Jj\&*ddlZddlmZmZddlmZddlmZmZddl m Z m Z ddl Z ddl mZddlZ ddl mZmZddlmZddlmZdd lmZdd lmZdd lmZdd l mZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(dd l)m*Z*ddl+m,Z,m-Z-ddl.m/Z/m0Z0ddl1m2Z2m3Z3m4Z4e j56ddZ7e j56dddZ8e j56dddZ9e j56dddZ:e j56dddZ;gdZ< drdddddde"e jzdzde$dzde%dzde j=dzde j>e?zdzf dZ@dd d d!d"e?d#e#eAe#d$fzd%ed&ed'e?d(ee jBdzd)eCd*eCfd+ZDGd,d-eZEdd.d/eEd0eAe'd$fdzd1e,fd2ZFd3ZGd4e?d/eEfd5ZHd4e?d/eEfd6ZId7ZJeHd8e jKd9eEjL:ZKeHd;e jMd9eEjN:ZMeHde jQd9eEjN:ZQeHd?e jRd9eEjN:ZReHd@e jSd9eEjN:ZSeHdAe jTd9eEjN:ZTeHdBe jUd9eEjN:ZUeHdCe jVjWd9eEjN:ZWeHdDe jVjXd9eEjN:ZXeHdEe jYd9eEjN:ZZeHdFe jVj[d9eEjN:Z\eHdGe jVj]d9eEjN:Z^eHdHe jVj_d9eEjN:Z`eHdIe jad9eEjN:ZadJe jd1efdKZbeHdLebd9eEjN:ZceHdMe jdd9eEjN:ZddNe'd1e'fdOZeeDdPeee jfdQe#jgRZhe jidSdNe'dTe jjd1e'fdUZkeDdVeke jldWe#jgdXYZleHdZe jmd9eEjN:ZmeHd[e jnd9eEjN:ZneHd\e jVjod9eEjN:ZpeHd]e jVjqd9eEjN:ZqeHd^e jVjrd9eEjN:ZreHd_e jsd9eEjN:ZseHd`e jVjtd9eEjN:ZteHdae jVjud9eEjN:ZudJe'dbe"d1e'fdcZveDdde#jgeve jwd9eZweHdfe 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j>d)eCd1e'fd*Z*d'eded(ede j=de j>d)eCd1e'fd+Z+eDd,e#jge*e+e)eZ,de$de j=de j>d)eCd1e'f d-Z-de$de j=de j>d)eCd1ef d.Z.d/Z/eDd0e-e.e#jge/Z0de$de%de j=de j>d)eCd1e'f d1Z1d2Z2eDd3e#jge1e j3e2eZ3de$d4ede j=de j>d)eCd1e'f d5Z4d6Z5eDd7e#jge4e j6e5eZ6de$d8e"de j=de j>d)eCd1e'f d9Z7de$d8e"de j=de j>d)eCd1ef d:Z8d;Z9eDd<e7e8e#jge9Z:dJe'd8e"de j=de j>d)eCd1e'f d=Z;dJed8e"de j=de j>d)eCd1ef d>Z<d?Z=eDd@e;edAe"de j=de j>d1e'fdBZ?dAe"de j=de j>d1efdCZ@dDZAeDdEe?e@e#jgeAZBdFe'dGeCd1eAe'e'e'ffdHZCdFe'dGeCd1eAeeeffdIZDdJZEeDdKeCeDe#jge#jge#jgfeEZFddLde$dMeGeHzdNeGde j=de j>d)eCdOe jIdzd1e'fdPZJddLde$dMeGeHzdNeGde j=de j>d)eCdOe jIdzd1efdQZKdRZLeDdSe#jgeJeKeLeZMddLde$dTeGdUeGde j=de j>de$dOe jIdzd1e'fdVZNddLde$dTeGdUeGde j=de j>de$dOe jIdzd1efdWZOdXZPeDdYe#jgeNeOePeZQdNe&dedZeCd1e'fd[ZRdNe&dedZeCd1e'fd\ZSd]ZTeDd^eReSe#jgeTZUdNe&ded_eCd1e'fd`ZVdNe&ded_eCd1e'fdaZWdbZXeDdceVeWe#jgeXZYdNe&dedded1e'fdeZZdNe&dedded1e'fdfZ[dgZ\eDdheZe[e#jge\Z]die'd1eAe'e'ffdjZ^eDdke^e#jge#jgfe j_d9iZ_d1e'fdlZ`eDdme`e#jge`dniZadudoZbeDdpebe#jcebdqiZde jejfgedeedS(vN)CallableSequence)Enum)partialreduce)OptionalUnion) sym_floatTensor)_get_default_devicenew_token_tensor)is_functional_schema)register_debug_prims)register_rng_prims) DimDimsSequenceTypeDimsTypeIntLikeNumber NumberType RETURN_TYPE ShapeType StrideType TensorLikeTensorLikeType type_to_dtypebackwards_not_supported) FakeTensorFakeTensorMode)handle_torch_functionhas_torch_function) tree_flattentree_maptree_unflattenprimsDEFIMPLCompositeExplicitAutograd BackendSelectAutogradMeta)rabsacosacoshasinasinhatanatanhcoscosh bessel_i0 bessel_i0e bessel_i1 bessel_i1e bessel_j0 bessel_j1 bitwise_notcbrtceil conj_physicaldigammaerferf_inverfcerfcxexpexpm1exp2fillfloorimagisfinitelgammaloglog1plog2log10ndtrinegreal reciprocalroundsignsignbitsinsinhspherical_bessel_j0sqrttantanhtruncaddatan2 bitwise_and bitwise_or bitwise_xordiveqfmaxfminfmodfrexpgcdgegthypotigammaigammacleltmaximumminimummulne nextafterpow remainderrsqrt shift_leftshift_right_arithmeticshift_right_logicalsubzeta as_stridedbroadcast_in_dim collapse_viewconj expand_dimsslice split_dimsqueeze transposeview_ofview_element_typeas_strided_scattercollapsecatreshaperevwherecloneconvert_element_type device_putitem maximum_value minimum_value copy_stridedcopy_toresizeamaxaminprodsumxor_sumvar empty_stridedempty_permuted scalar_tensoriotasvdnormal_uniform_helperfft_r2cfft_c2cfft_c2r _make_token _sink_tokensshapestridesdtypedevice tensorlikerrrrct|tr|r4t|tstdt ||r4t|tstdt |d}d}t t |}t jd}n|}t|t jstdt |t|j }t| }|j }|j}nD|td|td|td|td ||nt|}||nt|}||n|}||n|}t|trt j|}t j|||| S) Nz7shape must be None or a Sequence for Number input, got z9strides must be None or a Sequence for Number input, got cpuz%tensorlike must be torch.Tensor, got z.shape must be provided when tensorlike is Nonez0strides must be provided when tensorlike is Nonez.dtype must be provided when tensorlike is Nonez/device must be provided when tensorlike is Nonerr) isinstancerrAssertionErrortypertorchrr tuplerstriderstrr) rrrrrinferred_shapeinferred_stridesinferred_dtypeinferred_devices CC:\Projects\DSCons\.venv\Lib\site-packages\torch\_prims\__init__.py TensorMetars*f%%#T  E844  W$u++WW   :gx88  [DQXMM[[ +-,.&tJ'7'788,u--  *el33  JZ8H8HJJ z/00 !2!2!4!455#)$+ = !QRR R ? !STT T = !QRR R > !RSS S#mNNuE")/uW~~G#mNNE &__FF&#&f%%  ugU6 J J JJF)tagsuse_old_custom_ops_apiregister_conj_neg_fallthroughschema return_type.meta impl_atendocrrrcfdfd}fd} |ddd} |t| d}tj| |z} |st | st | |ztjj t | t | |t | nd | jD} tjd | zt#| | } | |t&jks|rV| j | tjjd | j | tjjdt/tjjj| }|j|r|_nt/tjj| dx}rd|D}t?|dj |dd!tjj"!tjj#t#fd|dD_ddl$m%tMfdj'jDrtQdkrtR | | |fD],}||_*||_+||_,|_ |_ |_--S)z) Creates a primitive operation. c$|i||i|SNr)argskwargsrrs r _prim_implz_make_prim.._prim_impl(s/ dfy$)&)))rc.t|i|Srr)rr_prims r_autograd_implz"_make_prim.._autograd_impl3s!-&u--t>v>>>rc|dr|djdkr|i|Std|Dr|i|S|i|S)Nrrc3`K|])}t|tjo |jdkV*dS)rN)rrrr).0xs r z;_make_prim.._backend_select_impl..9s:NNAz!U\**?qv/?NNNNNNr)getrany)rrrrs r_backend_select_implz(_make_prim.._backend_select_impl6s ::h   )F8$4$9V$C$C4((( ( NNNNN N N /4((( (:t.v.. .r()maxsplitrNrcBg|]}|j |jj|jSr) alias_infois_writename)rargs r z_make_prim..Is6   ~)cn.E) H)))rzprims::) mutates_argsr ConjugateNegativecg|] }|j Srr)roverloads rrz_make_prim..`sRRR8RRRrc3$K|] }|v|V dSrr)rttags_intersections rrz_make_prim..ks.RR!1@Q;Q;QA;Q;Q;Q;QRRr)contains_tensor_typesc38K|]}|jVdSr)r)rars rrz_make_prim..ps1OO!%%af--OOOOOOrzprims.device_put.default).splitlenr_C parse_schemarprimdefineTagpt2_compliant_tag prim_implimplprim_autograd_implprim_meta_impl argumentslibrary custom_opr register_fakerVIEW_libfallthrough_kernelgetattr_opsopsr'default_tagsaten op_overloadssetintersection_updatediscardcoredata_dependent_outputtorch._subclasses.fake_tensorrr_schemarprim_backend_select_impl__doc__rrr)rrrrrrrrrrr cpp_schemarprim_def _prim_packet aten_packet overload_tagsprrrrs `` @@@@r _make_primrs ******?????////// <<a< ( ( +D CIIKK F&&tf}55JS%9*%E%ES D6M (C DDDtZ(((n555D$''''  !+   =**   |,, +   t$$$ +* * *.K * M  tU]%E{ S S S M  tU]%Ez R R R5:>/66L  E S  d;; ; SRR{7O7O7Q7QRRR  a 011--}QRR/@AA !!%).111 !!%)"ABBBRRRR}Q'7RRRRR CCCCCC OOOOu}7NOOO O OB     &  & & !%%d,@AAAE "   #    LrceZdZdZdZdZdZdS)$ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND)r)))N)__name__ __module__ __qualname__DEFAULT INT_TO_FLOAT ALWAYS_BOOLCOMPLEX_TO_FLOATrrrrrs%GLKrr)args_with_fixed_dtypestype_promotionr(returnc&t|dkrtdtj|t |}|t ||z}tj|dditj|dditj|\}}tj|ddi}d}d}|D]`} t| tr%tj | s | j }n-| j }t@t&jt&jfstd t| | p t| t@t&jf} w| rtC| } tE| S) z Meta function for elementwise operations that produce outputs in the same dtype as their inputs. Stride logic is currently incorrect. rz4elementwise operation requires at least one argumentNallow_cpu_scalar_tensorsTz.dtype must not be None when device is not Nonez.shape must not be None when device is not None)rrFz.Expected int, float, SymInt, or SymFloat, got )#rrutilscheck_same_dtypelistcheck_same_devicecheck_same_shape3compute_elementwise_output_logical_to_physical_perm extract_shaperris_cpu_scalar_tensorrrrrrrr&rboolr%is_integer_dtypeis_boolean_dtypeget_default_dtyper'is_complex_dtypecorresponding_real_dtyperSymIntSymFloatintfloatr r)r)r(rargs_l2p_perm_rr scalar_typerrnumber seen_floatrs r_prim_elementwise_metarEs9 4yyA~~STTT D!! JJE)+,,u4 UBTBBB EADAAAKUSKHa   F F FE EK$$ c: & & $-c22 "   V $ $ $s))K }0#K00F F   c: & & )#.. > ZF V $ $ ~  = !QRR R AM M MJEE CP P P%e,, 20Fu0M0M 2/11 CT T T%e,, >6u== = !QRR R#E8F%PPPP J&5<899' N NAa#uelEN!KLL $NT!WWNN$Mz!eU^5L'M'MJJ  'v&&F f  rctjtj|djdt |i|S)NrcdS)NzOnly complex dtype is supportedrrrrz0_complex_only_elementwise_meta..s7Xr)r_checkr-r9rrErrs r_complex_only_elementwise_metarKsE L tAw}--/X/X "4 26 2 22rrc dtd|dtt|tjd|S)z, Creates an elementwise unary prim. z(Tensor self) -> Tensorr)rrrrrrrErNEWrr)rs r_make_elementwise_unary_primrRsL  /// +N K K KO      rc dtd|dtt|tjd|S)z- Creates an elementwise binary prim. z%(Tensor self, Tensor other) -> TensorrMrNrrOrQs r_make_elementwise_binary_primrTsL  === +N K K KO      rctrNotImplementedErrorrJs r _not_implrXs rr.)rrr)r/r0r1r2r3r4r5r6r;r<r7r8r9r:r=rctj| dtjtj|d|S)NcdS)NzJcbrt: Complex inputs not supported. Consider calling torch.pow(a, 1.0/3.0)rrrrrHz_cbrt_aten..s\rgUUUUUU?)rrI is_complexcopysignrxr.rs r _cbrt_atenr_sR L LLNN\\ >%)AEEGGU33Q 7 77rr>r?inputc|jjstdtj|}t ||S)Nz6prims.conj_physical is only defined for complex dtypes)r)rr\ RuntimeErrorr-"compute_elementwise_output_stridesr)r`rs r_conj_physical_metardsB ; !USTTT6u==G eW - - --rz$conj_physical(Tensor self) -> Tensorz4Returns the physical conjugation of a complex tensor)rrrrr memory_formatrfc|tjkr-tj|j|j|j|j|Stj|}tj |j||j|j|jS)N)rlayoutrrf)rrhr) rpreserve_formatemptyrrrhrr-rcr)r`rfcomputed_strides r _clone_metarls---{ K+<<'      B5II" K +<<     rzAclone(Tensor self, *, MemoryFormat? memory_format=None) -> TensorzReturns the copy of a tensorT)rrrrrrrArBrCrDrErFrGrHvaluec8t|tjS)NrMrErr$)rrms r _fill_metarps" ! >F   rz)fill(Tensor self, Scalar value) -> Tensor)rrrrrrJz!imag(Tensor(a) self) -> Tensor(a)rM)rrrrrrLrMrNrOrPrQz!real(Tensor(a) self) -> Tensor(a)rUrRrSrVrzrWrXrYrZr[r\r]r^r_r`)rrrr)rarbrcrdct|tttjfp2t|tjot j|j}|rtj ||dStj ||S)Nr_) rounding_mode) rr5r=rr;r r-r6rre true_divide)rb is_integrals r _div_atenrvswQsEL 9::1el##G(>qw(G(G'yAW5555 A&&&rrerfrgrhrirkrlrmrnrorprqrrrtcFt|tr2t|trt||j|j}nFt|tr1t|trt||j|j}t j||SNr)rrrrrrrrsrrts r _maximum_atenrzh!Z  =Z6%:%:= !1718 < < < Az " "=z!V'<'<= !1718 < < < =A  rrscFt|tr2t|trt||j|j}nFt|tr1t|trt||j|j}t j||Srx)rrrrrrrrtrys r _minimum_atenr}{r{rrtrurvrwrxryr{r|r~rsizerstorage_offsetct|t|kr/tdt|dt||dkrtd|tj|tj|t t j|dkrnCt|tj r)tj | |||tj ||||S)Nz len(size)=z != len(stride)=rz!storage_offset must be >= 0, got )rrr-validate_stridesvalidate_shaperoperatorrurrr check_in_bounds_for_storage_typed_storagerrr~rrs r_as_strided_metars 4yyCKKR#d))RRS[[RRSSSQQQRRR 6"""  hlD!!Q&& Au| $ $  )    fn     AtV^ < < Tensor(a!)rrrrrbroadcast_dimensionsc  ddlm}m}m}t t st dtt tst dtt |tst dt|j t|kr(t dj dt|dtj kr(t d td j dfd }t||d t|D]N\ tj|jd k jk fdOg}d t!tD]~|vr|j d krd|j kr.| ns|dn]tjj k fd|  d z |d kr|d j kr|d 6|  z|S)Nr)guard_or_false guard_or_truesym_ora must be TensorLike, got zshape must be a Sequence, got z-broadcast_dimensions must be a Sequence, got za.ndim (z ) != len(broadcast_dimensions) ()z len(shape) (z) must be >= a.ndim (ct|tstdt|||krtd|d||t kr"td|dt |S)Nz.broadcast_dimensions element must be Dim, got z1broadcast_dimensions must be strictly ascending: z <= zbroadcast_dimension z# out of bounds for shape of length )rrrrr)accrrs r_greater_than_reducez4_broadcast_in_dim_meta.._greater_than_reduces!S!!  JaJJ  88 PAPP3PP  E ?? YqYYSQVZZYY rrc4jdS)Nz must be broadcastable to r)ridxnew_idxrsrrHz(_broadcast_in_dim_meta..2sqws|OOuW~OOrc6djdS)Nz#non-broadcasting semantics require z == r)rr original_idxrsrrHz(_broadcast_in_dim_meta..Cs&i!',BWii]bcf]giir)%torch.fx.experimental.symbolic_shapesrrrrrrrrndimrr enumeraterrIrrangeappendrr~rr) rrrrrrr new_stridesrrrs `` @@@r_broadcast_in_dim_metars a $ $EC$q''CCDDD eX & &MKd5kkKKLLL *H 5 5  XDAU!',"75:"EFF*&&qxxzz,'?@@@@&&q)))) GL)U3Z7iiiiiii""188::l#;<<<'!+LL}U3Z1_-- V""1%%%%''""1%%%%""188::l#;affhh|>T#TUUUU <<{A,<,<,>,> ? ??rct|}|D]}d||<|}t|D] \}}|dkr||}!||SNr)r/r unsqueezeexpand)rrrsbroadcast_dimensionvrrs r_broadcast_in_dim_atenrSst U A3$$!#  AA,,!!Q 77 C  A 88E??raD Creates a view of a with the specified shape. Allows adding dimensions of any length and broadcasting dimensions of length one in a to any length. The location of the broadcast dimensions must be specified using the broadcast_dimensions argument. Changing the relative order of dimensions is not supported. zVbroadcast_in_dim(Tensor(a) a, SymInt[] shape, int[] broadcast_dimensions) -> Tensor(a)startendctd|}tj|tj|t jkfddS)NrcdddS)Nz Attempting to collapse but end, z, is less than start, !r)rrsrrHz)_validate_collapse_args..~sV3VVeVVVr)maxdimr- validate_idxr _check_value)rrrrs `` r_validate_collapse_argsrtsv q!%%''??D tU### tS!!!  u VVVVVrct|dkrdnt|}d}|||dzD]}||z}|d||fz||dzdzS)zZ Returns the shape of a with dims in [start, end) merged into a single dimension. rrrN)rr)rrr dim_lengthrs r_collapsed_shaperst JJ!OODDuEJ 537? #$$!^ 5>ZM )E#')),< <.s}r)NN)rrrrrrrrrrrrrnumelrrrIminr-make_contiguous_strides_for)rrrrrrrrrrvalid_oprrlength new_shapers ` r_collapse_view_helperrsQ a $ $EC$q''CCDDDAuc*** v{{((**v{{se||g~H~aggii1n%% q%!)R00  Cw#J!O#'Na'CLGC!G$4uS1W~$EEH~h%/00  vh Q//H X44445555~h'' :S\FS1Weai,,// :??..F3ZF}Vq[!!q%!)R00 ) )C~eCjAo.. eCj(FFfuf  )E#')),< Tensor(a)c"|jjstd||j||}tj || |S)Nz$Expected complex dtype in prims.conj) rr\rbrrrrrr _set_conjis_conj)routs r _conj_metarsq 7 CABBB ,,qw A,<,<,>,> ? ?C Hs O,,, Jrz2 Returns a conjugated view of the original tensor zconj(Tensor(a) a) -> Tensor(a) dimensionsc|#ttj|}n'ttj|j}t t |t |kr!dt }t|t|j }|D]}| |dfdtt |D}t|||S)z Creates a view of a with a.ndim + len(dimensions) dimensions, with new dimensions of length one at the dimensions specified by dimensions. Nz+Received duplicate dimensions to expand in rcg|]}|v| Srr)rrrs rrzexpand_dims..=s*:0E0E0E0E0Er) sortedr-canonicalize_dimsrrr r ValueErrorr/rinsertrr)rrrdimsmsgrrrs ` rrr)s e-dJ??@@e-afjAABB 3t99~~T""MC OOMMooQW I!!a    S^^,, Ay*> ? ??rr outer_lengthcFt|tstdt|t j|j|t j||j||z}|j||zdkr#d|j|d|d}t|g}g}t|jD]}||krb| ||f| | ||z| |fj| |j|| | |||||S)Nrrz(Attempting to split dimension of length z, but outer length of z divides it with a remainder!)rrrrr-rrvalidate_dim_lengthrrrextendrrrr)rrr inner_lengthrrrrs r_split_dim_metarCs a $ $EC$q''CCDDD qvs### l+++73<</L  |#)) Oqws| O O#/ O O O ooIKQV}}00 #::   lL9 : : :    3, > 3P Q Q Q Q   QWS\ * * *   qxxzz# / / / / << ;0@0@0B0B C CCrc|j||z}|jd|||fz|j|dzdz}||S)Nrr)rr)rrrrrs r_split_dim_atenr`sQ73<</L#, !==a @RRI 66)  rz Creates a view of a with the given dimension (of length l) split into two dimensions, with the outer of the two having length outer_length and the inner of the two having computed length inner_length such outer_length * inner_length = l. zAsplit_dim(Tensor(a) a, int dim, SymInt outer_length) -> Tensor(a)cRt|tstdt||D]N}t j|j||j|dkr!td|d|j|dOg}g}tt|jD]T}||vr| |j|| | |U| ||| S)NrrzCannot squeeze dimension z with size z (must be 1))rrrrr-rrrrrrrrr)rrrrrs r _squeeze_metarys2 a $ $EC$q''CCDDD 163''' 73<1   VCVVAGCLVVV   IKS\\"",, *   &&&188::c?++++ << ;0@0@0B0B C CCrz~ Creates a view of the tensor with the specified dimensions removed. The removed dimensions must each have length one. z3squeeze(Tensor(a) a, int[] dimensions) -> Tensor(a) permutationc|jt|kr*d|jdt|d}t|tj|j|sd|d}t|dg|jz}dg|jz}t |D]2\}}|j|||<||||<3|t|t|| S)Nz'Attempting to permute a tensor of rank z', but received a permutation of length rz!Received an invalid permutation, r) rrrr-is_valid_permutationrrrrrr)rrrrrrrs r_transpose_metars v[!!!!zzzgjkvgwgwzzzoo  %afk : :@+@@@ooaf I#,Kk**++S #88::c? C <<i((% *<*N>N>P>P Q QQrc,tj||Sr)rpermute)rrs r_transpose_atenrs =K ( ((rz Creates a view of the tensor with its dimensions permuted. The length of the permutation must be the rank of the tensor, and each element of the permutation specifies the new order for the corresponding dimension. z6transpose(Tensor(a) a, int[] permutation) -> Tensor(a)c||j||Sr)rrrrr^s r _view_of_metars. <<Q-=-=-?-? @ @@rc6||jSr)rrr^s r _view_of_atenrs 66!'??rz' Creates a view of the tensor. z!view_of(Tensor(a) a) -> Tensor(a)c,||Srrrrs r_view_element_type_metar 66%==rc,||Srrrs r_view_element_type_atenrrrz> Creates a view of the tensor with a different dtype. z9view_of_dtype(Tensor(a) a, ScalarType dtype) -> Tensor(a)srccxtjtjtjt jkfdt jtjjfdtj S)Ncdddddzdz S)Nzas_strided_scatter: sizes z , strides z, storage offset z and itemsize z requiring a storage size of z' are out of bounds for storage of size ) element_sizer)r` required_sizer~rrsrrHz*_as_strided_scatter_meta..s J J J J JR` J J"//11 J Ju11333 J J$);;==53E3E3G3G#G J JrcdjdS)NzCexpected src to have a size equal to the slice of self. src size = z, slice size = r)r~rsrrHz*_as_strided_scatter_meta.. svVYV_vvptvvr) r-rrcompute_required_storage_lengthrrIr is_same_shaperclone_preserve_strides)r`rr~rrrs`````@r_as_strided_scatter_metars  6"""9$WWM L &         L CIt,,vvvvv  ' . ..rz Creates a new tensor equivalent to ``out = input.clone()`` after mutation by ``out.as_strided(size, stride, storage_offset).copy_(src)``. zlas_strided_scatter(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt storage_offset) -> Tensorczt|||t|j||}||Sr)rrr new_emptyrs r_collapse_metar#s9Auc*** %55I ;;y ! !!rct|j||}||}tj5|||dddn #1swxYwY|Sr)rrrrno_gradview_ascopy_)rrrrrs r_collapse_atenr*s %55I ++i C    AQ               Js)A44A8;A8zn Collapse a span of neighboring dimensions into one. See collapse_view for the corresponding view operation. z0collapse(Tensor a, int start, int end) -> Tensortensorsc  dkrtd|dj}g}t|D]\ }t|t|jkr7tdt|d dt|jtt ||jD]D\\kr|$t jk fdEt|dj }t j ||<t|d|tj |S)Nrzdim must be non-negative, got z>All tensors must have the same number of dimensions. Expected z but tensor z has c (dddddd S)Nz0Sizes of tensors must match except in dimension z . Expected z in dimension z but got z for tensor number z in the listr) common_lengthrrr tensor_idxsrrHz_cat_meta..UsO0s00 -00=@00KQ00!000rrr)rrrrziprrrIr/copysym_sumrr-r) rrr sym_sum_argstensorrr rrr s ` @@@@r _cat_metarCs QwwCcCCDDD AJ EL'00 F u::V\** * * YJJYY4>YYEHEVEVYY -6c%6N6N,O,O   (C(-czz##F++++ m+00000000 WQZ%&&++--I]<00IcN  1)<<   rc,tj||Sr)rr)rrs r _cat_atenrcs 9Wc " ""rz Concatenates tensors along the specified dimension. The tensors' shapes must have the same rank and same length for other dimensions. z(cat(Tensor[] tensors, int dim) -> Tensorct|tstdt|t j|t tj|}|| kr*d| d|d}t|t||t j |S)Nrz$Attempting to reshape a tensor with z elements to a shape with elements!r ) rrrrr-rrrrurrrr)rrrrs r _reshape_metarvs a $ $EC$q''CCDDD  8< ' 'E  kQWWYYkkZ_kkkoo aue.OPU.V.V W W WWrch||tjSNre)rrrcontiguous_formatrrs r _reshape_atenrs( 99U   ! !0G ! H HHrz` Creates a contiguous tensor with the specified shape containing a copy of the data in a. z+reshape(Tensor a, SymInt[] shape) -> Tensorrcvtj|j|tj|tjSr)r-validate_dimension_indicesrr empty_likeri)rrs r _rev_metars0 $QVT222  AU-B C C CCrzD Reverses the order of elements along the given dimensions. z#rev(Tensor a, int[] dims) -> Tensorpredc>t||tj|fS)N)r)r(ro)r rrts r _where_metar"s, "  ;C $w    rz Selects elements from a and b according to pred. Where pred is true the result contains the element from a, and where pred is false the result contains the element from b. z0where(Tensor pred, Tensor a, Tensor b) -> Tensorct|tstdt|t|tjstdt|tj|r|}ntj |}t|||S)Nrzdtype must be torch.dtype, got )rr) rrrrrr _prims_common%is_non_overlapping_and_dense_or_falserr-rcr)rrrs r_convert_element_type_metar&s a $ $EC$q''CCDDD eU[ ) )NLtE{{LLMMM @@CC>((**:1== a 6 6 66rctj|sd}n |j}n#t$rd}YnwxYwt j||j||}t j5t||cdddS#1swxYwYdS)NF)rr requires_grad) r- is_grad_dtyper( Exceptionrrrrr)rrr(results r_convert_element_type_atenr,s  u % %"  "OMM " " "!MMM "  !(%}F ""vq!!""""""""""""""""""s! 00#BBBz6 Creates a copy of a tensor with the given dtype. z:convert_element_type(Tensor a, ScalarType dtype) -> Tensor)rrrrrrct|tstdt|t|tt jfstdt|t|tstdt|t|tj |S)Nrz(device must be str or torch.device, got znon_blocking must be bool, got )r) rrrrrrrr5rr-canonicalize_devicerr non_blockings r_device_put_metar1s a $ $EC$q''CCDDD fsEL1 2 2XVV VVWWW lD ) )UStL?Q?QSSTTT a 9& A A B B BBrc0|||S)N)r0)tor/s r_device_put_atenr4s 44\4 2 22rz5 Creates a copy of a tensor on the given device. zFdevice_put(Tensor a, Device device, bool non_blocking=False) -> Tensorcdtj|j}t|dSr)r- dtype_to_typerr)r number_types r _item_metar8s*%ag..K kk"oo & &&rz< Converts a tensor with one element to a Python number. ctjj5tjj|i|cdddS#1swxYwYdSr)r _dispatchpythonno_python_dispatcherr rrJs r_item_aten_no_python_dispatcherr=)s   4 4 6 622| $1&11222222222222222222sAA A zitem(Tensor a) -> ScalarcZtj|}t|dSrr-r6rrr7s r_maximum_value_metarA<(%e,,K kk"oo & &&rc|tjkrdS|js|jrtj|jStj|jS)NT)rr5r\is_floating_pointfinforiinfors r_maximum_value_atenrHAsO  t  &U4&{5!!%%{5!!%%rz2 Return the maximum finite value for a dtype. z)maximum_value(ScalarType dtype) -> ScalarcZtj|}t|dSrr?r@s r_minimum_value_metarJ\rBrc|tjkrdS|js|jrtj|jStj|jS)NF)rr5r\rDrErrFrGs r_minimum_value_atenrLasO  u  &U4&{5!!%%{5!!%%rz2 Return the minimum finite value for a dtype. z)minimum_value(ScalarType dtype) -> Scalarct|tstdt|t|tstdt|||kr Tensor(a!))rrrrrrct|tstdt|t j|j||j|j|j |j S)Nr)rrhrr() rrrrrrrrrhrr()rrs r_copy_strided_metarRsh a $ $EC$q''CCDDD   gxxo    rctj|||j|j|j|j}|||S)N)rrrhrr()rrr~rrhrr(r)rrrs r_copy_strided_atenrTsQ   gxxo    CIIaLLL Jrzp Copies the data in a to a new tensor, the new tensor has same shape with a size, but has different stride. z1copy_strided(Tensor a, SymInt[] stride) -> Tensorc,||Srresize_rs r _resize_metarX 99U  rc,||SrrVrs r _resize_atenr[rYrz Gives a tensor with no elements a new shape, returning the modified tensor. The tensor's strides are contiguous and its values are uninitialized. z2resize(Tensor(a!) a, SymInt[] shape) -> Tensor(a!) output_dtypect|tstdt|||j}t j|j|}t|t j |||j S)z\ Meta function for single output reduction operations Stride logic is incorrect zinp must be TensorLike, got Nr) rrrrrr-compute_reduction_output_shaperrrr)inprr] output_shapes r_reduction_metarbs c: & &IGDIIGGHHHy 7 4HHL 1,??z    rctj|jrtj|j}n|j}t |||S)Nr\)r-r9rr:rb)r`r correctionr]s r_var_reduction_metaresG ci((!5ci@@ y 3< @ @ @@rzx Computes the sum of elements in the input tensor over the list of dimensions specified in the dim argument z| Computes the xor sum of elements in the input tensor over the list of dimensions specified in the dim argument z| Computes the product of elements in the input tensor over the list of dimensions specified in the dim argument z Computes the maximum value of elements in the input tensor over the list of dimensions specified in the dim argument z Computes the minimum value of elements in the input tensor over the list of dimensions specified in the dim argument ze Computes the biased variance of x over the list of dimensions specified in the dim argument cNt|dt|tj|S)Creates a reduction prim.zE(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensorr)rrbrrPrrrs r_make_reduction_primri s3 ]]] O     rcNt|dt|tj|S)rgzZ(Tensor inp, int[]? dims, float? correction=1, *, ScalarType? output_dtype=None) -> Tensorr)rrerrPrhs r_make_var_reduction_primrk$ s3 rrr O     rrrhrGr`c td)Nz&xor_sum only implemented with inductorrV)r`rrs r _xor_sum_atenrm6 s F G GGrrc |lt|dkr|St|dD]1}|dkrtd|t j|||}2|St j||S)NrT)reversez$dimension must be non-negative, got rG)rrrrrr)r`rrds r _prod_atenrqF s   t99>>99;; d+++ 2 2A1uu$%OA%O%OPPP*S!5111CC z#U++++rrrc ,tj|f||d|S)N)rrd)rr)r`rrdrs r torch_varrs` s! 9U E E Ef E EErrrrzC Constructs a 1-D tensor t where ``t[i] == start + i * step``. rstepr(ctjtj|dtj|dkdtj||||S)NcdS)Nz'prims.iota only supports integer dtypesrrrrrHz_iota_meta.. s9rrcdS)Nzstep must be nonzerorrrrrHz_iota_meta.. s$:rrrr()rrIr-r6rj)rrrtrrr(s r _iota_metary~ sm L u%%99 L::;;; ;#    rcF|||zz}tj||||||SNrx)rarange)rrrtrrr(rs r _iota_atenr} s8 &4- C < sDfM   rzpiota(SymInt length, *, SymInt start, SymInt step, ScalarType dtype, Device device, bool requires_grad) -> TensorcPtj|}t||||SNrr-rr)rrrr(rs r _empty_metar s+/66G E7% O O OOrc2tj||||Sr{)rrj)rrrr(s r _empty_atenr s ;uE& V V VVrz\ Creates a tensor with uninitialized values and the specified shape, dtype, and device. zWempty(SymInt[] shape, *, ScalarType dtype, Device device, bool requires_grad) -> Tensorc(t||||Srr)rrrrr(s r_empty_strided_metar s E7% O O OOrz1 Creates a tensor with uninitialized values. zqempty_strided(SymInt[] shape, SymInt[] strides, *, ScalarType dtype, Device device, bool requires_grad) -> Tensorphysical_layoutc tjfdD}ttjtkfddgtz}t }t D]e\ tjd cxkoknc fdtj |vd| | <| ft|||S)Nc g|] }| Srr)rlrs rrz(_empty_permuted_meta.. s2U2U2U582U2U2Urc.ddtS)NzlNumber of dimensions in the tensor input does not match the length of the physical layout; i.e. len(size) = z( is not equal to len(physical_layout) = )r)rrsrrHz&_empty_permuted_meta.. s4 M?B M M69/6J6J M Mrrc"ddz dddS)Nz5Dimension out of range (expected to be between 0 and rz , but got z at index zL). NB: negative dims not currently supported; file an issue if you want it.r)rrrsrrHz&_empty_permuted_meta.. sDIaIIII !IIIrcdS)NzDuplicate dim not allowedrrrrrHz&_empty_permuted_meta.. s1Lrr) r-rrrrIr rr`r) rrrrr( p_stridesr seen_dimsrrrs `` @@@r_empty_permuted_metar sY12U2U2U2U_2U2U2UVVI e**C L O#     cCJJGI/**  1 LLLLSLLLL           Qi')L)LMMMq\  a     rz Creates a tensor with uninitialized values according to some physical layout, that is guaranteed to be non-overlapping and dense. zwempty_permuted(SymInt[] shape, int[] physical_layout, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor fill_valuecPtj|}t||||Srr)rrrrr(rs r _full_metar s+/66G E7% O O OOrc4tj|||||Sr{)rfull)rrrrr(s r _full_atenr$ s, : #    rzi Creates a tensor filled with the given fill value, and with the specified shape, dtype, and device. zifull(SymInt[] shape, Scalar fill_value, *, ScalarType dtype, Device device, bool requires_grad) -> Tensorctj|}|dkr|}t ||||S)Nr)rrr)r-rcrrr)rrrrr(rs r_full_like_metarD sI6q99GwwyyA~~((** af E E EErc4tj|||||Sr{)r full_like)rrrrr(s r_full_like_atenrS s, ? #    rz Creates a tensor filled with the given fill value, and the same shape, dtype, and device as the given tensor by default. The dtype and device settings can be overridden by specifying them explicitly. zhfull_like(Tensor a, Scalar fill_value, *, ScalarType dtype, Device device, bool requires_grad) -> TensorscalarcVg}tj|}t|||||Srr)rrrrrs r_scalar_tensor_metart s3 E/66G fE7%PV W W WWrct|tr%|tj|st dt j|||S)Nz-Complex scalar requires complex tensor dtype.r)rcomplexr-r9 TypeErrorrr)rrrs r_scalar_tensor_atenr sR &'""I U3E:: GHHH  vU6 B B BBrzG Wraps a Number into a Tensor with the specified dtype and device. zQscalar_tensor(Scalar s, *, ScalarType? dtype=None, Device? device=None) -> TensorA full_matricescftj|dtj|jdd|j}|dd}|dd\}}t ||}|||r|n|fz}tj|d}t|||j|j} ||fz} tj| } t| | | rtj |jn|j|j} ||r|n||fz} |jj dk}tj| |}t| ||j|j}| dkrF| r2tjr|}| | |fS) Nz linalg.svdF)allow_low_precision_dtypes) row_majorrcudar)r-check_is_matrixcheck_fp_or_complexrrrrrrr\r:rrrr is_availabler)rrA_shapebatchmnkshape_U strides_UUshape_S strides_SSshape_Vhis_cuda strides_VhVhs r _svd_metar s !\*** ag|PUVVVVgG CRCLE 233 (Tensor U, Tensor S, Tensor Vh) generatormeanstdrctjdkfdtjtjptjfdtj|}t |||S)NgcdS)Nz6expected non-negative standard deviation, but got std=r)rsrrHz_normal_meta.. sNNNrcdS)Nz:expected a floating-point or complex dtype, but got dtype=rrGsrrHz_normal_meta.. sTUTTrr)rrIr-is_float_dtyper9rr)rrrrrr(rrs `` r _normal_metar s L s NNNN  L U##Du'=e'D'DTTTT /66G E7% O O OOrctj||||}tj5||||dddn #1swxYwY|S)Nrxr)rrjrnormal_)rrrrrr(rrs r _normal_atenr s  Ev]SSSA 22 $y 111222222222222222 HsAAAz Constructs a tensor filled with values drawn from a normal distribution with the specified mean and standard deviation. Only supports floating-point types. znormal(SymInt[] shape, *, Scalar mean, Scalar std, ScalarType dtype, Device device, bool requires_grad, Generator? generator=None) -> Tensorlowhighc(t||||Srr)rrrrrrrs r _uniform_metar s E6v N N NNrcftj||||}|||||S)N)rrrr)rruniform_)rrrrrrrrs r _uniform_atenr' s: E&fMMMAJJsDIJ... HrzN Constructs a tensor filled with values drawn uniformly from low to high. zuniform(SymInt[] shape, *, Scalar low, Scalar high, ScalarType dtype, Device device, SymInt[] stride, Generator? generator=None) -> TensoronesidedcFtj|j|}tj|t |j}|r|d}||dzdz||<tj|j}tj|}t||||j S)Nrrrr) r-rrvalidate_no_repeating_dimsr/rcorresponding_complex_dtyperrrr)r`rrrlast_dimrrs r _fft_r2c_metarK s  !%*c 2 2C $S)))   E3r7/Q.2h  -ek : :E/66G E7% U U UUrc4d}tj||||SNr)r_fft_r2c)r`rr normalizations r _fft_r2c_atenr^ s M >%mX > >>rz7 Performs a real to complex Fast Fourier Transform z;fft_r2c(Tensor self, *, int[] dim, bool onesided) -> Tensorforwardctj|j|}tj||j}tj|}t |||j|jSr) r-rrrrrrrr)r`rrrrs r _fft_c2c_metarv sd  !%*c 2 2C $S))) KE/66G WEK    rc4d}tj||||Sr)r_fft_c2c)r`rrrs r _fft_c2c_atenr s M >%mW = ==rz> Performs either a Fast Fourier Transform, or its inverse z:fft_c2c(Tensor self, *, int[] dim, bool forward) -> Tensor last_dim_sizec&tj|j|}tj|t |j}|||d<tj|j}tj|}t||||j S)Nrr) r-rrrr/rr:rrrr)r`rrrrrs r _fft_c2r_metar s  !%*c 2 2C $S)))   E"E#b'N  *5; 7 7E/66G E7% U U UUrc4d}tj||||Sr)r_fft_c2r)r`rrrs r _fft_c2r_atenr s M >%m] C CCrz? 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