Ein7FSSKrSSKrSSKrSSKJr SSKJr SSKJrJ r J r SSK r SSK r SSK Jr SSKJr /SQr\ \\ R&\\ R&4r\ \ R&\4r\\ R.R0r\\ R.R0r\\r1Skr\"S S 9S 5r\"S S 9S \\\ R@RB4S \ R.R0S\4Sj5r"\"S S 9S \ R.R0S\#4Sj5r$\"S S 9"SS55r%\"S S 9S\ R.RLS\ R.RL4Sj5r'\"S S 9S\ R.RLS\ R.RL4Sj5r(g)N)Mapping) dataclass)AnyOptionalUnion) compatibility)_get_qualified_name)get_acc_ops_nameget_node_targetis_node_output_tensorFxNetAccFusionsFinderlegalize_graphstable_topological_sort> call_method call_module call_functionF)is_backward_compatiblec[U[5(aU$UR(aSUR;aSUR3$URR SS5nU(aUOSSUR3$)Nacc_opsacc_ops.z torch._opsz torch.ops.) isinstancestr __module____name__replace)kmodules Y/home/mande/repo/quber/.venv/lib/python3.13/site-packages/torch/fx/passes/tools_common.pyr r sl!S )q||3!**&&%% + #&+1QZZL99 submodulesnodereturncUR[;a1[SSR[5-SUR3-5eURS:Xao[ UR [ 5(d![S[UR 535eXR n[US[U55n[U5$URS:XaCUR nURbSUR;aS UR3$[U5$[ UR [ 5(d![S[UR 535eUR $) a  Given a `node` returns its target typename. For "call_method" node, return node.target which is the name of that method being called. This could potential lead to conflict but should be okay because normally it's on a tensor. For "call_function" node, return typename of node.target. For "call_module" node, return typename of the module that node.target point to. If seeing "_VariableFunctionsClass" in the target name string, it will be replaced by "torch". e.g. _VariableFunctionsClass.relu would become torch.relu. zExpect op types of z, z , but found rzExpected str target, got _base_class_originrrr) opCALLABLE_NODE_OPSAssertionErrorjoinrtargetrtypegetattrr rrr )r"r#submod submod_typer+s r r r -s;$ ww'' !ii)* +TWWI& '   ww-$++s++ #O>O1Ov' ( %V, $++s++ #inputsnodes_need_processcXR;agURRU5 URRU5 URR U5 URR UR Vs1sH,nUR[;dMX R;dM*UiM. sn5 gs snf)z Add a node to fusion group. N) r>rJaddrIdiscardupdateall_input_nodesr'r()rAr#ns r add_node*FxNetAccFusionsFinder.FusionGroup.add_nodeszz!  # # ' ' - JJNN4 KK   % KK  "111tt0056jj5H1 sC! C2CN) rr __qualname____firstlineno__int__annotations__NodeSetrQ__static_attributes__rSr!r FusionGrouprFrs%$# r!rZN fusion_grouprFrIvisitedc@UHnUbXC;aM URU5 UR[;aM4URUUR:aMSXAR ;a gUR XRU5(dMURU5 g g)z Start from inputs and going reverse topological order. If any upstream node is in the fusion group, add all the nodes in this path to fusion group. TF) rLr'r(r@rHr>recursive_add_noderOrQ)rAr[rIr\args r r^(FxNetAccFusionsFinder.recursive_add_nodesC"> C vv..s#l&?&??(((&&|5H5H'RR%%c*14r!r$c0n[UR5nUGHgnX1;aM UR[;aM!SUR;aM3X0R;aMDUR UR UU1[UR5U1S9nUR(GawURR5nURUUR[5S9 SUR;anURH^nUR[;aMXTR;aM*URU5 URUUR[5S9 M` URHnUR[;aMSUR;aM+XdR;aM<URU5 [!UR"UR U5UlURUUR[5S9 M UR(aGMw[UR5UR::d"U=RUR-slGMFURHnURX'M GMj U$)N tensor_meta)rHr>rIrJ)r\)r<r9r'r(r1rZr@setrOrJpopr^rIusersr>rQminrH)rAresultr9r#r[userr_rPs r __call__FxNetAccFusionsFinder.__call__s9/1( D~ww// )>>)>B>N>N!__T2f4//0$(6 ?O?L 111#66::<''  ''E(! 1 $ 77*;;$#5#55$$--d3//((//$'E0!+ //Cvv%66 $0 000  ))#.03$114??33G1L-++$$++ #,01111T **+t~~=,"4"44%++A , 2 2FI,{@ r!)r9rr@r>r;)rrrTrU__doc__r4fx GraphModulerXrCrrZrNodeListrr^dictNoderirYrSr!r r r es Iuxx33II F&* $9$gx'($'" $LD$uxx}}g56Dr!r gmcR^[R[R[R[R[R [R [R[R[R[R[R[R[RRR R"[RRR$R"[RRR&R([RRR*R"[RRR,R"/n[.R1UR2R4S5n[R6R95nUR2R4H"nUR:HnX%==S- ss'M M$ [<R>"5nUR2R4HnX$S:XdM URAU5 M 0m[CU5S:aURE5nURGUU4Sj5TU'UR:H]nX%==S-ss'X%S:XdMURHS:Xa#URJU;aURMU5 MLURAU5 M_ [CU5S:aM[CUR45[CUR2R45:a)[OSUVs/sHoBUS:wdM UPM sn35eUR2RPUl(X0lU$s snf)ay Replace the graph of the given GraphModule with one that contains the same nodes as the original, but in topologically sorted order. This is used by the merge_matmul transformation below, which disturbs the topologically sorted order of its input GraphModule, so that this order is restored before further transformation. Arguments: gm: The graph module to topologically sort. It is modified in-place. Returns: The graph module in-place sorted Warning: This topological sort is NOT stable, it will NOT preserve the original node order. If you need a stable topological sort, use stable_topological_sort instead. rc>TU$r;rSxenvs r  legalize_graph..; c!fr!r&Input graph has cycles, unable to add ))operatorrLmulsubfloordivtruedivmodleltgegteqner4opsatensym_constrain_rangedefaultsym_constrain_range_for_size _assert_asyncmsg scalar_tensor_assert_scalarrofromkeysr=r>rlGraphre collectionsdequeappendlenpopleft node_copyr'r+ appendleft RuntimeError_codegen) rqPRIORITIZED_OPSindeg new_graphr#rhqueuecurrws @r rrst2            **22 33;; $$(( $$,, %%--#O( MM"((..! ,E IJJD K1 K +002E ;!  LL /1C e*q.mmo&&s,<=CIID K1 K{a77o-$++2P$$T*LL&  e*q. 9??c"((..114u5autVZP[_`P`du5a4b c  **IH I 6bs " N$ 2N$ c"^ [RURRS5n[R R 5n[URR5VVs0sHup4XC_M nnnURRH"nURHnX==S- ss'M M$ /nURRH(nXS:XdM [R"XuUU45 M* 0m U(a~[R"U5upURU U 4Sj5T U 'U RH4nX==S-ss'XS:XdM[R"XuUU45 M6 U(aM~[UR5[URR5:wa)[SUVs/sHoAUS:wdM UPM sn35eURRUlX lU$s snnfs snf)a Replace the graph of the given GraphModule with one that contains the same nodes as the original, but in topologically sorted order while preserving the original node order as much as possible. This function performs a stable topological sort where nodes appear in an order that: 1. Respects data dependencies (topological ordering) 2. Preserves the original node order when there are no dependency constraints The algorithm uses Kahn's algorithm with a priority queue: nodes with all dependencies satisfied are added to a min-heap, ordered by their original position. This ensures we always process the earliest node in the original order among ready nodes. Arguments: gm: The graph module to topologically sort. It is modified in-place. Returns: The graph module in-place sorted rrsc>TU$r;rSrus r rx)stable_topological_sort..}rzr!r{)rorr=r>r4rlrr?reheapqheappushheappoprrr)r) rqrridxr# node_to_idrh ready_queue_rrws @r rrNs* MM"((..! ,E I$-RXX^^#<,#>224u5autVZP[_`P`du5a4b c  **IH IM,B6bs,H H H ))rrr|collections.abcr dataclassesrtypingrrrr4torch.fxtorch.fx._compatibilityr torch.fx.noder __all__tupler5r<TensorsTensorOrTensorsrlrprnrcrXrNamesr(r nnModuler boolr r rmrrrSr!r rs #!'' 1-  ell#T%,,%77 8 g-.   ehhmm  S Ce, :- :e,(UXX__,-(5:XX]]((-(Ve,A A$A-Ae,VV-Vre,Kuxx++K0D0DK-K\e,> 4 4>9M9M>->r!