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The function returns a signature of the forward() function. # TODO: Directly provide inspect.signature compatible TS-d module. rr"self) parameters) astparsecoder0rPOSITIONAL_OR_KEYWORDr/r1r"rmrinspect Signature) r8ast_mod ast_func_def arg_type_map param_listarg_type param_typea arg_name_listarg_names rB&_get_torch_jit_trace_forward_signaturerFnsii!G$+LLOLI;;>"c)ns;7G&GG  J G  , 388Y .+ . 8 88rQcg}|jjD]p}|jdk(st|jt j jsC|jjdk(r`|j\}}}}|jt||j|f|jt||j|f|jjdk(rG|jd|jdd}}|jt||j|f|jjdk(s7|j\} } } |jt|| j| | zfs|D]\}} i} t|jjD]q\}}|t| kr8| |j| |j<| |jx|_|_Lt| |j|j|_st!||j#y)a0 Propagate placeholder names from the top-level graph into HigherOrderOp subgraphs, and handle collisions with non-placeholders by count suffixing. 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For nested inputs from dictionaries, lists, tuples, or dataclasses, the names are a concatenation of the path to the tensor. e.g. x = { 'a': torch.randn(), 'b': [torch.randn(), torch.randn()] } produces nodes x_a, x_b_0, x_b_1. - Parameters/buffers/constants/custom objects: These follow the FQN of the object, prefixed by "p", "b", "c", "obj" respectively. e.g. self.bar.l0.weight produces "p_bar_l0_weight". - Effect tokens: These are named token, token_1, ... c|jdr|tdd}n|jdr|tdd}tjdd|}|S)N L__self___self_ [^a-zA-Z0-9]rl) startswithrrsubxs rB _strip_namez,placeholder_naming_pass.._strip_namesS << %#l#%&A \\' "#g,.!A FF?C +rQc0t|tr,tjddt |j }|St|t rt |jSt|tr |jStdt|d|)NrhrlzPytree key of type z not handled for ) r_rrrjrZkeyrrrr<rrrks rB_extract_pytree_keyz4placeholder_naming_pass.._extract_pytree_keyst a $SZ8AH ; 'quu:  : &66M!4T!WI=NqcRS SrQrlc3JK|]}|jywr)lower)rrlrps rBrz*placeholder_naming_pass..s La.q1779Ls #T)rr rhrtNz arg(\d+)_1) rRrr*r+r USER_INPUTrmr<rrplaceholder_prefixesjoinTOKENr.rrrrjr[r\r^rYr CUSTOM_OBJ output_specsrUSER_INPUT_MUTATIONr)r+r_rWrr)rprcr8rOrPfake_params_buffersr7rmr combined_argsrrlr:user_input_namesarg_pathrmuser_input_name base_namerjr<constantnew_namerps @rBplaceholder_naming_passrs6 T "H.c9kJM3MB+66  99 ,, ,  -00CEU,V(3  &$Y%9%9:((L8LLM#  '22  99 ,, ,  99  'I#DKK0668IFF?C; "  HHMM  +i 7   (C 77m # "8TYY BC , 77m #99( ((&.tyy&9 9DI YY( " +DI ,$B'LLN'224xx}}((( / II-- -$++2I"4;;/3DK 4'330 88==H $$TXX]]3DHHM 99 66 64;;(;R"4;;/DK 0Y^^%& $T? 8 J ell%  ~HD HH]D1 4Y5I5I JT QQ&. (#dO $Us:Prin_placec|r6|jD]!\}}t|dst||d#|Si}|jD]8\}}t|dr"|jj ||<4|||<:|S)z If `in_place` is false, return a new copy of `state_dict` with "proxy" removed from `v.__dict__`. `v` is the values in the dictionary. If `in_place` is true, modify `state_dict` in place. rg)r/hasattrdelattrclonedetach)rrrzr{new_state_dicts rBremove_proxy_from_state_dictrMs $$& 0DAqq'" 1 w/ 0$$& &DAqq'"$%GGI$4$4$6q!$%q!  & rQcDg}g}|jjD]}|jdk(rMd|jvr?|jd}|2t |t j sM|j|_t|dk(snd|jvsd|jvsd}d|jvr|jd}nd|jvr|jd}|t |t j s|j|t||zS)a For a given graph module, we look at the "val" of placeholder nodes to find the fake inputs. Additionally, if gm doesn't have placeholders, we further look at the "example_value" or "val" of other nodes. If no fake mode is found, we return None for fake_mode. rtrNr example_value) r[r\r^r]r_rWrrrr )rp fake_inps fake_valsrjfake_vals rBrrbs%'I$&I+ 77m #(:yy'H# 8U\\(J  * ^q tyy (ETYY,>H$))+99_5$))#99U+# 8U\\(J  *+ I 1 22rQ)r&r)rFrrDr)F)NNr)Xr7rr;rrrrtypingrrrrrr r r rW torch._guardsr torch._subclasses.fake_tensorr r)r torch.exportrtorch.export.graph_signaturerrrtorch.utils._pytreerrrrrrrrrrrrrt PARAMETERr,CONSTANT_TENSORrxrwrurCrGrXrYrZror}rboolrr&rrrr`rrrrrrrrrrr r rr3rarFrRrYrr _subclasses fake_tensorFakeTensorModerrrQrBrs   RRR *4H,A>    ""  d t & OOW <  # .D  3'(<(<3'c3h3'l)!%c3h) )$) )8 $uxx33 $&! 38n  :UXX]]+H)-,04 +/9==A$4 c4%4=)4 #3- 4 ""56 4$$9:44 4nE'Euxx}}EE    ((-- ehh  !  B(B B$B  ((--ell&R R ((--R R  ((--ell$--%((BVBV8<UXX]]+<UXX]]@S< 04     ehhmm 4tEHHMM*4s4 T%((--(T%((--=P(EHHMM(UXX]](t(@ @$@F442 9'(<(<''TJ$ J$2J$ J$CH~J$ J$ZTTd*3 3 ""113rQ