tj k<UddlZddlZddlZddlZddlZddlZddlZddlZddlm Z ddl Zddl m Z ddl m Z ddl mZddlmZmZmZmZmZmZddlmZmZ gdZdd d Zhd Zd Zd.d ededzdejfdZiZ e!edfe"d<GddZ#ej$GddZ%dedefdZ&deddfdZ'deddfdZ(d ede)eeffdZ*deddfdZ+d ej,de-fd!Z.ded"ejddfd#Z/d$ej0d%eddfd&Z1d'Z2d(Z3d/d*Z4d+Z5d0d-Z6dS)1N) infer_schema)get_ctx)BaseTyBaseTypeFunctionSchemaListType OperatorName SchemaKind)autograd_kernel_indirectionconstruct_autograd_kernel) custom_opCustomOprCPUCUDA)cpucuda>atatenprimprimstorchpytorchc>tjdtddS)Nzwtorch._custom_op is deprecated and will be removed in PyTorch 2.6, please use the equivalent torch.library API instead. stacklevel)warningswarnDeprecationWarningS/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/torch/_custom_op/impl.pywarn_deprecatedr$5s0 M 8 r"qualname manual_schemareturnc2tfd}|S)L This API is deprecated, please use torch.library.custom_op instead c tj|stdt|t \}}t ||j|krtd d|d|jd t|dn }||}tj |}t| t||tj |d}||t||j}t#|||||d }|j|_|j|_|j|_tj||jd t-t/j|t2j|t9jt<t/j||S) NzDcustom_op(...)(func): Expected `func` to be a Python function, got: zcustom_op(qualname='z-', ...)(func): expected `func` to have name 'z ' but got 'zX'. Please either change the name of `func` or the qualname that is passed to `custom_op`r!) mutates_argsFRAGMENTT_private_accessAutograd)inspect isfunction ValueErrortypeparse_qualnamevalidate_namespace__name__rrparsevalidate_schema validate_function_matches_schemalibraryLibrarydefinefind_ophandle_or_thrownamer __module____doc__impl_opnamer weakrefproxyr_C#_dispatch_set_report_error_callback functoolspartialreport_error_callback) funcnsr>schema schema_strfunction_schemalibophandleresultr&r%s r#innerzcustom_op..innerDs !$'' /"&t**//  "(++D2 =D ,x,,!%,,26-,,, $ B / / / /  &f&& (.z::(((  $ ,_d C C Cob*-- :)"o.BCC _dHd   - O5 S&.*55 ' f(=(= > >    44 i'(=w}V?T?TUU    r")r$)r%r&rRs`` r#rr>s8//////b Lr"rglobal_registryceZdZdZddfd ZdZddZdZd Zd Z d Z d Z dd e e je zde jfdZdZde jfdZdde jfdZdZdZdZdZddZddZxZS)rr)Fr-c0tt|std|d|}||_||_||_||_||_||_ d|_ i|_ d|_ |t|j <dS)Nz|The CustomOp constructor is private and we do not guarantee BC for it. Please use custom_op(...) to create a CustomOp object::F)super__init__r$ RuntimeError_schema_cpp_ns_lib _ophandlerB _qualnamer6_impls'_registered_autograd_kernel_indirectionrS) selfrOcpp_nsrL operator_namerPr.r> __class__s r#rXzCustomOp.__init__s  S ++M++  %( 5=) " :< 7<4*.'''r"c|jrtd|j|jt t j|dd|_dS)Nz.autograd kernel indirection already registeredr/T)r`AssertionErrorr\rArBr rCrDras r#%_register_autograd_kernel_indirectionz.CustomOp._register_autograd_kernel_indirectionsa  7 S !QRR R  L5gmD6I6IJJJ   8<444r"rc Z||rF|j|}|td|j}t d|d|jd|d|d t jtj |}|j d|j }t|||j|<dS)Nz&func_and_location is unexpectedly NonezAttempting to register a z impl for operator z that already has a z impl registered from Python at z. This is not supported.:) _has_implr_rflocationrYr^r0 getframeinfosys _getframefilenamelinenoFuncAndLocation)rakindrJrfunc_and_locationrlframes r#_register_implzCustomOp._register_impls >>$    $ D 1  ($%MNNN(1H6D66T^66&*66666  $S]:%>%>??n55u|55+D(;; Dr"c|j|SNr_rarss r# _get_implzCustomOp._get_impls{4  r"c||jvSrxryrzs r#rkzCustomOp._has_implst{""r"c|`ttj|j}t ||jrt||jt|j =dSrx) r\getattrropsr[hasattrrBdelattrrSr^)ra opnamespaces r#_destroyzCustomOp._destroysR Iei66 ; - - / K . . . DN + + +r"cd|jdS)Nz)r^rgs r#__repr__zCustomOp.__repr__s33333r"c:tj|jg|Ri|}|Srx)rE_dispatch_call_boxedr])raargskwargsrQs r#__call__zCustomOp.__call__s+(I$III&II r" device_typesr'crttrgD]}t|fd}|S)T This API is deprecated, please use torch.library.custom_op instead ctD]e}|||t|}t jjj||f|S)Nr)set_check_doesnt_have_library_implrvSUPPORTED_DEVICE_TYPE_TO_KEYr:rAr\rB)f device_type dispatch_key _stacklevelrras r#rRzCustomOp.impl..inners"<00 G G 44[AAA##K{#KKK;KH C TY lCCAFFFFHr") isinstancestrvalidate_device_type)rarrrrRs``` r#rAz CustomOp.implsj lC ( ( *(>L' . .K  - - - -        r"c||rdSt|}tj|j|rt d|d|jddS)Nzimpl(..., device_types=z): the operator zs already has an implementation for this device type via a pre-existing torch.library or TORCH_LIBRARY registration.)rkrrE._dispatch_has_computed_kernel_for_dispatch_keyr^rY)rarkeys r#rz(CustomOp._check_doesnt_have_library_impls >>+ & &  F*;7 .inners@    1 - - - BGLDL/ B B1 E E EHr"r!)rarRs` r# impl_factoryzCustomOp.impl_factorys#       r"cfd}|S)rc,ddjjt jfd}jj |dS)Nabstractrcfd}tjj|5|i|cdddS#1swxYwYdS)Nc.tdd)Nz.inner..f_with_ctx..error_on_ctxs6&)'))'))   r")r_library fake_implset_ctx_getter)rrrrrlr%s r# f_with_ctxz9CustomOp.impl_abstract..inner..f_with_ctxs      ^-<<\JJ..1d-f--..................sAAAMeta) $_check_doesnt_have_library_meta_implrvr{rlr^rGwrapsr\rArB)rrrlr%rras` @@r#rRz%CustomOp.impl_abstract..inner s  5 5 7 7 7    A+  F F F~~j11:H~H_Q   . . . . . .  . INN4<V < < <Hr"r!rarrRs`` r# impl_abstractzCustomOp.impl_abstracts)       8 r"c@fd}jtjkr |dj}js |dt |dkrt dt |td|D}|r |dttj dttj d ttj d ttj d ttjd tttjdd i}jD]B}|j|vr |dt#|d|jdCdS)Nc>td|djd)NzCCannot use torch._custom_ops APIs to register backward formula for z. Got operator z with schema: )rYr^)detailrLras r#errorz4CustomOp._check_can_register_backward..error+sC:%::>::17:: r"znon-functional operatorzoperator with no returnsrz"expected at least one return, got c3BK|]}|jduo |jj VdSrx) annotationis_write).0rs r# z8CustomOp._check_can_register_backward..<sG# # GHAL $ BQ\-B)B# # # # # # r"zoperator that returns viewsintSymIntboolfloatTensorz List[Tensor]zoperator with return not in z (got ))rZrsr functionalreturnslenrfanyrrrrrrrrr3listvalues)rarretsis_non_mutating_viewallowed_return_typesretrLs` @r#_check_can_register_backwardz%CustomOp._check_can_register_backward*s       ;;==J1 1 1 E+ , , ,~~ . E, - - - t99>> !Qc$ii!Q!QRR R"# # LP# # #     1 E/ 0 0 0 VZ % V] # #X V[ ! !6 V\ " "G V] # #X Xfm,,d 3 3^  >  Cx/// Eet4H4O4O4Q4Q/R/ReeZ]Zbeee      r"c|jrdStj|jdrt d|jddD]7}tj|j|rt d|jd|d8dS)NCompositeImplicitAutogradz3impl_backward/impl_save_for_backward: the operator a3 already has an implementation for this device type via a pre-existing registration to DispatchKey::CompositeImplicitAutograd.CompositeImplicitAutograd operators do not need an autograd formula; instead, the operator will decompose into its constituents and those can have autograd formulas defined on them.)r/ AutogradCPU AutogradCUDAz; already has an Autograd kernel registered to DispatchKey::z vi a pre-existing torch.library or TORCH_LIBRARY registration. Please either remove those registrations or don't use the torch._custom_ops APIs)r`rE%_dispatch_has_kernel_for_dispatch_keyr^rY)rars r#(_check_doesnt_have_library_autograd_implz1CustomOp._check_doesnt_have_library_autograd_implRs  7  F 3 N7  ?dn??? ?  C7LL "Z$(NZZ25ZZZ   r"ch|drdStj|jdrtj|jdsdStj|jdrt d|jdtj|jdrt d|jddS)NrCompositeExplicitAutogradrrz!impl_abstract(...): the operator a- already has an implementation for this device type via a pre-existing registration to DispatchKey::CompositeImplicitAutograd.CompositeImplicitAutograd operators do not need an abstract impl; instead, the operator will decompose into its constituents and those can have abstract impls defined on them.z already has a DispatchKey::Meta implementation via a pre-existing torch.library or TORCH_LIBRARY registration. Please either remove that registration or don't call impl_abstract.)rkrErr^rYrgs r#rz-CustomOp._check_doesnt_have_library_meta_implns >>* % %  F  3 N7   :4>6RR  F  3 N7   " " NN. / / 4 NN: & & +    J/////r"cfd}|S)zyRegister a function that tells us what to save for backward. Please see impl_backward for more details. cjsd|drdSdS)Nrrr)rrr`rhrvrkr)rrras r#rRz.CustomOp.impl_save_for_backward..inners  - - / / /  9 9 ; ; ;? =::<<<    3Q;  O O O~~j)) 1..00000 1 1r"r!rs`` r#impl_save_for_backwardzCustomOp.impl_save_for_backwards)  1 1 1 1 1 1 r"Nc|fd}tts |D]!}t|ts |"tjjtkr |fd}|S)rNc(td)Nzimpl_backward(output_differentiability): expected output_differentiability to be a list of bools with length equal to the number of outputs of this CustomOp got: r)output_differentiabilitysr#yellz$CustomOp.impl_backward..yells)"7577r"c"jsd|_drdSdS)Nrrr)rrr`rhrvrrkr)rrrras r#rRz%CustomOp.impl_backward..inners  - - / / /  9 9 ; ; ;? =::<<<    A+  F F F-ED *~~122 1..00000 1 1r")rrrrrZr)rarrrdiffrRs``` r# impl_backwardzCustomOp.impl_backwards $ /     6== 0  !$--DFFF4<'((C0H,I,III 1 1 1 1 1 1 1 r")r)Nr)r6r? __qualname__r@rXrhrvr{rkrrrrtypingIterableCallablerArrrrrrrrr __classcell__)rds@r#rrs PU///////8<<< < < < <!!!### , , ,444FOC00  .   fo!!fo!!!!F&&&P8%%%X 0 0 0"        r"c.eZdZUejed<eed<dS)rrrJrlN)r6r?rrr__annotations__rr!r"r#rrrrs' /MMMMMr"rrrbrccz|jdn|j}tj|dt|j|S)NrV) overload_namerE_dispatch_find_schema_or_throwrr>)rbrcrs r#r=r=sN)1}7R  , ..S+,,..   r"rKcrd|vrtd|d|tvrtd|d|ddS)N.zcustom_op(..., ns="zC"): expected ns to not contain any . (and be a valid variable name)zcustom_op(..., ns='z'): 'z9' is a reserved namespace, please choose something else. )r2 RESERVED_NS)rKs r#r5r5s| byy $" $ $ $    [ ." . .2 . . .   r"rLctjj|st d||jjt d|dS)Nzcustom_op only supports functional operators (ops that do not mutate any inputs, do not return views of the inputs, and has at least one return). Got the following non-functional schema: zUcustom_op does not support arguments named 'self'. Please rename your argument. Got: )rrutilsis_functional_schemar2 argumentsself_arg)rLs r#r8r8s >  4 4V < <  A9? A A    , 3*0 3 3   -,r"c|dd}t|dkrtd|dd|dvrtd||d|dfS) NrVr rz$Expected there to be a namespace in z;, i.e. The operator name should look something like ns::foorzThe torch.custom_ops APIs do not handle overloads, i.e. operator names with '.' in them. Please name your operator something like ns::foo. Got: r)splitrr2)r%namess r#r4r4 s NN4 # #E 5zzQ @8 @ @ @    eAh      8U1X r"rcr|tvr-td|dtddS)NzCustomOp.impl(device_types=[z(, ...]): we only support device_type in r)rr2keys)rs r#rrsW666 9; 9 9.3355 9 9 9   76r"paramcT|jtjjtjjfvSrx)rsr0 ParameterPOSITIONAL_OR_KEYWORD KEYWORD_ONLY)rs r#supported_paramr#s( :/& r"rJcvtj|tdjDst dt djDsjtjj urt ddj D}dj D}fdfdfd }||j j ||j j dS) Nc34K|]}t|VdSrx)rrps r#rz3validate_function_matches_schema../s*CCaq!!CCCCCCr"zcustom_op(..., manual_schema)(func): positional-only args, varargs, and kwargs are not supported. Please rewrite `func` to not have them. Got `func` with signature: c3FK|]}|jtjjuVdSrx)rr0remptyrs r#rz3validate_function_matches_schema..7s@  <=AL 1 7 7      r"zcustom_op(..., manual_schema)(func): When passing in a manual schema, we expect `func` to have no type annotations to avoid ambiguity. Got `func` with signature: cPg|]#\}}|jtjjk||f$Sr!)rsr0rrrr>rs r# z4validate_function_matches_schema..Bs= D% :*@ @ @ u @ @ @r"cPg|]#\}}|jtjjk||f$Sr!)rsr0rrr s r#r z4validate_function_matches_schema..Gs= D% :*7 7 7 u 7 7 7r"c.tdd)Nzcustom_op(..., manual_schema)(func): When passing in a manual schema, we expect `func`'s signature to match `manual_schema` (aside from type annotations). func's signature: , manual_schema: r2rLsigsr#rz/validate_function_matches_schema..errorM= @"% @ @8> @ @   r"c.tdd)Nzycustom_op(..., manual_schema)(func): neither func nor manual_schema should have default arguments. Got func's signature: rrrsr#error_default_argsz.error_default_argsUrr"c t|t|kr t||D]F\\}}}||jkr |jtjjus|j GdSrx)rzipr>defaultr0rr )sig_args schema_argsr>rargrrs r#comparez1validate_function_matches_schema..compare]s x==C ,, , , EGGG"%h "<"< % % MT53sx}G$5$;;;s{?V""$$$  % %r")r0 signatureall parametersrr2rreturn_annotation Signaturer itemsrflat_positionalflat_kwarg_only)rLrJ positional kwargonlyrrrrs` @@@r#r9r9*s  D ! !C CC3>+@+@+B+BCCC C C  B//11J >//11I             %%%%%% GJ(8999 GIv'788888r"rrc |dkrt|d|dkrt|d|dvr/|}t|d|d|d|d t|d |d ) N Undefineda: There were no Tensor inputs to this operator (e.g. you passed an empty list of Tensors). If your operator is a factory function (that is, it takes no Tensors and constructs a new one), then please use CustomOp.impl_factory to register an implementation for itrz: when running with device='Meta' tensors: there is no abstract impl registered for this CustomOp. Please register one via CustomOp.impl_abstract to get this CustomOp to work with Meta tensors)rrz: when running with device='z' tensors: there is no zW impl registered for this CustomOp. Please register one via CustomOp.impl(device_type='z')z%: No implementation for dispatch key z. It is likely that we have not added this functionality yet, please either open an issue or if you're feeling adventurous, use the low-level torch.library API)NotImplementedErrorlower)rrdevices r#rIrIjs k! ( ( (    f}}! U U U    o! 5 5f 5 5 5 5*0 5 5 5      3     r"c^|j}tj|d}|dd}t |j}|ddd}tj |}t|||||dS)Nr,rVr )maxsplitTr-) namespacerr:r;r>rrrZrsplitrr7r)oprKrOr>rMrLs r#custom_op_from_existingr2s B -  J / /C 7799??4  $DRZJ""4!"44R8J  !* - -F CVT2t D D DDr"cDfd}t\}}ttj|s |t tj|}t||s |t ||}t|ds ||jS)Nc*tdd)NzCould not find the operator z~. Please make sure you have already registered the operator and (if registered from C++) loaded it via torch.ops.load_library.r)r%sr#error_not_foundzget_op..error_not_founds+ 58 5 5 5   r"r)r4rrrr~r)r%r5rKr>rpackets` r#rrs     h''HB 59b ! !%)R((K ; % % [$ ' 'F 69 % % >r"Fc|tvr t|S|std|dt|}t|}|S)NzCould not find custom op "z5". Did you register it via the torch._custom_ops API?)rSrYrr2)r%also_check_torch_libraryoverloadrQs r#_find_custom_opr:si?""x(( #  * * * *   hH $X . .F Mr"c|tjjjvrdStjjj|}|dS|dsdS|djS)Nr)r _custom_oprArSrkr{rJ)r%rs r#get_abstract_implr=sku',<<<t %5h?It   z * *t   z * * //r"Tc @|d\}}||}tj|}t||rtjjjgng}tj |d}| ||t||j } t||||| d} | tj| t!jt$t'j| t+|S)NrVr,)tagsTr-)rrr7r8rrETagneeds_fixed_stride_orderr:r;r<r=r>rrhrFrGrHrIrCrDr) r%rLrArKr>rMrNr?rOrPrQs r#_custom_op_with_schemarBs ~~d##HB"&""J$*:66OO$$$6N VEHL 1 2 2TVD /"j ) )CJJzJ%%%%b/*>??H c2hPT U U UF 00222 H00)#$97=;P;PQQ (  r"rx)F)T)7 dataclassesrGr0rnrrrCrtorch._CrEtorch._library.infer_schema torch.libraryr:rrtorchgen.modelrrrrr r rr r __all__rrr$rrrrSdictrr dataclassrrr=r5r8tupler4rrrrr9AnyrIr2rr:r=rBr!r"r#rMs  """"444444!!!!!!MLLLLLLL / . .     777C$J7&/7777@*,c:o&+++YYYYYYYYx   3|  3  4      N t    "SU38_" c d    7,=9 =9"(/=9 =9=9=9=9@VZcd<EEE(    000r"