sgs+ddlmZmZmZmZmZmZmZmZddl Z ddl m Z ddl Z ddl Z ddlZ ddlmZddlmZgdZeee j(ee j(fZee j(efZee j.j0Zee j.j0ZeeZhdZed d Zed d eee j>j@fd e j.j0d efdZ!ed d e j.j0d e"fdZ#ed GddZ$ed de j.jJd e j.jJfdZ&y))ListTupleUnionDictAnySetMappingOptionalN) dataclass)_get_qualified_name) compatibility)get_acc_ops_nameget_node_targetis_node_output_tensorFxNetAccFusionsFinderlegalize_graph> call_method call_module call_functionF)is_backward_compatiblect|tr|S|jrd|jvrd|jS|jj dd}|r|ndd|jS)Nacc_opsacc_ops.z torch._opsz torch.ops.) isinstancestr __module____name__replace)kmodules O/var/www/html/venv/lib/python3.12/site-packages/torch/fx/passes/tools_common.pyrrsd!S )q||3!**&&%%lK@"&+1QZZL99 submodulesnodereturnc$|jtvs-Jddjtzd|jz|jdk(rLt|jt sJ||j}t |dt|}t|S|jdk(r@|j}|jd|jvrd|jSt|St|jt sJ|jS) 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 r_base_class_originrrr) opCALLABLE_NODE_OPSjoinrtargetrgetattrtyperrrr )r%r&submod submod_typer-s r#rr!s 77' ' *; <<dggY?WW ' ww-$++s+++DKK(f&:DLI  ,, O #kk  ,f>O>O1Ov' ( %V, $++s+++{{r$cz|jjdd}|duxrt|tjS)aChecks if the node output produces a Tensor or not. NOTE: This requires to run `ShapeProp` on the containing fx graph before calling this function. This is because it works by checking the `type` metadata on the node. This metadata is produced by the `ShapeProp`. r/N)metaget issubclasstorchTensor)r&type_s r#rrEs3 IIMM&$ 'E   @E5< C vv..zz$|'@'@@l(((&&|S5H5H'R%%c*1 4r$r'ci}t|j}|D]_}||vr |jtvrd|jvr+||jvr:|j |j j||ht|j|h}|jrs|jj}|j||jtd|jvrj|jD][}|jtvr||j vr%|j||j||jt]|jD]}|jtvrd|jvr%||j vr4|j|t!|j"|j j||_|j||jt|jrst|j |jks!|xj|j zc_@|j D]}|j ||<b|S)N tensor_meta)rDr?rErF)rU)r=r:r*r+r3rSr?rWsetrKrFpoprXrEusersrMminrD)r@resultr:r&rTuserrYrLs r#__call__zFxNetAccFusionsFinder.__call__s[/1( > 3Dv~ww// )4>>)>B>N>N!ZZ--d3f4//0$(6 ?O?L 11#66::<''  ''E(! 1 $ 77*;;$<#5#55$$--d3//((//$'E0  //Cvv%66 $0 l000  ))#.03$114::3C3CC3H1L-++$$++ #,111T **+t~~=,"4"44%++3A , 2 2F1I3{> 3@ r$r<)rrrN__doc__r6fx GraphModulerQrAr rSrNodeListr rXrNoderbrRr$r#rrPs #uxx33## F&* $9$gx'($'" $LD$uxx}}g56Dr$rgmc(tjtjtjtjtj tj tjtjtjtjtjtjtjjj j"tjjj$j"tjjj&j(tjjj*j"tjjj,j"g}t.j1|j2j4d}tj6j9}|j2j4D] }|j:D]}||xxdz cc<"t=j>}|j2j4D]}||dk(s |jA|itC|dkDr|jE}|jG|fd|<|j:D]X}||xxdzcc<||dk(s|jHdk(r |jJ|vr|jM|H|jA|ZtC|dkDrtC|j4tC|j2j4kr%tOd|Dcgc] }||dk7s |c}|j2jP|_(||_|Scc}w)a 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 rc|Sr<rR)xenvs r#z legalize_graph..!s c!fr$rz&Input graph has cycles, unable to add ))operatorrHmulsubfloordivtruedivmodleltgegteqner6opsatensym_constrain_rangedefaultsym_constrain_range_for_size _assert_asyncmsg scalar_tensor_assert_scalardictfromkeysr>r?rdGraphr^ collectionsdequeappendlenpopleft node_copyr*r- appendleft RuntimeError_codegen) rhPRIORITIZED_OPSindeg new_graphr&raqueuecurrms @r#rrs*            **22 33;; $$(( $$,, %%--#O( MM"((..! ,E IJJ D $K1 K  +002E ;!  LL /1C e*q.mmo&&s,<=CII 'D $K1 KT{a77o-$++2P$$T*LL&  ' e*q. 9??c"((..11CV[Dpd_dei_jno_oTDpCqrss**IBH IEqs  N N )'typingrrrrrrr r r dataclassesr ror6torch.fx torch.fx.noder torch.fx._compatibilityr __all__r7TensorsTensorOrTensorsrdrgrfrQrNamesr+rnnModulerboolrrrerrRr$r#rsHHH! -1 u ell#T%,,%77 8 g-.   ehhmm  S Ce,:-:e,!UXX__(< =!UXX]]!WZ!-!Fe,A A$A-Ae,UU-Upe,Euxx++E0D0DE-Er$