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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\)rrvs rXr.&s<(;1A$(;s)rrMr1rctree_flatten_with_path_check_input_constraintsr)rSrmrnrMflat_args_with_path received_spec flat_argss rX_get_flat_args_with_check)ExportedProgram._get_flat_args_with_checksg..((  #F4F-3-J-J N. * %%&9:<(;<< ''r[rmrncURRnURX5upEUb&[XS[5(d[ SUSU35e/nUR RGH nUR[R:XaM$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_inputs)s\..((#'#A#A$#O  } $A( ( Q)@ /# **66F{{i222##  !$$-&,,T^^FMM-JK%,,T__V]]-KL))$$!"(( )FG#7$""34!,,r[c[S5e)Nz\Unable to call ExportedProgram directly. You should use `exported_program.module()` instead.r)rSrmrns rX__call__ExportedProgram.__call__Ws B  r[cURRSSS9RSS5n[UR5RSS5nSUSUSUR S3nU$)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$)a Returns a self contained GraphModule with all the parameters/buffers inlined. - When `check_guards=True` (default), a `_guards_fn` submodule is generated and a call to a `_guards_fn` submodule is inserted right after placeholders in the graph. This module checks guards on inputs. - When `check_guards=False`, a subset of these checks are performed by a forward pre-hook on the graph module. No `_guards_fn` submodule is generated. r7)&_unlift_exported_program_lifted_states) check_guardsmodec[S5e)Nz%Calling train() is not supported yet.NotImplementedErrorrSrs rX_train&ExportedProgram.module.._trainys%&MN Nr[c[S5e)Nz$Calling eval() is not supported yet.r r"s rX_eval%ExportedProgram.module.._eval|s%&LM Mr[T)rrbooltypes MethodTypetraineval)rSrrrr#r&s rXrExportedProgram.modulejsY 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$3 3)nextrrrrrs rX_num_lifted_params_buffers*ExportedProgram._num_lifted_params_bufferssC %d&;&;&G&GH  %%11 2   r[rrcUc [5O [U5n[U[5(aUR 5n[ U5unn[ UUUSUS9$)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(< " $$|BT  m%6 7 7)557M" 9 G  + ' 3!(C   r[passesc j[[U55nSSKJn U"5 U"UR5nSSS5 Wb URO URnUceXPRLaUR (dU$S[ S[RRS[ 4Sjn[UURU"URU5UR[UUR5[ R""UR$5UR&UR(UR*S9 nURR,R/URR,5 URR,R/URR,5 U$!,(df  GNu=f)Nr)r old_signaturenew_gmryc &/n[URR5Hup4URS:wa OU[ UR 5:dS5eUR Un[ UR[[45(a URO$[UR5"UR5nUR[URUURUR 55 M [#URR5SnURS:Xde/n[UR$S5Hup4U[ UR&5:dS5eUR&Un [ U R[[45(a U RO$[U R5"UR5nUR[)U RXiR55 M [+X(S9n U $)z7 Update the graph signature's user_input/user_outputs. rz-Number of inputs changed after transformationr{rGrz.Number of outputs changed after transformationr)rrrrrrrrr9r:rrRrr=rrrrarmrr?r;) r=r>new_input_specsrVr+old_input_specrrnew_output_specsold_output_spec new_signatures rX_get_updated_graph_signatureKExportedProgram._transform_do_not_use.._get_updated_graph_signatures!O$V\\%7%7877m+3}8899C9"/!:!:1!="&**-=?P,Q#&&n001$))<  &&&++&--&11 90v||11226K>>X- --! $[%5%5a%893}99::D:#0"<"@Q-R$''o112499= !''33S:P:PQ: 1+M! r[ rrrrrrirrr)r6rarrrmodifiedr;rr[rfrErrrrrrr|rrrrrr)rSr;pmrrestransformed_gmrEtransformed_eps rX_transform_do_not_use%ExportedProgram._transform_do_not_usesh f & @ $ &T&&'C'-0_))$BSBS))) .. .s||K7 !/7 !HH((7 !"7 !r) &&8$$n<&&#mmD,C,CD..nnnn  ##((//0A0A0F0FG##((//0@0@0E0EFm' &s F## F2c`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.utilsrPrrrrrrrr<rr)rSrrPr7 placeholdersr1input_placeholderss rXr(ExportedProgram._check_input_constraints2sJ#'::#3#3M#3att}7L#3 ML*>*>*J*JK Kvv--- K  + T5K5K N sB%B% $B*B*c$UR5 grQ) _validaters rXrExportedProgram.validate?s  r[c[UR5S:dS5eURHnU"5RU5 M g)Nrz0ExportedProgram must have at least one verifier.)rrcheck)rSrTs rXrVExportedProgram._validateDs@4>>"Q& > &A CIIdO r[)rrrc "[UURUUbUO UR[R"UR 5[R"UR 5URUbUO URUbUS9 $URS9 $)NrG) rErrrr|rrrrr)rSrrrrrs rX_updateExportedProgram._updateMs$$+%/%;z"mmD,B,BC"mmD,C,CD..#,#8idnn#,#8i  ?Cnn  r[) rrrrrrrrr)NNr()NF)ryrE)Br]r^r_r`__doc__rr[rfrbr;r|rerrarFrrrr+rModuleGraphrrrpropertyr4rsetterrrrrrrrrrrirrrrrrrrr rrrr4rt_ops OperatorBaserr)r9PassTyperMrrrrVr\rfr\r[rXrErEsJ 88'''O**Mc3hU99E_--J5c?DcN:;dBBLS0011:T(^$$Is)IM>B%H26%HhhooS#X.%Hxx~~%H. %H ellUXX-?-???@ %H 5 %H 0%HeCHotCH~=>E%H334t;%HX'$.%HN%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 L\X\:K\|  %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%rTs rXrrcsHHNN "D 99== %  %"   / &I"""  a & &66## # s !B1B1old_range_constraintszdict[sympy.Symbol, Any] | NonercPUce[U5nUc0$[R"U5nUR5VVs0sHupEXBR;dMXE_M nnnURR5H!upEXBR;dMXC;dMXSU'M# U$s snnfrQ)rrr replacements var_to_range)rrjrrrrTs rXrrss ! ,, ,r"I  "78*0022a?U?U6U2 &&,,. ** *q/I#$a / s B"B"c[RRX5nU$![a[ [R "SSS9 [RRU[RR 55nXlU$f=f)NzUnable to execute the generated python source code from the graph. The graph module will no longer be directly callable, but you can still run the ExportedProgram, and if needed, you can run the graph module eagerly using torch.fx.Interpreter.r) stacklevel)rr[rf SyntaxErrorwarningswarnr`_graph)rrrs rXrrsy XX ! !$ ." I!    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HH r!-r! r!* r!  0 r!j4 //9:4ejj55x?@ 4 Dj 4 "& 4nn  n  b $$?C ;.,@r[