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If no node matches, returns None. When node_call_back is None, returns the first node in the node list. cyNTrr`s rKrznodes_first..hsrbrN)r"r)rTrrets rK nodes_firstr6as, unDU VC 3x!|1v rbc,tt||S)z:Returns the number of nodes that match the node_call_back.)rr")rTrs rK nodes_countr8ns |E>2 33rbc$|D] }|| |S)z Sequentially visit the nodes list and invoke node_call_back on each element. Returns the nodes list after the node_call_back is invoked on each element. rr!s rK nodes_mapr:ss t Lrbold_nodenew_nodec|j||jj|jj |y)z5 Replace all uses of old_node with new_node. N)replace_all_uses_withusersclearrS erase_node)r;r<s rK node_replace_rB}s4 ""8, NN NNh'rbct|tjjrGt |dr:d|j vr+|j j d|j diyyyy)Nr~r{)rZrVrWr|hasattrr~update)rrAs rK_update_gm_meta_if_possiblerFsY3,,- C   #((8"456 ! ! .rb call_mod_nodec  |jdk(sJ|jj}|Jt|jt sJt ||j}d|jjD}d|jjD}|jjDcgc]}|jdk(s|}}t||jD]7\}}t|tjjsJt||9|jj|5|D]}|jj|} |jdk(rt| j} t!|| r*d} d| } t!|| r| dz } d| } t!|| r| | _t#|| jt ||jt|| t%|d kDr4t%|dk(rt%|d jdk(sJ|d jd  t tjjr' j&j)t| nt t*t,fr~ D] }|j&j/|d "t1t+|j&j3d } t5| fd |jj7|n3t9d t; d |jj7|ddd|j=|j?|Scc}w#1swY0xYw)z Inline the submodule of the given node into the parent module. Note: we only support the case where submodule takes tensors inputs. rwNc3@K|]}|jdk(s|yw)rNrU.0r`s rK znode_inline_..s KD$'']2J4 Ksc3>K|]}|jdvs|yw))routputNrJrKs rKrMznode_inline_..s! tww>W/W srOrPrsubmod_rc^|jdk(xr|jtjk(S)Nrz)rUr7operatorgetitemr4s rKrznode_inline_..s)O!;"8 x'7'77rbc<t||jdSNr)rBargs) get_item_node new_outputs rKrznode_inline_..s"-%"=#5#5a#89+rbzUnsupported output type z2. 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The function returns a signature of the forward() function. # TODO: Directly provide inspect.signature compatible TS-d module. rrVself) parameters) astparsecoderbrPOSITIONAL_OR_KEYWORDr8r:rVrrinspect Signature) rAast_mod ast_func_def arg_type_map param_listarg_type param_typea arg_name_listarg_names rK&_get_torch_jit_trace_forward_signaturerzsii!G$+LLOLI;;>"c)ns;7G&GG  J G  , Ec *44 D DDrbcg}|jjD]p}|jdk(st|jt j jsC|jjdk(r`|j\}}}}|jt||j|f|jt||j|f|jjdk(rG|jd|jdd}}|jt||j|f|jjdk(s7|j\} } } |jt|| j| | zfs|D]\}} i} t|jjD]q\}}|t| kr8| |j| |j<| |jx|_|_Lt| |j|j|_st!||j#y)a0 Propagate placeholder names from the top-level graph into HigherOrderOp subgraphs, and handle collisions with non-placeholders by count suffixing. 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For nested inputs from dictionaries, lists, tuples, or dataclasses, the names are a concatenation of the path to the tensor. e.g. x = { 'a': torch.randn(), 'b': [torch.randn(), torch.randn()] } produces nodes x_a, x_b_0, x_b_1. - Parameters/buffers/constants/custom objects: These follow the FQN of the object, prefixed by "p", "b", "c", "obj" respectively. e.g. self.bar.l0.weight produces "p_bar_l0_weight". - Effect tokens: These are named token, token_1, ... c|jdr|tdd}n|jdr|tdd}tjdd|}|S)N L__self___self_ [^a-zA-Z0-9]r) startswithrrsubxs rK _strip_namez,placeholder_naming_pass.._strip_nameOsS << %#l#%&A \\' "#g,.!A FF?C +rbc0t|tr,tjddt |j }|St|t rt |jSt|tr |jStdt|d|)NrrzPytree key of type z not handled for ) rZr$rrr}keyr%rr!rErgrrs rK_extract_pytree_keyz4placeholder_naming_pass.._extract_pytree_keyWst a $SZ8AH ; 'quu:  : &66M!4T!WI=NqcRS Srbrc3JK|]}|jywr)lower)rLrrs rKrMz*placeholder_naming_pass..us La.q1779Ls #T)rr)rrrNz arg(\d+)_1)"rr'r3r4r USER_INPUTrrErrplaceholder_prefixesjoinTOKENr7rrrrSrTrUrZr~rrr] CUSTOM_OBJrerUSER_INPUT_MUTATIONrr]rVr[r)rrrArrfake_params_buffersr@rr combined_argsrrrCuser_input_namesarg_path_arguser_input_name base_namer`rEconstantnew_namers @rKplaceholder_naming_passr4s6 T "H.c9kJM3MB+66  99 ,, ,  .11DFV-W)4/  &$Y%9%9:((L8LLM#  '22  99 ,, ,  99  'I#DKK0668IFF?C; "  HHMM  +i 7   (C 77m # "8TYY BC  , 77m #99( ((&.tyy&9 9DI $))E*,=>(,  % % YY( " +DI ,$B'LLN'224xx}}((( / II-- -$++2I"4;;/3DK 4'330 88==H $$TXX]]3DHHM 99 66 64;;(;R"4;;/DK 0Y^^%& $T? 8 J ell%  ~HD HH]D1 4Y5I5I JT QQ&. (#dO $]s:Qr^in_placec|r6|jD]!\}}t|dst||d#|Si}|jD]8\}}t|dr"|jj ||<4|||<:|S)z If `in_place` is false, return a new copy of `state_dict` with "proxy" removed from `v.__dict__`. `v` is the values in the dictionary. If `in_place` is true, modify `state_dict` in place. r)r8rDdelattrdetachclone)r^rrrnew_state_dicts rKremove_proxy_from_state_dictrs $$& 0DAqq'" 1 w/ 0$$& &DAqq'"$%HHJ$4$4$6q!$%q!  & rbcDg}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. rrNr example_value) rSrTrUr~rZrVr[rrr )r fake_inps fake_valsr`fake_vals rKrrs%'I$&I+ 77m #(:yy'H# 8U\\(J  * ^q tyy (ETYY,>H$))+99_5$))#99U+# 8U\\(J  *+ I 1 22rbc#Kt|j}t|j}|jj|jj d||_||_y#||_||_wxYwwr)r_state_dict_hooks_state_dict_pre_hooksr@)rAstate_dict_hooksstate_dict_pre_hookss rK_disable_load_state_dict_hooksrsw,01F1F,G04S5N5N0O!##%9 0$8!!1$8!sAB"A5&B5BBrUrctjj|jtjjj xs0tjjj |j vSr)rV_C%_dispatch_has_kernel_for_dispatch_keyrE DispatchKeyCompositeImplicitAutograd py_kernelsrJs rK _is_cia_oprsX 66 GGIuxx++EE  K 88   9 9R]] J rbc2t|xr t|Sr)_check_valid_to_preserverrJs rK_is_preservable_cia_oprs #B ' :JrN:rbcL|jjdddk(S)N::raten)rEr9rJs rK _is_aten_oprs! 779??4  #v --rbct| Sr)rrJs rK _is_custom_oprs2 rbc xtjjd}|D]}t|j d\}}|j d}t |dk(st |dk(sJ|d}d}t |dk(r|d}t t t tj|||}y) z Utility function to query C++ dispatcher to get the all possible CIA ops and populate them into torch.ops namespace rrr1rrrdefaultN)rVr,_dispatch_get_registrations_for_dispatch_keyrr9rr:ops)cia_opsrU namespaceop_name split_listop_overload_namers rK_materialize_cpp_cia_opsrs hhCC#G  W"288D>2 7]]3' :!#s:!';;;Q-$ z?a )!}  GGEIIy97CEU V WrbctS)z] This is an special marker that tells our infra that we shouldn't decompose this op. )NotImplemented)rVkwargss rK_special_op_to_preserve_ciar3s  rbc|ddlm}||ry|tjvryt |dsyt |j jDcgc]}|j|c}}|dk7xs|j j}|rytjj|jsyycc}w)Nr)#_should_decompose_because_unsafe_opF_schemaT) torch._decomprr metadata_fnsrDrrr alias_info is_mutablerVr_dispatch_has_kernelrE) op_overloadrreris_mutating_or_aliasings rKrr>sA*;7&333 ; *''11NqQ\\5MNJ)AoO1D1D1O1O 88 ( ()9)9); <  Os B9B9r)maxsizectdS)Nr)(_collect_all_valid_cia_ops_for_namespacerrbrK-_collect_all_valid_cia_ops_for_aten_namespacerXs 3F ;;rbrc<tttj|sJt tj|}t }|D]L}t ||}|j D]+}t ||}t|s|j|-N|Sr) rrDrVrr:rR overloadsrr>)r op_namespacerrU op_packetoverloadrs rKrr]s 599i (( (599i0LeG)L"- !++- )H!)X6K%k2 K( )) Nrbct}tjjD]#}|dk7r|t |z}|t z}%|S)a This is an util function that gets the all CIA functional ops. The algorithm is in 2 steps: 1. We first query C++ dispatcher to get the list of CIA ops and then we call getattr on torch.ops.aten to lazily populate them. 2. Sometimes, handful of ops have CIA registered in python dispatcher but not on the C++ side, these can't be caught at the first step. So we walk again to get the final list. Note that the output of this function should never be modified r)rRrVr_dirrr)rop_namespace_names rK_collect_all_valid_cia_opsrnsSeG"YY^^G  & ?@QR RG DF FG G Nrbctjjj}||jvr@t |j|tjjs|j|Sd}t j||S)NcD|d}|d=tjjj}tjj |j tjjjr|j |g|i|Std|d)Nkernel Expected z) to have CompositeImplicitAutograd kernel)rVrrrrrE_op_dkAssertionError)rVrrdks rK_special_op_to_decompose_ciaz9_get_decomp_for_cia.._special_op_to_decompose_cias! 8 XX ! ! ; ; 88 9 9 KKM588//II !6==5d5f5 5 F8#LM rb)r)rVrrrrrZr-r.)rUrrs rK_get_decomp_for_ciarsk    7 7B R]]:bmmB.?AUAU#V}}R      9" EErbc#Ktjj}tjj} dtj_dtj_d|tj_|tj_y#|tj_|tj_wxYwwr3)rVcompiler_is_compiling_flag_is_exporting_flag)old_compiling_flagold_exporting_flags rK_compiling_state_contextr sy::::?,0),0) ,>),>)-?),>)s5C.B&+C,B==Crc2it|jdt|jd}i}i}|jD]G\}}t ||vr|t |}n!|j |d}||t |<|||<I|S)NF)ryT) static_shapes)rrrr8id from_tensor)rrAparams_buffersfaked_params_buffersmemorrF fake_tensors rK_fakify_params_buffersrs s##U#; < s  % 8 9N "$D$**,0 U e9 r%y/K#//T/JK)DEO$/S! 0 rbc  ttjjsJddl}G fdd|j  dt dttt tff fd }dtt d tdt f fd }dt dttt tff fd }| |}jd zjz}d } fd}t |||||dtt f fd } t| y)aB Registers a module as a valid input type for :func:`torch.export.export`. Args: mod: the module instance serialized_type_name: The serialized name for the module. This is required if you want to serialize the pytree TreeSpec containing this module. Example:: import torch class Module(torch.nn.Module): def __init__(self): super().__init__() self.linear = torch.nn.Linear(3, 3) def forward(self, x): return self.linear(x) torch._export.utils.register_module_as_pytree_node(InputDataClass) class Mod(torch.nn.Module): def forward(self, x, m): return m(x) + x ep = torch.export.export(Mod(), (torch.randn(3), Module())) print(ep) rNc2eZdZfdZdZfdZxZS)=register_module_as_pytree_input_node..PrototypeModulect||g|i|t|tjj sJt |drJ|_y)N _proto_cls)super__init__rZrVrrrDr)rimrVr __class__rs rKrzFregister_module_as_pytree_input_node..PrototypeModule.__init__sI G Q 0 0 0a1 11t\2 22!DOrbc4|j|jk(Sr)r)riothers rK__eq__zDregister_module_as_pytree_input_node..PrototypeModule.__eq__s??e&6&66 6rbc|Srr)rirPrototypeModules rK __deepcopy__zJregister_module_as_pytree_input_node..PrototypeModule.__deepcopy__s"46* *rb)__name__ __module__ __qualname__rr!r$ __classcell__)rr#rs@rKr#rs "  7 + +rbr#rr/ct|j}t|j}i||}t|j t|j |gfSr)rrrrrr])rrrrr#s rKrz@register_module_as_pytree_input_node..default_flatten_fnss 4 4 67S..01 >,> >N))+, $$& ' C /   rbrrc |\}}|| td|} J |\}}dtjjffd t dt ||Dr]tjj jj|tt ||dd5|}ddd|S|}|S#1swYSxYw)Nz!Module has been garbage collectedrAcFtj|}|jjDcic].\}}tj|tj|0c}}|_|jD]\}}t ||||Scc}}wr)copy__dict__r8named_childrenr=)rAr5rrrEchild copy_modules rKr0zWregister_module_as_pytree_input_node..default_unflatten_fn..copy_module s))C.CCF<.default_unflatten_fn..s=daqz=sT) tie_weightsstrict) rgrVrranyrutils stateless_reparametrize_moduler) rrrrefrrrr5r0rs @rKrzBregister_module_as_pytree_input_node..default_unflatten_fns! C ;#%-BC Ce%%%!# 1  UXX__  =c&)&<= =))??T#j&12T@ '"#& '  C  '  s 8 CCc|\}^}}t||Dcgc]\}}t||fc}}|g|fScc}}wrr)rrrrVrrrs rKrzJregister_module_as_pytree_input_node..default_flatten_fn_with_keyssW)3C& &J/2:y/IJtq!A"J M M   Js>r1cT|^}}tj|gdgt|zSr)jsondumpsr)rr]rs rKrzAregister_module_as_pytree_input_node..to_dumpable_context(s,qzz44D6CF?455rbctj|}tjj |d<|SrU)r<loadsrVrr)dumpablesr#s rKrzCregister_module_as_pytree_input_node..from_dumpable_context,s/ JJx uxx01!rbrc@|\}}||jk(sJ|Sr)r)rrCflatsrrs rKdefault_flatten_fn_speczEregister_module_as_pytree_input_node..default_flatten_fn_spec;s'#Cw$,,&&& rb) issubclassrVrrweakrefr9rrrrrr&r'rr) rrFrrrrrrrrDr#rs ` @@rK$register_module_as_pytree_input_noderGs@ c588?? ++ + +'++ +  d3i.@(A Xc]W8 # %S 78J2K $J'L>>C/#2B2BB6  19/3d3i ! rbc0t|t|yr)rr)rs rK&deregister_module_as_pytree_input_noderIFsC #C(rbct|tjjs Jd|dt|tjjs Jd|d|j|_|j |_y)Nrz to be a nn.Module)rZrVrr _parametersr;)srcdsts rK _sync_staterNKs    + 3%)*+     + 3%)*+ ooCO<rls   $%'@@ *D@?JH',AQQ$"  d t & OOW <%   %4 (  # .D  6'(<(<6'c3h6'r)!%c3h) )$) )8 $uxx33 $&! 38n  :{UXX]]+{ {@)-,04 +/9==A$4 c4%4=)4 #3- 4 ""56 4$$9:44 4nE'Euxx}}EE    ((-- ehh  !  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