9i04SSKrSSKJr SSKJr SSKrSSKrSSKJr SSK J r SSK J r J r Jr SSKJr SSKJr SS KJr SS KJr SS KJrJrJr SS KJr SS KJr "SS\5r \ "5r!\!RE\5S5r#\!RE\5S5r$\!RJS5r&\!RE\RN5"\"\!SS95 \!RE\RP5S5r)Sr*\S5r+Sr,Sr-S\.S\.4Sjr/Sr0Sr1Sr2g) N)contextmanager)wraps) DispatchKey)+_maybe_find_pre_dispatch_tf_mode_for_export)_ConstantFunction flat_apply to_graphable strict_mode)autograd_not_implemented)HigherOrderOperator)FakeTensorMode)PreDispatchTorchFunctionModeProxyTorchDispatchModetrack_tensor_tree)_pytree)"is_traceable_wrapper_subclass_typec4^\rSrSrU4SjrU4SjrSrU=r$)ExportTracepointc$>[TU]S5 g)N_export_tracepoint)super__init__)self __class__s V/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/torch/_export/wrappers.pyrExportTracepoint.__init__s -.c$>[TU]"U0UD6$N)r__call__)rargskwargsrs rr"ExportTracepoint.__call__ sw000r)__name__ __module__ __qualname____firstlineno__rr"__static_attributes__ __classcell__)rs@rrrs/11rrc[R"URRX45up4URR S[ X45n[ XSURS9$)N call_functionconstanttracer)pytreetree_mapr1 unwrap_proxy create_proxyrr)moder#r$p_argsp_kwargsproxys rexport_tracepoint_dispatch_moder:'sRt{{'?'?$PF KK $ $+V E T4 LLrc@U UsSSS5 $!,(df  g=fr!r&)r6r#r$s r"export_tracepoint_fake_tensor_moder<0s  s cURU5nURU5nUR5 [U0UD6 UsSSS5 $!,(df  g=fr!)unwrap_tensorsredispatch_to_nextr)ctxr#r$unwrapped_argsunwrapped_kwargss rexport_tracepoint_functionalrC6sL''-N))&1   !N?.>? " ! !s A  AT)deferred_errorcU$r!r&)r#r$s rexport_tracepoint_cpurFEs Krc:^^^^ [U[RR5(deTS:wde[RR R UT5nU4Sjm SmUU4SjnUUU 4SjnURUSS9nURUSS9nXg4$)NcX>UT;aTUSU:XdeTUSU:XdeXS.TU'g)Nin_specout_spec)rJrKr&)pathrJrKmodule_call_specss rupdate_module_call_signatures6_wrap_submodule..update_module_call_signaturesOsJ $ $$T*95@ @@$T*:6(B BB.5"L$rc UHJn[U[R[[[ [ 45(aM9UcM>[SU35e g)NzGOnly Tensors or scalars are supported as pytree flattened inputs, got: ) isinstancetorchTensorstrintfloatboolAssertionError) flat_argsas rcheck_flattened(_wrap_submodule..check_flattenedUsEAq5<<c5$"GHHAI$]^_]`arc>[R"X45up4T"U5 [USTS.6n[R"X45upX4$)Nmodule_call_inputskindrLr2 tree_flattenrtree_unflatten)moduler#r$rYrJr[rLs rpre_hook!_wrap_submodule..pre_hook\sJ#00$@  "& 8LSWX ,,Y@ |rc>[R"X45upE[R"U5upgT"U5 [UST S.6nT "T XW5 [R"Xg5$)Nmodule_call_outputsr_ra) rdr#r$res_rJflat_resrKr[rLrNs r post_hook"_wrap_submodule..post_hookcs](($8 #005!%x6KRVW%dG>$$X88rT) with_kwargs) rQrRnnModulefx graph_module _get_attrregister_forward_pre_hookregister_forward_hook) modrLrM submodulererl pre_handle post_handler[rNs `` @@r_wrap_submodulerzJs c588?? + ++ + 2::%%//T:IM 944X44PJ11)1NK  ""rc## /nUHnUR[XU55 M Sv UHnUR5 M g!UHnUR5 M f=f7fr!)extendrzremove)fpreserve_signaturemodule_call_signatureshandlesrLhandles r_wrap_submodulesrpsVG&D NN?14JK L' F MMOgF MMOsA&(AA&A##A&cSnXlU$)Nc[X5$r!r )rr#s rcall'_mark_strict_experimental..call~s 4&&r)r")clsrs r_mark_strict_experimentalr}s'L JrcUS-n[URU5(a0[URU5U:XdeURSUS05$UR U5n[ URXB5 URSUS05$)a This is a wrapper utility method on top of tracer to cache the already registered subclass spec attribute. This is useful because Subclass.__init__ will be same for each subclass. By default, fx will create multiple attributes/proxies for given attribute. 0get_attrr&)hasattrrootgetattrr5get_fresh_qualnamesetattr)r1namespecfx_namequalnames r#_register_func_spec_proxy_in_tracerrsSjGv{{G$$v{{G,444"":wB??((.H FKK(   z8R <T "U0UD6 [RR5(dg[[ US55(dJT R R S5(deT R S[S5*n[SUS35e[5nUcg[U[5(deURnUSn[USS5U4nSRT R R5R!S55n[ U5R"R5n[%UU[ U5UUUS9 g) NrrzCan't intercept z in export because this object is not a traceable tensor subclass. Please look at DTensor.__init__ implementation as an example of proper usage of this API.rj.r1rrrrr)rRcompiler is_exportingrtyper)endswithlen RuntimeErrorrrQrr1tuplejoinlowersplitr'r) r#r$obj_namer6r1subclass graphablerrconstructor_subclasss rwrapperBmark_subclass_constructor_exportable_experimental..wrappersAd-f-~~**,, 1$tAw-@@'44==jII II+889KC O;KLH"8*-}~  ;< < $ <====748_f- HH1>>DDFLLSQR "8n55;;=#'>$ 3   r)rrr')rrs` r1mark_subclass_constructor_exportable_experimentalrsI* ( ) )))=)F)F(GH} ~  #J Nrc^[T5(a [T5$[T5(d)TRS:Xd[STRS35e[ T5U4Sj5nU$)ao Experimental decorator that adds user function to export pre-dispatch graph. Note that we only support custom autograd function/subclass constructors today. To use this function: 1. For subclasses: 1. refer to instructions in mark_subclass_constructor_exportable_experimental 2. Define apply method on your custom autograd function and apply this decorator. Example: class MyCoolCustomAutogradFunc(autograd.Function): @classmethod @torch._export.wrappers.allow_in_pre_dispatch_graph def apply(cls, *args, **kwargs): return super(MyCoolCustomAutogradFunc, cls).apply(*args, **kwargs) applyztorch._export.wrappers.allow_in_pre_dispatch_graph can only be applied on subclass tensor.__init_ or custom_autograd_function.apply. But, you are adding it on a/ which is not supported. If __init__ doesn't exist on your subclass, please add it. Look at DTensor.__init__ implementation for example. If you are adding it on custom autograd function, please add it on apply method. If anything else, file an issue on github and we may consider extending our support. c >[RR5(dT "U0UD6$[R"US5(dT "U0UD6$[ US[R R5(dT "U0UD6$SSKJ n U"[RRR5nUcT "U0UD6$[RR5R[RRR 5n[RR#5[RR%[RRR 5-n[RR'XE5 T "U0UD6nSSS5 UR((dS5eUR*nUSR,SUSR.3nU/USSQ7U4n SSKJn SR5UR7S55n [9U 5R:R=5n [?UU U U WU S9 U$!,(df  N=f) Nr)_get_dispatch_mode_pre_dispatchz"Should only do this in predispatchrr)._call_custom_autograd_function_in_pre_dispatchrjr) rRrrinspectisclass issubclassautogradFunction torch._opsr_C_TorchDispatchModeKeyPROXY_dispatch_tls_local_include_setr}r PreDispatch_dispatch_tls_local_exclude_setDispatchKeySet_ForceDispatchKeyGuard pre_dispatchr1r(r)torch.export.custom_opsrrrrr'rr)r#r$rr6include_to_setexclude_to_setoutr1function_cls_namerrrrfuncs rr,allow_in_pre_dispatch_graph..wrappers~~**,,(( (tAw''(( ($q'5>>#:#:;;(( (>.uxx/M/M/S/ST <(( (AACJJ HH , ,  HH 4 4 6hh%%ehh&:&:&F&FG H  XX , ,^ L''CM  F"FF #Aw112!DG4H4H3IJ'3$qr(3V<  HH.44S9: " : (557  #Q$ 3   3M Ls  I  I)rrr'rr)rrs` rallow_in_pre_dispatch_graphrst"~~@FF TNNdmmw6))-8d e   4[33j Nr)3r contextlibr functoolsrrRtorch._custom_opstorch._Crtorch._export.utilsr"torch._higher_order_ops.flat_applyrrr #torch._higher_order_ops.strict_moder torch._higher_order_ops.utilsr rr torch._subclasses.fake_tensorr"torch.fx.experimental.proxy_tensorrrr torch.utilsrr2torch.utils._python_dispatchrrrpy_implr:r<py_functionalize_implrCAutogradCPUrFrzrrrrTrrrrr&rrrsR%  K <B*8 *K1*1&'23M4MN+, ))*;//0/E KOO,-##L  ="LLL>6AHTr