tjv7ddlZddlZddlZddlmZddlmZddlmZddl Z ddl Z ddl m Z ddl mZgdZee jee jzZe jezZee jjZee jjZeeZhdZe d d eezd efd Ze d deee jj fde jjd efdZ!e d de jjd e"fdZ#e d GddZ$e d de jj%d e jj%fdZ&e d de jj%d e jj%fdZ'dS)N)Mapping) dataclass)Any) compatibility)_get_qualified_name)get_acc_ops_nameget_node_targetis_node_output_tensorFxNetAccFusionsFinderlegalize_graphstable_topological_sort> call_method call_module call_functionF)is_backward_compatiblekreturnct|tr|S|jrd|jvr d|jS|jdd}|r|ndd|jS)Nacc_opsacc_ops.z torch._opsz torch.ops.) isinstancestr __module____name__replace)rmodules Z/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/torch/fx/passes/tools_common.pyrrs|!S: :)q|33&!*&&&%% +  #*&&99QZ999 submodulesnodec|jtvr5tddtzd|jz|jdkrxt |jt s$tdt|j||j}t|dt|}t|S|jdkr0|j}|j d |j vr d |j nt|St |jt s$tdt|j|jS) a, Given a `node` returns its target typename. For "call_method" node, return node.target which is the name of that method being called. This could potential lead to conflict but should be okay because normally it's on a tensor. For "call_function" node, return typename of node.target. For "call_module" node, return typename of the module that node.target point to. If seeing "_VariableFunctionsClass" in the target name string, it will be replaced by "torch". e.g. _VariableFunctionsClass.relu would become torch.relu. zExpect op types of z, z , but found rzExpected str target, got _base_class_originrNrr) opCALLABLE_NODE_OPSAssertionErrorjoinrtargetrtypegetattrrrrr)r!r"submod submod_typer)s rr r ,sS$ w''' !ii)** +&TW&& '    w-$+s++ R !PT$+=N=N!P!PQQ QDK(f&:DLLII  ,,, O # #k ,f>O1O1O )v ( ( ($V,, $+s++ R !PT$+=N=N!P!PQQ Q{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 rr r Xs5 IMM&$ ' 'E   @E5z2FxNetAccFusionsFinder.__init__..osHHHwq$4HHHr )rlistgraphnodesr6 enumerate node_index)selfrr6s r__init__zFxNetAccFusionsFinder.__init__ksG &,,-- "HH)DJ2G2GHHHr c\eZdZUeed<eed<eed<eed<dejjddfdZ dS) !FxNetAccFusionsFinder.FusionGroup top_node_idxr?inputsnodes_need_processr"rNc|jvrdSj|j|j|jfd|jDdS)z5 Add a node to fusion group. NcBh|]}|jtv|jv|Sr9)r%r&r?)r:nrBs r z=FxNetAccFusionsFinder.FusionGroup.add_node..s=t000Qdj5H5H5H5H5Hr )r?rHaddrGdiscardupdateall_input_nodes)rBr"s` radd_nodez*FxNetAccFusionsFinder.FusionGroup.add_nodestz!!  # ' ' - - - JNN4 K   % % % K  !1     r ) rr __qualname__int__annotations__NodeSetr2fxNoderQr9r r FusionGrouprEqsn$###  4      r rX fusion_grouprErGvisitedc|D]}|||vr |||jtvr-|j||jkrD||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&rArFr?recursive_add_noderPrQ)rBrYrGrZargs rr\z(FxNetAccFusionsFinder.recursive_add_nodes  C"'>> C   v...s#l&???l(((tt&&|S5H'RR %%c***tt ur c(i}t|j}|D]}||vr|jtvrd|jvr!||jvr+||j||ht|j|h}|j rD|j }| ||j td|jvra|j D]Y}|jtvr||jvr||| ||j tZ|jD]}|jtvrd|jvr||jvr%||t!|j|j||_| ||j t|j Dt|j|jks|xj|jzc_|jD] }|j||< |S)N tensor_meta)rFr?rGrH)rZ)r=r6r%r&r/rXrAsetrPrHpopr\rGusersr?rQminrF)rBresultr6r"rYuserr]rKs r__call__zFxNetAccFusionsFinder.__call__s/1(( > 3> 3Dv~~w/// ))4>))>B>N>N!_T2f4/00$(6 ?O??L 1( #6::<<''  'EE(! 11 $  7*;;;$<#555$$--d333//((/$'EE0 /Cv%666 $00 l000  ))#...03$14?33G11L-++$$+ #,I1( T *++t~==,"44%+33A , 2F1II3 r N)rrrR__doc__r2rV GraphModulerUrCrrXNodeListboolr\dictrWrfr9r rr r ds Iux3IIDIIII YF#' $$9$("$4 $  $$$$LD$ux}g56DDDDDDr r gmctjtjtjtjtjtjtjtjtj tj tj tj tjjjjtjjjjtjjjjtjjjjtjjjjg}t.|jjdtj}|jjD]}|jD]}|xxdz cc<t=j}|jjD]#}|dkr| |$itC|dkr|"}|#|fd|<|jD]]}|xxdzcc<|dkr?|j$dkr|j%|vr|&|H| |^tC|dktC|jtC|jjkrtOdfdD|jj(|_(||_|S)a Replace the graph of the given GraphModule with one that contains the same nodes as the original, but in topologically sorted order. This is used by the merge_matmul transformation below, which disturbs the topologically sorted order of its input GraphModule, so that this order is restored before further transformation. Arguments: gm: The graph module to topologically sort. It is modified in-place. Returns: The graph module in-place sorted Warning: This topological sort is NOT stable, it will NOT preserve the original node order. If you need a stable topological sort, use stable_topological_sort instead. rc|Srgr9xenvs rz legalize_graph..: c!fr r&Input graph has cycles, unable to add c,g|]}|dk|Srr9r:r"indegs r z"legalize_graph..F+5a5a5atPUVZP[_`P`P`dP`P`P`r ))operatorrMmulsubfloordivtruedivmodleltgegteqner2opsatensym_constrain_rangedefaultsym_constrain_range_for_size _assert_asyncmsg scalar_tensor_assert_scalarrlfromkeysr>r?rVGraphrb collectionsdequeappendlenpopleft node_copyr%r) appendleft RuntimeError_codegen) rmPRIORITIZED_OPS new_graphr"requeuecurrsrzs @@rr r sz2            *2 3; $( $, %-#O( MM"(.! , ,E  IJ  D $KKK1 KKKK .9.?.A.AE ;!   LL   .0C e**q..mmoo&&s,<,<,<,<==CI ' 'D $KKK1 KKKT{a7o--$+2P2P$$T****LL&&& e**q.. 9?c"(.1111 c5a5a5a5au5a5a5a c c   *IBH Ir c^ t|jjd tj}dt|jjD}|jjD]}|jD]} |xxdz cc<g}|jjD]+} |dkrtj ||||f,i|rxtj |\}}| |fd|<|jD];} |xxdzcc< |dkrtj ||||f<|xt|jt|jjkrtd fd D|jj|_||_|S)aR 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 rci|]\}}|| Sr9r9)r:idxr"s rr<z+stable_topological_sort..fs+,,,c4c,,,r roc|Srgr9rqs rrtz)stable_topological_sort..|rur rvc,g|]}|dk|Srxr9rys rr{z+stable_topological_sort..r|r )rlrr>r?r2rVrr@rbheapqheappushheappoprrr'r) rmr node_to_idr"re ready_queue_rrsrzs @@rr r Ms* MM"(.! , ,E  I,,#,RX^#<#<,,,J J  D $KKK1 KKKK  46KBB ;!   N;D)94(@ A A A.0C  F{++3&&s,<,<,<,<==CI F FD $KKK1 KKKT{a{Z-=t,DEEE  F 9?s28>2222 c5a5a5a5au5a5a5a c c   *IBH Ir )(rrr}collections.abcr dataclassesrtypingrr2torch.fxtorch.fx._compatibilityr torch.fx.noder__all__tupler3r=TensorsTensorOrTensorsrVrWrjr`rUrNamesr&r*rnnModuler rkr r rir r r9r rrs ######!!!!!! 111111------     U\ 2 2,(   ehm  S CCCe,,, :d :s : : :-, :e,,,(UX_,-(5:X]((((-,(Ve,,,A A$AAA-,Ae,,,VVVVVVV-,Vre,,,Kux+K0DKKK-,K\e,,,> 4>9M>>>-,>>>r