Eim&SSKrSSKrSSKrSSKrSSKrSSKrSSKrSSKrSSKrSSK J r SSK J r J r SSKJr SSKJrJr SSKJrJrJrJr SSKrSSKJr SSKJrJr SS KJr SS KJ r SS K!J"r" SS K#J$r$ \(aSSK%r%SS K&J'r' SSK(J)r) SSK*J+r+ SSK,J-r- SSK,J.r.J/r/J0r0 SSK1J2r2J3r3 SSK4J5r5J6r6J7r7J8r8J9r9J:r:J;r;Jr>J?r?J@r@JArA \/RS\/RS\/RS\/RS\/RS\/RS0rHSqISSjrJS\RR4SjrMSS jrNSS"jrOS\RRS\P\Q\44S#jrRS$rSS%\P\Q\4S&\RRS!SSS4S'jrTS&\RR4S(jrUSS)\P\Q\Q4S*\P\Q\V4S+\W\QS,\QS-\QS.\X4 S/jjrYS0\;S\Q4S1jrZSS2\\V\R4S3\VS4\;S5\\VSS4 S6jjr\S7\]\RRSS4S8jr_SSSSSS9.S:\`\S;\\8S<\\AS=\\QS>\\?S?\\9S@\XSS4SAjjjraSBSCSD\RRS\X4SEjrbSBSCSD\RRS\\RR"4SFjrdSBSCSD\RRS\X4SGjreSBSCSD\RRS\\R4SHjrgSBSCSD\RRS\X4SIjrhSBSCSD\RRS\\R4SJjriS&\RRSK\ \RR/\\RR\X44S\RR4SLjrjSM\]\RRS\]\RR4SNjrk\SO5rlS&\RRSS4SPjrmS&\RR4SQjrnSSM\]\RRS\\RR4SRjjroSM\]\RRS\V4SSjrpSM\]\RRS\]\RR4STjrqSU\RRSV\RRSS4SWjrrS&\RRS\RRSS4SXjrtSY\RRS\\RR4SZjruS\RRS\R4S[jrwS\rxS]ryS&\RRSS4S^jrzS&\RRS)\P\Q\Q4S_\P\Q\4SS4S`jr{S&\RRSaSS\RRSb\P\Q\4SS4 Scjr|Sd\PSe\XS\P4Sfjr}S&\RRS\\RRR84Sgjr\S\RR4Shj5rSiSjS\X4SkjrSiSjS\X4SljrSiSjS\X4SmjrSiSjS\X4SnjrSSojrSprSSqjr\GR"SrSs9S\WSj4Stj5rSu\GRGRS\WSj4SvjrS\WSj4SwjrSSxjr\Sy5rSz\S\RRS\P\Q\\R\RR"444S{jrS:\`\RRSS4S|jrS:\`\RRSS4S}jrS~rSr"SS\RR5rSrg)N) defaultdict)CallableIterable)contextmanager)ismethod Parameter)AnyOptional TYPE_CHECKINGUnion)detect_fake_mode) FakeTensorFakeTensorMode)FunctionalTensor)#first_call_function_nn_module_stack)PreDispatchTorchFunctionMode)insert_deferred_runtime_assertsConstantAttrMap) OperatorBase)ExportedProgram)ExportGraphSignature)CustomObjArgument InputKind OutputKind)_deregister_pytree_flatten_specregister_pytree_flatten_spec) _deregister_pytree_node_register_pytree_nodeContext FlattenFuncFromDumpableContextFn GetAttrKeyKeyPathkeystr MappingKey SequenceKeyToDumpableContextFntree_flatten_with_path UnflattenFuncp_b_c_obj_tokenFreturncSSKJn U"5nURVs1sHBnUR[R :XdM#UR (aM6URiMD nnUR5HnupxXv;aM Un URS5GtpU Hn [X5n M U RRU S5 [XU5 URX5 Mp U$s snf)Nrr.)(torch._export.passes.lift_constants_passr input_specskindrBUFFER persistenttargetitemssplitgetattr_bufferspopsetattradd) graph_signature constantsmodrconstant_attrsspecnon_persistent_buffersnamevalue_modatomsattratoms P/home/mande/repo/quber/.venv/lib/python3.13/site-packages/torch/_export/utils.py_collect_and_set_constant_attrsrNDsI$&N$///D 99 (( ( 15  / !(   ) zz# D4&D $%E"5') #s"C+C+C+rCcrSSKJnJn [5nURR HnUR S:XdM[RRRXR5n[U[R5(dMiURU;dM{URU;dM[RRRXR5 U"XpURURS5 UR!UR5 M UR#5 U$)Nr) _assign_attr _AttrKindget_attrF)torch.export.unflattenrPrQsetgraphnodesoptorchfx graph_module _get_attrr9 isinstanceTensor _del_attrr7r@ recompile)rC state_dictrFrPrQtemp_registered_constantsnoder9s rM_register_constants_as_buffersrcbs? #  77j XX**44S++FF&%,,//KKz1KK'==HH))33CE dkk9;K;KUS-11$++> MMO $$rc@URH1nURU;dM[RUlSUlM3 UR HLnUR[R:XdM#URU;dM5[SURS35e U$)Nz Constant z< is mutated in the forward method. 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If you meant for this dimension to be dynamic, please re-export and specify dynamic_shapes (e.g. with Dim.DYNAMIC))torch.export.dynamic_shapesrr\rXrrbexpr is_numbersympy@torch._export.passes.add_runtime_assertions_for_constraints_passrtorch.utils._sympy.solverr free_symbolsnextitersubsrriSymbolrEqmathinf)rrrange_constraintsunification_maprrrrrrsymbol existing_dimpathsolutionmin_valmax_vals rM _check_symintr]s<8 3 %% '' c ' ' 3&%,,''C 0@0@0M0M,NRS,Sd6;;++889:  $!;;++00AL"!'*=gaSN*D"(.>|nJWZV[\ #&++**ELL99 +.c('$UXXfkk.>.>%DfM#%g.D}'!A.&)$s3%8''-{{'7'7&8X /2(1+.>O+ ;;  0 04!++"2"23  G{=%g.D}'!A.&,TF*WIZPSuU!=%g.D}'!A.&,TF*WIZPSuU !" 1* FELL ) )&++2B2B2L2L F '" = gaSN "D &6vhjN& &  rdinput_placeholdersc [U5[U5:wa$[S[U5S[U5S35e0n[X5GHuupEnURR S5n[ U[ 5(a[ U[R5(d#[S[U5S[U535e[UR5[UR5:wa2[S[U5SURSURS35e[[URUR55Hunup[XX#XH5 M GM[ U[[[ 45(a<[U5[U5LdXW:wa[S[U5S US U35eGMz[ U[R"5(dGM[UUUUUS5 GM g) Nz&Unexpected number of inputs (expected z, got )rrz to be a tensor, but got z,Unexpected number of dimensions in input at z.shape (expected rr)rriziprrr\rrXr]rrshape enumeraterrfloatrr) rflat_args_with_pathrrrrrbnode_valjarg_dimnode_dims rM"_check_input_constraints_for_graphrs 3'9#:: /01>WX\]`XaWbc8>>"c#))n4"B:hCWBXY!!) 0syykD +4C 8>>4R*S&&G'88+T 3s"3 4 4CyX.#/"(H)=(>>NxjXbcfbgh3B%,, / / !  3"Nrd)serialized_type_nameto_dumpable_contextfrom_dumpable_contextreturn_none_fieldscls flatten_fn unflatten_fnrrrrc `^^^[R"T5(d[ST35e[RR S[ S[[[ [44U4Sjj5n[RR S[[ S[S[ 4U4Sjj5n[RR S[ S[[[ [44U4Sjj5n TbTOUmUbUOUnUSLUSL- (a[S TS 35e[TTUUU UUS 9 g) Nz7Only dataclasses can be registered with this function: objr1c>/n/n/n[R"U5HbnUR[XR5peUcT(a$UR U5 UR U5 MQUR U5 Md XU/4$N) dataclassesfieldsrGr<append)r flattened flat_names none_namesfrGrrs rMdefault_flatten_fn=register_dataclass_as_pytree_node..default_flatten_fns}   ##C(AVV 4#"4  %!!$'!!$' )z222rdrcontextc j>Uup#T"S0[[X 55D[RU5D6$)N)dictrfromkeys)rrr r rs rMdefault_unflatten_fn?register_dataclass_as_pytree_node..default_unflatten_fns1!( PT#j12PdmmJ6OPPrdc>T"U5unup#[X!5VVs/sHupE[U5U4PM snnU4$s snnfrrr&)rr r  _none_namesrrrs rMdefault_flatten_fn_with_keysGregister_dataclass_as_pytree_node..default_flatten_fn_with_keys sC/9#, ,J/2:/IJ/ItqA"/IJJVVJs<z7Both to_dumpable_context and from_dumpable_context for z must be None or registered.rflatten_with_keys_fnrr) r is_dataclassrtrX_dynamodont_skip_tracingr tuplelistr r ValueErrorr) rrrrrrrrrrs `` ` rM!register_dataclass_as_pytree_noder$sN  # #C ( (EcU K   ]]$$ 3 3d3i.@(A 3% 3 ]]$$QXc]QWQQ%Q ]]$$W#W%S 78J2KW%W *5;MJ#/#;TU$rr)rb split_maps rM"sequential_split..s Yt_rdT)keep_original_orderkeep_original_node_name)torch.fx.passes.split_moduler8rUrV_codegenr_)rr6r8split_idrbnew_gmr:s @rMsequential_splitrCus:IH $   MH" $   $ $ FHH--FLL  MrdrVcNUVs/sHo!"U5(dMUPM sn$s snf)z:Returns the nodes that match the node_call_back as a list.rrVr6rbs rM nodes_filterrFs!" ;UTnT&:DU ;; ;s""c#8# [nSqSv Uqg!Uqf=f7fNT)_DISABLE_ATEN_TO_ASSERTION_PASS)orig_vals rM$_disable_aten_to_metadata_assertionsrKs&/H&*#3 *2'('s c SSKJnJn [(ag[R R RR[R R RR[R R RR/nURRGHnURU;dMURR[R R RR La,UR"SURR"S:XaMUR"SR$R'S5=ncMURR)U5 U"U[*R,"UUR$R'S5UR$R'S5S.S95 URR/[R R RR UR"S4URURUR0S.S 9 SSS5 SSS5 GM g!,(df  N=f!,(df  GM=f) Nr_node_metadata_hook_set_node_metadata_hookrrr)rrr)dtypedevicelayoutargskwargs)(torch._export.passes._node_metadata_hookrNrOrIrXopsatentorRrQ dtype_layoutrUrVr9prev_assert_tensor_metadatadefaultrUrrinserting_before functoolspartialr~rS)rrNrOaten_to_variantsrb tensor_vals rM$_insert_aten_to_metadata_assert_passrds '&      &&  ;;* *   EIINN$J$J$R$RRIIaLDIINN1$55"iil//33E:: GHH--d3+!))//3yy}}]/K3799==AR3S& HH** >>FF"iil_%/%5%5&0&7&7&0&7&7 + 43  43s&AI4A8I# I4# I1 -I44 J c SSKJnJn SSKJn [ R RR(dhSnU"U[R"USU0S95 [U5nU(a"[UUS[UR53SS 9 SSS5 [U5 UR!5 U"U5UlX4$!,(df  N9=f) NrrM)_graph_output_nameszUFile "torch/fx/passes/runtime_assert.py", line 24, in insert_deferred_runtime_assertsrrPzexported program: T)export)rWrNrO4torch._functorch._aot_autograd.input_output_analysisrfrXrconfigdo_not_emit_runtime_assertsr`rarrrrUrdr_ user_outputs)rrArNrOrfrrs rMapply_runtime_assertion_passrlsY ==   ; ; 1 %    #}k.J  /r2I/()LRXX)V(WX  -R0LLN#6r#:O +  s 5C CcV[X(aUOS5n[U5S:aUS$g)z Returns the first node that matches the node_call_back. If no node matches, returns None. When node_call_back is None, returns the first node in the node list. cgrHrrbs rMr;nodes_first..sQUrdrN)rFr)rVr6rets rM nodes_firstrrs, unDU VC 3x!|1v rdc*[[X55$)z:Returns the number of nodes that match the node_call_back.)rrF)rVr6s rM nodes_countrts |E2 33rdc(UH nU"U5 M U$)z Sequentially visit the nodes list and invoke node_call_back on each element. Returns the nodes list after the node_call_back is invoked on each element. rrEs rM nodes_maprv s t Lrdold_nodenew_nodecURU5 URR5 URR U5 g)z- Replace all uses of old_node with new_node. N)replace_all_uses_withusersclearrU erase_node)rwrxs rM node_replace_r~s4 ""8, NN NNh'rdc[U[RR5(aN[ US5(a<SUR ;a+UR R SUR S05 gggg)Nrr)r\rXrYrhasattrrupdate)rrCs rM_update_gm_meta_if_possiblers_3,,-- C   #((8"456 ! ! .rd call_mod_nodec H^ URS:wa[SUR35eURRnUc [S5e[ UR [ 5(d+[S[UR 5R35e[XR 5nSURR5nSURR5nURRVs/sHoURS:XdMUPM nn[X0R5HPupx[ U[RR5(d[S [U535e[!Xx5 MR URR#U5 UHnURR%U5n URS :Xa~U R n ['X5(a3S n S U 3n ['X5(aU S - n S U 3n ['X5(aMXl[)XR [X%R 55 [!XY5 M [+U5S :GaY[+U5S :wd[+US R5S :wa[S[+U5S35eUS RS m [ T [RR5(a'T R,R/5 [!UT 5 O[ T [0[245(aT H!nUR,R5US 5 M# [7[1UR,R955S5n [;U U 4Sj5 URR=U5 O3[?S[T 5S35eURR=U5 SSS5 URA5 URC5 U$s snf!,(df  N5=f)z Inline the submodule of the given node into the parent module. Note: we only support the case where submodule takes tensors inputs. r{zexpected call_module op, got Nz owning_module should not be Nonezexpected target to be str, got c3H# UHoRS:XdMUv M g7f)rNrW.0rbs rM node_inline_..7s K.D'']2J44." "c3H# UHoRS;dMUv M g7f))routputNrrs rMrr8s  +ww>W/W+rrzexpected fx.Node, got rRrsubmod_rz*expected exactly 1 output with 1 arg, got z outputscfURS:H=(a UR[RL$)Nr~)rWr9operatorgetitemros rMr;node_inline_..fs)O!;"8 x'7'77"8rdc<>[UTURS5$Nr)r~rU) get_item_node new_outputs rMr;rls -%"=#5#5a#89+rdzUnsupported output type z2. Expect it to be a Node or a list/tuple of Nodes.)"rWrtrU owning_moduler\r9rrrr<rVrrUrXrYNoder~r_ node_copyrr?rr{r|r"r!r>rFkeysrvr}NotImplementedErrordelete_all_unused_submodulesr_)rrsub_gmphsbodyrbrphrrxnew_target_namerget_item_usersrs @rM node_inline_r&sm =(<]=M=MP"GP P!c[R"UR5nURSnS[R 0n/nUR 5HjupV[URU5Vs/sHowRPM nnUH0n U S:XaM UR[R"X55 M2 Ml [R"US9$s snf)z Get source code and parse argument names using AST. The function returns a signature of the forward() function. # TODO: Directly provide inspect.signature compatible TS-d module. rrUself) parameters) astparsecoderrPOSITIONAL_OR_KEYWORDr:r<rUrr inspect Signature) rCast_mod ast_func_def arg_type_map param_listarg_type param_typea arg_name_listarg_names rM&_get_torch_jit_trace_forward_signaturer~sii!G$+LLOLI;; >"0"FqcJ001s2OP[\e\ePfghPiOjjk!m# !< (#**;yy+AB$))E*,=>>(,  % %? YY( " +DI3rdexport_graph_signaturerBc  ^0n[U[RR5(aJURR H0nSUR ;dMUR SXxR'M2 Sn Sm0n [[5n [5n [X#U5n [U 5upURVs/sH9nUR[R :XdM#UR"RPM; nn[%UU5HQuunnnU(dM['U U U U[([R SR+U4SjU55-SS9 MS URHnUR[R :XaM#UR[R,:XaSnO U "UR.5R15n[2R4"S SU5n['U U U UR"R[(URU-SS9 UU;dMUUXzUR"R'UU M URR H6nUR6S :XaM['XXRUR5 M8 [9X U5 [;U5 UR=5 URHnUR"RU ;a$[?S UR"R<S 35eU UR"RUR"lUR[R@:XdMUR.U ;dMU UR.S SUlM URBHnUR"RU ;a(U UR"RUR"lUR[DRF:XdMeUR.U ;dMwU UR.UlM [IURK55HnUUnUU ;dM[U[RL5(aM1U UnUU:wdM>[2RN"SU5(dM\U[([R@U-:wdM|UUU'UU M gs snf)a This pass is run at the end of _export_non_strict() to assign better placeholder node names: - User inputs: These follow the signature of mod.forward(), e.g. forward(x, y) produces nodes x, y. For nested inputs from dictionaries, lists, tuples, or dataclasses, the names are a concatenation of the path to the tensor. e.g. x = { 'a': torch.randn(), 'b': [torch.randn(), torch.randn()] } produces nodes x_a, x_b_0, x_b_1. - Parameters/buffers/constants/custom objects: These follow the FQN of the object, prefixed by "p", "b", "c", "obj" respectively. e.g. self.bar.l0.weight produces "p_bar_l0_weight". - Effect tokens: These are named token, token_1, ... rcURS5(aU[S5SnO$URS5(aU[S5Sn[R"SSU5nU$)N L__self___self_ [^a-zA-Z0-9]r) startswithrrsubxs rM _strip_name,placeholder_naming_pass.._strip_name)sY << % %#l#%&A \\' " "#g,.!A FF?C +rdcP[U[5(a-[R"SS[ UR 55nU$[U[ 5(a[ UR5$[U[5(a UR$[S[U5SU35e)NrrzPytree key of type z not handled for ) r\r&rrrrr'rr#rGrirrs rM_extract_pytree_key4placeholder_naming_pass.._extract_pytree_key1s} a $ $SZ8AH ; ' 'quu:  : & &66M!4T!WI=NqcRS Srdrc3P># UHnT"U5R5v M g7fr)lower)rrrs rMr*placeholder_naming_pass..Ss#L8a.q177998s#&T)rr+rrzinput spec arg rNz arg(\d+)_1)(r\rXrYrrUrVrrGrrrTrr)r5r6r USER_INPUTrrrplaceholder_prefixesjoinTOKENr9rrrrWrrr_rt CUSTOM_OBJrgrUSER_INPUT_MUTATIONr"rr]r)rrrCrrfake_params_buffersrBrrbrrrr combined_argsrrrEuser_input_namesarg_path_arguser_input_name base_namerGconstantrrs @rMplaceholder_naming_passrs6#%K#uxx++,,IIOOD499$)-8)< II&$ T "H%0%5N5J.ckJM3MB+666D 99 ,, ,  6.11DFV-W)4/ ? &$Y%9%9:((L8LLM# .X'22 99 ,, ,  99  'I#DKK0668IFF?C; "    HHMM  +i 7    #4?y3IK/ 0I&-38 77m # " j))TYY 25$B'LLN'22 88== ( ?488==2CCS!TU U / II-- -$++2I#4;;/3DK3'33 88==H $$TXX]]3DHHM 99 66 64;;(;R"4;;/DK 4Y^^%&T? 8 J ell% %  ~HD HH]D11 4Y5I5I JT QQ&. (#dO'is ?"S%Sr`in_placec0U(a<UR5H&up#[US5(dM[XS5 M( U$0nUR5H<up#[US5(a"UR5R 5XB'M8X4U'M> U$)z If `in_place` is false, return a new copy of `state_dict` with "proxy" removed from `v.__dict__`. `v` is the values in the dictionary. If `in_place` is true, modify `state_dict` in place. r)r:rdelattrdetachclone)r`rrrnew_state_dicts rMremove_proxy_from_state_dictrs $$&DAq'"" w/'$$&DAq'""$%HHJ$4$4$6!$%q! ' rdc/n/nURRGHnURS:XaXSUR;aHURSnUb4[ U[ R 5(aURU5 MhMjMl[U5S:XdM}SUR;dSUR;dMSnSUR;aURSnOSUR;aURSnUcM[ U[ R 5(dGMURU5 GM [X-5$)a For a given graph module, we look at the "val" of placeholder nodes to find the fake inputs. Additionally, if gm doesn't have placeholders, we further look at the "example_value" or "val" of other nodes. If no fake mode is found, we return None for fake_mode. rrNr example_value) rUrVrWrr\rXr]r rr )r fake_inps fake_valsrbfake_vals rMrrs%'I$&I 77m #(:yy'H# 8U\\(J(J  *)K# ^q tyy (ETYY,>H$))+99_5$))#99U+# 8U\\(J(J  * I1 22rdc# # [UR5n[UR5nURR5 URR5 Sv XlX lg!XlX lf=f7fr)r_state_dict_hooks_state_dict_pre_hooksr|)rCstate_dict_hooksstate_dict_pre_hookss rM_disable_load_state_dict_hooksr sn,01F1F,G04S5N5N0O!##%9 0$8!!1$8!sAB"A3& B3BBrWrc[RRUR5[RRR 5=(d1 [RRR UR ;$r)rXr%_dispatch_has_kernel_for_dispatch_keyrG DispatchKeyCompositeImplicitAutograd py_kernelsrs rM _is_cia_oprsX 66 GGIuxx++EE  K 88   9 9R]] J rdc<[U5=(a [U5$r)_check_valid_to_preserverrs rM_is_preservable_cia_oprs #B ' :JrN:rdcLUR5RS5SS:H$)N::rrY)rGr;rs rM _is_aten_oprs! 779??4  #v --rdc"[U5(+$r)rrs rM _is_custom_oprs2 rdc [RRS5nUHn[UR S55up#UR S5n[ U5S;a[ S[ U535eUSnSn[ U5S:XaUS n[[[[RU5U5U5nM g ) zu Utility function to query C++ dispatcher to get the all possible CIA ops and populate them into torch.ops namespace rrr3)rrzexpected 1 or 2 parts, got rr^rrN) rXr,_dispatch_get_registrations_for_dispatch_keyr!r;rrtr<rX)cia_opsrW namespaceop_name split_listop_overload_namers rM_materialize_cpp_cia_opsr s hhCC#G "288D>2 ]]3' z?& ( #>s:>O!PQ QQ-$ z?a )!}  GGEIIy97CEU Vrdc[$)zU This is an special marker that tells our infra that we shouldn't decompose this op. )NotImplementedrTs rM_special_op_to_preserve_ciar#s  rdcSSKJn U"U5(agU[R;ag[ US5(dg[ UR RVs/sHo"RcMUPM sn5nUS:g=(d UR RnU(ag[RRUR55(dggs snf)Nr)#_should_decompose_because_unsafe_opF_schemaT) torch._decompr%r metadata_fnsrrr&r alias_info is_mutablerXr_dispatch_has_kernelrG) op_overloadr%rr)is_mutating_or_aliasings rMrr!sA*;77&333 ; * *''11N1q\\1NJ)AoO1D1D1O1O 88 ( ()9)9); < <  Os C-Cr)maxsizecH[[RR5$r)(_collect_all_valid_cia_ops_for_namespacerXrXrYrrdrM-_collect_all_valid_cia_ops_for_aten_namespacer1;s 3EIINN CCrd op_namespacec[5 [5nUHSn[X5nUR5H1n[X45n[ U5(dM UR U5 M3 MU U$r)r rTr< overloadsrr@)r2rrW op_packetoverloadr,s rMr0r0@sbeGL- !++-H!)6K%k22 K(. Nrdc[5n[RRHnUS:wa[ [RU5(d[ SU<35e[ [RU5n[U[RR5(aU[U5-nMMU[5-nM U$)a This is an util function that gets the all CIA functional ops. The algorithm is in 2 steps: 1. We first query C++ dispatcher to get the list of CIA ops and then we call getattr on torch.ops.aten to lazily populate them. 2. Sometimes, handful of ops have CIA registered in python dispatcher but not on the C++ side, these can't be caught at the first step. So we walk again to get the final list. Note that the output of this function should never be modified rYz"torch.ops does not have attribute ) rTrXrX_dirrrtr<r\r _OpNamespacer0r1)rop_namespace_namer2s rM_collect_all_valid_cia_opsr;QseG"YY^^  &599&788$89J8MN#599.?@L, (?(?@@CLQQA DF FG, Nrdc [RRRnXR;aE[ URU[RR5(dURU$Sn[ R"X S9$)NcNUSnUS [RRRn[RR UR 5[RRR5(aUR "U/UQ70UD6$[SUS35e)Nkernel Expected z) to have CompositeImplicitAutograd kernel)rXrr rr rG_op_dkrt)rUrVr>dks rM_special_op_to_decompose_cia9_get_decomp_for_cia.._special_op_to_decompose_cia{s! 8 XX ! ! ; ; 88 9 9 KKM588//II  ==5d5f5 5 F8#LM rd)r>)rXrr rrr\r`ra)rWrArBs rM_get_decomp_for_ciarDpsj    7 7B ]]:bmmB.?AUAU#V#V}}R      9 EErdc## [RRn[RRnS[RlS[RlSv U[RlU[Rlg!U[RlU[Rlf=f7frH)rXcompiler_is_compiling_flag_is_exporting_flag)old_compiling_flagold_exporting_flags rM_compiling_state_contextrKsx::::?,0),0) ,>),>)-?),>)s5C.B&+C,B==Crc"0[URSS95E[URSS95En0n0nUR5HDupV[ U5U;aU[ U5nOUR USS9nXt[ U5'XsU'MF U$)NF)r}T) static_shapes)rrrr:id from_tensor)rrCparams_buffersfaked_params_buffersmemorrH fake_tensors rM_fakify_params_buffersrTs s##U#; < s  % 8 9N "$D$**,  e9 r%y/K#//T/JK)EO$/S! - rdc F^^ ^ [T[RR5(d[ ST35eSSKn"U U4SjSUR 5m S[S[[[[44U 4SjjnS [[S [S[4U 4S jjnS[S[[[[44U 4S jjnUm UnTRS -TR-nSnU 4Sjn[TT UUUUUS9 S[[4U 4Sjjn [TU 5 g)a Registers a module as a valid input type for :func:`torch.export.export`. Args: mod: the module instance serialized_type_name: The serialized name for the module. This is required if you want to serialize the pytree TreeSpec containing this module. Example:: import torch class Module(torch.nn.Module): def __init__(self): super().__init__() self.linear = torch.nn.Linear(3, 3) def forward(self, x): return self.linear(x) torch._export.utils.register_module_as_pytree_node(InputDataClass) class Mod(torch.nn.Module): def forward(self, x, m): return m(x) + x ep = torch.export.export(Mod(), (torch.randn(3), Module())) print(ep) z!expected nn.Module subclass, got rNc>>^\rSrSrUU4SjrSrU4SjrSrU=r$)=register_module_as_pytree_input_node..PrototypeModuleic>[TU]"U/UQ70UD6 [U[RR 5(d![ S[U5R35e[US5(a [ S5eTUl g)Nzexpected nn.Module, got _proto_clsz_proto_cls should not be set) super__init__r\rXrrrtrrrrY)rmrUrV __class__rs rMr[Fregister_module_as_pytree_input_node..PrototypeModule.__init__sm G Q 0 0 0a11$'?Q@P@P?Q%RSSt\**$%CDD!DOrdc4URUR:H$rrY)rothers rM__eq__Dregister_module_as_pytree_input_node..PrototypeModule.__eq__s??e&6&66 6rdc>T"U"55$rr)rrRPrototypeModules rM __deepcopy__Jregister_module_as_pytree_input_node..PrototypeModule.__deepcopy__s"46* *rdr`) r __module__ __qualname____firstlineno__r[rbrf__static_attributes__ __classcell__)r]rers@rMrerWs " 7 + +rdrerr1c>[UR55n[UR55n0UEUEn[UR 55[UR 55T"U5/4$r)rrrr"rr)rrrrPres rMr@register_module_as_pytree_input_node..default_flatten_fnss 4 4 67S..01 >,> >N))+, $$& ' C /   rdrrc >^Uup#UbU"5c [S5eU"5nT c [S5eT "U5upVS[RR4U4Sjjm[ S[ X555(aY[RRRRU[[ X 55SSS9 T"U5nSSS5 U$UnU$!,(df  W$=f)Nz!Module has been garbage collectedzflatten_fn should not be NonerCcP>[R"U5nURR5VVs0sH0up#[R"U5[R"U5_M2 snnUlUR5HupE[ XT"U55 M U$s snnfr)copy__dict__r:named_childrenr?)rCrqrrrGchild copy_modules rMruWregister_module_as_pytree_input_node..default_unflatten_fn..copy_modules{))C.CCF<.default_unflatten_fn..s=&.default_unflatten_fns! ;#%-BC Ce   !@A A!#  UXX__  =c&&<= = =))??T#j12T@"#&  C   s  C C*c>T"U5untp#[X!5VVs/sHupE[U5U4PM snnU/UQ4$s snnfrr)rr r rUrrrs rMrJregister_module_as_pytree_input_node..default_flatten_fn_with_keys sW)3C& &J/2:/IJ/ItqA"/IJ M M   Js>r3cTUtp[R"U/S/[U5-Q5$r)jsondumpsr)rrrs rMrAregister_module_as_pytree_input_node..to_dumpable_contexts*zz44D6CF?455rdc>[R"U5nT"[RR 55US'U$r)rloadsrXrr)dumpablesres rMrCregister_module_as_pytree_input_node..from_dumpable_contexts/ JJx uxx01!rdrcp>T"U5up#X1R:wa[SUSUR35eU$)Nzcontext mismatch: z != )rrt)rrEflatsrrs rMdefault_flatten_fn_specEregister_module_as_pytree_input_node..default_flatten_fn_spec,s:#C ll " #5gYd4<<.!QR R rd) issubclassrXrrrtweakrefrr r!r"r rrhrirr) rrrrrrrrrrrers ` @@rM$register_module_as_pytree_input_nodersH c588?? + +@FGG + +'++ +  d3i.@(A Xc]W: # %S 78J2K $J'L>>C/#2B2BB6  19/3d3i ! rdc0[U5 [U5 gr)rr)rs rM&deregister_module_as_pytree_input_noder8sC #C(rdc([U[RR5(d[ SUS35e[U[RR5(d[ SUS35eUR UlUR Ulg)Nr?z to be a nn.Module)r\rXrrrt _parametersr=)srcdsts rM _sync_stater=sn c588?? + +y-?@AA c588?? + +y-?@AA ooCO<[TU]5 [URU5 Xlgr)rZr[r__self__r)rmethodr]s rMr[_WrappedMethod.__init__Vs! FOOT* rd)r)rrhrirjr[rkrl)r]s@rMrrUs rdrcV[U5(d[SUS35e[U5$)z Wrap a method as a module so that it can be exported. The wrapped module's forward points to the method, and the method's original module state is shared. r?z to be a method)rrtr)rs rM wrap_methodr^s. F  y@AA & !!rd)r1r)rjr)rnrrlr)Fr)NN)r1N)r,r)rWr)rrqrr`rrrrr collectionsrcollections.abcrr contextlibrrrtypingr r r r rX torch._guardsr torch._subclasses.fake_tensorrr#torch._subclasses.functional_tensorrtorch.fx._utilsr"torch.fx.experimental.proxy_tensorrtorch.fx.passes.runtime_assertrrr4r torch._opsr torch.exportrtorch.export.graph_signaturerrrrtorch.fx._pytreerrtorch.utils._pytreerrr r!r"r#r$r%r&r'r(r)r*r PARAMETERr7rfrrrrIrNrYrrcrkrorrrrrrrrTboolrrrrr"rrrr$r(rr+r-r]r/r2r5rCrFrKrdrlrrrtrvr~rrrrrrrrrrr _subclassesrSrr rrrrr r#r lru_cacher1rr9r0r;rDrKrTrrrrrrrrdrMrs   #.%'66 *D@?KJH',AQQ$"  d t & OOW #(<%   %4 (  # .D  7'(<(<7'c3h7't0)!%c3h) )$) )8 $uxx33 $*! %38n%cN%C% %  %  %P//S/< [ #u||# $[ [  [ } [  [ |,UXX]]+, ,b)-,08 +/9==A$8 c8%8=)8 #3- 8 ""56 8$$9:88 8vE'Euxx}}EE    ((-- ehh  !  B(B B$B  ((--ell&R R ((--R R  ((--ell$ ehhmm_eEHHMM44G.HHI XX><UXX]]+<UXX]]@S< 33-UXX-A-A-d-`!UXX%9%9!J04     ehhmm 4tEHHMM*4s4 T%((--(T%((--=P(EHHMM(UXX]](t(7EHH$8$87uxx7SW7U U(588;O;O2PUp44GDUDU42E(,-(<(<--`!, !,38n!,c3h!, !,H\$ \$2\$ \$CH~\$ \$~TTd*3 3 e++::;3< 9 9 9>d;~;$;.N.t.n W04 QDs>7JD D**))"C$7>F: ? ?    #uU\\588#5#556 67 *Fd588??.CFFR)UXX__0E)$)  $UXX__"rd