+ ܳj ^RIt^RIt^RIt^RIt^RIt]P !]4tRt!RR4t RRlt RRlt RRlt R t R tR ^R ^R ^R^/t]P!4UUu/uFwrWbK upptRRltR#uuppi)Nc \V\PP4'gQh\P 4pVP VP4pW2P9Ed\VP4pVPPV4VPPV4\VP4^8dVPR,P4^8dDVPR,P4VPP ^,8gQhVPR,VPV&R#V^,P#RV^,P R,4p\V^^R7p\P$!V^,4^8P'\)\+\V^,P 4^, 44R7p\P,!V^84^,p\P.!WG.4P14pWBPV&R#VP2FpVP54K R#)) reduction_dimzscore)dimN) isinstancetorchnnLinear OutlierTracer get_instance get_hvalueweighthvalue2outlier_idxfind_outlier_dimsoutliersappendhvalueslennumelmaxshapeviewabssumlistrangewherecatuniquehooksremove) moduleinputtracerhvalue outlier_idxmergeddims outlier_idx2hooks && S/home/luca/screen-checking/.venv/lib/python3.14/site-packages/bitsandbytes/utils.py outlier_hookr. s fehhoo . .. .  ' ' )F   v}} -F ...' 6 {+f% v ! #r"((*Q.r*..06==3F3Fq3IIII060CF % %f -1X]]2uQx~~b'9:F+F!ANKIIeAh'!+00T%E!HNN@SVW@W:X5Y0ZD ;;tax03L))[$?@GGIK0; % %f -LLD KKM!cRa]tRt^,toRtRtRtRtRtRt ] R4t Rt Vt R#) r Nc\R4h)zCall get_instance() instead) RuntimeErrorselfs&r-__init__OutlierTracer.__init__/s899r/cNRVnRVn.Vn.Vn/VnRVn.VnVP4F_wr#\V\PP4'gK1VP PVP\44Ka R#)NT)last_wcurrent_outlier_dimsrrr initializedr" named_modulesr r r r rregister_forward_pre_hookr.)r4modelnms&& r- initializeOutlierTracer.initialize2s} $(!  "$ '')DA!UXX__-- !!!"="=l"KL*r/c\VRR4#)r:F)getattrr3s&r-is_initializedOutlierTracer.is_initialized?st]E22r/cRVPP4P4#N)datastoragedata_ptr)r4rs&&r-rOutlierTracer.get_hvalueBs{{""$--//r/cVP4'g\PR4R#VPV4pW P9dVPV,#R#)z$Outlier tracer is not initialized...N)rDloggerwarningrr)r4rr's&& r- get_outliersOutlierTracer.get_outliersEsO""$$ NNA B( ,, ,**62 2r/cbVPfVPV4VnVP#rG) _instance__new__)clss&r-rOutlierTracer.get_instanceOs& == KK,CM}}r/)r9r"rrr:r8r)__name__ __module__ __qualname____firstlineno__rRr5r@rDrrO classmethodr__static_attributes____classdictcell__) __classdict__s@r-r r ,s8I: M30r/r cV'dE\P!^VP^,V3VPR7P 4#VP V4pVP 4pVP 4pWV, V, pVe,\P!VP4V^R7wrV #\P!W84^,p V #)r)sizedevice)kr) r randintrr`longstdmeantopkrr) rrrrfrdmrdstdmstdstdzstd_idxs &&&&& r-rrVs }}Q QtgfmmTYY[[ **] #C 88:D WWYF J& D CGGI15 Jkk$-(+ Jr/cRV^8dQhR\R\\\3,/#)command_stringreturn)strtuple)formats"r- __annotate__rths"suS#Xr/c0aRoV3RlpV!V4wr#W#3#)cZ\;QJd.RV4FNK 5#!RV44#)c3^"TF#qPR4P4xK% R#5i)zUTF-8N)decodestrip).0 to_decodes& r- 6execute_and_return.._decode..js'aH`9%%g.4466H`s+-)rr)subprocess_err_out_tuples&r-_decode#execute_and_return.._decodeis&uaH`auauaH`aaar/c<S!\P!\P!V4\P\PR7P 44#))stdoutstderr) subprocessPopenshlexsplitPIPE communicate)rors&r-&execute_and_return_decoded_std_streamsBexecute_and_return..execute_and_return_decoded_std_streamslsA    N+!! km   r/)rorstd_outstd_errrs& @r-execute_and_returnrhs&b >nMG  r/c|VP4EF&wrV\\VP444^8d\ WaW#V4\ V\ PP4'gKfWR9gKnVPV,pV!VPVPVPRJ4VPV&V'dEVPVPV,n VPVPV,n VfEK \WdR4pVfEKV!V4EK) V#)a Replace linear modules with a new Linear module. Parameters: model (`torch.nn.Module`): Input model or `torch.nn.Module` as the function is run recursively. linear_replacement (`torch.nn.Module`): The linear module that replaces the old one. Only expects standard arguments. If other arguments need to be passed, use a lambda. skip_modules (`List[str]`, *optional*, defaults to `lm_head`): List of modules names not to convert. Defaults to `lm_head`. copy_weights (`bool`): Copy the weights from the old linear module to the new one post_processing_function (`str`): A function name of the replacement linear class that is called after processing. N)named_childrenrrchildrenreplace_linearr r r r _modules in_features out_featuresbiasrrC) r=linear_replacement skip_modules copy_weightspost_processing_functionnamer$ old_modulefuncs &&&&& r-rrys.,,.  tFOO%& '! + 6|Sk l fehhoo . .43K-J#5""## 4'$ENN4 .8.?.?t$+,6OOt$)'3vF#L%/& Lr/c\P!V4pVPR4p\P!\ V4\P R7pV#)z Pack a dictionary into a torch tensor for storing quant_state items in state_dict. Parameters: - source_dict: The dictionary to be packed. Returns: A torch tensor containing the packed data. utf-8)dtype)jsondumpsencoder tensorruint8) source_dictjson_str json_bytes tensor_datas& r-pack_dict_to_tensorrs?zz+&H)J,,tJ/u{{CK r/c\VP4P44pVPR4p\P !V4pV#)z Unpack a torch tensor into a Python dictionary. Parameters: - tensor_data: The torch tensor containing the packed data. Returns: A Python dictionary containing the unpacked data. r)bytescpunumpyrxrloads)rrr unpacked_dicts& r-unpack_tensor_to_dictrsB{(..01J  )HJJx(M r/rowcol32 col_turing col_amperec8V^8dQhR\P/#)rnt)r Tensor)rss"r-rtrts    r/cVPPR8Xd!\PP 4R#VPPR8Xd!\P P 4R#R#)cudaxpuN)r`typer r synchronizer)rs&r-sync_gpursGxx}}  %   r/)rg@NF))lm_headFN)rloggingrrr getLoggerrVrMr.r rrrrr"LINEAR_8BIT_WEIGHTS_FORMAT_MAPPINGitems*INVERSE_LINEAR_8BIT_WEIGHTS_FORMAT_MAPPINGr)rvals00r-rs     8 $B''T$"*Z""',QL!\[\%]"KmKsKsKu-vKuKTciKu-v* .ws' B