g^(h<.2ddlZddlmZddlmZddlmZmZddlm Z 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 jjjmddZ7e jjjmdddZ8e jjjmdddZ9e jjjmdddZ:e jjjmdddZ;gdZ< dpdddddde e e"e j$fde e$de e%de e jzde e e j|e?ff dZ@dd d d!d"e?d#e e#eAe#d$ffd%e d&e d'e?d(e ee jd)eCd*eCfd+ZDGd,d-eZEdd.d/eEd0e eAe'd$fd1e,fd2ZFd3ZGd4e?d/eEfd5ZHd4e?d/eEfd6ZId7ZJeHd8e jd9eEj:ZKeHd;e jd9eEj:ZMeHde jd9eEj:ZQeHd?e jd9eEj:ZReHd@e jd9eEj:ZSeHdAe jd9eEj:ZTeHdBe jd9eEj:ZUeHdCe jjd9eEj:ZWeHdDe jjd9eEj:ZXeHdEe jd9eEj:ZZeHdFe jjd9eEj:Z\eHdGe jjd9eEj:Z^eHdHe jjd9eEj:Z`eHdIe jd9eEj:ZadJe j$d1efdKZbeHdLebd9eEj:ZceHdMe jd9eEj:ZddNe'd1e'fdOZeeDdPeee jdQe#jRZhe jdSdNe'dTe jd1e'fdUZkeDdVeke jdWe#jdXYZleHdZe jd9eEj:ZmeHd[e jd9eEj:ZneHd\e jjd9eEj:ZpeHd]e jjd9eEj:ZqeHd^e jjd9eEj:ZreHd_e jd9eEj:ZseHd`e jjd9eEj:ZteHdae jjd9eEj:ZudJe'dbe"d1e'fdcZveDdde#jeve jd9eZweHdfe jd9eEj:ZxeDdgeeGeEjhe#je jd9iZzeHdje jd9eEj:Z{eHdke jd9eEj:Z}eHdle jd9eEj:Z~eHdme jd9eEj:ZeHdne jd9eEj:ZeHdoe jd9eEj:ZeDdpeeGeEjhe#je jd9iZeHdqe jd9eEj:ZeHdre jjd9eEj:ZeHdse j d9eEj:ZeHdte j d9eEj:ZeHdue jd9eEj:ZeHdve jd9eEj:ZeHdwe jd9eEj:ZeHdxe jd9eEj:ZeHdye jd9eEj:ZeHdze jjd9eEj:ZeHd{e jd9eEj:ZeHd|e jd9eEj:ZeHd}e jd9eEj:ZeHd~e j d9eEj:ZeIde j"d9eEjZeIde j$d9eEjZeIde j&d9eEj:ZeIde j(d9eEj:ZeIde j*d9eEj:ZdZeIded9eEj:ZeIde j0d9eEj:ZeIde j2d9eEj:ZeIde j4d9eEj:ZeIde j6d9eEj:ZeIde j8d9eEj:ZeIde j:d9eEj:ZeIde j<d9eEj:ZeIde j>d9eEj:ZeIde jj@d9eEj:ZeIde jjDd9eEj:ZeIde jHd9eEj:ZeIde jJd9eEj:ZdJe e'e"fde e'e"fd1e'fdZeIded9eEj:ZdJe e'e"fde e'e"fd1e'fdZeIded9eEj:ZeIde jTd9eEj:ZeIde jVd9eEj:ZeIde jXd9eEj:ZeIde jZd9eEj:ZeIde j\d9eEj:ZeIde j^d9eEj:ZeIde jbd9eEj:ZeJZeIde jhd9eEj:ZeIde jjjd9eEj:ZdJe'de$de%ded1e'f dZdJede$de%ded1ef dZdZeDdeee#jeZdJe'de$deefdZdZdZeDdeee#jeZdJededed1dfdZde$deded1eAed$ffdZdJe'deded1eAe e$e e%ffdZdJe'deded1e'fdZdJededed1efdZdZeDdeee#jeĬZdJe'd1e'fdZdZeDdee je#jeǬZ dpdJe'ded1e'fdZdJe'deded1e'fdZdJededed1efdZdZeDdeee#je̬ZdJe'ded1e'fd„ZdZeDdee je#jeϬZdJe'ded1e'fdƄZdJeded1efdDŽZdZeDdeee#jeӬZdJe'd1e'fdʄZdJed1efd˄ZdZeDdeee#je׬ZdJe'de jzd1e'fd΄ZdJede jzd1efdτZdZeDdeee#je۬ZdNe'de'de$de%ded1e'f dӄZdZeDdee je#jeެZdJededed1efdքZdJededed1efdׄZdZeDdeee#je⬨Zdee'ded1e'fdۄZde eAed$feefded1efd܄ZdZeDdeee#je笨ZdJe'de$fd߄ZdJede$d1efdZdZeDdeee#je묨ZdJe'ded1e'fdZdZeDdee je#jeZde'dJe'de'd1e'fdZdZeDdee je#je򬨫ZdJe'de jzd1e'fdZdJede jzd1efdZdZeDdeee#jee jjfZ dqdJe'de e?e j|fd1e'fdZ dqdJede e?e j|fd1efdZdZeDdeee#jeZdJe'd1e,fdZdZdZeDdeee#jeZde jzd1e,fdZde jzfdZdZeDdeee#jeZde jzd1e,fdZde jzfdZdZeDdeee#jeZdJe'de'fdZ dJeded1efdZ dZ eDde e e#je dXZ dJe'de$fdZdJede$d1efdZdZeDdeee#jeZdJe'de$fd ZdJede$d1efd Zd ZeDd eee#jeZdd dZdZdZdZdZdZdZdZd4e?fdZd4e?fdZede j@eZ ddde'de ede e jzd1efdZ!ede!eZ"ddde'de ede e jzd1efdZ#ede#eZ$drd Z%ed!e%eZ&ed"e jNeZ'ed#e jPeZ(d$Z)d%eded&ede jzde j|d'eCd1e'fd(Z*d%eded&ede jzde j|d'eCd1e'fd)Z+eDd*e#je*e+e)eZ,de$de jzde j|d'eCd1e'f d+Z-de$de jzde j|d'eCd1ef d,Z.d-Z/eDd.e-e.e#je/Z0de$de%de jzde j|d'eCd1e'f d/Z1d0Z2eDd1e#je1e jfe2eZ3de$d2ede jzde j|d'eCd1e'f d3Z4d4Z5eDd5e#je4e jle5eZ6de$d6e"de jzde j|d'eCd1e'f d7Z7de$d6e"de jzde j|d'eCd1ef d8Z8d9Z9eDd:e7e8e#je9Z:dJe'd6e"de jzde j|d'eCd1e'f d;Z;dJed6e"de jzde j|d'eCd1ef d<Z<d=Z=eDd>e;ed?e"de jzde j|d1e'fd@Z?d?e"de jzde j|d1efdAZ@dBZAeDdCe?e@e#jeAZBdDe'dEeCd1eAe'e'e'ffdFZCdDe'dEeCd1eAeeeffdGZDdHZEeDdIeCeDe#je#je#jfeEZFddJde$dKe eGeHfdLeGde jzde j|d'eCdMe e jd1e'fdNZJddJde$dKe eGeHfdLeGde jzde j|d'eCdMe e jd1efdOZKdPZLeDdQe#jeJeKeLeZMddJde$dReGdSeGde jzde j|dMe e jd1e'fdTZNddJde$dReGdSeGde jzde j|dMe e jd1efdUZOdVZPeDdWe#jeNeOePeZQdNe&dedXeCd1e'fdYZRdNe&dedXeCd1e'fdZZSd[ZTeDd\eReSe#jeTZUdNe&ded]eCd1e'fd^ZVdNe&ded]eCd1e'fd_ZWd`ZXeDdaeVeWe#jeXZYdNe&dedbed1e'fdcZZdNe&dedbed1e'fddZ[deZ\eDdfeZe[e#je\Z]dge'd1eAe'e'ffdhZ^eDdie^e#je#jfe jd9iZ_d1e'fdjZ`eDdke`e#je`dliZadsdmZbeDdnebe#jebdoiZdeey(tN)Sequence)Enum)partialreduce)CallableOptionalUnion) 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 slice_in_dim 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|trZ|s|t|tsJ|s|t|tsJd}d}tt |}t j d}nu|ct|t jsJt|j}t|j}|j}|j }n|J|J|J|J|n t|}|n t|}|n|}|n|}t|trt j |}t j||||S)Ncpurr) isinstancerrrtypetorchrr tuplerstriderstrr) rrrrrinferred_shapeinferred_stridesinferred_dtypeinferred_devices S/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/torch/_prims/__init__.py TensorMetarsN*f%emz%/JKK:gx3PQQ*,,.&tJ'78,,u-  *ell333z//0 !2!2!45#))$++   """   !!!#mNuE")/uW~G#mNE &_FF&#f%   ugU6 JJF)tagsuse_old_custom_ops_apiregister_conj_neg_fallthroughschema return_type.meta impl_atendocrrrc:fdfd}fd} |jdd} |t| d}tjj | |z} |s t | svt j| |ztjjtj| tj| |tj| n| jD cgc]0} | j"| jj r | j"2} } tj$j'd| zt)| | }|j+|t,j.k(s|rj|j0j| tj$j2d |j0j| tj$j2d t5tj6j8j:| }|j<|r|_nt5tj8j@| dx}r|jCDcgc]}t5||jD}}tG|djH|d djKtjjLjKtjjNt)fd |dD_ddl(m)tUfdjVjDr tYdvrtZj| | |fD],}||_.||_/||_0|_ |_ |_1.Scc} wcc}w)z) Creates a primitive operation. c$|i||i|SNr)argskwargsrrs r _prim_implz_make_prim.._prim_impls" df$)&))rc&t|i|Srr)rr_prims r_autograd_implz"_make_prim.._autograd_impl&s-&u-t>v>>rc|jdr|djdk(r|i|Std|Dr|i|S|i|S)Nrrc3rK|]/}t|tjxr|jdk(1yw)rN)rrrr).0xs r z;_make_prim.._backend_select_impl..,s,NAz!U\\*?qvv/??Ns57)getrany)rrrrs r_backend_select_implz(_make_prim.._backend_select_impl)s_ ::h F8$4$9$9V$C(( ( NN N(( (t.v. .r(rN)rzprims::) mutates_argsr ConjugateNegativec3,K|] }|vs| ywrr)rttags_intersections rrz_make_prim..`sR!1@Q;QARs )contains_tensor_typesc3BK|]}|jywr)r)rars rrz_make_prim..dsN$QVV,Ns)zprims.device_put.default)2splitlenr_C parse_schemarprimdefineTagpt2_compliant_tag prim_implimplprim_autograd_implprim_meta_impl arguments alias_infois_writenamelibrary custom_opr register_fakerVIEW_libfallthrough_kernelgetattr_opsopsr'default_tagsaten overloadsrsetintersection_updatediscardcoredata_dependent_outputtorch._subclasses.fake_tensorrr_schemarprim_backend_select_impl__doc__rrr)rrrrrrrrrrr cpp_schemaargrprim_def _prim_packet aten_packetoverload overload_tagsprrrrs `` @@@@r _make_primr s *?/ << Q D CIK F&&tf}5J%9*%E D6M (C(C DtZ(n5D$'"++ ~~)cnn.E.E HH  ==**   |, +  t$ +** *.K MM  tU]]%E%E{ S MM  tU]]%E%Ez R5::>>//6L  E   d; ; ;@K@U@U@W 4t@t&j:t&j<fsJd| xs t| t@t&j<f} c| r tC| } tE| S)z Meta function for elementwise operations that produce outputs in the same dtype as their inputs. Stride logic is currently incorrect. rNallow_cpu_scalar_tensorsT)rrFNYI)#rutilscheck_same_dtypelistcheck_same_devicecheck_same_shape3compute_elementwise_output_logical_to_physical_perm extract_shaperris_cpu_scalar_tensorrrrrrrrr!rboolr is_integer_dtypeis_boolean_dtypeget_default_dtyper"is_complex_dtypecorresponding_real_dtyperSymIntSymFloatintfloatr r) r$r#rargs_l2p_permrr scalar_typerrnumber seen_floatrs r_prim_elementwise_metar@s t9q== D! JE)+,u4 UBTB EADAHH%PH    F FE EK$ c: &--c2   V $s)K$ }0##K0F F  c: &))#.> ZZF V $~ "   AII IE COO OJJE CPP P%%e,0F0Fu0M//1 CTT T%%e,66u=   ##E8F%PP J&5<<89 NAa#uellENN!KL Se SL#Mz!eU^^5L'MJ N v&F f rctjtj|djdt |i|S)Nrcy)NzOnly complex dtype is supportedrrrrz0_complex_only_elementwise_meta..r)r_checkr)r5rr@rrs r_complex_only_elementwise_metarGs9 LL tAw}}-/X "4 26 22rrc `td|dtt|tjd|S)z, Creates an elementwise unary prim. z(Tensor self) -> Tensorr$rrrrrrr@rNEWrr$rs r_make_elementwise_unary_primrNs=  ./ +N KOO    rc `td|dtt|tjd|S)z- Creates an elementwise binary prim. z%(Tensor self, Tensor other) -> TensorrIrJrrKrMs r_make_elementwise_binary_primrPs=  <= +N KOO    rctrNotImplementedErrorrFs r _not_implrTs rr.)rrr$r/r0r1r2r3r4r5r6r;r<r7r8r9r:r=rctj|j dtjtj|j d|S)Ncy)NzJcbrt: Complex inputs not supported. Consider calling torch.pow(a, 1.0/3.0)rrrrrCz_cbrt_aten..rDrgUUUUUU?)rrE is_complexcopysignrxr.rs r _cbrt_atenr[~sA LL LLN\ >>%))AEEGU3Q 77rr>r?inputc|jjs tdtj|}t ||S)Nz6prims.conj_physical is only defined for complex dtypes)r)rrX RuntimeErrorr)"compute_elementwise_output_stridesr)r\rs r_conj_physical_metar`s8 ;; ! !STT66u=G eW --rz$conj_physical(Tensor self) -> Tensorz4Returns the physical conjugation of a complex tensor)rrrrr memory_formatrbcZ|tjk7rBtj|j|j|j |j |Stj|}tj|j||j|j |j S)N)rlayoutrrb)rrdr) rpreserve_formatemptyrrrdrr)r_r)r\rbrs r _clone_metargs---{{ KK++<<<<'   66u=G    kk||||  rzAclone(Tensor self, *, MemoryFormat? memory_format=None) -> TensorzReturns the copy of a tensorT)rrrrrrrArBrCrDrErFrGrHvaluec8t|tjS)NrIr@rr)rrhs r _fill_metarks ! >FF rz)fill(Tensor self, Scalar value) -> Tensor)rrrrrrJz!imag(Tensor(a) self) -> Tensor(a)rI)rrrrrrLrMrNrOrPrQz!real(Tensor(a) self) -> Tensor(a)rUrRrSrVrzrWrXrYrZr[r\r]r^r_r`)rrrr$rarbrcrdc&t|tttjfxs;t|tj xrt j|j}|rtj||dStj||S)Nr_) rounding_mode) rr1r9rr7r r)r2rre true_divide)rb is_integrals r _div_atenrqsnQsELL 9:1ell#G(>(>qww(GyyAW55  A&&rrerfrgrhrirkrlrmrnrorprqrrroc8t|tr3t|tr#t||j|j }nBt|tr2t|tr"t||j|j }t j||SNr)rrrrrrrrsrros r _maximum_atenruWg!Z Z6%: !177188 < Az "z!V'< !177188 < ==A rrsc8t|tr3t|tr#t||j|j }nBt|tr2t|tr"t||j|j }t j||Srs)rrrrrrrrtrts r _minimum_atenrxjrvrrtrurvrwrxryr{r|r~rsizerstorage_offsetct|t|k(sJ|dk\sJtj|tj|t t j |dk(rn@t|tjr&tj|j|||tj||||SNr) rr)validate_stridesvalidate_shaperoperatorrurrr check_in_bounds_for_storage_typed_storagerrryrrzs r_as_strided_metars t9F ## # Q   6"  hllD!Q& Au|| $ ))   fn    AtV^ < Tensor(a!)rrrrrbroadcast_dimensionscddlm}ttsJttsJt|tsJj t |k(sJt j k\sJfd}t||dt|D]M\|jdk(rtjjk(fdOg}d}tt D]|vrU|j|k7r|jdn"|jj||dz}\|dk7r|jd||j k(r|jd|jj|j|zj!|j#S)Nrguard_size_obliviouscZt|tsJ||kDsJ|tksJ|Sr)rrr)accrrs r_greater_than_reducez4_broadcast_in_dim_meta.._greater_than_reduces3!S!!!3ww3u:~~rrc2jdS)Nz must be broadcastable to r)ridxnew_idxrsrrCz(_broadcast_in_dim_meta.. s 1773<.(B5>BRSr)%torch.fx.experimental.symbolic_shapesrrrrndimrr enumeraterrrErangeappendrryrrz) rrrrr new_strides original_idxrrs `` @@r_broadcast_in_dim_metarsK a $$ $ eX && & *H 55 5 66S-. .. . u:    !5r:""67 W#AGGCLA$56 LL g.S KLSZ V & &$AGGL$9U3Z$GH""1%""188:l#;<'!+L#E#J!O4""1%'""1%""188:l#;affh|>T#TUV" <<{A,<,<,> ??rct|}|D]}d||< |}t|D]\}}|dk7s |j|}|j|SNr)r+r unsqueezeexpand)rrrsbroadcast_dimensionvrrs r_broadcast_in_dim_atenr#si U A3$!# $ AA,!Q 7 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|j}tj|tj|t j k\fdy)NrcdddS)Nz Attempting to collapse but end, z, is less than start, !r)rrsrrCz)_validate_collapse_args..Ns23%7MeWTUVr)maxdimr) validate_idxr _check_value)rrrrs `` r_validate_collapse_argsrDsN q!%%'?D tU# tS!  u Vrct|dk(rdn t|}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_shaperRsf J!ODuEJ 537 #$!^ $ 5>ZM )E#'),< < AA14fu  )E#'),< Tensor(a)c|jjs td|j|j|j |j }tjj||j |S)Nz$Expected complex dtype in prims.conj) rrXr^rrrrzrr _set_conjis_conj)routs r _conj_metarsa 77  ABB ,,qww A,<,<,> ?C HHs O, Jrz2 Returns a conjugated view of the original tensor zconj(Tensor(a) a) -> Tensor(a) dimensionsc| ttj||}n)ttj|j|}t t |t |k7rdt |}t|t|j}|D]}|j|dtt |Dcgc] }||vs| }}t|||Scc}w)z Creates a view of a with a.ndim + len(dimensions) dimensions, with new dimensions of length one at the dimensions specified by dimensions. z+Received duplicate dimensions to expand in r) sortedr)canonicalize_dimsrrrrrr+rinsertrr)rrrdimsrrrrs rrrs e--dJ?@e--affjAB 3t9~T";C O;LMoQWW I!a !S^,:0E Ay*> ??s C&C&r outer_lengthct|tsJtj|j|tj ||j ||z}|j ||zdk7r!d|j |d|d}t|g}g}t|jD]}||k(rL|j||f|j|j||z|j|fT|j|j ||j|j||j|||jS)Nrz(Attempting to split dimension of length z, but outer length of z divides it with a remainder!)rrr)rrvalidate_dim_lengthrrrextendrrrrz)rrr inner_lengthrrrrs r_split_dim_metarsD a $$ $ qvvs# l+773<</L  |#)6qwws|nE##/.0M O oIKQVV}0 #:   lL9 :    3, > 3P Q   QWWS\ *   qxxz# / 0 << ;0@0@0B CCrc|j||z}|jd|||fz|j|dzdz}|j|S)Nrr)rr)rrrrrs r_split_dim_atenrsP773<</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)ct|tsJ|D]6}tj|j||j |dk(r6Jg}g}t t|j D]G}||vr|j|j ||j|j|I|j|||jSNr) rrr)rrrrrrrrrz)rrrrrs r _squeeze_metar(s a $$ $! 1663'wws|q   !IKS\", *  &188:c?+ , << ;0@0@0B 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|k7r'd|jdt|d}t|tj|j|sd|d}t|dg|jz}dg|jz}t |D]-\}}|j |||<|j|||</|jt|t||jS)Nz'Attempting to permute a tensor of rank z', but received a permutation of length rz!Received an invalid permutation, r) rrrr)is_valid_permutationrrrrrrz)rrrrrrrs r_transpose_metarJsvv[!!7x?fgjkvgwfxxyzo  % %affk :1+a@oaff I#,Kk*+S #88:c? C+ <<i(% *N>N>P QQrc.tj||Sr)rpermute)rrs r_transpose_atenr\s ==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)ct|j|j|j|jSr)rrrrzrZs r _view_of_metarqs( <<Q-=-=-? @@rc8|j|jSr)rrrZs r _view_of_atenrus 66!''?rz' Creates a view of the tensor. z!view_of(Tensor(a) a) -> Tensor(a)c$|j|Srrrrs r_view_element_type_metar 66%=rc$|j|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)srcctjtjtjt j j k\fdt j tjjfdtjS)Ncddddjdjzdjjz 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_sizeryrzrsrrCz*_as_strided_scatter_meta..so(j@QR`Qab"//122Ou113345##(;;=53E3E3G#G"H Jrc(djdS)NzCexpected src to have a size equal to the slice of self. src size = z, slice size = r)ryrsrrCz*_as_strided_scatter_meta..s UVYV_V_U``optouvr) r)r~r}compute_required_storage_lengthrrEr is_same_shaperclone_preserve_strides)r\rryrrzrs`````@r_as_strided_scatter_metars  6"99$WM LL &  LL CIIt,v  ' ' ..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) -> Tensorclt|||t|j||}|j|Sr)rrr new_emptyrs r_collapse_metars/Auc* %5I ;;y !!rct|j||}|j|}tj5|j |j |ddd|S#1swY|SxYwr)rrrrno_gradview_ascopy_)rrrrrs r_collapse_atenrs[ %5I ++i C   AQ J Js !A((A2zn Collapse a span of neighboring dimensions into one. See collapse_view for the corresponding view operation. z0collapse(Tensor a, int start, int end) -> Tensortensorsc . dk\sJ|dj}g}t|D]\ }t|t|jk(sJtt||jD]?\\k(r|j  t j k( fdAt|djj}t j||<t|d|tj|S)Nrc (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_idxsrrCz_cat_meta..s2NseT -nSE6(Re!l,0rrr) rrrziprrrEr+copysym_sumrr)r) rrr sym_sum_argstensorrrrrrs ` @@@@r _cat_metars !8O8 AJ  EL'0  F5zS....,5c%6N,O  (C(-cz##F+ m+0  WQZ%%&++-I]]<0IcN  11)< rc.tj||Sr)rr)rrs r _cat_atenr s 99Wc ""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) -> Tensorc0t|tsJtj|t t j |}||jk7r"d|jd|d}t|t||tj|S)Nz$Attempting to reshape a tensor with z elements to a shape with elements!r) rrr)r~rrrurrrr)rrrrs r _reshape_metar s a $$ $  8<< 'E  4QWWYK?YZ_Y``jko aue.O.OPU.V WWrc`|j|jtjSNra)rrrcontiguous_formatrrs r _reshape_atenr*s% 99U  ! !0G0G ! HHrz` Creates a contiguous tensor with the specified shape containing a copy of the data in a. z+reshape(Tensor a, SymInt[] shape) -> Tensorrctj|j|tj|tj Sr )r)validate_dimension_indicesrr empty_likere)rrs r _rev_metar;s/ $$QVVT2   AU-B-B CCrzD Reverses the order of elements along the given dimensions. z#rev(Tensor a, int[] dims) -> Tensorpredc>t||tj|fS)N)r$r#rj)rrros r _where_metarQs% "  ;CC $w  rz Selects elements from a and b according to pred. Where pred is true the result contains the element from a, and where pred is false the result contains the element from b. z0where(Tensor pred, Tensor a, Tensor b) -> Tensorct|tsJt|tjsJtjj |r|j }ntj|}t|||S)N)rr) rrrr _prims_commonis_non_overlapping_and_denserr)r_r)rrrs r_convert_element_type_metarosf a $$ $ eU[[ )) ) 77:((*::1= a 66rc tj|sd}n |j}t j ||j ||}t j5t||cdddS#t$rd}YZwxYw#1swYyxYw)NF)rr requires_grad) r) is_grad_dtyper Exceptionrrrrr)rrrresults r_convert_element_type_atenr}s   u %  "OOM   !((%}F "vq!""  "!M " ""s A3 B3 BBB z6 Creates a copy of a tensor with the given dtype. z:convert_element_type(Tensor a, ScalarType dtype) -> Tensor)rrrrrrct|tsJt|ttjfsJt|t sJt |tj|S)N)r) rrrrrr1rr)canonicalize_devicerr non_blockings r_device_put_metar$sU a $$ $ fsELL1 22 2 lD )) ) a 9 9& A BBrc(|j||S)N)r#)tor"s r_device_put_atenr's 44\4 22rz5 Creates a copy of a tensor on the given device. zFdevice_put(Tensor a, Device device, bool non_blocking=False) -> Tensorcbtj|j}t|dSr)r) dtype_to_typerr)r number_types r _item_metar+s%%%agg.K k"o &&rz< Converts a tensor with one element to a Python number. ctjjj5tjj |i|cdddS#1swYyxYwr)r _dispatchpythonno_python_dispatcherr rrFs r_item_aten_no_python_dispatcherr0sD    4 4 62||  $1&1222s AAzitem(Tensor a) -> ScalarcNtj|}t|dSrr)r)rrr*s r_maximum_value_metar4!%%e,K k"o &&rc|tjk(ry|js |jrtj|j Stj |j S)NT)rr1rXis_floating_pointfinforiinfors r_maximum_value_atenr;sL     U44{{5!%%%{{5!%%%rz2 Return the maximum finite value for a dtype. z)maximum_value(ScalarType dtype) -> ScalarcNtj|}t|dSrr2r3s r_minimum_value_metar=r5rc|tjk(ry|js |jrtj|j Stj |j S)NF)rr1rXr7r8minr9r:s r_minimum_value_atenr@sL     U44{{5!%%%{{5!%%%rz2 Return the minimum finite value for a dtype. z)minimum_value(ScalarType dtype) -> Scalarct|tsJt|tsJ|j|jk7r0d|jd|jd}t||S)NzAttempting to copy z elements to a tensor with r)rrrr^)rrors r _copy_to_metarBsj a $$ $ a $$ $ wwyAGGI#AGGI;.I!'')T^_3 Hrc$|j|Sr)rrts r _copy_to_atenrD1s 771:rz; Copies the data in b to a and returns the modified a. z-copy_to(Tensor(a!) a, Tensor b) -> Tensor(a!))rrrrrrct|tsJtj|j||j |j |j|jS)N)rrdrr) rrrrrrrdrr)rrs r_copy_strided_metarFDsK a $$ $    ggxxxxoo  rctj|j||j|j|j |j }|j||S)N)rrrdrr)rrryrrdrrr)rrrs r_copy_strided_atenrHPsL    ggxxxxoo  CIIaL 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$|j|Srresize_r s r _resize_metarLk 99U rc$|j|SrrJr s r _resize_atenrOorMrz Gives a tensor with no elements a new shape, returning the modified tensor. The tensor's strides are contiguous and its values are unitialized. z2resize(Tensor(a!) a, SymInt[] shape) -> Tensor(a!) output_dtypect|tsJ| |j}tj|j |}t |tj|||jS)z\ Meta function for single output reduction operations Stride logic is incorrect r) rrrr)compute_reduction_output_shaperrrr)inprrQ output_shapes r_reduction_metarVsa c: && &yy 77 4HL 11,?zz  rctj|jr tj|j}n |j}t |||S)NrP)r)r5rr6rV)rTr correctionrQs r_var_reduction_metarYs@ cii(55cii@ yy 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 cLt|dt|tj|S)Creates a reduction prim.zE(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensorr)rrVrrLrrrs r_make_reduction_primr]s* \] OO   rcLt|dt|tj|S)r[zZ(Tensor inp, int[]? dims, float? correction=1, *, ScalarType? output_dtype=None) -> Tensorr)rrYrrLr\s r_make_var_reduction_primr_s* qr OO   rrr\r:rTctd)Nz&xor_sum only implemented with inductorrR)rTrrs r _xor_sum_atenras F GGrrc|Qt|dk(r|jSt|dD]!}|dk\sJtj|||}#|Stj|||S)NrT)reverser:)rrrrr)rTrrds r _prod_atenresn   t9>99; d+ 2A6M6**S!51C 2 zz#t511rrc 4tj|f||d|S)N)rrX)rr)r\rrXrs r torch_varrgs 99U E Ef EErrrrzC Constructs a 1-D tensor t where ``t[i] == start + i * step``. rsteprctjtj|dtj|dk7dtj||||S)Ncy)Nz'prims.iota only supports integer dtypesrrrrrCz_iota_meta..$ rDrrcy)Nzstep must be nonzerorrrrrCz_iota_meta..& rDrrrr)rrEr)r2rf)rrrhrrrs r _iota_metarm sS LL u%9 LL:; ;;#  rcH|||zz}tj||||||SNrl)rarange)rrrhrrrrs r _iota_atenrq/ s0 &4- C << sDfM rzpiota(SymInt length, *, SymInt start, SymInt step, ScalarType dtype, Device device, bool requires_grad) -> TensorcJtj|}t||||SNrr)rr)rrrrrs r _empty_metaruI s%//6G E7% OOrc4tj||||Sro)rrf)rrrrs r _empty_atenrwP s ;;uE& 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||||Srs)r)rrrrrs r_empty_strided_metaryc s E7% OOrz1 Creates a tensor with uninitialized values. zqempty_strided(SymInt[] shape, SymInt[] strides, *, ScalarType dtype, Device device, bool requires_grad) -> Tensorphysical_layoutc tjDcgc]}|| c}}t| tjt k( fddgt|z}t }t D]`\ tjdcxkxr knc  fdtj|vd| |<|jbt||||Scc}w)Nc&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)rrzsrrCz&_empty_permuted_meta.. s) ??BeD669/6J5K 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)rlrsrrCz&_empty_permuted_meta.. s-GayPZ#Zs#IIrcy)NzDuplicate dim not allowedrrrrrCz&_empty_permuted_meta.. rDrr) r)rrrrErrr`r) rrzrrrr~ p_stridesr seen_dimsrrs ` ` @@r_empty_permuted_metar| s11_2U582UVI e*C LL O# cCJGI/* 1 LSL    Qi')LMq\  a    13Vs C2z 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_valuecJtj|}t||||Srsrt)rrrrrrs r _full_metar s%//6G E7% OOrc6tj|||||Sro)rfull)rrrrrs r _full_atenr s  :: zv] 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|}|jdk(r|j}t ||||S)Nr)rrr)r)r_rrr)rrrrrrs r_full_like_metar s=66q9GwwyA~((* af EErc6tj|||||Sro)r full_like)rrrrrs r_full_like_atenr s  ?? :U6 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) -> TensorscalarcPg}tj|}t|||||Srsrt)rrrrrs r_scalar_tensor_metar s- E//6G fE7%PV WWrct|tr"|tj|s t dt j |||S)Nz-Complex scalar requires complex tensor dtype.r)rcomplexr)r5 TypeErrorrr)rrrs r_scalar_tensor_atenr sA &'" U33E:GHH   vU6 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_matricesctj|dtj|jdd|j}|dd}|dd\}}t ||}|||r|n|fz}tj |d}t|||j|j} ||fz} tj | } t| | |jrtj|jn |j|j} ||r|n||fz} |jjdk(}tj | |}t| ||j|j}|jdk7r>|jr.tjjr|j!}| | |fS) Nz linalg.svdF)allow_low_precision_dtypes) row_majorrcudar)r)check_is_matrixcheck_fp_or_complexrrr?rrrrXr6rrrr is_availabler)rrA_shapebatchmnkshape_U strides_UUshape_S strides_SSshape_Vhis_cuda strides_VhVhs r _svd_metar3 sw !\* agg|PUVggG CRLE 23 (Tensor U, Tensor S, Tensor Vh) generatormeanstdrc tjdk\fdtjtjxstjfdtj |}t |||S)NgcdS)Nz6expected non-negative standard deviation, but got std=r)rsrrCz_normal_meta..~ sHNrcdS)Nz:expected a floating-point or complex dtype, but got dtype=rr:srrCz_normal_meta.. sLUGTrr)rrEr)is_float_dtyper5rr)rrrrrrrrs `` r _normal_metarr sm LL s N  LL U#Du'='=e'DT //6G E7% OOrctj||||}tj5|j|||ddd|S#1swY|SxYw)Nrlr)rrfrnormal_)rrrrrrrrs r _normal_atenr sQ  Ev]SA 2 $y 12 H2 Hs A  Az 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) -> TensorlowhighcJtj|}t||||Srsrt)rrrrrrrs r _uniform_metar s%//6G E7% OOrc^tj|||}|j||||S)Nrr)rrfuniform_)rrrrrrrs r _uniform_atenr s-  Ev6AJJsDIJ. HrzN Constructs a tensor filled with values drawn uniformly from low to high. zyuniform(SymInt[] shape, *, Scalar low, Scalar high, ScalarType dtype, Device device, Generator? generator=None) -> TensoronesidedcZtj|j|}tj|t |j }|r|d}||dzdz||<tj |j}tj|}t||||jS)Nrrrr) r)rrvalidate_no_repeating_dimsr+rcorresponding_complex_dtyperrrr)r\rrrlast_dimrrs r _fft_r2c_metar s  ! !%**c 2C $$S)  Er7/Q.2h  - -ekk :E//6G E7% UUrc6d}tj||||Sr|)r_fft_r2c)r\rr normalizations r _fft_r2c_atenr s M >>%mX >>rz7 Performs a real to complex Fast Fourier Transform z;fft_r2c(Tensor self, *, int[] dim, bool onesided) -> Tensorforwardctj|j|}tj||j}tj |}t |||j|jSrs) r)rrrrrrrr)r\rrrrs r _fft_c2c_metar s_  ! !%**c 2C $$S) KKE//6G WEKK  rc6d}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||||jS)Nrr) r)rrrr+rr6rrrr)r\rrrrrs r _fft_c2r_metar/ s|  ! !%**c 2C $$S)  E"E#b'N  * *5;; 7E//6G E7% UUrc6d}tj||||Sr|)r_fft_c2r)r\rrrs r _fft_c2r_atenr? s M >>%m] CCrz? Performs a complex to real Inverse Fast Fourier Transform zBfft_c2r(Tensor self, *, int[] dim, SymInt last_dim_size) -> Tensorselfctj|jjdtj|tj|tj fS)Ncy)Nz1torch.frexp() only supports floating-point dtypesrrrrrCz_frexp_meta..Z rDrr:)rrErr7rint32)rs r _frexp_metarW sG LL $$C   D !5#3#3D #L LLrz8frexp(Tensor self) -> (Tensor mantissa, Tensor exponent)ctSrr rrr_make_token_atenrh s  rz_make_token() -> Tensorz7Creates a token used for keeping track of side effects.cyrr)tokenss r_sink_tokens_atenru srz#_sink_tokens(Tensor[] tokens) -> ()zTSink all of the tokens which were previously used for keeping track of side effects.r)Fr)r%N(ercollections.abcrenumr functoolsrrtypingrrr rtorch._prims_commonrr) torch.libraryr r torch._Cr torch._higher_order_ops.effectsrtorch._library.utilsrtorch._prims.debug_primsrtorch._prims.rng_primsrrrrrrrrrrrrrtorch._prims_common.wrappersrr r r!torch.overridesr"r#torch.utils._pytreer$r%r&rLibraryrrr rr__all__rrrrrrr1rrr@rGrNrPrTr.r"r/rr0r1r2r3r4r5r6specialr;r<i0r7i0er8i1r9i1er:r=r[r>r?r`_conj_physicalrLr@rerbrgrrArBerfinvrCrDrErFrGrHrkrIrJrrKrLr!rMrNrOrPrQrTrUrRrSrVrzrWrXrYrZr[r\r]r^r_r`rarbrcrdrqrerfrgrhrirkrlrmrngammaincro gammainccrprqrrrursrxrtrurvrwrxrybitwise_left_shiftr{bitwise_right_shiftr|r}r~rr9rr_as_strided_docrrr_broadcast_in_dim_docrrrrrr_collapse_view_docrr _conj_docrrrr_split_dim_docrr _squeeze_docrrr_transpose_docrrr _view_of_docrrr_view_element_type_docrr_as_strided_scatter_docrrr _collapse_docrrr+r_cat_docrr r _reshape_docrr_rev_docfliprr _where_docrrr_convert_element_type_doc pointwiserr$r'_device_put_docrr+ _item_docr0rr4r;_maximum_value_docrr=r@_minimum_value_docrrBrD _copy_to_docINPLACErrFrH_copy_strided_docrrLrO _resize_docrrVrY_sum_doc _xor_sum_doc _prod_doc _amax_doc _amin_doc_var_docr]r_rrarrerrgrrr _iota_docrmrqrrurw _empty_docrfry_empty_strided_docrr_empty_permuted_docrrr _full_docrrr_full_like_docrrr_scalar_tensor_docrrr_svd_docrr:r Generatorrr _normal_docrrr _uniform_docrrr _fft_r2c_docrrr _fft_c2c_docrrr _fft_c2r_docrrrjrrrNONErrrrr/s0"$%,, ##(<595    ADEFF }}We, MM ! !'63N O  ==00&/R]]**7FJG&&w?p h=A(K"&$(#'15 (Kz5<<789(K I (Kj ! (K EKK (K U5<<,- . (Kd+/#(*/j j{E+s*:$;;<j  j  j j 8EII& 'j!j$(jZ4DH\8\%U>3+>%?@\ \~3   "F    "F # ii 7HH $ jj 7??  % kk 7??  $ jj 7??  % kk 7??  $ jj 7??  % kk 7??  # ii 7?? $ jj 7??  )mm%% 7??    )mm%% 7??    )hh 7??   *mm 7??   )mm 7??   *mm 7??  + 7??   8%,, 86 8$  7?? $ jj 7?? .~... 1 "">  DICXCX -2-@-@.  N kk&"&   ' mm 7??  # ii 7??  ' mm"" 7??  $ mm   7??  % mm!! 7??  # ii 7??  % mm!! 7??  $ mm   7?? . 6 jj  % kk 7??   . &;LL   jj   (nn 7CC   & ll 7??  # ii 7??  % kk 7??  $ jj 7??  % kk 7??   . &;LL   jj  * 7??   % mm!! 7??  # ii 7??  % kk 7??   % kk 7??  $ jj 7??  ' mm 7??  # ii 7?? $ jj 7?? 3mm// 7?? $ jj 7?? # ii 7?? $ jj 7??  % kk 7??  $ ii 7??  & kk 7??  , 7??  + 7??  , 7??  '$  7?? #hh 7CC % jj 7?? % jj 7?? % jj 7?? $ ii 7?? #hh 7CC #hh 7CC  & kk 7??   ' mm$$ 7??   ( mm%% 7??  #hh 7CC #hh 7CC  ^Z '(-2>:3M-N (  7??   ^Z '(-2>:3M-N (  7??  $ ii 7?? #hh 7CC  *oo 7??   $ ii 7??  *oo 7??   +&& 7??  7'' 7??  # ii 7?? % mm   7?? ==&=0:=LO==(= ==(2=DG= =   j     7@7@'7@?G}7@t   c $    v c    =I =c = =c3h =7"7"!7"(+7" 8I  4 457"tD>D#DCDND6#CF " H !    .^   + jj   ;?@@#3@@4D~DCDsD~D8vCsv  N      D^DDnD&  @ mm    R~R4DRR$)v),<))  C      A^AAV   .     ~ekknvekkf F %   / / / /  /  /  /8  y !&& "f"S"s"v"fSsv  =   x/cn6#uU63;/f=>#S#V#   5   X^ XI XIVIII&I   8   DD'7DND   0 jj  +0>   = kk   7. 7 7 7"&"""$" G #(! ))    GLCC$S%,,%67CC?D3 3S%,,./3 3  S   '.'Z'   2 % - 'u{{'z' &u{{& 6 !  'u{{'z' &u{{& 6 !   ^  &V6  : ##"&   . )  & )    >    N9F9   ? ##  04"A          s3 ii$( H H # $H EKK H  H   $( 2 2 # $2 EKK 2  2" F   jj  jj       ;;  LL ,        ;;  LL    }  P P % P5:\\PRVPPW W % W5:\\WRVW W    d   P P P ;; P LL P  PP  ~ !!  % %%% ;; % LL %  %%P  E ""  P  P P ;; P LL P  P P     ;;  LL        v   F F F ;; F LL F  F F     ;;  LL       u    X X ;;X LL X  X C  C ;; C LL C  C  ^ !  "")-" >>> 9:"J<<)-< 666 !"<    T +//;??C ,,0P P w P  P ;; P LL PP(PP@,0   w     ;;  LL   (   "   W  $,0 P  P  P  P ;; P LL P( P P(,0          ;;  LL  (       D V V V V  V&? ? ? ?  ?  H            > > > >  >  G    V  V  V V  V D D D D  D  O   MnM~~/M)NM  E +//2kk  . $ A    0   ^  r