o 8wiu@sddlmZddlZddlZddlZddlmZddlZddlZddlm Z m Z ddl m Z m Z ddlmZddlmZddlmZd d lmZmZmZmZmZmZd d lmZd d ZddZ d4ddZ d5d6ddZ!d7d#d$Z"   d8d9d&d'Z#d(d)d*d+Z$ d:d;d2d3Z%dS)<) annotationsN)Optional) file_existshf_hub_download)EntryNotFoundErrorLocalEntryNotFoundError) load_file)http_user_agent)PEFT_TYPE_TO_PREFIX_MAPPING)EMBEDDING_LAYER_NAMESSAFETENSORS_WEIGHTS_NAME WEIGHTS_NAMEAuxiliaryTrainingWrappercheck_file_exists_on_hf_hub infer_device)PeftTypecCs"t|dot|jtjjtjjfS)z.Check if the layer has an embedding base layer base_layer)hasattr isinstancertorchnnLinear Embedding)layerrU/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/peft/utils/save_and_load.pyhas_valid_embedding_base_layer)s"rcCs:|D]\}}|s||ks|t|ddkr|SqdS)z7Get the name of the embedding module for a given layer.rN) named_modulesgetattr)modelris_embedding_in_target_modulesnamemodulerrrget_embedding_layer_name.s r$defaultFautoc s|rt|d|}|jdur|jtjtjfvrj}|dkr/fddD}n9|dkr=fddD}n+|dkrfi}D]}d |vrd|||<|d d d }|vrd|||<qEnt fd d| D}jtjkrj } | durfd d| D} | _ | | |}j rddfddfdd| D}njtjkrj}|dkrʇfddD}nw|dkrهfddD}nh|dkri}D]!}d|vr|||<|dd d }|vr|||<qn;t jtjkrfddD}n'jrUi}jtjkr?|jj|d<|jj|d<|jjj} njrK|jjj} n|} | |d<njtjkrtjfddD}jrdvr{tdd|d<d|d<njtjkrfddD}njtjkr!i}jd krt j!} njd!krt j"} n jd"krt j#} nt j$} j%r D]:}d#|vr |&j&\} } |'|d$| j(| d%i|'|d&t j)| d'd(dddddd'f*iqn fd)dD}d*|d*<n jt+tvr9tjfd+dD}ntd,j|,D](\}t-|t.rlfd-d D}|'fd.d|/| DqEd/}|d0krt0d1rt1fd2d3t2Drjtj3krt45d4d5}}n`|d0krtt|d6dd7d}td8d}d/}|durt6j78t6j79|d9}|pt:|d9}|durt45d:|d;d/}n|}|r|r|r||j;j<=|j>krt45d<d5}nd/}|r-t0|d=r-|?|@fD]"}|rtA|r*tB|||r*|'fd>d Dq n|r5t45d?fd@d| D}|S)AuQ Get the state dict of the Peft model. Args: model ([`PeftModel`]): The Peft model. When using torch.nn.DistributedDataParallel, DeepSpeed or FSDP, the model should be the underlying model/unwrapped model (i.e. model.module). state_dict (`dict`, *optional*, defaults to `None`): The state dict of the model. If not provided, the state dict of the passed model will be used. adapter_name (`str`, *optional*, defaults to `"default"`): The name of the adapter whose state dict should be returned. unwrap_compiled (`bool`, *optional*, defaults to `False`): Whether to unwrap the model if torch.compile was used. save_embedding_layers (`Union[bool, str]`, , *optional*, defaults to `auto`): If `True`, save the embedding layers in addition to adapter weights. If `auto`, checks the common embedding layers `peft.utils.other.EMBEDDING_LAYER_NAMES` in config's `target_modules` when available. Based on it sets the boolean flag. This only works for 🤗 transformers models. _orig_modNnoneci|] }d|vr||qS)lora_r.0k state_dictrr Xz-get_peft_model_state_dict..allc&i|]}d|vs d|vr||qSr*biasrr+r.rrr0Z& lora_onlyr*rr5cs.i|]\}}d|vr|vsd|vr||qSr4rr,r-v adapter_namerrr0es.c$i|]\}}|dd|qS.replacer8r:rrr0i$lora_magnitude_vector..weightcs|r |dd}|S)Niendswithr-)new_dora_suffixrrrenamed_dora_weightsss  z7get_peft_model_state_dict..renamed_dora_weightsci|] \}}||qSrrr8rIrrr0xcr))boft_rr+r.rrr0}r1cr3)rMr5rr+r.rrr0r6 boft_onlyrMcs*i|]}|dddr||qS)r> adaption_)split startswithr+r.rrr0s*prefix_task_colsprefix_task_rowsprompt_embeddingscsi|] }|vr||qSrrr+)r/ vera_prefixrrr0r1zbase_model.vera_A.zModel was initialised to not save vera_A and vera_B but config now specifies to save projection! Set `config.save_projection` to `False`.zbase_model.vera_B.cr))internal_xlora_classifierrr+r.rrr0r1il vblora_logits _topk_indices)dtype _topk_weightsrOdimcr))rYrr+r.rrr0r1zbase_model.vblora_vector_bank.csi|] }|vr||qSrrr+)prefixr/rrr0r1zUnknown PEFT type passed: cs2i|]\}}|dr|d|qSr>)rR removeprefixr8r"rrr0s cs i|] \}}d||qSr`rr8rbrrr0s Fr&target_modulesc3s|]}|jvVqdS)N)rcr+)configrr sz,get_peft_model_state_dict..zXSetting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.Trd vocab_sizebase_model_name_or_pathz config.jsonz Could not find a config file in z4 - will assume that the vocabulary was not modified.zdSetting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.get_input_embeddingscsi|] \}}|vr||qSrrr8)embedding_module_namerrr0 r1uYCould not identify embedding layer(s) because the model is not a 🤗 transformers model.cr<r=r@r8r:rrr0rB)Cr peft_configr/ peft_typerLORAADALORAr5rQNotImplementedErroritems rank_pattern!resize_state_dict_by_rank_patternuse_doraBOFTADAPTION_PROMPTis_prompt_learningMULTITASK_PROMPT_TUNINGprompt_encoderrSrT embeddingweightinference_modeget_prompt_embedding_to_saveVERAr save_projection ValueErrorXLORAVBLORA num_vectorsruint8int16int32int64save_only_topk_weightstopkupdatetosoftmax contiguouslistrrradapter_state_dictranyr TRAINABLE_TOKENSwarningswarnospathexistsjoinrrd __class__from_pretrainedrfrhget_output_embeddingsrr$)r r/r;unwrap_compiledsave_embedding_layersr5 to_returnr- bias_namerprU indices_dtypelogitsindicesr#module_state_dictr!rfmodel_idhas_base_configlocal_config_existsrrr) r;rdrir"rHr_rIr/rVrget_peft_model_state_dict6s8                   6             rr torch.nn.Modulepeft_model_state_dictdict[str, torch.Tensor]ignore_mismatched_sizesboolreturnRtuple[dict[str, torch.Tensor], list[tuple[str, tuple[int, ...], tuple[int, ...]]]]cCs|s|gfSg}|}|D]3\}}||vrq||jddkr/||d|kr/q||j|jkrC|||j||jfq|D]\}}}||=qF||fS)NrOr )r/roshapenumelappend)r rr mismatchedr/keytensor_rrr_find_mismatched_keyss*rr/r;strparameter_prefixcCsi}|D];\}}||vr=||d}d|vr1d|ddd}|||d|}n|d|}|||<q|||<q|S)zbUtility function to remap the state_dict keys to fit the PEFT model by inserting the adapter name.r r>N)rorQrrA)r/r;rrrvalsuffixsuffix_to_replacerrr$_insert_adapter_name_into_state_dict.s  rlow_cpu_mem_usagecs||j|}|}|D],\}}t|tr7||} | D]} |d| } |d| | } || || <|| =qq |jsA|jtjkrE|}n|jtj krN|}n|jt vrJi}t |j} |jtj kr|j r|j |j\}}t|}|D][} d| vr|| tj}| dd}|| dd}tj|d|jdddgdd }t|}tg|jd d|td |jd||}|||<|| =|| dd=qtt||| d }|jtjkr|j}|d ur| ||na|jtj!kr|j"rd |vrt#d|j"s d |vr t$%dn@|j"st$%dn6|jtj&kr4d|fddfdd|'D}n|jtj(krIt)dd|DrIt#dnt*t+|||d\}}|rt|j,|ddd}|-D]}t.|drq|/|qdn|j,|dd}|jr|j0|j1j,d|didd|jtj2kr|j0|j,|dd|rd 3d!d"|D}d#|j4j5d$|d}t$%||S)%a Set the state dict of the Peft model. Args: model ([`PeftModel`]): The Peft model. peft_model_state_dict (`dict`): The state dict of the Peft model. adapter_name (`str`, *optional*, defaults to `"default"`): The name of the adapter whose state dict should be set. ignore_mismatched_sizes (`bool`, *optional*, defaults to `False`): Whether to ignore mismatched in the state dict. low_cpu_mem_usage (`bool`, `optional`, defaults to `False`): This argument must be `True` if the `model` was loaded with adapter weights on the meta device, e.g. after calling `inject_adapter_in_model` with `low_cpu_mem_usage=True`. Otherwise, leave it as `False`. r>rZr?r\r rOT)keepdimr]Nz-inf)r;rzbase_model.vera_AzXSpecified to load vera_A and vera_B from state dictionary however they were not present!zSpecified to not load vera_A and vera_B from state dictionary however they are present in state dictionary! Consider using them to ensure checkpoint loading is correct on all platforms using `peft_config.save_projection = True`zSpecified to not load vera_A and vera_B from state dictionary. This means we will be relying on PRNG initialisation to restore these projections using `config.projection_prng_key`, which may not be accurate on all system configurations.rCcs|r |d}|S)NrDrErG)old_dora_suffixrrrIs z7set_peft_model_state_dict..renamed_dora_weightscrJrrr8rKrrr0rLz-set_peft_model_state_dict..css|]}d|vVqdS)z.oft_r.Nr)r,rrrrresz,set_peft_model_state_dict..zTrying to load old OFT checkpoint, which is no longer supported. Please install PEFT <= v0.15.2 to load it or train a new OFT adapter.)rF)strictassign%_move_adapter_to_device_of_base_layer)rryrU c Ss*g|]\}}}d|d|d|dqS)z- z: found shape z in the checkpoint and z in the model instantiatedr)r,rshape1shape2rrr sz-set_peft_model_state_dict..zSome weights of zy were not initialized from the model checkpoint and are being ignored because you passed `ignore_mismatched_sizes=True`: )6rjrrradapter_state_dict_load_maprurkrrtrr rrvblora_vector_bankrrkeysrrlongrAcatsumlogzerosfill_floatdevicescatterrrmrpresize_modules_by_rank_patternr|r}r~rrrlroOFTrrnrload_state_dictmodulesrrrwrxrvrr__name__)r rr;rrrdr/r"r#key_mapr- lookup_key store_keyrrrstate_dict_keysr9 original_key topk_weights topk_logitsmatrixrpmismatched_keys load_resultmismatched_warningmsgr)rrIrset_peft_model_state_dictAs                         rT) weights_onlycOstj|d|i|S)zCall torch.load and handle weights_only. Defaults to weights_only=True to anticipate upcoming switch on the PyTorch side. r)rload)rargskwargsrrr torch_loadsrrr Optional[str] key_mappingOptional[dict[str, str]]dictc sdddurtj|dn|}|durt}dfdd }dvr*td<tjtj|tr?tj|t}d}ntjtj|trTtj|t}d}nt j j r|dd} d dzt ||fd di}d}Wnvty|dd}t ||fd di}d}Yn\wd d} | durd d} |dd}t||d dd d| d} | }| rt |tfi}n'z t |tfi}Wntytd|d|dtdtd|d w|r ttjdr|tdkrt|dd} nt||d} n t|t|d} |s| } | Si} | D]F\} }| dr,d}n | dr5d}ntd| |} |D]\}}t||| \}}|dkrX|} nqB|| }|| |<q| S)aP A helper method to load the PEFT weights from the HuggingFace Hub or locally Args: model_id (`str`): The local path to the adapter weights or the name of the adapter to load from the HuggingFace Hub. device (`str`): The device to load the weights onto. key_mapping (dict, *optional*, defaults to None) Extra mapping of PEFT `state_dict` keys applied before loading the `state_dict`. When this mapping is applied, the PEFT-specific `"base_model.model"` prefix is removed beforehand and the adapter name (e.g. `"default"`) is not inserted yet. Only pass this argument if you know what you're doing. hf_hub_download_kwargs (`dict`): Additional arguments to pass to the `hf_hub_download` method when loading from the HuggingFace Hub. subfolderNTcs2|rtnt}dddurtjd|S|S)Nr)r rgetrrr)use_safetensors weights_namehf_hub_download_kwargsrrget_hub_filenames z+load_peft_weights..get_hub_filename user_agentF)rlocal_files_onlytokenuse_auth_tokenrevision repo_type)repo_idfilenamerrrzCan't find weights for z in z8 or in the Hugging Face Hub. Please check that the file z or z is present at r>mpscpu)r) map_locationzbase_model.model.z base_model.zAn error occurred while trying to load a PEFT state_dict with key_mapping. This should not happen. Please open an issue on https://github.com/huggingface/peft/issues and report the error.r)T)rrrrrr rr rhuggingface_hub constantsHF_HUB_OFFLINEpoprrrrr~rrbackendsrsafe_load_filerrorRraresubn)rrrrrrrr hub_filenamerhas_remote_safetensors_fileadapters_weightsremapped_adapters_weightsrrr_pattern replacementkey_new n_replacekey_with_prefixrrrload_peft_weightss                 r)Nr%Fr&)F)r rrrrrrr)r/rr;rrrrr)r%FF)rrrr)NN)rrrrrrrr)& __future__rrrrtypingrrrrrhuggingface_hub.errorsrrsafetensors.torchrrtransformers.utilsr peft.mappingr otherr r rrrr peft_typesrrr$rrrrrrrrrrs<        ^  "