Vpf:^rdZddlmZddlmZddlmZddlZddlm Z m Z ddl Z ddl Z ddlZddlmZddlmZdd lmZdd lmZmZdd lmZdd lmZdd lmZddlmZddlmZddlm Z ddl!m"Z"ddl#m$Z$ddl%m&Z&m'Z'm(Z(ddl)m Z*e Z+e,e-dfZ.Gdde Z/ej0GddeZ1ej2dZ3dUdZ4dVd"Z5d#Z6e3j7d$Z8ej9e6d%&Z:d'Z;d(Ze3eZTdYdZdBZUd%dCd[dDZVdEZWeTj7dFZXej9eWd%&ZYdGZZdHZ[dIZ\dJZ]ej>eTe[dde\e]ej?eT<ej@eTeYejAeTe"jBr"ej@eTeeZe"j^d*+e"jDr"ej@eTeeZe"j_d,+ej2dKZ`d%dCd\dMZad%dCd]dNZbdOZce`j7dPZdej9ecd%&ZedQZfdRZgdSZhdTZiej>e`egddeheiej?e`<ej@e`eeejAe`e"jBr"ej@e`eefe"jjd*+e"jDr$ej@e`eefe"jkd,+dSdS)^z@COO (coordinate format) matrix object and associated primitives.) annotations)Sequence)partialN)Any NamedTuple)lax)mlir) JAXSparse) _coo_extractCuSparseEfficiencyWarning) tree_util)core)dispatch)ad)_const)hlo) gpu_sparse)promote_dtypes)Array ArrayLike DTypeLike.c6eZdZUded<dZded<dZded<dS)COOInfoShapeshapeFbool rows_sorted cols_sortedN)__name__ __module__ __qualname____annotations__rr[/var/www/html/nettyfy-visnx/env/lib/python3.11/site-packages/jax/experimental/sparse/coo.pyrr.s=,,,++r$rcteZdZUdZded<ded<ded<ded<edZed Zed Zed Z d ed <d ed<dddd7fdZ e de j dd8d Zd9d!Ze dd"d#d:d'Ze dd"d#d;d,Zdd0Zd?d2Ze d3Zd@d6ZxZS)ACOOaExperimental COO matrix implemented in JAX. Note: this class has minimal compatibility with JAX transforms such as grad and autodiff, and offers very little functionality. In general you should prefer :class:`jax.experimental.sparse.BCOO`. Additionally, there are known failures in the case that `nse` is larger than the true number of nonzeros in the represented matrix. This situation is better handled in BCOO. z jax.Arraydatarowcolztuple[int, int]rc|jjSN)r(sizeselfs r%z COO.Ds dinr$c|jjSr,)r(dtyper.s r%r0z COO.Es  r$cDt|j|j|jS)Nrrr)rr _rows_sorted _cols_sortedr.s r%r0z COO.Fs% JD$5#!%!%!%r$c*|j|j|jfSr,)r(r)r*r.s r%r0z COO.IsDHdh ?r$rr5r6F)rrargstuple[Array, Array, Array]rrrcttj|\|_|_|_||_||_t ||dS)Nr) mapjnpasarrayr(r)r*r5r6super__init__)r/r8rrr __class__s r%r@z COO.__init__MsT$' T$:$:!DItx#D#D GGT'''''r$Nnse index_dtypematrrC int | NonerDrreturnc&t|||SNrB) coo_fromdense)clsrErCrDs r% fromdensez COO.fromdenseTs #; ? ? ??r$c|jr|Stj|j|j|jfd\}}}||||f|jdS)zReturn a copy of the COO matrix with sorted indices. The matrix is sorted by row indices and column indices per row. If self._rows_sorted is True, this returns ``self`` without a copy. )num_keysTrr)r5rsortr)r*r(rAr)r/r)r*r(s r% _sort_indiceszCOO._sort_indicesXsf  kXtx49=JJJNCd >>4c*$*&*  , ,,r$int32r2rD Sequence[int]r2DTypeLike | Nonect|}t|dkrtd|tjd|}tjd|x}}||||f|ddS)z>Create an empty COO instance. Public method is sparse.empty().rNz COO must have ndim=2; got shape=rTr4)tuplelen ValueErrorr=empty)rKrr2rDr(r)r*s r%_emptyz COO._emptyes %LLE 5zzQ .r$cSr,r#r`sr%r0zCOO._eye..ar$cSr,r#rcsr%r0zCOO._eye..rer$cSr,r#rgsr%r0zCOO._eye..rhr$Tr4) minr\r=onesarangerrsubcondadd) rKr]r_r`r2rD diag_sizer(idxr)r*rds ` @r%_eyezCOO._eyeqs 1uuaQ--iia!eQ--iA~~ ZZAeZ E EE 8IU + + +D *Yk 2 2 2C #q>>DsAA '#sxQ iiii@@ A AC '#sxQ iiii@@ A AC 3c31v4T R R RRr$c t|Sr,) coo_todenser.s r%todensez COO.todenses t  r$axestuple[int, ...] | Nonec|tdt|j|j|jf|jddd|j|jS)Nzaxes argument to transpose()r4)NotImplementedErrorr'r(r*r)rr6r5)r/rws r% transposez COO.transposes[   > ? ??  48TX.dj26F,$:K M M MMr$1tuple[tuple[Array, Array, Array], dict[str, Any]]c\|j|j|jf|jfSr,)r(r)r*_info_asdictr.s r% tree_flattenzCOO.tree_flattens( Itx *DJ,>,>,@,@ @@r$c t|}|\|_|_|_|hdkrt d||d|_|d|_|d|_ |S)N>rrrz%COO.tree_unflatten: invalid aux_data=rrr) object__new__r(r)r*keysrZrr5r6)rKaux_datachildrenobjs r%tree_unflattenzCOO.tree_unflattens ..  C!)CHcgsw}}AAAAA AhAA B BB!CI .C .C Jr$otherrct|trtdtj|}t |j|\}}t||j|j ffi|j }|j dkrt||S|j dkrt||Std|j)Nz"matmul between two sparse objects.rNzmatmul with object of shape ) isinstancer r{r=r>rr(r'r)r*rrndim coo_matvec coo_matmatr)r/rr( self_promoteds r% __matmul__zCOO.__matmul__s%##F  D E EE K  E E22KD%tx2KKdj6H6H6J6JKKM zQ  u - -- q  u - --  Lu{ L L M MMr$)r8r9rrrrrrrErrCrFrDrrGr')rGr')rrUr2rVrDrrGr') r]r^r_r^r`r^r2rVrDrrGr')rGrr,)rwrxrGr')rGr})rrrGr)rr r!__doc__r"propertyrCr2r_bufsr@ classmethodnprSrLrRr\rsrvr|rrr __classcell__)rAs@r%r'r'4s%  ///......,,--# (// 0 0% (%% & &% (?? @ @%$)e((((((((6:UWU]@@@@@;@ , , , ,EI&- ! ! ! ! !; !EI$+SSSSS;S&MMMMM AAAA; N N N N N N N Nr$r'rurErGrcPt|j|j|j|jS)zConvert a COO-format sparse matrix to a dense matrix. Args: mat : COO matrix Returns: mat_dense: dense version of ``mat`` spinfo) _coo_todenser(r)r*r)rEs r%rurus# ch C C CCr$r(r)r*rc>t||||S)aIConvert CSR-format sparse matrix to a dense matrix. Args: data : array of shape ``(nse,)``. row : array of shape ``(nse,)`` col : array of shape ``(nse,)`` and dtype ``row.dtype`` spinfo : COOInfo object containing matrix metadata Returns: mat : array with specified shape and dtype matching ``data`` r) coo_todense_pbindr(r)r*rs r%rrs!   D#s6  : ::r$ctj|j|jj||f|Sr,)r=zerosrr2atrprs r%_coo_todense_implrs2 6< , , /S 9 = =d C CCr$c@tj|j|jSr,)r ShapedArrayrr2rs r%_coo_todense_abstract_evalrs &, 3 33r$F)multiple_resultscJ|j\}}}|j} tj| tjsQtj| tjs2t jd| dtt|||||S|j r |j } d} nL|j r||}}d} |j ddd} n.t jdtt|||||S||||| | |j} | r*tj| tjd d ggn| gS) Nz@coo_todense cusparse/hipsparse lowering not available for dtype=). Falling back to default implementation.rFTrzzcoo_todense GPU lowering requires matrices with sorted rows or sorted cols. To sort the rows in your matrix, use e.g. mat = mat._sort_indices(). Falling back to the default implementation.)r data_dtyperDrr)avals_inr2r issubdtypefloatingcomplexfloatingwarningswarnr _coo_todense_loweringrrrrr|r dense_int_array) coo_todense_hloctxr(r)r*r data_avalrow_aval_r2rr|results r%_coo_todense_gpu_loweringrso<)Xq /% -r{ + +Er}UBDV/W/WE M ) ) ) ) TY_ $ $ $ $ c3 # #S# --r$cuda)platformrocmrJTrBrCrFrDrc|%t|dk}tjtj|d}t t||||jdS)aECreate a COO-format sparse matrix from a dense matrix. Args: mat : array to be converted to COO. nse : number of specified entries in ``mat``. If not specified, it will be computed from the input matrix. index_dtype : dtype of sparse indices Returns: mat_coo : COO representation of the matrix. Nrzcoo_fromdense nse argumentrBTrP) r^sumrconcrete_or_erroroperatorindexr'_coo_fromdenser)rErCrDnse_ints r%rJrJsj [ saxnn  C  "8>38T U U' ^CW+ F F F9$ 0 0 00r$)rDr^r9ctj|}tjtj|d}t |||S)aCreate COO-format sparse matrix from a dense matrix. Args: mat : array to be converted to COO. nse : number of specified entries in ``mat`` index_dtype : dtype of sparse indices Returns: data : array of shape ``(nse,)`` and dtype ``mat.dtype`` row : array of shape ``(nse,)`` and dtype ``index_dtype`` col : array of shape ``(nse,)`` and dtype ``index_dtype`` znse argument of coo_fromdense()rB)r=r>rrrrcoo_fromdense_pr)rErCrDs r%rr"sE C# x~s4UVV#   cs  D DDr$chtj|}|jdksJtj||\}}|||f}tj||dkk}tj||d}|||||fS)NrN)r-r)r=r>rnonzerormrwhereastype)rErCrDr)r*r( true_nonzeross r%_coo_fromdense_implr3s C# Q [3 ' ' '(#s S#X$*S//SAXNN$4$44- =$ * *$ szz+&& ;(?(? ??r$crtj|f|j}tj|f|x}}|||fSr,)rrr2)rErCrDr(r)r*s r%_coo_fromdense_abstract_evalr?s= 3&#) , ,$v{333# sCr$c |jdj}tj|tjsPtj|tjs1t jd|dtt||||S||||tj|tj tj|\}}}|||gS)NrzBcoo_fromdense cusparse/hipsparse lowering not available for dtype=rrB)nnzrrD index_type) rr2rrrrrrr _coo_fromdense_loweringr dtype_to_ir_type) coo_fromdense_hlorrErCrDr2r(r)r*s r%_coo_fromdense_gpu_loweringrHs ,q/ % -r{ + +Or}UBDV/W/WO Mrrr2rrp)r(r)r*rrr| out_shapedvs r%_coo_matvec_implrsq k!nn!CC!*?fl1oo Q) af}" 9bh ' ' *3 / 3 3B 7 77r$c|j|jcxkr |jksnJ|j|jksJ|j|jksJt|jdksJ|jdksJ|jd|r |jdn |jdksJ|r |jdn |jd}t j|f|jS)NrNrr)rr2rYrrr)r(r)r*rrr|rs r%_coo_matvec_abstract_evalrs sy - - - -CI - - - - - - qw     ci     V\  a     1 9I Q&,q/ J J J J!*?fl1oo Q) 9, 3 33r$c |j\}} } } |j} | tjtjtjtjfvr4tjd| dtt|||||||S|j r|j } nO|j r||}}| }|j ddd} n0tjdtt|||||||S|||||| || j| | j gS)Nz?coo_matvec cusparse/hipsparse lowering not available for dtype=rrrzzcoo_matvec GPU lowering requires matrices with sorted rows or sorted cols. To sort the rows in your matrix, use e.g. mat = mat._sort_indices(). Falling back to the default implementation.)rr|rDrx_dtype)rr2rfloat32float64 complex64 complex128rrr _coo_matvec_loweringrrr)coo_matvec_hlorr(r)r*rrr|rrrx_avalr2rs r%_coo_matvec_gpu_loweringrsJ#&< )Xq& /% 2:rz2<GGG M)rr(r)r*rrr|s r%_coo_matvec_transposers  #C ( ((((  #C ( ((((A) c;tS#r&XaTabbb bb AA c7QsV S#q ((r$rBcL|j\}}}t|||||j|S)aProduct of COO sparse matrix and a dense matrix. Args: mat : COO matrix B : array of shape ``(mat.shape[0] if transpose else mat.shape[1], cols)`` and dtype ``mat.dtype`` transpose : boolean specifying whether to transpose the sparse matrix before computing. Returns: C : array of shape ``(mat.shape[1] if transpose else mat.shape[0], cols)`` representing the matrix vector product. r)r _coo_matmatr)rErr|r(r)r*s r%rrrr$cBt||||||S)aDProduct of COO sparse matrix and a dense matrix. Args: data : array of shape ``(nse,)``. row : array of shape ``(nse,)`` col : array of shape ``(nse,)`` and dtype ``row.dtype`` B : array of shape ``(shape[0] if transpose else shape[1], cols)`` and dtype ``data.dtype`` shape : length-2 tuple representing the matrix shape transpose : boolean specifying whether to transpose the sparse matrix before computing. Returns: C : array of shape ``(shape[1] if transpose else shape[0], cols)`` representing the matrix vector product. r) coo_matmat_pr)r(r)r*rrr|s r%rrrr$c"tj|}|r||}}|r |jdn |jd}|dddf||z}tj||jdf|jj||Srr)r(r)r*rrr|rdBs r%_coo_matmat_implr&s k!nn!CC!*?fl1oo Q) AAAtG}qv" Iqwqz*BH 5 5 8 = A A" E EEr$c|j|jcxkr |jksnJ|j|jksJ|jdksJt|jdksJ|jd|r |jdn |jdksJ|r |jdn |jd}t j||jdf|jS)NrNrr)rr2rrYrr)r(r)r*rrr|rs r%_coo_matmat_abstract_evalr.s sy - - - -CI - - - - - - qw     1 V\  a     9I Q&,q/ J J J J!*?fl1oo Q) 9agaj14: > >>r$c |j\}} } } |j} | tjtjtjtjfvr4tjd| dtt|||||||S|j r|j } nO|j r||}}| }|j ddd} n0tjdtt|||||||S|||||| || j|j| j gS)Nz>coo_matmat cusparse/hipsprse lowering not available for dtype=rrrzzcoo_matmat GPU lowering requires matrices with sorted rows or sorted cols. To sort the rows in your matrix, use e.g. mat = mat._sort_indices(). Falling back to the default implementation.)rr|rrrD)rr2rrrrrrrr _coo_matmat_loweringrrr)coo_matmat_hlorr(r)r*rrr|rrrB_avalr2rs r%_coo_matmat_gpu_loweringr:sM#&< )Xq& /% 2:rz2<GGG Mr)rr(r)r*rrr|s r%_coo_matmat_transposer'\s  #C ( ((((  #C ( ((((A2 c;tS#r&XaTabbb bb AA sGaf  ! !! $ $c3 11r$)rEr'rGr) r(rr)rr*rrrrGrr)rErrCr^rDrrGr9)F)rEr'rrr|rrGr)r(rr)rr*rrrrrr|rrGr)rEr'rrr|rrGr)r(rr)rr*rrrrrr|rrGr)lr __future__rcollections.abcr functoolsrrtypingrrrnumpyrjaxrjax.interpretersr jax.experimental.sparse._baser jax.experimental.sparse.utilr r r jax._srcrrjax._src.interpretersrjax._src.lax.laxrjax._src.lib.mlir.dialectsr jax._src.librjax._src.numpy.utilrjax._src.typingrrr jax.numpyr=DtyperXr^rrregister_pytree_node_classr' Primitiverrurrdef_abstract_evalr lower_funrrrrdefjvpprimitive_transposesregister_lowering simple_implcuda_is_supportedcuda_coo_todenserocm_is_supportedrocm_coo_todenserrrSrJrrrrrrrprimitive_jvpscuda_coo_fromdenserocm_coo_fromdenserrrrrrrr r rcuda_coo_matvecrocm_coo_matvecrrrrrrrr"r%r'cuda_coo_matmatrocm_coo_matmatr#r$r%rMs GF""""""$$$$$$"""""""" !!!!!!333333PPPPPPPP$$$$$$######******######......7777777777  c3hj  %rNrNrNrNrN)rNrN&%rNn}-- DDDD ; ; ; ;DDD 44! 4'///"""8999 . . .  -)4666)? &}&;<<<]### $ g')DEE$ g')DEE!$.11#' 37RUR[000000$FIYEEEEEE" @ @ @"#" )$.$000   ### JJJ&8/"+C((?@@@_%%% $ g):+HII$ g):+HIIt~l++ OOOOO"fkRRRRRR&88844 4&t~u...KKK6PPPPPP ) ) )  ,+T49LMMM(= %|%9:::\""" $ g& (BCC$ g& (BCCt~l++ 8=OOOOOO"fkRRRRRR&FFF?? ?&t~&6OOOCCC8PPPPPP222  ,,dD:OPPP(= %|%9:::\""" $ g& (BCC$ g& (BCCr$