ϑiPWSrSSKJr SSKrSSKrSSKrSSKJr SSKJ r SSK J r J r SSK r\ (aSSKJ r /r"SS\5r"S S \5rSS jrS rSS jrSSjrSrSSjrSSjr\R6"5q0qSrSSjr\R@SS4SSjjr!\RDSS4SSjjr#g)z# Utilities of Auto SParsity (ASP). ) annotationsN)Enum) permutations) TYPE_CHECKINGAnyc$\rSrSrSrSrSrSrSrg)MaskAlgo$z A collection of all mask generating algorithms. There currently are three algorithms, `MASK_1D`, `MASK_2D_GREEDY` and `MASK_2D_BEST` get_mask_1dget_mask_2d_greedyget_mask_2d_bestN) __name__ __module__ __qualname____firstlineno____doc__MASK_1DMASK_2D_GREEDY MASK_2D_BEST__static_attributes__rY/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/incubate/asp/utils.pyr r $s G)N%Lrr c4\rSrSrSrSrSr\SSj5rSr g) CheckMethod/zn A collection of all sparsity checking approaches. There currently are two methods, `CHECK_1D` and `CHECK_2D` check_mask_1d check_mask_2dc[U[5(dS5eU[R:Xa[R$[R $)a} Get sparsity checking method by mask generating algorithm. Args: mask_algo (MaskAlgo): The algorithm of mask generating. Returns: CheckMethod: The corresponded sparsity checking method. Examples: .. code-block:: python >>> import numpy as np >>> from paddle.incubate.asp import CheckMethod, MaskAlgo >>> print(CheckMethod.get_checking_method(MaskAlgo.MASK_1D)) CheckMethod.CHECK_1D >>> print(CheckMethod.get_checking_method(MaskAlgo.MASK_2D_GREEDY)) CheckMethod.CHECK_2D >>> print(CheckMethod.get_checking_method(MaskAlgo.MASK_2D_BEST)) CheckMethod.CHECK_2D z!mask_algo should be MaskAlgo type) isinstancer rrCHECK_1DCHECK_2D) mask_algos rget_checking_methodCheckMethod.get_checking_method8sI*)X.. / . (( ('' ''' 'rrN)r#r returnr) rrrrrr!r" staticmethodr$rrrrrr/s% HH((rrcUR5n[[R"U5SR5UR- $)a Return the density of the input tensor. Args: x (nparray): The input tensor. Returns: float, The density of :attr:`x`. Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> x = np.array([[0, 1, 3, 0], ... [1, 1, 0, 1]]) >>> out = paddle.incubate.asp.calculate_density(x) >>> print(out) 0.625 r)flattenfloatnpnonzerosize)x x_flatteneds rcalculate_densityr0Vs90))+K K(+00 1K4D4D DDrc[UR5S:XdS5eURSU-nURSU-S:an[R"URSURSX- -45nXSS2SURS24'URnUR SU5U4$UR SU5UR4$)a Reshape the input 2D matrix to shape (-1, m). If the second dimension of :attr:`mat` is not a multiples of :attr:`m`, then this function would pad the remainder with 0 before reshaping. .. math:: remainder = mat.shape[1] % m Args: mat (nparray): The input 2D matrix. m (int): The second dimension of reshaped matrix. Returns: tuple: A pair of the reshaped and padded matrix and the shape of padded matrix (non-reshaping). $The input mat should be a 2D matrix!rN)lenshaper+zerosreshape)matm remainder mat_paddedr7s r _reshape_1dr>rs syy>Q F FF  ! q I yy|a!XXsyy|SYYq\Q]-KLM (+1n ! n$%  !!"a(%//{{2q!399,,rc[UR5S::a,[URSURS5U5up4O [X5up4UH.n[R "U5SR X!- :dM. g g)aj Check if every row of the input matrix :attr:`mat` is in 1D `n:m` sparse pattern. This function would pad the second dimension of :attr:`mat` by zero to be a multiples of :attr:`m` if necessary. 1D `n:m` sparse pattern: At least :attr:`n` zeros in every :math:`1 \times m` block. Args: mat (nparray): The input matrix. n (int): n of `n:m` sparse pattern. m (int): m of `n:m` sparse pattern. Returns: bool: True if every row of :attr:`mat` is in 1D n:m sparse pattern, else False. Examples: .. code-block:: python >>> import numpy as np >>> import paddle.incubate.asp as sparsity >>> x = np.array([[0, 1, 3, 0], ... [1, 0, 0, 1]]) >>> y = sparsity.check_mask_1d(x, 2, 4) >>> print(y) True >>> x = np.array([[0, 1, 5, 4], ... [1, 0, 0, 1]]) >>> y = sparsity.check_mask_1d(x, 2, 4) >>> print(y) False >>> # x would be padded to shape (2, 8) >>> x = np.array([[0, 1, 0, 4, 6], ... [1, 0, 0, 1, 7]]) >>> y = sparsity.check_mask_1d(x, 2, 4) >>> print(y) True r4rFT)r6r7r>r9r+r,r-)r:nr; mat_flattenr7sub_mats rrrsvN 399~(Q ! )EqI U(0  ::g q ! & &!% 0 rc[X5up4[R"U5n[R"U5n[URS5HHnX7n[R "[R "U55n SXWU SUR54'MJ URU5nUSS2SURS24USS2SS24'U$)ab Generate 1D `n:m` sparse pattern mask of the input matrix :attr:`mat` in row-directory. This function would pad the second dimension of :attr:`mat` by zero to be a multiples of :attr:`m` before mask generation. 1D `n:m` sparse pattern: At least :attr:`n` zeros in every :math:`1 \times m` block. Args: mat (nparray): The input matrix. n (int): n of `n:m` sparse pattern. m (int): m of `n:m` sparse pattern. Returns: nparray: The 1D `n:m` sparse mask of :attr:`mat`. Examples: .. code-block:: python >>> import numpy as np >>> import paddle.incubate.asp as sparsity >>> mat = np.array([[0, 1, 5, 4], ... [2, 7, 3, 6]]) >>> mask = sparsity.get_mask_1d(mat, 2, 4) >>> print(mask) [[0 0 1 1] [0 1 0 1]] >>> y = sparsity.check_mask_1d(mask, 2, 4) >>> print(y) True rNr4) r>r+ ones_likeranger7argsortabsolutetolistr9) r:r@r;rAr7 mask_flattenmaskirBmin_order_indicess rr r s:%S,K<< ,L << D ;$$Q' (.JJr{{7';<:; )"1-44667) ''.La399Q</0DAJ Krc[UR5S:XdS5eURSU-nURSU-nUS:XaURSOURSX- -US:XaURSOURSX- -4n[R"U5nXSURS2SURS24'[R"U5R SX-5nSn[ SURSU5HanX-n [ SURSU5H<n X-n [R"XXU 2X24R S55n XU'US- nM> Mc XeR4$)a Reshape the input 2D matrix to shape (-1, :math:`m \times m`). In each dimension of :attr:`mat`, if it is not a multiples of :attr:`m`, then this function would pad the remainder with 0 before reshaping. .. math:: remainder_0 = mat.shape[0] % m \\ remainder_1 = mat.shape[1] % m Args: mat (nparray): The input 2D matrix. m (int): The square root of second dimension of reshaped matrix. Returns: tuple: A pair of the reshaped and padded matrix and the shape of padded matrix (non-reshaping). r2r3rr4Nr5)r6r7r+r8emptyr9rEsqueeze) r:r; remainder_0 remainder_1 new_shaper=rAcurr_idx row_startrow_end col_startcol_endrBs r _reshape_2drXs" syy>Q F FF ))A,"K))A,"K$q( ! ciilao.N#q( ! ciilao.NI)$J14~1~~1~-.((9%--b!%8KH1j..q115 -q*"2"21"5q9ImGjjW,i.??@HHLG%, ! MH :6 (( ((rc x[X5up4UHn[R"[R"UR X"555S:n[R "[R "USS9X!- :5S:wdMs[R "[R "USS9X!- :5S:wdM g g)a Check if every :math:`m \times m` block of the input matrix :attr:`mat` is in 2D `n:m` sparse pattern. This function would pad each dimension of :attr:`mat` by zero to be a multiples of :attr:`m` if necessary. 2D `n:m` sparse pattern: At least :math:`n \times n` zeros in every :math:`m \times m` block under the constraint of at least :attr:`n` zeros for each row and column. Args: mat (nparray): The input matrix. n (int): n of `n:m` sparse pattern. m (int): m of `n:m` sparse pattern. Returns: bool: True if every :math:`m \times m` block of the input matrix :attr:`mat` is in 2D `n:m` sparse pattern, else False. Examples: .. code-block:: python >>> import numpy as np >>> import paddle.incubate.asp as sparsity >>> x = np.array([[0, 8, 9, 0], ... [9, 0, 0, 10], ... [5, 0, 0, 6], ... [0, 4, 6, 0]]) >>> y = sparsity.check_mask_2d(x, 2, 4) >>> print(y) True >>> x = np.array([[0, 8, 0, 9], ... [9, 0, 0, 10], ... [0, 5, 0, 6], ... [0, 4, 6, 0]]) >>> y = sparsity.check_mask_2d(x, 2, 4) >>> print(y) True >>> # x would be padded to shape (8, 8) >>> x = np.array([[0, 8, 0, 9], ... [9, 0, 7, 0], ... [0, 5, 0, 6], ... [3, 0, 6, 0], ... [1, 1, 0, 1]]) >>> y = sparsity.check_mask_2d(x, 2, 4) >>> print(y) True rr4axisFT)rXr+rGrOr9sum)r:r@r;r=r7rBsub_masks rrrs^$C+J;;rzz'//!*?@AAE FF266(+qu5 6! ; FF266(+qu5 6! ;  rc[X5up4[R"U5RSX"5n[ [ U55GHn[R "[R"X655n[R"XV5n[R"U5n U V s/sHn [X- 5X-4PM n n [R"5n [R"5n [ [ U 5S- SS5HInXnXSU:Xd XSU:XaMSXSUS4'XS==S- ss'XS==S- ss'MK GM [R"U5nSn[ SUSU5H7nUU-n[ SUSU5HnUU-nUUUUU2UU24'US- nM M9 USURS2SURS24$s sn f)a Greedily generate 2D `n:m` sparse pattern mask of the input matrix :attr:`mat`. This function would pad each dimension of :attr:`mat` by zero to be a multiples of :attr:`m` before mask generation. 2D `n:m` sparse pattern: At least :math:`n \times n` zeros in every :math:`m \times m` block under the constraint of at least :attr:`n` zeros for each row and column. Greedily generating: For each :math:`m \times m` block, selecting values to keep in descent order. Args: mat (nparray): The input matrix. n (int): n of `n:m` sparse pattern. m (int): m of `n:m` sparse pattern. Returns: nparray: The 2D `n:m` sparse mask of :attr:`mat`. Examples: .. code-block:: python >>> import numpy as np >>> import paddle.incubate.asp as sparsity >>> mat = np.array([[9, 8, 3, 7], ... [9, 2, 1, 10], ... [5, 1, 3, 6], ... [2, 4, 6, 1]]) >>> mask = sparsity.get_mask_2d_greedy(mat, 2, 4) >>> print(mask) [[1. 1. 0. 0.] [1. 0. 0. 1.] [0. 0. 1. 1.] [0. 1. 1. 0.]] >>> y = sparsity.check_mask_2d(mask, 2, 4) >>> print(y) True r5r4rg?N)rXr+ zeros_liker9rEr6rGrOrFint collectionsCounterrNr7)r:r@r;r=r7 mask_paddedidxrBr]min_order_1d_indicesr.min_order_2d_indices row_counter col_counterrK matrix_entryrJrSrTrUrVrWs rr r NsJ$C+J-- +33B=KS_%++bjj9:::k./!zz'2)= )=ASZ )=  "))+ !))+ s/014b"=A/2LO,1O,19&, 88E?DH1eAh* a-q%(A.I!mG9DX9ND7"Ig$55 6 MH/+ #))A,#))A,. //3 s*G$c tUSU3nU[;a [U$[R"U5nSUSU&[[ [ UR 5555nX3-n[R"[[ [ X15555nURSS9U:*RSS9U:HR5SRS5n[R"URSX45nX4SSUSS&[R5 U[U'[R5 U$)a Compute all valid 2D `n:m` sparse patterns. 2D `n:m` sparse pattern: At least :math:`n \times n` zeros in every :math:`m \times m` block under the constraint of at least :attr:`n` zeros for each row and column. Args: n (int): n of `n:m` sparse pattern. m (int): m of `n:m` sparse pattern. Returns: dictionary: A dictionary with key: *m_n* (string) and value: all valid 2D `n:m` sparse patterns. _r4NrZrr5)_valid_2d_patternsr+r8listsetrrHasarrayr\r,r9rNr7_valid_2d_patterns_lockacquirerelease)r@r; valid_keypatternsvalidvalid_patternss r_compute_valid_2d_patternsrws #Qqc I&&!),,88A;! L):;<=&::d3|H'@#ABClll"a' , ,! , 4 9 WYq  WR[  5;;q>1"89$1X.q'')(69%'')rc <[X5n[X5upE[R"U5R SX"5n[R "[R "XCR URSX"-5R5SS9nX7SSUSS&[R"U5nSn [SUSU5H2n X-n [SUSU5Hn X-n XiXU 2X24'U S- n M M4 USURS2SURS24$)aH Generate 2D `n:m` sparse pattern mask of the input matrix :attr:`mat` to form sparse matrix with maximum L1 norm .This function would pad each dimension of :attr:`mat` by zero to be a multiples of :attr:`m` before mask generation. 2D `n:m` sparse pattern: At least :math:`n \times n` zeros in every :math:`m \times m` block under the constraint of at least :attr:`n` zeros for each row and column. *Note*: L1 norm of sparse matrix from `Best` API is greater than or equal to the one from `Greedy`. Args: mat (nparray): The input matrix. n (int): n of `n:m` sparse pattern. m (int): m of `n:m` sparse pattern. Returns: nparray: The 1D `n:m` sparse mask of :attr:`mat`. Examples: .. code-block:: python >>> import numpy as np >>> import paddle.incubate.asp as sparsity >>> mat = np.array([[2, 8, 9, 9], ... [9, 1, 3, 9], ... [5, 6, 3, 9], ... [2, 4, 6, 9]]) >>> mask_greedy = sparsity.get_mask_2d_greedy(mat, 2, 4) >>> mask_best = sparsity.get_mask_2d_best(mat, 2, 4) >>> print("L1 norm of `greedy` sparse matrix", np.multiply(mat, mask_greedy).sum()) L1 norm of `greedy` sparse matrix 56.0 >>> print("L1 norm of `best` sparse matrix", np.multiply(mat, mask_best).sum()) L1 norm of `best` sparse matrix 61.0 r5rr4rZN) rwrXr+rDr9argmaxmatmulr7TrNrE)r:r@r;rtrAr7rIpmaxrJrSrTrUrVrWs rr r sD*!/H$S,K<< ,44R>L 99 +//q0A15IKKL  D Aw'LO 88E?DH1eAh* -q%(A.ImG9E9OD7"I$55 6 MH/+ #))A,#))A,. //rr2cURnURnUR[5n[ U[ 5(dS[ U535e[[R[URS5n[U5S:XaURSUS5nO[U5S:XaURUSUS5nO[U5S:XaURUSUS-US5nO[U5S:Xa}UR/SQ5RUSUS-US-US5nU"XbUS 9nURUSUSUSUS/5R/SQ5RU5$[S [U535eU"XbUS 9nURU5RU5$) a Create `n:m` sparse pattern mask of the input tensor via function given by :attr:`func_name`. Currently only support tensor with dimension less than or equal to 4. Args: tensor (nparray): The input tensor. func_name (MaskAlgo, optional): The function name to generate sparse mask. Default is `MaskAlgo.MASK_1D`. All options please refer to `MaskAlgo`. n (int, optional): n of `n:m` sparse pattern. Default is 2. m (int, optional): m of `n:m` sparse pattern. Default is 4. Returns: nparray: The `n:m` sparse mask of :attr:`tensor` generated by :attr:`func_name`. Examples: .. code-block:: python >>> import numpy as np >>> import paddle.incubate.asp as sparsity >>> tensor = np.array([[2, 8, 9, 9], ... [9, 1, 3, 9], ... [5, 6, 3, 9], ... [2, 4, 6, 9]]) >>> mask_1d = sparsity.create_mask(tensor, func_name=sparsity.MaskAlgo.MASK_1D) >>> print(mask_1d) [[0 0 1 1] [1 0 0 1] [0 1 0 1] [0 0 1 1]] >>> mask_2d = sparsity.create_mask(tensor, func_name=sparsity.MaskAlgo.MASK_2D_BEST) >>> print(mask_2d) [[0 1 1 0] [1 0 0 1] [1 1 0 0] [0 0 1 1]] zMfunc_name argument of create_mask is only accepted as type MaskAlgo. But got Nr4rr2r}rr4rr2r@r;gThe dimension of input tensor is not supported in create_mask, Only dimension < 4 is supported but got )r7dtypeastyper*r r typegetattrsysmodulesrvaluer6r9 transpose ValueError) tensor func_namer@r;r7rtfuncrJs r create_maskrsP LLE LLE eA i * *  ?# % * 3;;x()//4 @D 5zQ IIaq " Uq IIeAha ) Uq IIeAhq)58 4 Uq KK % - - !HuQx %( *E!H Aa  LL%(E!HeAhaA B Y| $ VE]  77:5zl D  ! D <<  % %e ,,rcURnUR[5n[U5[:XdS[U535e[ [ R[URS5n[U5S:XaURSUS5nO[U5S:XaURUSUS5nO[U5S:XaURUSUS-US5nO][U5S:Xa7UR/SQ5RUSUS-US-US/5nO[S [U535eU"XRUS 9$) a Check if input tensor is in `n:m` sparse pattern via function given by :attr:`func_name`. Currently only support tensor with dimension less than or equal to 4. Args: tensor (nparray): The input tensor. func_name (CheckMethod, optional): The function name to generate sparse mask. Default is `CheckMethod.CHECK_1D`. All options please refer to `CheckMethod`. n (int, optional): n of `n:m` sparse pattern. Default is 2. m (int, optional): m of `n:m` sparse pattern. Default is 4. Returns: bool: True if tensor pass checking of function given by :attr:`func_name`, else False. Examples: .. code-block:: python >>> import numpy as np >>> import paddle.incubate.asp as sparsity >>> tensor = np.array([[2, 8, 9, 9], ... [9, 1, 3, 9], ... [5, 6, 3, 9], ... [2, 4, 6, 9]]) >>> mask_1d = sparsity.create_mask(tensor, func_name=sparsity.MaskAlgo.MASK_1D) >>> print(mask_1d) [[0 0 1 1] [1 0 0 1] [0 1 0 1] [0 0 1 1]] >>> y = sparsity.check_sparsity(mask_1d, func_name=sparsity.CheckMethod.CHECK_1D) >>> print(y) True >>> y = sparsity.check_sparsity(mask_1d, func_name=sparsity.CheckMethod.CHECK_2D) >>> print(y) True zSfunc_name argument of check_sparsity is only accepted as type CheckMethod. But got Nr4rr2rr}rrr)r7rr*rrrrrrrr6r9rr)rrr@r;r7rrs rcheck_sparsityrHsLP LLE eA  ?k )  ?# % ) 3;;x()//4 @D 5zQ IIaq " Uq IIeAha ) Uq IIeAhq)58 4 Uq KK % - - 1Xa 58 +U1X 6  77:5zl D  ! r)r.npt.NDArray[Any]r&r*)r:rr@r`r;r`r&bool)r:rr@r`r;r`r&r) rrrr r@r`r;r`r&r) rrrrr@r`r;r`r&r)$r __future__rrar threadingenumr itertoolsrtypingrrnumpyr+ numpy.typingnpt__all__r rr0r>rr rXrr Lockrprlrwr rrr!rrrrrsL# "% &t&$($$(NE8-8/d'T()V6rF0 F0!F0&)F0F0R$..*&R50t#**   I- I-I- I- I-  I-\)11   A AA A A  Ar