Vh6ddlZddlZddlmcmZddlmZdgZdZ GddejjZ dejjde fd Z dejjd e fd Zdejjd e fd Zd ejjfdZd ejjfdZd ejjfdZy)N)_assert_and_get_unique_devicemodel_is_exported_exported_trainingc(eZdZdZfdZdZxZS)_WrapperModulezrClass to wrap a callable in an :class:`torch.nn.Module`. Use this if you are trying to export a callable. c0t|||_yN)super__init__fn)selfr __class__s W/home/dcms/DCMS/lib/python3.12/site-packages/torch/ao/quantization/pt2e/export_utils.pyr z_WrapperModule.__init__s c&|j|i|S)zUSimple forward that just calls the ``fn`` provided to :meth:`WrapperModule.__init__`.)r )r argskwargss rforwardz_WrapperModule.forwardstww'''r)__name__ __module__ __qualname____doc__r r __classcell__)rs@rrrs(rrmreturnct|tjjxr&t d|j j DS)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). c38K|]}d|jvyw)valN)meta).0ns r z$model_is_exported..#s77s) isinstancetorchfx GraphModuleanygraphnodes)rs rrrs> a-- . 37!"74r train_to_evalc ddlm}|jj|j dD] fd} fd}t j df}|r%|t||}|t||}n$|t||}|t||}ddlm }||||gd |j y ) 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_pattern)FTc6tj|ddS)N?TptraininginplaceFdropoutxr4s r dropout_trainz'_replace_dropout..dropout_train<s99Q#gF Frc6tj|ddS)Nr0Fr1r5r8s r dropout_evalz&_replace_dropout..dropout_eval?s99Q#wG Grrreplace_pattern_with_filtersT match_filtersignore_literalsN) utilsr.r(eliminate_dead_code recompiler$randnrtorch.fx.subgraph_rewriterr>) rr*r.r:r<example_inputs match_patternreplacement_patternr>r4s @r_replace_dropoutrJ(s:GG!KKM % G H ++a.* >}-M#E|,#  ?|,M#E}-#  L$     K%rc nddlm}|jj|j dt j dt j dt j dt j dt j f d}dt j dt j dt j dt j dt j f d }t jddd d t jdt jdt jdt jdf}t|}|d uxr|jd k(}|t|||}|t|||} |r|} | } n| } |} d dl m } | || | gd|j y )a 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,r-r9 bn_weightbn_biasbn_running_meanbn_running_varc8tj|||||dS)NTr3r6 batch_normr9rLrMrNrOs rbn_trainz$_replace_batchnorm..bn_trainus"||  7T  rc8tj|||||dS)NFrQrRrTs rbn_evalz#_replace_batchnorm..bn_evals"||  7U  rNcudarr=Tr?) rBr.r(rCrDr$TensorrErtyperrFr>) rr*r.rUrWrGdeviceis_cuda bn_train_aten bn_eval_atenrHrIr>s r_replace_batchnormr`bs:GG!KKM  <<  <<          <<  <<          Aq!Q A A A A N+1 -FD :V[[F%:G6x M 6wL % *$ +G   KKMrmodelct|td}|s|St|tdt|dt |d|S)a8 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_ATTRsetattrrJr`ra is_trainings r_move_exported_model_to_evalrjsA%!8$?K   E*E2U$/uD1 Lrct|td}|r|St|tdt|dt |d|S)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. FTrcrdrhs r_move_exported_model_to_trainrlsA%!8%@K  E*D1U%0uE2 Lrcddtfd}d}tj|||_tj|||_|S)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. modec6|r t|yt|yr )rlrj)r rns r_trainz0_allow_exported_model_train_eval.._trains  )$ / ( .rct|yr )rj)r s r_evalz/_allow_exported_model_train_eval.._evals $T*r)T)booltypes MethodTypetraineval)rarprrs r _allow_exported_model_train_evalrxsB/4/ +""651EK!!%/EJ Lr)rtr$torch.nn.functionalnn functionalr6torch.ao.quantization.utilsr__all__rfModulerrsrr%r&rJr`rjrlrxrrrs E / (UXX__ (T7,,7T7tN%((..NtNd(<(<$)=)=$EHH,@,@r