קgs+ddlmZmZmZmZmZmZmZmZddl Z ddl m Z ddl Z ddl Z ddlZ ddlmZddlmZgdZeee jee jfZee jefZee jjZee jjZeeZhdZed d Zed d eee jj fd e jjd efdZ!ed d e jjd e"fdZ#ed GddZ$ed de jj%d e jj%fdZ&dS))ListTupleUnionDictAnySetMappingOptionalN) dataclass)_get_qualified_name) compatibility)get_acc_ops_nameget_node_targetis_node_output_tensorFxNetAccFusionsFinderlegalize_graph> call_method call_module call_functionF)is_backward_compatiblect|tr|S|jrd|jvr d|jS|jdd}|r|ndd|jS)Nacc_opsacc_ops.z torch._opsz torch.ops.) isinstancestr __module____name__replace)kmodules X/var/www/html/ai-engine/env/lib/python3.11/site-packages/torch/fx/passes/tools_common.pyrrsu!S: :)q|33&!*&&&%%lK@@"*&&99QZ999 submodulesnodereturnc|jtvs0Jddtzd|jz|jdkrVt|jt sJ||j}t |dt|}t|S|jdkr0|j}|j d|j vr d |j nt|St|jt sJ|jS) a, Given a `node` returns its target typename. For "call_method" node, return node.target which is the name of that method being called. This could potential lead to conflict but should be okay because normally it's on a tensor. For "call_function" node, return typename of node.target. For "call_module" node, return typename of the module that node.target point to. If seeing "_VariableFunctionsClass" in the target name string, it will be replaced by "torch". e.g. _VariableFunctionsClass.relu would become torch.relu. zExpect op types of z, z , but found r_base_class_originrNrr) opCALLABLE_NODE_OPSjoinrtargetrgetattrtyperrrr )r%r&submod submod_typer-s r#rr!s 7' ' ' ' *; < <O1O1O )v ( ( ($V,, $+s+++++{r$ct|jdd}|duot|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#rrEs5 IMM&$ ' 'E   @E5eZdZUeed<eed<eed<eed<dZdS)!FxNetAccFusionsFinder.FusionGroup top_node_idxr?inputsnodes_need_processc|jvrdSj|j|j|jfd|jDdS)z5 Add a node to fusion group. NcBh|]}|jtv|jv|S)r*r+r?).0nr@s r# z=FxNetAccFusionsFinder.FusionGroup.add_node..us=t000Qdj5H5H5H5H5Hr$)r?rFaddrEdiscardupdateall_input_nodes)r@r&s` r#add_nodez*FxNetAccFusionsFinder.FusionGroup.add_nodejstz!!  # ' ' - - - JNN4 K   % % % K  !1     r$N)rr __qualname__int__annotations__NodeSetrQrIr$r# FusionGrouprC\sW$###     r$rVN fusion_grouprCrEvisitedc.|D]}|||vr |||jtvr-|j||jkrQ||jvrdS|||j|r||dSdS)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. NTF) rMr*r+r?indexrDrecursive_add_noderPrQ)r@rWrErXargs r#r[z(FxNetAccFusionsFinder.recursive_add_node|s  C"'>> C   v...z$$|'@@@l(((tt&&|S5H'RR %%c***tt ur$r'c\i}t|j}|D]}||vr|jtvrd|jvr!||jvr+||j||ht|j |h}|j rQ|j }| ||j td|jvra|jD]Y}|jtvr||jvr||| ||j tZ|j D]}|jtvrd|jvr||jvr%||t!|j|j||_| ||j t|j Qt|j|jks|xj|jzc_|jD] }|j||< |S)N tensor_meta)rDr?rErF)rX)r=r:r*r+r3rVr?rZsetrPrFpopr[rEusersrQminrD)r@resultr:r&rWuserr\rKs r#__call__zFxNetAccFusionsFinder.__call__s/1(( > 3> 3Dv~~w/// ))4>))>B>N>N!Z--d33f4/00$(6 ?O??L 1( #6::<<''  'EE(! 11 $  7*;;;$<#555$$--d333//((/$'EE0 /Cv%666 $00 l000  ))#...03$14:3C3CC3H3H11L-++$$+ #,I1( T *++t~==,"44%+33A , 2F1II3 r$r<)rrrR__doc__r6fx GraphModulerUrAr rVrNodeListr r[rNodererIr$r#rrPs #ux3##### YF&* $$9$gx'($'" $$$$LD$ux}g56DDDDDDr$rgmctjtjtjtjtjtjtjtjtj tj tj tj tjjjjtjjjjtjjjjtjjjjtjjjjg}t.|jjdtj}|jjD]}|jD]}|xxdz cc<t=j}|jjD]#}|dkr| |$itC|dkr|"}|#|fd|<|jD]]}|xxdzcc<|dkr?|j$dkr|j%|vr|&|H| |^tC|dktC|jtC|jjkrtOdfdD|jj(|_(||_|S)a Replace the graph of the given GraphModule with one that contains the same nodes as the original, but in topologically sorted order. This is used by the merge_matmul transformation below, which disturbs the topologically sorted order of its input GraphModule, so that this order is restored before further transformation. Arguments: gm: The graph module to topologically sort. It is modified in-place. Returns: The graph module in-place sorted rc|Sr<rI)xenvs r#z legalize_graph..!s c!fr$rz&Input graph has cycles, unable to add c,g|]}|dk|S)rrI)rJr&indegs r# z"legalize_graph..,s+DpDpDpd_dei_jno_o_oT_o_o_or$))operatorrMmulsubfloordivtruedivmodleltgegteqner6opsatensym_constrain_rangedefaultsym_constrain_range_for_size _assert_asyncmsg scalar_tensor_assert_scalardictfromkeysr>r?rgGraphra collectionsdequeappendlenpopleft node_copyr*r- appendleft RuntimeError_codegen) rkPRIORITIZED_OPS new_graphr&rdqueuecurrprss @@r#rrss*            *2 3; $( $, %-#O( MM"(.! , ,E  IJ  D $KKK1 KKKK *022E ;!   LL   .0C e**q..mmoo&&s,<,<,<,<==CI ' 'D $KKK1 KKKT{a7o--$+2P2P$$T****LL&&& e**q.. 9?c"(.1111rDpDpDpDpV[DpDpDprrsss*IBH Ir$)'typingrrrrrrr r r dataclassesr rur6torch.fx torch.fx.noder torch.fx._compatibilityr __all__r7TensorsTensorOrTensorsrgrjrirUrNamesr+rnnModulerboolrrrhrrIr$r#rsqHHHHHHHHHHHHHHHHHHHH!!!!!! ------111111 u u u el#T%,%77 8 g-.   ehm  S CCCe,,,::-,:e,,,!UX_(< =!UX]!WZ!!!-,!Fe,,,A A$AAA-,Ae,,,UUUUUUU-,Upe,,,Eux+E0DEEE-,EEEr$