uki\@dZddlmZddlmZddlmZddlZddlm Z m Z ddl Z ddl Z ddlZddlmZddlmZdd lmZdd lmZdd lmZmZdd 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/ej`GddeZ1ejddZ3dGdZ4dHdZ5dZ6e3jndZ8ejre6dZ:d Z;d!ZZXdd6dNd?ZYdd6dOd@ZZdAZ[eXjndBZ\ejre[dZ]dCZ^dDZ_dEZ`dFZaej|eXe_dde`eaej~eX<ejeXe]ejeXejeXee^d#$d%&ejeXee^d'$d(&y)Pz@COO (coordinate format) matrix object and associated primitives.) annotations)Sequence)partialN)Any NamedTuple)lax)mlir) _lowerings) JAXSparse) _coo_extractCuSparseEfficiencyWarning) tree_util)core)dispatch)ad)_const)hlo)promote_dtypes)Array ArrayLike DTypeLike.c4eZdZUded<dZded<dZded<y)COOInfoShapeshapeFbool rows_sorted cols_sortedN)__name__ __module__ __qualname____annotations__rrV/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/jax/experimental/sparse/coo.pyrr.s ,+t+tr$rc`eZdZUdZded<ded<ded<ded<edZed Zed Zed Z d ed <d ed<ddd dfdZ e de jdd dZd!dZe ddd d"dZe ddd d#dZd$dZd%d&dZd'dZe dZd(dZxZS))COOaExperimental 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.Dsdiinnr$c.|jjSr,)r(dtyper.s r%r0z COO.Es r$cZt|j|j|jS)Nrrr)rr _rows_sorted _cols_sortedr.s r%r0z COO.Fs# JJD$5$5##!%r$cH|j|j|jfSr,)r(r)r*r.s r%r0z COO.IsDHHdhh ?r$rr5r6F)rrcttj|\|_|_|_||_||_t|%||y)Nr) mapjnpasarrayr(r)r*r5r6super__init__)r/argsrrr __class__s r%r>z COO.__init__MsB$' T$:!DItx#D#D GT'r$Nnse index_dtypect|||SNrA) coo_fromdense)clsmatrBrCs r% fromdensez COO.fromdenseTs #; ??r$c|jr|Stj|j|j|j fd\}}}|j |||f|jdS)zReturn a copy of the COO matrix with sorted indices. The matrix is sorted by row indices and column indices per row. If self._rows_sorted is True, this returns ``self`` without a copy. )num_keysTrr)r5rsortr)r*r(r@r)r/r)r*r(s r% _sort_indiceszCOO._sort_indicesXsc  kXXtxx499=JNCd >>4c*$**&*  ,,r$int32r2rCct|}t|dk7rtd|tjd|}tjd|x}}||||f|ddS)z>Create an empty COO instance. Public method is sparse.empty().rKz COO must have ndim=2; got shape=rTr4)tuplelen ValueErrorr;empty)rGrr2rCr(r)r*s r%_emptyz COO._emptyesm %LE 5zQ :E8< == 99Q D ![))C# c3u$ !!r$c  dkDrt||z }nt|z|}|dkr|j||f||Stj||}tj||}t |d t |t j|t jdk\ fdfd} t j|t jdk fdfd} ||| | f||fdd S) NrrQ)r2cSr,r#zerosr%r0zCOO._eye..r$cSr,r#ksr%r0zCOO._eye..ar$cSr,r#rZsr%r0zCOO._eye..r\r$cSr,r#r^sr%r0zCOO._eye..r`r$Tr4) minrWr;onesarangerrsubcondadd) rGNMr_r2rC diag_sizer(idxr)r*r[s ` @r%_eyezCOO._eyeqs 1uaQ-ia!eQ-iA~ ZZAeZ EE 88IU +D **Yk 2C #q>DsAA ''#sxxQ i@ AC ''#sxxQ i@ AC c31v4T RRr$ct|Sr,) coo_todenser.s r%todensez COO.todenses t r$c| tdt|j|j|jf|j ddd|j |jS)Nzaxes argument to transpose()r4)NotImplementedErrorr'r(r*r)rr6r5)r/axess r% transposez COO.transposesX   > ??  488TXX.djj26F,,$:K:K MMr$c||j|j|jf|jj fSr,)r(r)r*_info_asdictr.s r% tree_flattenzCOO.tree_flattens- IItxx *DJJ,>,>,@ @@r$ctj|}|\|_|_|_|j hdk7rt d||d|_|d|_|d|_ |S)N>rrrz%COO.tree_unflatten: invalid aux_data=rrr) object__new__r(r)r*keysrUrr5r6)rGaux_datachildrenobjs r%tree_unflattenzCOO.tree_unflattenso .. C!)CHcgsw}}AA ?h[A BB!CI .C .C Jr$ct|tr tdtj|}t |j |\}}t||j|jffi|jj}|jdk(r t||S|jdk(r t||Std|j)Nz"matmul between two sparse objects.rKzmatmul with object of shape ) isinstancer rsr;r<rr(r'r)r*rwrxndim coo_matvec coo_matmatr)r/otherr( self_promoteds r% __matmul__zCOO.__matmul__s%#  D EE KK E E2KD%txx2Kdjj6H6H6JKM zzQ  u -- q  u -- ">u{{m L MMr$)r?tuple[Array, Array, Array]rrrrrrrHrrBz int | NonerCrreturnr')rr')rz Sequence[int]r2DTypeLike | NonerCrrr') riintrjrr_rr2rrCrrr')rrr,)rtztuple[int, ...] | Nonerr')rz1tuple[tuple[Array, Array, Array], dict[str, Any]])rrrr)rr r!__doc__r"propertyrBr2rw_bufsr> classmethodnprPrIrOrWrmrpruryrr __classcell__)r@s@r%r'r'4s#  /.. ,-# / 0% % &% ? @%$)e( (7;(6:UWU]U]@@ ,EI&- !# !25 ! !EI$+S!S03SS&M A Nr$r'rocpt|j|j|j|jS)zConvert a COO-format sparse matrix to a dense matrix. Args: mat : COO matrix Returns: mat_dense: dense version of ``mat`` spinfo) _coo_todenser(r)r*rw)rHs r%roros% chh CCr$c4tj||||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$ctj|j|jj||fj |Sr,)r;zerosrr2atrhrs r%_coo_todense_implrs5 6<< , / /S 9 = =d CCr$cVtj|j|jSr,)r ShapedArrayrr2rs r%_coo_todense_abstract_evalrs  &,, 33r$F)multiple_resultsc>|j\}}}|j} tj| tjsRtj| tj s.t jd| dtt|||||S|jr|j} d} nO|jr||}}d} |jddd} n*t jdtt|||||S|} | rR|j\} tj| jddd| j} | j!| g } t#j$| |||| | }| r,t'j(|t+j,d d ggS|gS) Nz@coo_todense cusparse/hipsparse lowering not available for dtype=). Falling back to default implementation.rFTrrzcoo_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.)rr2) avals_out)rtarget_name_prefixrr)avals_inr2r issubdtypefloatingcomplexfloatingwarningswarnr _coo_todense_loweringrrrrrrreplacer coo_todense_gpu_loweringrrur dense_int_array)ctxr(r)r*rr data_avalrow_aval_r2rrusub_ctxout_avalresults r%_coo_todense_gpu_loweringrs|<<)Xq //% --r{{ +r}}UBDVDV/W MMUuhW<<=VX dCV DD  LLEI CCI LL2 E MM89RT !dCV DD ' IHhnnTrT&:(..QHoo o3G  . . tS#U?Q S&  }}VT111a&9:;""r$c t||||S)Nr)r)data_dotr(r)r*rs r%_coo_todense_jvprs hS 88r$ctj|sJtj|stj|r td|j|jk(sJ|jj |jj k(sJ|j |jj k(sJt |||||fS)N/Cannot transpose with respect to sparse indices)ris_undefined_primalrUravalr2r )ctr(r)r*rs r%_coo_todense_transposers   %% %C B$:$:3$? F GG V\\ !! ! 388>> )) ) TYY__ $$ $ c3 #S# --r$cu)rcuda)platformhiprocmrFTrAc|t|dk7j}tjtj |d}t t||||jdS)aECreate a COO-format sparse matrix from a dense matrix. Args: mat : array to be converted to COO. nse : number of specified entries in ``mat``. If not specified, it will be computed from the input matrix. index_dtype : dtype of sparse indices Returns: mat_coo : COO representation of the matrix. rzcoo_fromdense nse argumentrATrM) rsumrconcrete_or_erroroperatorindexr'_coo_fromdenser)rHrBrCnse_ints r%rFrFsX [ saxnn C  " "8>>38T U' ^CW+ F99$ 00r$)rCctj|}tjtj |d}t j|||S)aCreate COO-format sparse matrix from a dense matrix. Args: mat : array to be converted to COO. nse : number of specified entries in ``mat`` index_dtype : dtype of sparse indices Returns: data : array of shape ``(nse,)`` and dtype ``mat.dtype`` row : array of shape ``(nse,)`` and dtype ``index_dtype`` col : array of shape ``(nse,)`` and dtype ``index_dtype`` znse argument of coo_fromdense()rA)r;r<rrrrcoo_fromdense_pr)rHrBrCs r%rr%sC C# x~~s4UV#   cs  DDr$cVtj|}|jdk(sJtj||\}}|||f}tj||dk7j k}tj ||d}||j||j|fS)NrK)r-r)r;r<rnonzerorerwhereastype)rHrBrCr)r*r( true_nonzeross r%_coo_fromdense_implr6s C# Q [[3 '(#s S#X$**S/SAXNN$44- =$ *$ szz+& ;(? ??r$ctj|f|j}tj|f|x}}|||fSr,)rrr2)rHrBrCr(r)r*s r%_coo_fromdense_abstract_evalrBs?  3&#)) ,$v{33# sCr$cR|jdj}tj|tjsQtj|tj s-t jd|dtt||||Stj|||||S)NrzBcoo_fromdense cusparse/hipsparse lowering not available for dtype=rrA)nnzrCr) rr2rrrrrrr _coo_fromdense_loweringr coo_fromdense_gpu_lowering)rrHrBrCrr2s r%_coo_fromdense_gpu_loweringrKs ,,q/  % --r{{ +r}}UBDVDV/W MMWQVPXY<<=VX "3+ NN  . . 3C[+ --r$cP|\}|\}t|||}|\}}} t|tjur tjj |} n t || |} | tjj |tjj | f} || fSrE)rtyperZerofrom_primal_valuer ) primalstangentsrBrCrjMdot primals_outr(r)r*r tangents_outs r%_coo_fromdense_jvprUs"! %$qc{C+.$S $Z277ww((.HCd+HBGG55c:BGG>r$c ,|j\}} } } |j} | tjtjtj tj fvr0tjd| dtt|||||||S|jr |j} nR|jr||}}| }|jddd} n,tjdtt|||||||Stj||||||| |S)Nz>coo_matmat cusparse/hipsprse lowering not available for dtype=rrrrzcoo_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.r)rr2rrrrrrrr _coo_matmat_loweringrrrr _coo_spmm_gpu_lowering)rr(r)r*rrrurrrrB_avalr2rs r%_coo_matmat_gpu_loweringr4s#&<< )Xq& //% 2::rzz2<<GG MMSUHU<<=VX T3QvQZ [[  LLE CC I LL2 E MM89RT T3Qv*3 55  * * 4c1 + --r$c$t||||||Srr)rr(r)r*rrrus r%_coo_matmat_jvp_leftr"Or r$c$t||||||Srr!)B_dotr(r)r*rrrus r%_coo_matmat_jvp_rightr%Rrr$c tj|rJtj|rJtj|r|||t|||||| fStj|}||||zj d|||fS)Nrr)rrrr;r<r)rr(r)r*rrrus r%_coo_matmat_transposer'Us  # #C (( (  # #C (( (A c;tS#r&XaTab bb AA sGaf  ! !! $c3 11r$)rHr'rr) r(rr)rr*rrrrrr)rHrrBrrCrrr)F)rHr'rrrurrr)r(rr)rr*rrrrrrurrr)rHr'rrrurrr)r(rr)rr*rrrrrrurrr)br __future__rcollections.abcr functoolsrrtypingrrrnumpyrjaxrjax.interpretersr jax.experimental.sparser jax.experimental.sparse._baser jax.experimental.sparse.utilr r rjax._srcrrjax._src.interpretersrjax._src.lax.laxrjax._src.lib.mlir.dialectsrjax._src.numpy.utilrjax._src.typingrrr jax.numpyr;DtyperSrrrregister_pytree_node_classr' Primitiverrorrdef_abstract_evalr lower_funrrrrdefjvpprimitive_transposesregister_lowering simple_implrrrPrFrrrrrrrprimitive_jvpsrrrrrrrr r rrrrrrrrr"r%r'r#r$r%rCsG"$" !.3P$#*.77  c3hj  %%rN)rN&rNn}- D ;D  4!4'/"B9 .  -)46)? &}&;<]# %$?  %%@ !$..1#' 37RUR[R[0$FIYYE" @""# )$..$0-# J&8/"+C((?@_% 'DA  'EB t~~l+ O"fkR&84 4&t~~u.-6PP )  ,+T49LM(= %|%9:\" $>  $? t~~l+ 8=O"fkR&F? ?&t~~&6O-6PP2  ,,dD:OP(= %|%9:\" $>  $? r$