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all_mappingsstatic_quant_module_classs r*rrs}'.*,'$  7 1' L !- 0 01CT J (*3/A+B*C DE F   ==2 33r+ additional_dynamic_quant_mappingcUc0n[[U5nURUS5nUc[S[ U53S-5e[ R "U5$)z^n Get the dynamically quantized module class corresponding to the floating point module class Nr=r>)r rr?r@rAr-r.)r9rDrBdynamic_quant_module_classs r*rrsu(/+-($-/OL".!1!12Dd!K!)*3/A+B*C DE F   ==3 44r+c6[R"[5$)z:Get default module mapping for quantization aware training)r-r.rr/r+r*rrs ==4 55r+c[R"[5n[RU[ R'[R U[ R 'U$)zGet module mapping for quantization aware training This is includes default values in addition to enabling qat for embeddings. )r-r.rr4r3rr5r6s r*rr!s> mm78G$11GBOO!OOGBLL Nr+c[$)z9Get module mapping for post training dynamic quantization)rr/r+r*rr,s 00r+c[$)z@Get module mapping for post training dynamic sparse quantization)rr/r+r*rr1s 77r+c[[R55[[R55-[[R55-[ -n[ R"U5$)zWGet the default list of module types that we'll attach qconfig attribute to in prepare )setr keysrrr#r-r.)#QCONFIG_PROPAGATE_MODULE_CLASS_LISTs r*rr6se 0 5 5 78 )..0 1 2 388: ; < * *( ==< ==r+c[[R55[[R55-[[R55-[[R 55-[[R 55-[[R 55-[ -n[R"U5$)zKGet list of module class types that we will record output in numeric suite ) rLr valuesrrrMr#r-r.).NUMERIC_SUITE_COMPARE_MODEL_OUTPUT_MODULE_LISTs r*r r Cs 0 7 7 9: )002 3 4 3::< = > 2779 : ; )..0 1  2 388: ;  < *  *3 ==G HHr+c6[R"[5$N)r-r.rr/r+r*r!r!Ss ==E FFr+float_opcf[RU5nUc[S[U5S35eU$)z>Get the quantized operator corresponding to the float operatorz Operator z) does not have corresponding quantized op)rr?r@rA)rT quantized_ops r*r"r"Zs>?CCHMLH &O P   r+modulec>[R[U55$)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 )rr?r rWs r*_get_special_act_post_processrZds . 1 12Nv2V WWr+cLUR=(a [U5[;$rS)trainingtyperrYs r*_has_special_act_post_processr^ns ?? QtF|/QQQr+)NFrS)r-collections.abcrtypingrtorch torch.ao.nnaorao_nntorch.ao.nn.intrinsic intrinsicnnitorch.ao.nn.intrinsic.qatqatnniqattorch.ao.nn.intrinsic.quantized quantizednniq'torch.ao.nn.intrinsic.quantized.dynamicdynamicnniqdtorch.ao.nn.qatr4torch.ao.nn.qat.dynamicnnqatdtorch.ao.nn.quantizedr2torch.ao.nn.quantized.dynamicnnqdtorch.ao.nn.quantized.reference referencennqrtorch.ao.nn.sparsetorch.nn.functional functionalF#torch.ao.quantization.fake_quantizerrtorch.ao.quantization.stubsrr torch.ao.quantization.utilsr torch.nn.utils.parametrizer __all__Quantize DeQuantizeLinearConv1dConv2dConv3dConvTranspose1dConvTranspose2dConvTranspose3dr5r3GRUCellLSTMCellRNNCellr'r dict__annotations__ BatchNorm2d BatchNorm3dDropoutELU GroupNorm HardswishInstanceNorm1dInstanceNorm2dInstanceNorm3d LayerNorm LeakyReLUmoduleslinearNonDynamicallyQuantizableLinearReLU6PReLUFloatFunctional QFunctionalBNReLU2dBNReLU3d ConvReLU1d ConvReLU2d ConvReLU3d ConvAdd2d ConvAddReLU2d LinearReLULinearLeakyReLU LinearTanhConvBn1dConvBn2dConvBn3d ConvBnReLU1d ConvBnReLU2d ConvBnReLU3d LinearBn1dr rGRUr Sequentialr#rLeluops hardswish instance_norm layer_norm leaky_reludropoutrrA HardsigmoidSigmoidSoftmaxTanhrsparserrrrrrrboolrrrrrrrr r!r"ModulerZr^r/r+r*rs  $ ##**..777((##,,..?9C 8s||IIt{{IIt{{IIt{{IIt{{,,,,,,LL$..OOT&&JJ KKJJ GGTYYG.Xs]0C&5= s||5=5=NNCOO5=NNCOO 5= JJ 5= IIszz 5=IIszz5=IIszz5=++5=++5=++5=FFCGG5=LL#--5=OOS%%5=LL#--5= LL#--!5="s))#5=$s))%5=&s))'5=(LL#--)5=*LL#--+5=,JJ55szz-5=.IIszz/5=0HHcii15=2HHcii35=675=:LL$--;5=<LL$--=5=>NNDOO?5=@NNDOOA5=BNNDOOC5=DMM4>>E5=Ft))G5=HNNDOOI5=J--NNDOO OOSZZ OOSZZ OOSZZ    t t t szz LL#** LL#** LL#**i5=$d8S=&95rIIu||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""** P,d8c>83K.LNN?JJ;JJ;GG ; @"D8);$<IIu||%%,,D+T(C--@ IIu||%%--44E,d8S=.A S ?$x}2E? 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