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all_mappingsstatic_quant_module_classs r.rrs}'.*,'$  7 1' L !- 0 01CT J $ 0 &s+='>&?@ A B 0 ==2 33r/ additional_dynamic_quant_mappingcUc0n[[U5nURUS5nUcS[U53S-5e[R "U5$)z^n Get the dynamically quantized module class corresponding to the floating point module class NrArB)rrrCrDr1r2)r=rGrEdynamic_quant_module_classs r.rrsu(/+-($-/OL".!1!12Dd!K % 1 &s+='>&?@ A B 1 ==3 44r/c6[R"[5$)z:Get default module mapping for quantization aware training)r1r2rr3r/r.rrs ==4 55r/c[R"[5n[RU[ R'[R U[ R 'U$)zGet module mapping for quantization aware training This is includes default values in addition to enabling qat for embeddings. )r1r2rr8r7r r9r:s r.r r s> mm78G$11GBOO!OOGBLL Nr/c[$)z9Get module mapping for post training dynamic quantization)rr3r/r.r!r!*s 00r/c[$)z@Get module mapping for post training dynamic sparse quantization)rr3r/r.r"r"/s 77r/c[[R55[[R55-[[R55-[ -n[ R"U5$)zWGet the default list of module types that we'll attach qconfig attribute to in prepare )setrkeysrrr'r1r2)#QCONFIG_PROPAGATE_MODULE_CLASS_LISTs r.r#r#4se 0 5 5 78 )..0 1 2 388: ; < * *( ==< ==r/c[[R55[[R55-[[R55-[[R 55-[[R 55-[[R 55-[ -n[R"U5$)zKGet list of module class types that we will record output in numeric suite ) rOrvaluesrrrPr'r1r2).NUMERIC_SUITE_COMPARE_MODEL_OUTPUT_MODULE_LISTs r.r$r$As 0 7 7 9: )002 3 4 3::< = > 2779 : ; )..0 1  2 388: ;  < *  *3 ==G HHr/c6[R"[5$N)r1r2rr3r/r.r%r%Qs ==E FFr/float_opc`[RUS5nUcS[U5S35eU$)z>Get the quantized operator corresponding to the float operatorNz Operator z) does not have corresponding quantized op)rrCrD)rW quantized_ops r.r&r&Xs@?CCHdSL L 3x=/!JKL r/modulec@[R[U5S5$)zGet the special activation post process for `module`, this has higher priority than the activation post process in `qconfig` e.g. input: torch.nn.Sigmoid output: default_affine_fixed_qparam_fake_quant N)rrCrrZs r._get_special_act_post_processr]as! . 1 1$V,d r/cLUR=(a [U5[;$rV)trainingtyperr\s r._has_special_act_post_processrams ?? QtF|/QQQr/)NFrV)r1typingrrrrrrtorch torch.ao.nnaor ao_nntorch.ao.nn.intrinsic intrinsicnnitorch.ao.nn.intrinsic.qatqatnniqattorch.ao.nn.intrinsic.quantized quantizednniq'torch.ao.nn.intrinsic.quantized.dynamicdynamicnniqdtorch.ao.nn.qatr8torch.ao.nn.qat.dynamicnnqatdtorch.ao.nn.quantizedr6torch.ao.nn.quantized.dynamicnnqdtorch.ao.nn.quantized.reference referencennqrtorch.ao.nn.sparsetorch.nn.functional functionalF#torch.ao.quantization.fake_quantizer r torch.ao.quantization.stubsr r torch.ao.quantization.utilsrtorch.nn.utils.parametrizer__all__Quantize DeQuantizeLinearConv1dConv2dConv3dConvTranspose1dConvTranspose2dConvTranspose3dr9r7GRUCellLSTMCellRNNCellr+r__annotations__ BatchNorm2d BatchNorm3dDropoutELU GroupNorm HardswishInstanceNorm1dInstanceNorm2dInstanceNorm3d LayerNorm LeakyReLUmoduleslinearNonDynamicallyQuantizableLinearReLU6PReLUFloatFunctional QFunctionalBNReLU2dBNReLU3d ConvReLU1d ConvReLU2d ConvReLU3d ConvAdd2d ConvAddReLU2d LinearReLULinearLeakyReLU LinearTanhConvBn1dConvBn2dConvBn3d ConvBnReLU1d ConvBnReLU2d ConvBnReLU3d LinearBn1drrGRUr Sequentialr'eluops hardswish instance_norm layer_norm leaky_reludropoutrrD HardsigmoidSigmoidSoftmaxTanhrsparserrrrrrrboolrrrr r!r"r#r$r%r&Moduler]rar3r/r.rs  << ##**..777((##,,..?9C 8s||IIt{{IIt{{IIt{{IIt{{,,,,,,LL$..OOT&&JJ KKJJ GGTYYG.Xs]0C&6= s||6=6=NNCOO6=NNCOO 6= JJ 6= IIszz 6=IIszz6=IIszz6=++6=++6=++6=FFCGG6=LL#--6=OOS%%6=LL#--6= LL#--!6="s))#6=$s))%6=&s))'6=(LL#--)6=*LL#--+6=,JJ55szz-6=.IIszz/6=0HHcii16=2JJ 36=4HHcii56=896=<LL$--=6=>LL$--?6=@NNDOOA6=BNNDOOC6=DNNDOOE6=FMM4>>G6=Ht))I6=JNNDOOK6=L--M6=NNNDOOO6=P OOSZZQ6=R OOSZZS6=T OOSZZU6=V W6=X Y6=Z [6=\ t]6=^ t_6=` ta6=b szzc6=f LL#**g6=h LL#**i6=j LL#**k6=$d8S=&96tIIu||IIu||IIu||JJ55u||LL&//LL&//LL&//f))f))f))NNF%%NNF%%NNF%%NNF%%4T(C-0(JJ IIt{{ MM4;;JJ55t{{GGTYYFFDHHKKJJ NNE$$OOS%%LL#-->%tHcM':0MM2XEE599   " "KK$$..OOUYY((66LL%))%%00LL%))%%00IIuyy""** V,d533G3Q.RNN?JJ;JJ;GG ; @"D8);$<IIu||%%,,D+T(C--@ IIu||%%--44E,d8S=.A S ?$x}2E? 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