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N)rrrrrr)rTr buffer_names rYrzExportedProgram.named_bufferss "%T%9%P!Q!Q/7 @ @K444!4>+#>>>>>>!4?;#??????  @ @r[c|jSrR)rrs rYrz!ExportedProgram.range_constraints &&r[c td)Nz.CallSpecrNrON)r\r]r^r rarbr`rdr[rYCallSpecrs8fo. . . .v/ / / / / /r[rr)rNrO)r rrrrfrgrNrO)rTrs rYrzExportedProgram.call_specs 0 0 0 0 0z 0 0 0 t% & &! + +8D4888 8%a(,2222x*1-7?+A.8A    r[c td)Nz4Unable to set ExportedProgram's call_spec attribute.rrs rYrzExportedProgram.call_specQRRRr[c|jdSNrrrs rYverifierzExportedProgram.verifiersq!!r[c td)Nz3Unable to set ExportedProgram's verifier attribute.rrs rYrzExportedProgram.verifiersPQQQr[c8|jJ|jdjSr)rdialectrs rYrzExportedProgram.dialects"***q!))r[c td)Nz2Unable to set ExportedProgram's dialect attribute.rrs rYrzExportedProgram.dialectsOPPPr[c|jSrRrrs rYrzExportedProgram.verifiers r[c td)Nz4Unable to set ExportedProgram's verifiers attribute.rrs rYrzExportedProgram.verifiersrr[c|jSrRrrs rYtensor_constantsz ExportedProgram.tensor_constantsrr[c td)Nz;Unable to set ExportedProgram's tensor_constants attribute.rrs rYrz ExportedProgram.tensor_constantss I   r[c|jSrRrrs rYrzExportedProgram.constantsrr[c td)Nz4Unable to set ExportedProgram's constants attribute.rrs rYrzExportedProgram.constants rr[c|jj}|t||}tj||f\}}||t d|D}||fS)aFlatten 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) Nc3&K|] }|dV dS)r9Nrd)rrs rYrz.&s&<<1!A$<<<<<Trying to flatten user inputs with exported input tree spec: z- but actually got inputs with tree spec of: F)rrNrr2ryrrrrr>r%r,r&rr rrrCONSTANT_TENSOR CUSTOM_OBJr)rTrjrkrNrradditional_inputsinput_s rY_graph_module_flat_inputsz)ExportedProgram._graph_module_flat_inputs)sz.(#'#A#A$#O#O =  } 7$A( ( ###!## *6 H HF{i222# !$--&,,T^FM-JKKKK%,,T_V]-KLLLL)$!"(( )FGGG!"344!9,,r[c td)Nz\Unable to call ExportedProgram directly. You should use `exported_program.module()` instead.r)rTrjrks rY__call__zExportedProgram.__call__Ws B   r[c|jdddd}t|jdd}d|d|d|jd}|S)NF) print_outputcolored z zExportedProgram: z Graph signature: z Range constraints: )rprint_readablereplacercrr)rTrrstrings rY__str__zExportedProgram.__str__]s(778  '$ ! ! d233;;D(KK = = = / = =#'"8 = = =   r[Tcddlm}|||}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_guardsTmodec td)Nz%Calling train() is not supported yet.NotImplementedErrorrTrs rY_trainz&ExportedProgram.module.._trainys%&MNN Nr[c td)Nz$Calling eval() is not supported yet.rrs rY_evalz%ExportedProgram.module.._eval|s%&LMM Mr[T)rrbooltypes MethodTypetraineval)rTrrrrrs rYrzExportedProgram.modulejs DCCCCC77<XXX O Ot O O O O N Nd N N N N'77 &uf55  r[ctdt|jjDt |jjS)Nc3JK|]\}}|jtjk|VdSrRrr>r%)rrWss rYrz=ExportedProgram._num_lifted_params_buffers..sB  Aq6Y1111111  r[)nextr#rrrrs rY_num_lifted_params_buffersz*ExportedProgram._num_lifted_params_bufferssQ  %d&;&GHH    %1 2 2    r[rrc|tnt|}t|tr|}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_decompositionssP)5(< " $ $ $$|BTBT  m%6 7 7 8)5577M" 9 G G  + ' 3!(C     r[passesc tt|}ddlm}|5||j}dddn #1swxYwY||jn|j}|J||jur |js|Sdt dtjj dt fd}t||j ||j ||j t||jt!j|j|j|j|j }|jj|jj|jj|jj|S)Nr)r old_signaturenew_gmruc g}t|jjD]\}}|jdkrn|t |jks Jd|j|}t |jttfr|jn!t|j|j }| t|j||j|jt#|jjd}|jdksJg}t|jdD]\}}|t |jks Jd|j|} t | jttfr| jn!t| j|j }| t)| j|| jt+||} | S)zO Update the graph signature's user_input/user_outputs. rz-Number of inputs changed after transformationrrQrz.Number of outputs changed after transformationr)r#rrrrrrrr;r<rrSr r?rrrr_rjrrAr=) r*r+new_input_specsrWrold_input_specrrnew_output_specsold_output_spec new_signatures rY_get_updated_graph_signaturezKExportedProgram._transform_do_not_use.._get_updated_graph_signatures-!O$V\%788  47m++E3}899999C:99"/!:1!="&*-=?P,Q=N&&2n011$)<<  &&&+&-&1 v|12226K>X----! $[%5a%899  43}9:::::D;::#0"@Q-R>O''3o12249== !''3S/:PQQ1+:JM! r[ rrrrrrrr rr)r8r_rrrmodifiedr=rrdrorGrrrrrrrrr rrrr)rTr(pmrrestransformed_gmr2transformed_eps rY_transform_do_not_usez%ExportedProgram._transform_do_not_uses f & & @????? $ $ & & ( ("T&''C ( ( ( ( ( ( ( ( ( ( ( ( ( ( (-0_))$BS))) T. . .s| .K7 !/7 !H(7 !"7 !7 !7 !7 !r) &88$n<&#mD,CDD.nn    #(//0A0FGGG#(//0@0EFFFsA  AAcddlm}d|jjD}dt ||jjD}||||jdS)Nr)"_check_input_constraints_for_graphc(g|]}|jdk |Srr)rrs rYrz.3s$MMMaqt}7L7L7L7L7Lr[cBg|]\}}|jtjk|Srdr )rrr!s rYrz.4s6   1v--- ---r[)torch._export.utilsr;rrrrrr)rTrr; placeholdersinput_placeholderss rYrz(ExportedProgram._check_input_constraints0sJJJJJJMM4:#3MMM   L$*>*JKK    +*  3T5K     r[c.|dSrR) _validaters rYrzExportedProgram.validate=s r[ct|jdks Jd|jD]}|| dS)Nrz0ExportedProgram must have at least one verifier.)rrcheck)rTr]s rYrBzExportedProgram._validateBs\4>""Q&&& >'&&  A ACCIIdOOOO  r[)rrrc t||j|||n|jtj|jtj|j|j||n|j||n|j  S)Nr3) rGrrrrrrr rr)rTrrrrrs rY_updatezExportedProgram._updateKsw$+%/%;zz"mD,BCC"mD,CDD.#,#8iidn#,#8iidn    r[)NNr)NF)rurG)Br\r]r^__doc__rrdror`r=rxrcrr_rHr rrrr-r rModuleGraphr0rrpropertyr6rsetterrrrrrrrrrrrr rrrrrrrrrr rr#rp_ops OperatorBaserrr'PassTyper9rrrrBrFrdr[rYrGrGsd   8''''O****Mc3hU9999E_----JeE#s(OT#s(^$CDEEEELS001111:T(^$$$$Is)LPAE%X59%X%X%XEHOT#s(^34%Xx~%X. %X eEL%(2D$DEEF %X 5 %X 0%X!uS#XS#X'F!GH%XD&%XDh01%X%X%X%XN]%000""10X"]%000VV10V]%000''10X' \]%000OO10\O]%000%%10X%]%000YY10Y]%000  10X ]%000TT10T]%000HUX%7810]%000:(5eh6H1H+I"J:::10:]%000%,/10]%000 @xc5<.?(@A @ @ @10 @]%000''10X']%000  10 ]%000''10X']%000  10 ]%000$$10X$]%000%%10%2]%000   10X  ]%000SS10S]%000"#"""10X"_]%000RR10_R]%000****10X* ^]%000QQ10^Q]%00010X]%000SS10S]%00010X]%000  10 ]%00010X]%000SS10S(((.,-c,-3,-3,-,-,-,-\ c S S        58+?2   $KO,1D D tEJ$;X$EFGD &*D   D D D $#D LZXZ:KZZZZx    ]%00010  U         r[cd|jjD}ddlm}||}||jS|D]*}t |t jr|jjcS+dS)Nc^g|]*}|jdd|jd+S)rN)rr*rs rYrz"_get_shape_env..bs?     9== % % 1 % 1 1 1r[rr.) rr torch._guardsr/rrrSymIntr)rvalsr/r5r]s rYr"r"as  HN   D /.....  &&I"" $$ a & & $6# # # # $$$r[old_range_constraintsz!Optional[dict[sympy.Symbol, Any]]rc|Jt|iStj|}fd|D}jD]\}}|jvr ||vr|||<|S)Nc.i|]\}}|jv||Srd) replacements)rrr]rs rYrz2_get_updated_range_constraints..|s3Aay?U6U6U16U6U6Ur[)r"rr var_to_rangerW)rrTrrr]rs @rYrrqs ! , , ,r""I  "788*0022 &,,..%%1 I* * *q8I/I/I#$ a r[c tj||}ng#t$rZt jdtj|tj}||_YnwxYw|S)NzUnable to execute the generated python source code from the graph. The graph module will no longer be directly callable, but you can still run the ExportedProgram, and if needed, you can run the graph module eagerly using torch.fx.Interpreter.)rrdro SyntaxErrorwarningswarnrI_graph)rrrs rYrrs X ! !$ . .       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H r!-r! r!* r!  0 r!r!r!r!j4 /9:4ej5x?@ 4 sm 4 "& 4444nl  l  l  l  l  l  l  l  ^ $ $ $$BF >.*r[