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Preserving submodule that takes subclass parameter is not supported in inference IR because we desugar them, resulting in more tensorsrZ)$rdeepcopyrrrRr"r#rrrafilterrrrvrrerrrrrjoinsplitrrjrKrL ValueErrorrrarto dtype_layoutdevicedtyperm)rqrrrrrsnew_module_call_graphr- old_nodesnew_graph_params_buffers provenanceuser_input_counterold_user_input_namesnew_user_input_nameshistoryold_nameold_param_to_desugaredrRr old_targetrorjrwrVrxs ` @rX_get_updated_module_call_graphrfs!MM*?@-3\\-?-?@-?TD-?I@   2 2   / /    . .   + + "$J & 2 2 2gg6N  2  :  !# .'']2J  iimmK4 47 4+/99Jr{'' ( WW % 77;;/">">>;;"66%dii55,@dii@QR 4GH%h44,?X?OP,099J(&!+&/8).0668 f  !455//#' 4 0&,,s"3Ab"9:J ": . 5 5d ;9'OO   89##8i&7&78Avv// $QVVH-Z[ vv##AFF+9$$QVV,;;/1hooIINN%%22IINN%%,,IINN%%++J7 "*q!1!6!6J!3(,ZL9bc ( 3AFG9 'R ! _A sQ+Q00Q04Q5 Q5rc h[UUUUUS9unnn[URURUUUR5nUR R URR 5 [UUR5n [UURUUU UURURS9n U $)Nrrrr)rootrrrrange_constraintsrjrr) rXrrrrjrr_get_updated_range_constraintsrrErrr) rrrrrrr8rrnew_range_constraintsexported_programs rX_decompose_exported_programrs ; #/)$?    ;     GGNN2??''(:   ' hh+//((,,  r[c \rSrSr%Sr\R R\S'\ \S'\ \ \ 4\S'S\S'\ \\S'\\\ S 4\ \ \ 44S -\S '\ \ \4\S '\ \\\S '\ \ \S'SOS S.S\R&R(\ \ \ 4-S\R R*S\ S\ \ \R,\R&R.-4SSS\ \S\\\ S 4\ \ \ 44S -S\ \ \4S -S\ \\S -4Sjjjr\\"SS9S55r\R8\"SS9S55r\\"SS9S55r\R8\"SS9S 55r\\"SS9S!55r\R8\"SS9S"55r\\"SS9S#55r\R8\"SS9S$55r\"SS9S%\ \R&R.4S&j5r!\"SS9S%\ \\ \R&R.44S'j5r"\"SS9S%\ \R,4S(j5r#\"SS9S%\ \\ \R,44S)j5r$\\"SS9S*55r%\%R8\"SS9S+55r%\\"SS9S,55r&\&R8\"SS9S-55r&\\"SS9S.55r'\'R8\"SS9S/55r'\\"SS9S055r(\(R8\"SS9S155r(\\"SS9S%\ 4S2j55r)\)R8\"SS9S355r)\\"SS9S%\ 4S4j55r*\*R8\"SS9S555r*\\"SS9S655r+\+R8\"SS9S755r+\\"SS9S855r,\,R8\"SS9S955r,\\"SS9S:55r-\-R8\"SS9S;55r-S<r.S=\ S>\ S%\ 4S?jr/S=\ S>\ S%\ 4S@jr0S%\ 4SAjr1SPS%\R R4SBjjr2SCr3\4SQSD\ \RjRl\74S -SE\8S%S4SFjj5r9SG\:S%S4SHjr;SIr<\"SS9SJ5r=\>SK5r?S S S SL.SRSMjjr@SNrAg )SrEia Package of a program from :func:`export`. It contains an :class:`torch.fx.Graph` that represents Tensor computation, a state_dict containing tensor values of all lifted parameters and buffers, and various metadata. You can call an ExportedProgram like the original callable traced by :func:`export` with the same calling convention. To perform transformations on the graph, use ``.module`` property to access an :class:`torch.fx.GraphModule`. You can then use `FX transformation `_ to rewrite the graph. Afterwards, you can simply use :func:`export` again to construct a correct ExportedProgram. _graph_module_graph_signature _state_dictzdict[sympy.Symbol, ValueRanges]_range_constraints_module_call_graph.N_example_inputs _constants _verifiers _guards_code) verifiersrrrrrdict[sympy.Symbol, Any]rjrrrc [RRR5Ul[ X5Ul[U[RR5(a/UR RRUR5 [UR X65 X0l X@l XPlUc [S5eX`lXplU=(d 0UlU =(d [&/n [)SU 55(d[SU 35eXlUR-5 [/UR 5Ulg)Nz"module_call_graph must not be Nonec3B# UHn[U[5v M g7frQ)r{r+).0rWs rX +ExportedProgram.__init__..cs>Iq:a**Isz2all verifiers must be subclasses of Verifier, got )rrrCodeGenr_create_graph_module_for_exportrrrrrrprrrrrrrr+allrvalidate_convert_guards_to_coder) rSrrrrrrjrrrs rX__init__ExportedProgram.__init__Bs//1I>>> DYKP $ 3D4F4FGr[F)is_backward_compatiblecUR$rQ)rrSs rXrExportedProgram.graph_modulems!!!r[c[S5e)Nz7Unable to set ExportedProgram's graph_module attribute.rrSvalues rXrrrsTUUr[c.URR$rQ)rrrs rXrExportedProgram.graphws  &&&r[c[S5e)Nz0Unable to set ExportedProgram's graph attribute.rrs rXrr|sMNNr[cUR$rQ)rrs rXrExportedProgram.graph_signatures$$$r[c[S5e)Nz:Unable to set ExportedProgram's graph_signature attribute.rrs rXrrsWXXr[cUR$rQ)rrs rXrExportedProgram.state_dictsr[c[S5e)Nz5Unable to set ExportedProgram's state_dict attribute.rrs rXrrsRSSr[ryc#F# UR5H upUv M g7f)z8 Returns an iterator over original module's parameters. N)r)rSr=params rX parametersExportedProgram.parameterss! --/HAK0!c#j# URRHnXRU4v M g7f)z Returns an iterator over original module parameters, yielding both the name of the parameter as well as the parameter itself. N)rrr)rS param_names rXr ExportedProgram.named_parameterss/ ..99Jooj99 9:s13c#F# UR5H upUv M g7f)z3 Returns an iterator over original module buffers. N)r)rSr=bufs rXbuffersExportedProgram.bufferss! ((*FAI+rc## [URR5nURRH.nX!;aX RU4v MX R U4v M0 g7f)zv Returns an iterator over original module buffers, yielding both the name of the buffer as well as the buffer itself. N)rrrrrr)rSr buffer_names rXrExportedProgram.named_bufferss^ "%T%9%9%P%P!Q//77K4!>>+#>>>!??;#??? 8sA)A+cUR$rQ)rrs rXr!ExportedProgram.range_constraints&&&r[c[S5e)Nz.CallSpeciNrMrNr\)r]r^r_r`rcrdrbrfr\r[rXCallSpecrs __t+ +oo, ,r[rr)rMrNz=expected first module_call_graph fqn to be empty string, got )r r rjrirrjrMrN)rSrs rXrExportedProgram.call_specs -z - t%% &! +D48 8  ! !! $ ( (B . OPTPfPfghPiPmPmOpq **1-77??++A.88AA  r[c[S5e)Nz4Unable to set ExportedProgram's call_spec attribute.rrs rXrrQRRr[c URS$)Nrrrs rXverifierExportedProgram.verifier sq!!r[c[S5e)Nz3Unable to set ExportedProgram's verifier attribute.rrs rXrrsPQQr[cdURc [S5eURSR$)Nz_verifiers must not be Noner)rrdialectrs rXrExportedProgram.dialects/ ?? " !>? ?q!)))r[c[S5e)Nz2Unable to set ExportedProgram's dialect attribute.rrs rXrrsOPPr[cUR$rQrrs rXrExportedProgram.verifiers r[c[S5e)Nz4Unable to set ExportedProgram's verifiers attribute.rrs rXrr%rr[cUR$rQrrs rXtensor_constants ExportedProgram.tensor_constants*rr[c[S5e)Nz;Unable to set ExportedProgram's tensor_constants attribute.rrs rXrr/s I  r[cUR$rQrrs rXrExportedProgram.constants6rr[c[S5e)Nz4Unable to set ExportedProgram's constants attribute.rrs rXrr;rr[cURRnUb [X#5n[R"X45upEUR U5 [ SU55nXe4$)agFlatten args, kwargs using pytree, then, check specs. Args: args: List[Any] original args passed to __call__ kwargs: Dict[str, Any] original kwargs passed to __call Returns: A tuple of (flat_args, received_spec) flat_args is flattened args / kwargs received_spec is the pytree spec produced while flattening the tuple (args, kwargs) c3*# UH oSv M g7f)r7Nr\)rrws rXr.Ts<(;1A$(;s)rrMr1rctree_flatten_with_path_check_input_constraintsr)rSrmrnrMflat_args_with_path received_spec flat_argss rX_get_flat_args_with_check)ExportedProgram._get_flat_args_with_check@sg..((  #F4F-3-J-J N. * %%&9:<(;<< ''r[rmrncURRnURX5upEUb&[XS[5(d[ SUSU35e/nUR RGH nUR[R:XaM$UR[R[R4;acURSLa*URURUR 5 MURUR"UR 5 MUR[R$[R&4;dMURURUR 5 GM [)U5nXd-$)aTransform args, kwargs of __call__ to args for graph_module. self.graph_module takes stuff from state dict as inputs. The invariant is for ep: ExportedProgram is ep(args, kwargs) == ep.postprocess(ep.graph_module(ep.graph_module_flat_inputs(args, kwargs))) z>Trying to flatten user inputs with exported input tree spec: z- but actually got inputs with tree spec of: F)rrMrr0r}rrrrr<rrrrrrrrCONSTANT_TENSOR CUSTOM_OBJr)rSrmrnrMrradditional_inputsinput_s rX_graph_module_flat_inputs)ExportedProgram._graph_module_flat_inputsWs\..((#'#A#A$#O  } $A( ( Q)@ /# **66F{{i222##  !$$-&,,T^^FMM-JK%,,T__V]]-KL))$$!"(( )FG#7$""34!,,r[c[S5e)Nz\Unable to call ExportedProgram directly. You should use `exported_program.module()` instead.r)rSrmrns rX__call__ExportedProgram.__call__s B  r[cURRSSS9RSS5n[UR5RSS5nSUSUSUR S3nU$)NF) print_outputcolored z zExportedProgram: z Graph signature: z Range constraints: )rprint_readablereplacererr)rSrrstrings rX__str__ExportedProgram.__str__s((778 '$ ! d223;;D(K .! /01""&"8"8!9 =   r[cSSKJn U"XS9nSS[4SjjnSS[4Sjjn[R"XC5Ul[R"XS5UlU$)a Returns a self contained GraphModule with all the parameters/buffers inlined. - When `check_guards=True` (default), a `_guards_fn` submodule is generated and a call to a `_guards_fn` submodule is inserted right after placeholders in the graph. This module checks guards on inputs. - When `check_guards=False`, a subset of these checks are performed by a forward pre-hook on the graph module. No `_guards_fn` submodule is generated. r7)&_unlift_exported_program_lifted_states) check_guardsmodec[S5e)Nz%Calling train() is not supported yet.NotImplementedErrorrSrs rX_train&ExportedProgram.module.._trains%&MN Nr[c[S5e)Nz$Calling eval() is not supported yet.r r"s rX_eval%ExportedProgram.module.._evals%&LM Mr[T)rrbooltypes MethodTypetraineval)rSrrrr#r&s rXrExportedProgram.modulesY D7X Ot O Nd N''7 &&u5  r[c[S[URR55[ URR55$)Nc3j# UH)upUR[R:XdM%Uv M+ g7frQ)rr<r)rrVss rXr=ExportedProgram._num_lifted_params_buffers..s- HDA66Y111Hs$3 3)nextrrrr rs rX_num_lifted_params_buffers*ExportedProgram._num_lifted_params_bufferssC %d&;&;&G&GH  %%11 2   r[rrcUc [5O [U5n[U[5(aUR 5n[ U5unn[ UUUSUS9$)a Run a set of decompositions on the exported program and returns a new exported program. By default we will run the Core ATen decompositions to get operators in the `Core ATen Operator Set `_. For now, we do not decompose joint graphs. Args: decomp_table: An optional argument that specifies decomp behaviour for Aten ops (1) If None, we decompose to core aten decompositions (2) If empty, we don't decompose any operator Some examples: If you don't want to decompose anything .. code-block:: python ep = torch.export.export(model, ...) ep = ep.run_decompositions(decomp_table={}) If you want to get a core aten operator set except for certain operator, you can do following: .. code-block:: python ep = torch.export.export(model, ...) decomp_table = torch.export.default_decompositions() decomp_table[your_op] = your_custom_decomp ep = ep.run_decompositions(decomp_table=decomp_table) Nr)rHr|rr3 materializerr)rSrr _decomp_tablerrs rXrun_decompositions"ExportedProgram.run_decompositionssnP)5(< " $$|BT  m%6 7 7)557M" 9 G  + ' 3!(C   r[passesc |[[U55nSSKJn U"5 U"UR5nSSS5 Wb URO URnUc [ S5eXPRLaUR (dU$S[S[RRS[4Sjn[UURU"URU5UR[UUR 5["R$"UR&5UR(UR*UR,S9 nURR.R1URR.5 URR.R1URR.5 U$!,(df  GN~=f) Nr)rztransformed_gm must not be None old_signaturenew_gmryc /n[URR5Hup4URS:wa OU[ UR 5:a%[ SUS[ UR 5S35eUR Un[UR[[45(a URO$[UR5"UR5nUR[URUUR UR"55 M [%URR5SnURS:wa[ SUR<35e/n[UR&S5Hup4U[ UR(5:a%[ S US[ UR(5S 35eUR(Un [U R[[45(a U RO$[U R5"UR5nUR[+U RXiR 55 M [-X(S 9n U $) z7 Update the graph signature's user_input/user_outputs. rz9Number of inputs changed after transformation: got index z but only z input_specsr|rIz,expected last node to have op='output', got rz:Number of outputs changed after transformation: got index z output_specsr)rrrrr rrrrr9r:rrRrr=rrrrarmrr?r;) r=r>new_input_specsrVr-old_input_specrr new_output_specsold_output_spec new_signatures rX_get_updated_graph_signatureKExportedProgram._transform_do_not_use.._get_updated_graph_signaturesU!O$V\\%7%7877m+M5566(STUSVW$$' (A(A$B#C<Q"/!:!:1!="&**-=?P,Q#&&n001$))<  &&&++&--&11 #94v||11226K~~)$B;>>BTU " $[%5%5a%89M6677(TUVTWX$$' (B(B$C#DMS#0"<"@Q-R$''o112499= !''33S:P:PQ:$1+M! r[ rrrrrrjrrr)r6rarrrrmodifiedr;rrrrErrrrrrr}rrrrrr)rSr;pmrrestransformed_gmrEtransformed_eps rX_transform_do_not_use%ExportedProgram._transform_do_not_usesn f & @ $ &T&&'C'-0_))$BSBS  ! !BC C .. .s||K> !/> !HH((> !"> !@) &&8$$n<&&#mmD,C,CD..nnnn  ##((//0A0A0F0FG##((//0@0@0E0EF}' &s F,, F;c`SSKJn URRVs/sHo3RS:XdMUPM nn[ X@R R5VVs/sH'up5UR[R:XdM%UPM) nnnU"XaUR5 gs snfs snnf)Nr)"_check_input_constraints_for_graphr) torch._export.utilsrPrrrr rrrr<rr)rSrrPr9 placeholdersr1input_placeholderss rXr(ExportedProgram._check_input_constraintshsJ#'::#3#3M#3att}7L#3 ML*>*>*J*JK Kvv--- K  + T5K5K N sB%B% $B*B*c$UR5 grQ) _validaters rXrExportedProgram.validateus  r[c[UR5S:Xa [S5eURHnU"5RU5 M g)Nrz0ExportedProgram must have at least one verifier.)r rrcheck)rSrWs rXrVExportedProgram._validatezs: t~~ ! # !ST TA CIIdO r[)rrrc "[UURUUbUO UR[R"UR 5[R"UR 5URUbUO URUbUS9 $URS9 $)NrG) rErrrr}rrrrr)rSrrrrrs rX_updateExportedProgram._updates$$+%/%;z"mmD,B,BC"mmD,C,CD..#,#8idnn#,#8i  ?Cnn  r[) rrrrrrrrr)NNr()NF)ryrE)Br]r^r_r`__doc__rrrrbr;r|rerrarFrrrr+rModuleGraphr!rrpropertyr4rsetterrrrrrrrrrrjrrrrrrrrr rrrr4rt_ops OperatorBaserr)r9PassTyperMrrrrVr\rfr\r[rXrErEsJ 88'''O**Mc3hU99E_--J5c?DcN:;dBBLS0011:T(^$$Is)IM>B)H26)HhhooS#X.)Hxx~~)H. )H ellUXX-?-???@ )H 5 )H 0)HeCHotCH~=>E)H334t;)HX'$.)HV%0"1"%0V1V%0'1' \\%0O1O%0%1%%0Y1Y%0 1 %0T1T%0HUXX%7%781%0:(5ehh6H6H1H+I"J:1:%0%,,/1%0 @xc5<<.?(@A @1 @%0'1'%0 1 %0'1'%0 1 %0$1$%0%1%2%0 1 %0S1S%0"#"1"__%0R1R%0**1*  ^^%0Q1Q%01%0S1S%01%0 1 %01%0S1S(.,-c,-3,-3,-\ c S S  588+?+?2 $HL,1D 5::22H<=DD &*D   D $D LdXd:KdL  %01      r[clURRVs/sH1nURRSS5cM"URSPM3 nnSSKJn U"U5nUb UR $UH:n[U[R5(dM$URR s $ gs snf)Nrrr,) rrrr torch._guardsr-rrrSymIntr-)rr-valsr-r'rWs rXrrsHHNN "D 99== %  %"   / &I"""  a & &66## # s !B1B1old_range_constraintszdict[sympy.Symbol, Any] | NonercbUc [S5e[U5nUc0$[R"U5nUR5VVs0sHupEXBR;dMXE_M nnnUR R5H!upEXBR;dMXC;dMXSU'M# U$s snnf)Nz&old_range_constraints must not be None)rrrr replacements var_to_range)rrjrrrrWs rXrrs$EFFr"I  "78*0022a?U?U6U2 &&,,. ** *q/I#$a / s B+!B+c[RRX5nU$![a[ [R "SSS9 [RRU[RR 55nXlU$f=f)NzUnable to execute the generated python source code from the graph. The graph module will no longer be directly callable, but you can still run the ExportedProgram, and if needed, you can run the graph module eagerly using torch.fx.Interpreter.r) stacklevel)rrr SyntaxErrorwarningswarnr`_graph)rrrs rXrrsy XX ! !$ ." I!    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HH x!-x! x!* x!  0 x!v4 //9:4ejj55x?@ 4 Dj 4 "& 4n}  }  @ $$?C ;0,@r[