ZjSSKJr SSKJr SSKJrJr \"5(a SSKrSSKJ r \R"\ 5r "SS\5r "SS \RR5r"S S \ R"5rSS \\S-4S jjrg)) ConversionOps)should_convert_module)is_torch_availableloggingNc ~\rSrSrSrS S\\\\R4S\S-S\\\R44Sjjr Sr g) EetqQuantizecXlgN hf_quantizer)selfrs o/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/integrations/eetq.py__init__EetqQuantize.__init__s(N input_dictfull_layer_namereturnc f[UR55SupEUSnURn[R"U5R 5R 5n[RU[RS5upxURU5nURU5nX'US3U0$)NrF_scales) tupleitemsdevicetorcht contiguouscpueetq_kernels_hub quant_weightsint8to) rrrkwargs_value value_device int8_weightscaless rconvertEetqQuantize.converts))+,Q/a|| ggen//1557 .<<[%**V[\ !nn\2 <(0A.I6RRrr r ) __name__ __module__ __qualname____firstlineno__rdictstrlistrTensorr*__static_attributes__rrr r s[)X\ SsD$667 SJMPT* S c5<<  S Srr c8\rSrSr\SSj5r\S5rSrg)EetqLinearMMFunction/NclURXX45 [RXU5nUbXT-nU$UnU$r )save_for_backwardr w8_a16_gemm)ctxxweightr)biasoutputs rforwardEetqLinearMMFunction.forward0sC a6!--a@"&"2 9? rcURup#pE[R"URS5R UR 5R UR 5n[RXcU5nURS(a?URS5RURSS55RS5nWSSS4$)Nr) saved_tensorsreyeshaper#rdtyper r;needs_input_gradsqueezematmul transpose unsqueeze)r< grad_outputinputr>r)r?identity grad_inputs rbackwardEetqLinearMMFunction.backward8s&)&7&7#v99V\\!_-00?BB5;;O"--hG    "$,,Q/66v7G7G17MNXXYZ[J4t++rr5r )r,r-r.r/ staticmethodrArRr4r5rrr7r7/s( , ,rr7cJ^\rSrSr\R S4U4SjjrSrSrU=r $) EetqLinearGFc>[TU]5 [R"[R "X4US9SS9Ul[R"[R "U[RS95UlU(a=[R"[R "U[RS95Ul gSUl g)N)rHF) requires_grad) superrnn Parameterremptyr>float16 weight_scalesr?)r in_features out_featuresrHr? __class__s rrEetqLinear.__init__Hsy ll5;; /JRW#Xhmn \\%++|EMM*Z[  U[[,u}}%UVDIDIrcp[RXRURUR5nU$r )r7applyr>r_r?)rrOr@s rrAEetqLinear.forwardQs,%++E;;@R@RTXT]T]^ r)r?r>r_) r,r-r.r/rr"rrAr4 __classcell__)rbs@rrVrVGs8= rrVmodules_to_not_convertcSSKJn U"S5qSnU(a0OSS0nUR5Hupg[ Xa5(dM[ R "S5 [U[R5(aB[URUR4SURSL0UD6nURXh5 S nSSS5 M U(d[R!S 5 U$!,(df  M=f) a A helper function to replace all `torch.nn.Linear` modules by `EetqLinear` modules. Parameters: model (`torch.nn.Module`): Input model or `torch.nn.Module` as the function is run recursively. modules_to_not_convert (`list[`str`]`, *optional*, defaults to `None`): Names of the modules to not convert in `EetqLinear`. In practice we keep the `lm_head` in full precision for numerical stability reasons. rD) get_kernelz#kernels-community/quantization-eetqFrHNmetar?TzYou are loading your model using eetq but no linear modules were found in your model. Please double check your model architecture, or submit an issue on github if you think this is a bug.) hub_kernelsrjr named_modulesrrr isinstancer[LinearrVr`rar? set_submoduleloggerwarning) modelrh pre_quantizedrjhas_been_replaced module_kwargs module_namemodule new_modules rreplace_with_eetq_linearrzVs(""GH'Bgt_M$224 $[II  \\& !&")),,'&&(;(;BH++UYBY]j ##K<$(! " ! 5   L" !s A"C'' C6 )NF)core_model_loadingrquantizers.quantizers_utilsrutilsrrrtorch.nnr[ get_loggerr,rqr autogradFunctionr7ModulerVr2r1rzr5rrrs|/?/   H %S=S(,5>>22,0  %DI