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You should use `exported_program.module()` instead.r)rTrjrks rY__call__zExportedProgram.__call__Ws B  r[c|jjddjdd}t|jjdd}d|d|d|j d}|S)NF) print_outputcolored z zExportedProgram: z Graph signature: z Range constraints: )rprint_readablereplacercrr)rTrrstrings rY__str__zExportedProgram.__str__]s((778 '$ ! d223;;D(K .! /01""&"8"8!9 =   r[cddlm}|||}ddtfd}ddtfd}tj|||_tj|||_|S)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. r9)&_unlift_exported_program_lifted_states) check_guardsmodectd)Nz%Calling train() is not supported yet.NotImplementedErrorrTrs rY_trainz&ExportedProgram.module.._trainys%&MN Nr[ctd)Nz$Calling eval() is not supported yet.rrs rY_evalz%ExportedProgram.module.._eval|s%&LM Mr[T)rrbooltypes MethodTypetraineval)rTrrrrrs rYrzExportedProgram.modulejs_ D7<X Ot O Nd N''7 &&uf5  r[ctdt|jjDt |jjS)Nc3`K|]&\}}|jtjk(r|(ywrR)rr>r )rrWss rYrz=ExportedProgram._num_lifted_params_buffers..s. Aq66Y111 s,.)nextrrrrrs rY_num_lifted_params_buffersz*ExportedProgram._num_lifted_params_bufferssC %d&;&;&G&GH  %%11 2   r[rrc| tn t|}t|tr|j }t |\}}t |||d|S)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)rJrxrr5 materializerr)rTrr _decomp_tablerrs rYrun_decompositionsz"ExportedProgram.run_decompositionsskP)5(< " $$|BT  m%6 7)557M" 9 G  + ' 3!(C   r[passesc Ttt|}ddlm}|5||j}ddd |jn |j}|J||jur|j s|Sdt dtjjdt fd}t||j||j||jt||jt!j"|j$|j&|j(|j* }|jj,j/|jj,|jj,j/|jj,|S#1swYjxYw)Nr)r old_signaturenew_gmruc g}t|jjD]\}}|jdk7rn|t |j ksJd|j |}t |jttfr |jn$t|j|j}|jt|j||j|j t#|jjd}|jdk(sJg}t|j$dD]\}}|t |j&ksJd|j&|} t | jttfr | jn$t| j|j}|jt)| j|| jt+||} | S)zO Update the graph signature's user_input/user_outputs. rz-Number of inputs changed after transformationrrr?rz.Number of outputs changed after transformationr)rrrrrrrrr;r<rrSrr?rrr r_rjrrAr=) rrnew_input_specsrWr#old_input_specrrnew_output_specsold_output_spec new_signatures rY_get_updated_graph_signaturezKExportedProgram._transform_do_not_use.._get_updated_graph_signatures!O$V\\%7%78 477m+3}8899C9"/!:!:1!="&**-=?P,Q#&&2n001$))<  &&&++&--&11  0v||11226K>>X- --! $[%5%5a%89 43}99::D:#0"<"@Q-R$''3o112499= !''33S/:P:PQ  1+:JM! r[ rrrrrrarrr)r8r_rrrmodifiedr=rrSr^rGrrrrrrrsrrrrrr)rTrpmrrestransformed_gmrtransformed_eps rY_transform_do_not_usez%ExportedProgram._transform_do_not_useso f & @ $ & (T&&'C (-0_))$BSBS))) T.. .s||K7 !/7 !HH((7 !"7 !r) &&8$$n<&&#mmD,C,CD..nnnn  ##((//0A0A0F0FG##((//0@0@0E0EFi ( (s FF'cRddlm}|jjDcgc]}|jdk(s|}}t ||j jDcgc]$\}}|jtjk(r|&}}}||||jycc}wcc}}w)Nr)"_check_input_constraints_for_graphr) torch._export.utilsr'rrrrrrrr>r r)rTrr'r/ placeholdersr input_placeholderss rYrz(ExportedProgram._check_input_constraints0sJ#'::#3#3Maqtt}7LM ML$*>*>*J*JK 1vv---   +  3T5K5K N sBB)B#c$|jyrR) _validaters rYrzExportedProgram.validate=s  r[ct|jdkDsJd|jD]}|j|y)Nrz0ExportedProgram must have at least one verifier.)rrcheck)rTrLs rYr,zExportedProgram._validateBsE4>>"Q& > & A CIIdO r[)rrrc $t||j|||n |jtj|j tj|j |j||n |j|| S|j S)Nr) rGrrrrsrrrrr)rTrrrrrs rY_updatezExportedProgram._updateKs$$+%/%;z"mmD,B,BC"mmD,C,CD..#,#8idnn#,#8i  ?Cnn  r[)NNr)NF)rurG)Br\r]r^__doc__rrSr^r`r=rxrcrr_rHr rrrr-r rModuleGraphrrrpropertyr6rsetterrrrrrrrrrrarrrrrrrrrrrrrrp_ops OperatorBaserrrPassTyper%rrrr,r0rdr[rYrGrGsO 88'''O**Mc3hU99E_--JeE#s(OT#s(^$CDEELS0011:T(^$$Is)LPAE%X59%XEHHOOT#s(^34%Xxx~~%X. %X eELL%((2D2D$DEEF %X 5 %X 0%X!uS#XS#X'F!GH%XD&%XDh01%XN%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 $KO,1D tEJJ$;$;X$EFGD &*D   D $D LZXZ:KZx  %01     r[cP|jjDcgc]-}|jjdd|jd/}}ddlm}||}| |j S|D]4}t|tjs|jj cSycc}w)Nrrr.) rrrr torch._guardsr/rrrSymIntr#)rr#valsr/rrLs rYrrasHHNN   99== % 1 % D  / &I""" $ a &66## #$ s2B#old_range_constraintsz!Optional[dict[sympy.Symbol, Any]]rc<|Jt|}|iStj|}|jDcic]\}}||jvs||}}}|jjD]\}}||jvs||vs|||< |Scc}}wrR)rrr replacements var_to_range)rr=rrrrLs rYrrqs ! ,, ,r"I  "78*002Aay?U?U6U1 &&,,.%1 I** *q8I/I#$ a % s BBc tjj||}|S#t$r\t j dtjj|tjj }||_Y|SwxYw)NzUnable to execute the generated python source code from the graph. The graph module will no longer be directly callable, but you can still run the ExportedProgram, and if needed, you can run the graph module eagerly using torch.fx.Interpreter.)rrSr^ SyntaxErrorwarningswarnr3_graph)rrrs rYrrsx XX ! !$ . I     G XX ! !$(8 9 I s $A!B B c|gS|jjDchc]\}}td|Dr|}}}tjj j j|jd|j}|jDcgc]B}|jjj|r|j|jDc}Scc}}wcc}w)Nc3zK|]3}t|tjjj 5ywrR)rrrrJConstantSource)rrJs rYrz*_convert_guards_to_code..s2 65==#7#7#F#FG G s9;c"|jSrRr)r s rYrpz)_convert_guards_to_code..s AFFHr[) var_to_sourcesrrrrS experimentalsymbolic_shapesShapeGuardPythonPrinterguardsexpr free_symbolsissubsetdoprint)rvarsources local_vars py_printerguards rYrrs &44::< C  !   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