+ "ݳj^RIt^RIt^RIHuHt^RIHtR.tRt !RR]PP4t RRlt R R lt R R ltR RltRRltRRltR#)N)_assert_and_get_unique_devicemodel_is_exported_exported_trainingc<aa]tRt^toRtV3RltRtRtVtV;t #) WrapperModulezjClass to wrap a callable in an :class:`torch.nn.Module`. Use this if you are trying to export a callable. c0<\SV`4WnR#N)super__init__fn)selfr __class__s&&g/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torchao/quantization/pt2e/export_utils.pyr WrapperModule.__init__s c &VP!V/VB#)zUSimple forward that just calls the ``fn`` provided to :meth:`WrapperModule.__init__`.r )r argskwargss&*,rforwardWrapperModule.forwardsww'''rr) __name__ __module__ __qualname____firstlineno____doc__r r__static_attributes____classdictcell__ __classcell__)r __classdict__s@@rrrs((rrcXV^8dQhR\PPR\/#)mreturn)torchnnModulebool)formats"r __annotate__r*$s"Trc\V\PP4;'d]\;QJd0RVP P 4F 'gK R# R#!RVP P 44#)z Return True if the `torch.nn.Module` was exported, False otherwise (e.g. if the model was FX symbolically traced or not traced at all). c3@"TFpRVP9xK R#5i)valN)meta).0ns& r $model_is_exported..)s7!.AsTF) isinstancer%fx GraphModuleanygraphnodes)r#s&rrr$sl a-- .  337!"733337!"74rcXV^8dQhR\PPR\/#r"r# train_to_evalr%r4r5r()r)s"rr*r*.s$77,,7T7rc a ^RIHpVPP4VP 4RFo V 3RlpV 3Rlp\ P !^43pV'd&V!\V4V4pV!\V4V4pM$V!\V4V4pV!\V4V4p^RIH pV!VVV.RR7VP 4K R#) a Switch dropout patterns in the model between train and eval modes. Dropout has different behavior in train vs eval mode. For exported models, however, calling `model.train()` or `model.eval()` does not automatically switch the dropout behavior between the two modes, so here we need to rewrite the aten dropout patterns manually to achieve the same effect. See https://github.com/pytorch/pytorch/issues/103681. "_get_aten_graph_module_for_patternTc8<\P!VRRSR7#)?TptraininginplaceFdropoutxrEs&r dropout_train'_replace_dropout..dropout_trainBs99Q#gF Frc8<\P!VRRSR7#)rAFrBrFrIs&r dropout_eval&_replace_dropout..dropout_evalEs99Q#wG Grreplace_pattern_with_filters match_filtersignore_literalsN)FT) utilsr?r7eliminate_dead_code recompiler%randnrtorch.fx.subgraph_rewriterrQ) r#r;r?rKrNexample_inputs match_patternreplacement_patternrQrEs && @r_replace_dropoutr].s:GG!KKM  G H ++a.* >m,M#El+#  ?l+M#Em,#  L$     K!rcXV^8dQhR\PPR\/#r:r<)r)s"rr*r*hs(NN%((..NtNrcZ^RIHpVPP4VP 4RRlpRRlp\ P !^^^^4\ P !^4\ P !^4\ P !^4\ P !^43p\V4pVRJ;'dVPR8HpV!\V4VV4pV!\V4VV4p V'dTp T p MT p Tp ^RI H p V !VV V .R R 7VP 4R#) ao Switch batchnorm patterns in the model between train and eval modes. Batchnorm has different behavior in train vs eval mode. For exported models, however, calling `model.train()` or `model.eval()` does not automatically switch the batchnorm behavior between the two modes, so here we need to rewrite the aten batchnorm patterns manually to achieve the same effect. r>c V^8dQhR\PR\PR\PR\PR\P/#r"rJ bn_weightbn_biasbn_running_meanbn_running_varr%Tensor)r)s"rr*(_replace_batchnorm..__annotate__{N   <<  <<        rc 6\P!WWAVRR7#)TrDrG batch_normrJrbrcrdres&&&&&rbn_train$_replace_batchnorm..bn_train{s|| 7T  rc V^8dQhR\PR\PR\PR\PR\P/#rarf)r)s"rr*rhrirc 6\P!WWAVRR7#)Frkrlrns&&&&&rbn_eval#_replace_batchnorm..bn_evals|| 7U  rNcudarPTrR) rUr?r7rVrWr%rXrtyperrYrQ) r#r;r?rorsrZdeviceis_cuda bn_train_aten bn_eval_atenr[r\rQs && r_replace_batchnormr{hs :GG!KKM      Aq!Q A A A A N+1 -FD ::V[[F%:G6hM 6gL % *$ +G   KKMrcLV^8dQhR\PP/#r"modelr%r4r5)r)s"rr*r*s(<(<rc\V\R4pV'gV#\V\R4\VRR7\ VRR7V#)a$ Move an exported GraphModule to eval mode. This is equivalent to model.eval() but only for certain special ops like dropout, batchnorm. QAT users should call this before performing inference on the model. This call is idempotent; if the model is already in eval mode, nothing will happen. TFr;getattr_EXPORTED_TRAINING_ATTRsetattrr]r{r~ is_trainings& r_move_exported_model_to_evalrsA%!8$?K   E*E2U$/uD1 LrcLV^8dQhR\PP/#r}r)r)s"rr*r*s)=)=rc\V\R4pV'dV#\V\R4\VRR7\ VRR7V#)a& Move an exported GraphModule to train mode. This is equivalent to model.train() but only for certain special ops like dropout, batchnorm. QAT users should call this before performing training on the model. This call is idempotent; if the model is already in train mode, nothing will happen. FTrrrs& r_move_exported_model_to_trainrsA%!8%@K  E*D1U%0uE2 LrcLV^8dQhR\PP/#r}r)r)s"rr*r*sEHH,@,@rcRRRllpRp\P!W4Vn\P!W 4VnV#)a Allow users to call `model.train()` and `model.eval()` on an exported model, but with the effect of changing behavior between the two modes limited to special ops only, which are currently dropout and batchnorm. Note: This does not achieve the same effect as what `model.train()` and `model.eval()` does in eager models, but only provides an approximation. In particular, user code branching on `training` flag will not function correctly in general because the branch is already specialized at export time. Additionally, other ops beyond dropout and batchnorm that have different train/eval behavior will also not be converted properly. c$V^8dQhR\/#)r"mode)r()r)s"rr*6_allow_exported_model_train_eval..__annotate__s//4/rcFV'd\V4R#\V4R#r )rr)r rs&&r_train0_allow_exported_model_train_eval.._trains  )$ / ( .rc\V4R#r )r)r s&r_eval/_allow_exported_model_train_eval.._evals $T*r)T)types MethodTypetraineval)r~rrs& r _allow_exported_model_train_evalrs7/ +""61EK!!%/EJ Lr)rr%torch.nn.functionalr& functionalrG torchao.utilsr__all__rr'rrr]r{rrrrrrs_ 7 / (EHHOO (7tNd$$r