(HJj VddlZddlZddlmZddlmZddlmZddl m Z ddl m Z ddlmZmZddlmZmZddlmZdd lmZmZdd lmZmZmZd efd Z dd e j<dedede fdZ!d e j<de"de j<fdZ#d e j<de j<fdZ$dZ%y)N)Path)Dict) tree_flattentree_unflatten)QuantizedSwitchLinear SwitchLinear)get_total_parameters) DoRAEmbedding DoRALinear) LoRAEmbedding LoRALinearLoRASwitchLinearschedule_configc8ttj|d}|d}|d}||}|jddx}rY|jdd}tjj |||}tjj ||g|dzgS|S)z? Build a learning rate schedule from the given config. name argumentsrwarmup warmup_initgr )getattropt schedulersgetlinear_schedulejoin_schedules)r schedule_fnr initial_lrbound_schedule_fn warmup_stepsr warmup_fns \/Users/ahmed/devFolder/claude-voice/.venv/lib/python3.12/site-packages/mlx_lm/tuner/utils.pybuild_scheduler#s#../&*ABK ,I1J#Y/&**8Q77|7%))-= NN22 \ ~~,, ) *\A-=,>  ! model num_layersconfiguse_dorac & fd}jddx 1t fd}|jD]}|j||jt |d dD]N}|j Dcgc]\}}| vs |||f} }}| s5|j t| P|j Dcgc]\}}| vs |||f} }}| r|j t| yycc}}wcc}}w)a Convert some of the models linear layers to lora layers. Args: model (nn.Module): The neural network model. num_layers (int): The number of blocks to convert to lora layers starting from the last layer. config (dict): More configuration parameters for LoRA, including the rank, scale, and optional layer keys. use_dora (bool): If True, uses DoRA instead of LoRA. Default: ``False`` c>s)t|dr|jdddSt|tjtj frrt nt}nt|ttfr*r!tt|jdt}n[t|tjtjfrrt nt"}n"tdt|jd|j%|dddS) Nto_lorarankscaledropout)rr-r.z doesn't support DoRA yet.zCan't convert layer of type z to LoRA)hasattrr+ isinstancennLinearQuantizedLinearr rr r ValueErrortype__name__r EmbeddingQuantizedEmbeddingr r from_base)layer LoRALayerr'r(s r"r+z&linear_to_lora_layers..to_lora9sGE95==.Woy)!  ebii););< =&. JI  .CD E DK$8$8#99S!TUU(I  b.C.CD E)1 }I.tE{/C/C.DHM "" Vn/9% #  r$keysNctjtjtttj tj f}t|ds t||rj|yy)Nr+) r2r3r4r rr8r9r0r1add)pmtypesr=s r"get_keys_for_loraz0linear_to_lora_layers..get_keys_for_loraXsU ""% %% Eq)$ 1e(< )=r$r)rsetlayersapply_to_modulesmax named_modulesupdate_modulesr) r%r&r'r(r+rClkrA lora_layers lora_modulesr=s `` @r"linear_to_lora_layersrN&s & 8 64((1u A  0 1\\3z1--/ 034??3DR3D41aT 71:3D R   ^K8 91 160C0C0ES0E1dQ O0ELS ^L9: STs? D D D  D  adapter_pathreturnct|}|jstd|t|dz d5}t j d it j|}dddtdd}|dk7r&t||j|j|dk( |jt|d z d |S#1swYbxYw)a Load any fine-tuned adapters / layers. Args: model (nn.Module): The neural network model. adapter_path (str): Path to the adapter configuration file. Returns: nn.Module: The updated model with LoRA layers applied. z!The adapter path does not exist: zadapter_config.jsonr/Nfine_tune_typelorafulldora)r(zadapters.safetensorsF)strict)rexistsFileNotFoundErroropenrBSimpleNamespacejsonloadrrNr&lora_parameters load_weightsstr)r%rOfidr'rRs r" load_adaptersrbqs %L    "CL> RSS l22C 8C&&838 9V%5v>N      " "$.   s<*@@A%P L 9 8s )CC cg}|jD]3\}}t|ts|j||jf5t |dkDr|j t||S)z Remove the LoRA layers from the model. Args: model (nn.Module): The model with LoRA layers. Returns: nn.Module: The model without LoRA layers. r)rHr1rappendlinearlenrIr)r% reset_layersrmodules r"remove_lora_layersrishL++- f fj )   v}} 5 6. <1 ^L9: Lr$c t|dz }tdt|jDdz }t d|dz|z dd|dd|ddy) Ng.Ac3:K|]\}}|jyw)N)size).0_vs r" z-print_trainable_parameters..s JItq!AFFIszTrainable parameters: dz.3fz% (zM/zM))r sumrtrainable_parametersprint)r%total_p trainable_ps r"print_trainable_parametersrwsr"5)C/G J|E,F,F,HI JJSP +"3g"=s CD  Bwsm2 /r$)F)&r\rBpathlibrtypingrmlx.corecoremxmlx.nnr2mlx.optimizers optimizersr mlx.utilsrrmodels.switch_layersrr utilsr rUr r rSrrrr#ModuleintboolrNr`rbrirwrWr$r"rs 2F(+==!D!0 H; 99H;H; H; H;V#"))8biiBII&r$