft.tuners.lorar r r r otherrr peft_typesr save_and_loadrrLORArr&r3rIrSrZrorrrr'r%rs# $ "QBB0 R! 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