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"+-'(9:  %'   . # " ( > )b1 H #B ' " %'!/$?  S .- #" "!  ^)/: HM>>AG G Hsl C? C.C'C ?CC.C?3D C C C+ 'C.. C< 8C?? D E #EEc 4[R"UR5nUR"U6Rn/nUR 5HupgUR UR[RR:Xa6UR[U5VV s/sH upUSU3PM sn n5 MpURU5 M U(a4URUR 5V V s/sHupU PM sn n 5 U$s sn nfs sn n f)a Gets the argument names to forward that are used, for restoring the original signature when unlifting the exported program module. - Positional args: retain the original argument names, and enumerate *args as args_0, args_1, ... - Keyword args: retain the original kwarg names in the order specified by the user. This order seems to matter for the current state of export lifted modules. r) rrforward bind_partial argumentsrr]r_ParameterKindVAR_POSITIONALextendrr) rdrrrs_argsnamesrr@rrkwargs rn_get_forward_arg_namesrs   CKK (C   d # - -EE{{}  >>$  $ $(>(>(M(M M LLIe4DE4DDATF!A3-4DE F LL  % FLLN;NeN;< LF # UH nTSU3v M g7f)rNra).0brs rn ._get_non_persistent_buffers..sO1NATF!A3-1Ns)rkrr_non_persistent_buffers_set)rdresultrrs @rnraras\uF$$e$<a  MMO1N1NO O MM!77 8 = Mrmorig_mod_bufferstraced_mod_bufferscURHjnUR[R:XdM#URceXRnXP;dMG[R UlXSUR'Ml g)zh Dynamo erroneously marks tensor attributes on modules as buffers. Rewrite them to be tensor constants. N)rrrLrrr3)rrr=rtrr@s rn _rewrite_dynamo_tensor_constantsrsc ++ 99 (( (;;* **&{{3E,&55 ).$++&,rmorig_modc URH~nUR[R:XdM#UR(aM6UR ceUR U;deUR UR 5X#R 'M g)z3 Moves non-persistent buffers to tensor constants. N)rrrLr persistentr get_buffer)rr=rtrs rn0_move_non_persistent_buffers_to_tensor_constantsrsk ++ 99 (( (;;* **;;i/ //%-%8%8%EIkk " ,rm graph_modulec [U/[UR55-5GH6up[U[R R 5(dM1URRHnURS;aUS:XaURRSS5=nc[SUSURS35e[SUR555(d[SUSURS U35eMMURS ;dMURRSS5(dM[SUSURS 35e GM9 g) af Perform nn_module_stack checks on the graph. Current constraints: For the top level graph: - populated for 'call_function', 'get_attr' - None for 'placeholder', 'output' For submodule graphs: - None for 'placeholder', output' TODO(pianpwk): make this a consistent node-level check once nn_module_stack is populated for cond submodules. rr{rrNNode of type z$ is missing nn_module_stack metadatac3# UH\up[U[5=(a> [U[5=(a' [U5S:H=(a [ SU55v M^ g7f)c3B# UHn[U[5v M g7fr)rry)rrs rnr4_verify_nn_module_stack...s>Aq 1c 2 2AsN)rryrrall)rkvs rnr*_verify_nn_module_stack.. s] %A>>?% rzPlaceholder node name z does not follow spec for z, name should have prefix: N)rrsrrr rrurvrwrrrr!rr")rrrsr name_to_kindrdr node_kindprefixs rn_verify_placeholder_namesr 7s:=IHHMM499,LIzz|#uxx3344 IIOODww-'99L0(3 -i8yy++F33,0 ;UV_U``{}C|DE$Js*DepcSn[5nURR5Hn[U[R R 5(dM.URRHnURS:waMUS- n[URS5(de[URS5(deURURRSURR35 M M XS.$)NrrrIrcrbr)op_countop_set)rkrr rrurvrwrrrr}rrmrcrb)r r r rrs rn get_ep_statsrOsH UF __ $ $ &!UXX1122 GGMMDww/) MH4;; 55 554;; 33 33 JJ$++0014;;3G3G2HI J "'! 33rm _EXPORT_FLAGSrcF^[R"T5U4Sj5nU$)Nc`>[R"5nT "U0UD6n[R"5n[S SXB- [S.[U5D6 SqSqU$![an[ U5nUR S-UR-n[U5nUb8[R[U55 [SU[U5[S9 O[SU[U5[S9 [US5(a"[UR[ R"S9 UeSnAff=f!SqSqf=f) Nz export.time)rmetricsrrzexport.error.classified)rrmessagerzexport.error.unclassifiedpartial_fx_graph)filera)timer3rrrrrcrdrlogerrorr ryr}printrsysstderrr) rrstartr endrt error_typerfns rnwrapper$_log_export_wrapper..wrapperds*' ,IIKET$V$B))+C  # # r"  D!M'+ $ ? QA+annELL$#788 ((*#!  //rmc[URSS95n[URSS95nX-n[U5nUHnUR US5 M U$r)rxr"r$rarS)rdr"r$original_state_dictnon_persistent_buffersrs rn_get_original_state_dictr-skC00%0HI**E*BCM*:8= #4($ rmc[U[5(d&[[RS[ U535eUbUO0n[ R"X45upE[U[RR5(aURnURXU5nOASn[U[RR5(aURXU5nOUnXXWU4$)a Process and validate export inputs for the torch.export API. This function validates the input arguments, normalizes kwargs, computes input tree specs, and handles special dynamic shapes cases like AdditionalInputs and ShapesCollection. Args: mod: The PyTorch module to be exported. args: Tuple of example positional inputs for the module. kwargs: Optional dictionary of example keyword inputs. dynamic_shapes: Optional specification for dynamic shapes. Can be: - dict mapping argument names to dynamic shape specifications - tuple/list specifying dynamic shapes for each input in order - torch.export.AdditionalInputs object with verification callback - torch.export.ShapesCollection object Returns: A tuple containing: - args: Validated tuple of positional inputs - kwargs: Normalized dictionary of keyword inputs (empty dict if None was passed) - original_in_spec: TreeSpec representing the flattened input structure - dynamic_shapes: Processed dynamic shapes specification - verify_additional_inputs: Callback function for additional input verification Raises: UserError: If args is not a tuple. rcgrrar s rnr(_process_export_inputs..sdrm)rrr r rrr tree_flattenrurAdditionalInputsverifyrShapesCollection)rdrrrroriginal_in_specverify_additional_inputsout_dynamic_shapess rn_process_export_inputsr9sZ dE " "  ' 'OPTUYPZ| \  )VrF --tn=A.%,,"?"?@@#1#8#8 +::3fM#2 nell&C&C D D!/!>!>s&!Q !/  )?W WWrmexport_artifactstrict_mode_exportc URRnURRnURnURnUR n0n UR 5H.upU(d [U 5OU n [//U SU SSS9X'M0 [U5S:a4U(d [U5 [X5"U5n U ceU Rn[ce[UUU U5nXN4$)zt In-place modify the graph module in export_artifact, remove _export_tracepoint nodes and return module_call_graph. rrNrr)r~rrrsrrrrrrNrrrrrr)r:rr;rrrrhrrrrrspecsmod_fqnresmodule_call_graphs rn_get_module_call_graphrAs /3366B3B3G3G3K3K))(//G(11H>@'--/ *<+c"#*=)$:&" + '0 )*Q.! $R ($%;TUWX    # // //    rmcpURRnURRnURn[ S[ UR 55[UR 55n[XU5n 0n U Hn X;dM XX'M UH n X<X'M U n [UUU UU5n U $)Nc3j# UH)upUR[R:XdM%Uv M+ g7fr)rrLr2)rrss rnr)_get_range_constraints..3s- Evv--- AEs$3 3) r~rrrsrr rrrr7r)rdr:rrrrrrhr num_liftedrcombined_args_traced_orderrsrrange_constraintss rn_get_range_constraintsrI(s /3366B3B3G3G3K3K / 9 9I !"8"D"DE " . ./ J"#V4M "$  .;.@ & +*0+"'/M(   rmrcURceURRR5VVs0sHup[U5(dMX_M snn$s snnfr)rrOrrB)rrrs rn_get_inline_constraintsrKTs^    ** *''44::<   --n=--n=s>B A% B %!BB  orig_in_spec _to_aten_funcc B[UUUUUSUSS9n[XU5un n n [X5n URRHn U R S:XdMSU R ;dM'[XR5n[U[RR5(aMgU cS5eU RUSS9U R S'M [URR[RRR 5(deURRR"R$=nnUceUceUR&[([*4;a[,R."[*SU/5nURRR"R0n[![3UUR4U55URlUR75 [9U['U55 [;U5n[=U5n[?X5n[AU5nURC5VVs0sH unnUU_M nnnUVs1sHnUU;dM UUiM snUl"[GU 5nU  [IU5 U"U[KUX50U U5nSSS5 SSS5 WRLnURNnURPn[SUUU5 [U[WURY55[[UR]55UUS 9 [_UU5 [aUUU5 [cUUU5 [eUU5 [gUUUU UR S S 9$s snnfs snf!,(df  N=f!,(df  N=f) R _to_aten_func can either be `_export_to_aten_ir_make_fx` or `_export_to_aten_ir` F)rrr`rr{rNzbCannot find dynamo_fake_mode. This could be due to the exported graph module have no placeholders.T) static_shapes)rrr=rtrr~rrrr)4rrrrrrrr~rrrurr from_tensor_codegenrvrE pytree_inforrrrrrG orig_argsrF_in_specrtrrrr,rarrr/r.rrrrsrtrrrkr^rxr$rrrCryr|)rdrrrrrYr`rZrrrdynamo_fake_moder-rrr orig_out_specrparams_buffers_to_node_metarerr,tracedorigreverse_name_lookuprtxaten_export_artifactrrrhrts rn_strict_exportrlys})  'E4_ N ^6: 11AR $$** 77j U$))%;>;;7DdEHHOO44'3x3$4#?#?$@$ % + n**33UXX^^5R5R S SS S .33<<HHQQQH}     $$ $}}T5M)??5$ ;#))22>>HHN$2   # #  %N!~tCy9"@"P,C0N)B3)W 9=?.  24JK5 #Y ^%;YGB 67  !"(--.AB  eX2';  s6M4 M:, M: NM?-N? N N NcU$rrars rnrrrrmc Sn[RRRR UUSSSS9 [ 5 [ 5 U"U5"UUSUS9upURRHQn U RS:XdM[U R5S:XdM0SU R;dMBSU RS'MS [U[RR5(a6[!US 5(a%URR#UR5 SSS5 SSS5 SSS5 U(a U"W5 [1WUUW SUUUS 9$!,(df  N>=f!,(df  NG=f!,(df  NP=f![$[&4a(n [)[*R,[/U 55eSn A ff=f) Nc^%^&U=(d 0n[URSS95n[URSS95n0UEUEn[R"U5upx[ U5n[ U5n [ U5n [ U5n [XU SS9n /n U RU5 U RU5 [XU5um&n[R"X45unn[R"T&5U&4Sj5n[5 [5n[USS5nUb[U5n0n[!UU5nSm%["U%4Sj5n["S 5nU U"U5 U"5 [%USSS 9"U6nSSS5 SSS5 SSS5 UGb9['W5n[)UR*R-55VVVs0sHununnUcMUUU U-_M nnnn[/UR0R25S nWR-5HunnUU5n[5UU5n UR0R7U5 UR0R9S [:R<R>R@RBUU 4S 9n!U RDU!l"SSS5 M WRG5 Sn"WR0RIU"5 SSS5 URJcS5e['W5n[MU5n#[O[/U5[/U5UU SU#[[QUSU U55[[QUXU -U55000UURJS//S9n$UU$4$!,(df  GN=f!,(df  GN=f!,(df  GN'=fs snnnf!,(df  GM=f!,(df  N=f)NFr T)store_orig_modc >[T"U65$r)r)rflat_fns rn wrapped_fnG_export_to_aten_ir_make_fx.._make_fx_helper..wrapped_fn7s$( (rmrcU"X5nX;a[RR5(a[U[R5(a[R R 5nUHn[U[5(dMURn[X5RnURS[RRRRX405n [!XISUS9 M U$)z The idea here is that we override subclass getattr methods to proxy inner tensors and metadata. Because of infinite loop shenanigans, we have to manually construct the getattr proxy nodes without relying on torch function system. rN)r:tracer)rurT_is_torch_function_mode_enabledrr overrides _get_current_function_mode_stackr?rvr=proxy create_proxyrraccess_subclass_inner_tensordefaultr@) rroriginal_getattrattrs_to_proxyouttorch_function_mode_stackmodervrz inner_proxys rncustom_getattributeP_export_to_aten_ir_make_fx.._make_fx_helper..custom_getattributeHs't2)xx??AA%c5<<88!& P P R6)B#-d4P#Q#Q-1[[F,:4,H,N,NE282E2E(7(- (8(8(U(U(](]). (* 3&K %6(+4PV%&)B rmc3*># 0nUHn[U5nUHknXA;dM [X45S:deX4Sn[[U55[[[R 55- nUR U4X'Mm M UR5H%ununn[R"T UUS9n XlM' Sv UR5HunupXlM g!UR5HunupXlM f=f7f)z~ Context manager that temporarily monkey patches tensor.__getattribute__ so that we can intercept it at torch_function layer. r)r~rN) r(rrkdirrur__getattribute__rr# partialmethod) rtensor_type_to_old_getattributerssubclass_types_to_instances subclass_typeinstancerr old_getattrcustomrrs rn$override_getattribute_for_subclassesa_export_to_aten_ir_make_fx.._make_fx_helper..override_getattribute_for_subclassesls40 C6c:0*E (O#&'B'Q#RUV#VV#V'B'QRS'TH-0X-?#c%,,FWBX-XN - > > .N;J *E  99>>@#&!*!8!8/-8+9" .4* A/N/T/T/V++K-8*0W/N/T/T/V++K-8*0Ws#DA)D =C, #D,$DDc3# [R"5nSv [RRU5 g![RRU5 f=f7f)z When pre-dispatch export accidentally change grad state, we restore it back. This can happen when we are calling torch._C._set_grad_enabled directly in the forward. N)ruis_grad_enabledrT_set_grad_enabled) old_states rn_maybe_restore_grad_stateV_export_to_aten_ir_make_fx.._make_fx_helper.._maybe_restore_grad_statesA"113 :HH..y9EHH..y9sA!= A!!AA!)record_module_stackrfrr)rcURS:XaUR[RRR R [RRRR [RRRR[RRRR [RRRR 4;agg)NrFT)rrrurprofiler_record_function_enterr}_record_function_enter_new_record_function_exit_RecordFunctionr~detachrr|)rs rn _is_impureG_export_to_aten_ir_make_fx.._make_fx_helper.._is_impures77o-$++ II&&==EEII&&AAIIII&&<<LLIINN))11II$$AAIIB3!rmzout_spec.spec is None!r)r]r^ user_inputs user_outputsinputs_to_parametersinputs_to_buffersbuffers_to_mutateparameters_to_mutateuser_inputs_to_mutaterrbackward_signaturer_ output_tokens))rxr"r$rr2rrr$rr)r#r$r rr~r+r*rr>r%r_buffersrrrr _find_noderrrurr~copy_r}rremoveeliminate_dead_coderr&r'r0)'rdrrrr"r$params_and_buffersparams_and_buffers_flat params_spec param_len buffer_len params_lenfunctional_callparams_buffers_argsr flat_argsrrsrnon_strict_rootassigned_buffershookrrrr input_namesrrbufbuffer_input_names output_nodebuf_node name_noderr output_namesrsrrrs' @@rn_make_fx_helper3_export_to_aten_ir_make_fx.._make_fx_helpers2 4 4e 4 LMS...FG B 0BMB/5/B/BCU/V,"'(?"@() ' +, 0 j *,""#:;""4(4 & $002E1NO 7  ! ) " )& '-C%c>4@O*2?C46 6#%5" H) 9) 9V : :4Y?)+(,!% ,@*04 +4O4L4L4R4R4T*U&*U;D#5D+i!m44*U#& #288>>226 !1!7!7!9IC)".@.EFH *2t 4I22;?#%88#7#7+!IINN0088"*I!6$8$ )2 @?":  & HH ( ( 4}(B}}(B*BB((, *2. ,-'#JK0%!%c+a *BDT&U!V"K ,BC]S!!#"$]]#! $3wS,+@?&@?C( 's AO N-&N.N =NN- >O N?  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D ! ##0#'(@*$ !5 > > D D F. FHCj>N.O.O%c*UXX F. * $ E K 2  ) >5 ! % %';'E'E        !+  . $ # E D K J 21   )(  s- H8 H*G1.G <G  F> *F-*F'. F-7F> ?G G G1HH'F-- F;7F> > G G  GG G. *G11 G? ;H H H H!rrr`rc [U5q[XX#5unnnnn[U5n U(a[O[ n /n U(d[ RR[S5S:Xax[R"5 [R"5V s/sHCn [U [RR R"5(dM8[%U 5PME n n U "UUUUUUU[&S9n U R(R*n[-XU5n[/U R05nUU R(R2R4S'[7UU UUU5n[9U UUU5unn[;U5 [=U5 [?UU5 [AUU5 SSK!J"n [GUURHUU UUX4U R(RJU/S9 nU"U5 U(Gd[ RR[S5S:XGamSSK&J'n [R"5V s/sH:n [U [RR R"5(dM8U PM< nn [PRR"5nUH%n[%U5U ;dMURUU5 M' AA [WU5n/nU5S:aUHqn[%U5U;dMU[%U5R4RS S 5nS URZS UR\3nS USU3nUR_U5 Ms SRaU5n [bRd"S[YU5SU S35 AU$s sn fs sn f)N01rdrrrrrYr`rZinline_constraintsr)TrainingIRVerifier rrr= state_dictrHr@r'rt verifiers)'_FAKE_TENSOR_ID_TO_PROXY_MAP_FOR_EXPORTr[zzshape=z, dtype=z FakeTensor(z): z z Detected z fake tensors that are still alive after export. This is likely result of torch.export.export not being able to track side effects that is happening outside of model scope. Leaked tensors: zU Alternatively, please file a bug report to PyTorch team for further debugging help.)3rrr9r-rlrosenvironrrQgccollect get_objectsrru _subclasses fake_tensor FakeTensorr#rr~rsrrKrrrrrIrArrr rtorch._export.verifierrrKrrt"torch.fx.experimental.proxy_tensorrweakrefWeakSetrmr4rshapedtyperrwarningswarn)!rdrrrrrr`rYr7r+ export_func"alive_fake_input_ids_before_exportrr:rhrrrHrrr@rexported_programr fakes_after active_fakesr legit_leak leak_sourcesfake_valr[ shape_info leak_info leak_detailss! rn_export_for_trainingrsh 5S9 s&A   337%+.0BK46& bjjnn%DcJcQ ^^%. %!U..::EEF BqE% +. "  %'E!4_0 O-1155.s&A01J1JK:LO  !56.   3& BBb"89C(9& hh.&++~!&&00%& -. bjjnn%DcJcQ ^^%G %!U..::EEF % G )0(9 &K+&HH  -'  .&El&S "$ z?Q &h<#JJ"I8 #d33}.EF $*(..)9(..AQ!RJ"-j\[M JI '' 2'";;|4L MMC O,-&'3^4fg  Y. HG s7M=M=+7N&N)rrrfr`rfc$^^SSKJn [U5q[ 5n U R U(aSOS5 U R U(aSOS5 U q[S[ S9 [S S S 9 U(a+U"5(a[UUUUUUUS 9m[S U4SjS 9 T$[XX#5unnn nn [U5n U(a[O[n U "UUUUUU U[R"[ US9S9nUR"R$n['XU5n[)UR*5nUUR"R,R.S'[1UUUUU5n[3UUUU5unn[5U5 [7U5 [9UU5 [:R<R>RAU SS9 SSK!J"n [GUU5 [IUURJUU UUX4UR"RLU/S9 m[S U4SjS 9 U "T5 T$)a Traces either an nn.Module's forward function or just a callable with PyTorch operations inside and produce a ExportedProgram. Args: mod: the `nn.Module` to trace. args: example positional inputs. kwargs: optional example keyword inputs. dynamic_shapes: An optional argument where the type should either be: 1) a dict from argument names of ``f`` to their dynamic shape specifications, 2) a tuple that specifies dynamic shape specifications for each input in original order. If you are specifying dynamism on keyword args, you will need to pass them in the order that is defined in the original function signature. The dynamic shape of a tensor argument can be specified as either (1) a dict from dynamic dimension indices to :func:`Dim` types, where it is not required to include static dimension indices in this dict, but when they are, they should be mapped to None; or (2) a tuple / list of :func:`Dim` types or None, where the :func:`Dim` types correspond to dynamic dimensions, and static dimensions are denoted by None. Arguments that are dicts or tuples / lists of tensors are recursively specified by using mappings or sequences of contained specifications. preserve_module_call_signature: A list of submodule paths for which the original calling conventions are preserved as metadata. prefer_deferred_runtime_asserts_over_guards: With the current dynamic shapes language for dims and derived dims, we can run into constraints that are not expressible with the language. For example, flattening a matrix and adding to a vector, both fully dynamic (i.e. x.reshape([-1]) + y) emits a guard s0 * s1 = s2, which is not expressible. By default, we either raise a constraint violation error or specialize to static values. If this flag is set to True, we avoid erroring out and instead allow complex constraints to exist as runtime assertions in the graph. The sympy interpreter (torch/utils/_sympy/interp.py) will produce the math ops required to compute and assert the value of the guard (e.g. sym_size_int, eq, _assert_scalar). Additionally, if TORCH_DYNAMO_DO_NOT_EMIT_RUNTIME_ASSERTS=1 is specified, we will allow complex constraints while not emitting runtime asserts, returning a cleaner graph with lesser guarantees around dynamic shapes. 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