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N)rrrrrr)rRr buffer_names rWrzExportedProgram.named_bufferssm "%T%9%9%P%P!Q//77 @K44!4>>+#>>>!4??;#???  @sA'A)c|jSrP)rrs rWrz!ExportedProgram.range_constraints&&&rYctd)Nz.CallSpecNrLrM)rZr[r\r_r`r^rbrYrWCallSpecrs __t+ +oo, ,rYrr)rLrM)r rr`rdrerLrM)rRrs rWrzExportedProgram.call_specs -z - t%% &! +D48 8%%a(,,222**1-77??++A.88AA  rYctd)Nz4Unable to set ExportedProgram's call_spec attribute.rrs rWrzExportedProgram.call_specQRRrYc |jdSNrrrs rWverifierzExportedProgram.verifiersq!!rYctd)Nz3Unable to set ExportedProgram's verifier attribute.rrs rWrzExportedProgram.verifiersPQQrYcP|jJ|jdjSr)rdialectrs rWrzExportedProgram.dialects)***q!)))rYctd)Nz2Unable to set ExportedProgram's dialect attribute.rrs rWrzExportedProgram.dialectsOPPrYc|jSrPrrs rWrzExportedProgram.verifiersrYctd)Nz4Unable to set ExportedProgram's verifiers attribute.rrs rWrzExportedProgram.verifiersrrYc|jSrPrrs rWtensor_constantsz ExportedProgram.tensor_constantsrrYctd)Nz;Unable to set ExportedProgram's tensor_constants attribute.rrs rWrz ExportedProgram.tensor_constantss I  rYc|jSrPrrs rWrzExportedProgram.constantsrrYctd)Nz4Unable to set ExportedProgram's constants attribute.rrs rWrzExportedProgram.constants rrYc|jj}| t||}tj||f\}}|j |t d|D}||fS)aFlatten args, kwargs using pytree, then, check specs. 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You should use `exported_program.module()` instead.r)rRrhris rW__call__zExportedProgram.__call__Ws B  rYc|jjddjdd}t|jjdd}d|d|d|j d}|S)NF) print_outputcolored z zExportedProgram: z Graph signature: z Range constraints: )rprint_readablereplacerarr)rRrrstrings rW__str__zExportedProgram.__str__]s((778 '$ ! d223;;D(K .! /01""&"8"8!9 =   rYcddlm}|||}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. r7)&_unlift_exported_program_lifted_states) check_guardsmodectd)Nz%Calling train() is not supported yet.NotImplementedErrorrRrs rW_trainz&ExportedProgram.module.._trainys%&MN NrYctd)Nz$Calling eval() is not supported yet.rrs rW_evalz%ExportedProgram.module.._eval|s%&LM MrYT)rrbooltypes MethodTypetraineval)rRrrrrrs rWrzExportedProgram.modulejs_ D7<X Ot O Nd N''7 &&uf5  rYctdt|jjDt |jjS)Nc3`K|]&\}}|jtjk(r|(ywrP)rr<r )rrUss rWrz=ExportedProgram._num_lifted_params_buffers..s. Aq66Y111 s,.)nextrrrrrs rW_num_lifted_params_buffersz*ExportedProgram._num_lifted_params_bufferssC %d&;&;&G&GH  %%11 2   rYrrc| 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)rHrvrr3 materializerr)rRrr _decomp_tablerrs rWrun_decompositionsz"ExportedProgram.run_decompositionsskP)5(< " $$|BT  m%6 7)557M" 9 G  + ' 3!(C   rYpassesc Ttt|}ddlm}|5||j}ddd |jn |j}|J||jur|j s|Sdt dtjjdt fd}t||j||j||jt||jt!j"|j$|j&|j(|j* }|jj,j/|jj,|jj,j/|jj,|S#1swYjxYw)Nr)r old_signaturenew_gmrsc g}t|jjD]\}}|jdk7rn|t |j ksJd|j |}t |jttfr |jn$t|j|j}|jt|j||j|j t#|jjd}|jdk(sJg}t|j$dD]\}}|t |j&ksJd|j&|} t | jttfr | jn$t| j|j}|jt)| j|| jt+||} | S)zO Update the graph signature's user_input/user_outputs. rz-Number of inputs changed after transformationrqr>rz.Number of outputs changed after transformationr)rrrr}rrrrr9r:rrQrr=rrrr]rhrr?r;) rrnew_input_specsrUr"old_input_specrrnew_output_specsold_output_spec new_signatures rW_get_updated_graph_signaturezKExportedProgram._transform_do_not_use.._get_updated_graph_signatures!O$V\\%7%78 477m+3}8899C9"/!:!:1!="&**-=?P,Q#&&2n001$))<  &&&++&--&11  0v||11226K>>X- --! $[%5%5a%89 43}99::D:#0"<"@Q-R$''3o112499= !''33S/:P:PQ  1+:JM! rY rrrrrr`rrr)r6r]rrrmodifiedr;rrRr]rErrrrrrrrrrrrrr)rRrpmrrestransformed_gmrtransformed_eps rW_transform_do_not_usez%ExportedProgram._transform_do_not_usesq f & @ $ & (T&&'C (-0_))$BSBS))) T.. .s||K7 !/7 !HH((7 !"7 !r) &&8$$n<&&#mmD,C,CD..nnnn  ##((//0A0A0F0FG##((//0@0@0E0EFm ( (s FF'cRddlm}|jjDcgc]}|jdk(s|}}t ||j jDcgc]$\}}|jtjk(r|&}}}||||jycc}wcc}}w)Nr)"_check_input_constraints_for_graphr) torch._export.utilsr&rrr}rrrrr<r r)rRrr&r. placeholdersr input_placeholderss rWrz(ExportedProgram._check_input_constraints2sJ#'::#3#3Maqtt}7LM ML$*>*>*J*JK 1vv---   +  3T5K5K N sBB)B#c$|jyrP) _validaters rWrzExportedProgram.validate?s  rYct|jdkDsJd|jD]}|j|y)Nrz0ExportedProgram must have at least one verifier.)rrcheck)rRrKs rWr+zExportedProgram._validateDsE4>>"Q& > & A CIIdO rY)rrrc $t||j|||n |jtj|j tj|j |j||n |j|| S|j S)Nr) rErrrrrrrrrr)rRrrrrrs rW_updatezExportedProgram._updateMs$$+%/%;z"mmD,B,BC"mmD,C,CD..#,#8idnn#,#8i  ?Cnn  rY)NNr)NF)rsrE)@rZr[r\__doc__rrRr]r^r;rvrarr]rFrrrr+rModuleGraphrrrpropertyr4rsetterrrrrrrrrrr`rrrrrrrrrrrrrrn_ops OperatorBaserrrPassTyper$rrrr+r/rbrYrWrErEsE 88'''O**Mc3hU99E_--J5c?DcN:;dBBLS0011:T(^$$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     rYcP|jjDcgc]-}|jjdd|jd/}}ddlm}||}| |j S|D]4}t|tjs|jj cSycc}w)Nrrr,) rrrr torch._guardsr-rrrSymIntr")rr"valsr-rrKs rWrrcsHHNN   99== % 1 % D  / &I""" $ a &66## #$ s2B#old_range_constraintszdict[sympy.Symbol, Any] | Nonerc<|Jt|}|iStj|}|jDcic]\}}||jvs||}}}|jjD]\}}||jvs||vs|||< |Scc}}wrP)rrr replacements var_to_range)rr<rrrrKs rWrrss ! ,, ,r"I  "78*002Aay?U?U6U1 &&,,.%1 I** *q8I/I#$ a % s BBc tjj||}|S#t$r^t j ddtjj|tjj }||_Y|SwxYw)NzUnable to execute the generated python source code from the graph. The graph module will no longer be directly callable, but you can still run the ExportedProgram, and if needed, you can run the graph module eagerly using torch.fx.Interpreter.r) stacklevel)rrRr] SyntaxErrorwarningswarnr2_graph)rrrs rWrrs} XX ! !$ ." I!    G  XX ! !$(8 9 I!s $A#B  B c(t|}|gS|jjDchc]\}}td|Dr|}}}tj j jj|jd|j}|jDcgc]B}|jjj|r|j|jD}}|Dcgc] }d|vs| c}Scc}}wcc}wcc}w)Nc3zK|]3}t|tjjj 5ywrP)rrrrIConstantSource)rrIs rWrz*_convert_guards_to_code..s2 65==#7#7#F#FG G s9;c|jSrPr)r s rWroz)_convert_guards_to_code..s AFFrYz L['self'])rvar_to_sourcesrrrrR experimentalsymbolic_shapesShapeGuardPythonPrinterguardsexpr free_symbolsissubsetdoprint)rrvarsources local_vars py_printerguardrets rWrrs |,I &44::< C  !   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HH r!-r! r!* r!  0 r!j4 //9:4ejj55x?@ 4 Dj 4 "& 4nn  n  b $$?C ;.,@rY