o * iPW@sdZddlmZddlZddlZddlZddlmZddlm Z ddl m Z m Z ddl Ze r4ddlm ZgZGdddeZGd d d eZd.ddZddZd/ddZd0ddZddZd/ddZd0d d!Zeaiad"d#Zd0d$d%Zej d&d'fd1d*d+Z!ej"d&d'fd2d,d-Z#dS)3z# Utilities of Auto SParsity (ASP). ) annotationsN)Enum) permutations) TYPE_CHECKINGAnyc@seZdZdZdZdZdZdS)MaskAlgoz A collection of all mask generating algorithms. There currently are three algorithms, `MASK_1D`, `MASK_2D_GREEDY` and `MASK_2D_BEST` get_mask_1dget_mask_2d_greedyget_mask_2d_bestN)__name__ __module__ __qualname____doc__MASK_1DZMASK_2D_GREEDYZ MASK_2D_BESTrre/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/incubate/asp/utils.pyr$s rc@s&eZdZdZdZdZed ddZd S) CheckMethodzz A collection of all sparsity checking approaches. There currently are two methods, `CHECK_1D` and `CHECK_2D` check_mask_1d check_mask_2d mask_algorreturncCs(t|ts Jd|tjkrtjStjS)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) isinstancerrrCHECK_1DCHECK_2D)rrrrget_checking_method8s  zCheckMethod.get_checking_methodN)rrrr)r r r rrr staticmethodrrrrrr/s rxnpt.NDArray[Any]rfloatcCs"|}tt|dj|jS)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)flattenrnpnonzerosize)rZ x_flattenedrrrcalculate_densityVsr#cCst|jdks Jd|jd|}|jd|dkrDt|jd|jd||f}||ddd|jdf<|j}|d||fS|d||jfS)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_paddedr)rrr _reshape_1drs"r0r,nintr-boolcCsft|jdkrt|d|jd|\}}nt||\}}|D]}t|dj||kr0dSqdS)a 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 r&rFT)r(r)r0r+r r!r")r,r1r- mat_flattenr)sub_matrrrrs'rc Cst||\}}t|}t|}t|jdD]}||}tt|} d||| d|f<q||}|ddd|jdf|ddddf<|S)a 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 rNr&) r0r ones_likeranger)argsortabsolutetolistr+) r,r1r-r4r) mask_flattenmaskir5Zmin_order_indicesrrrrs   *rc Cs8t|jdks Jd|jd|}|jd|}|dkr"|jdn|jd|||dkr3|jdn|jd||f}t|}||d|jdd|jdf<t|d||}d}td|jd|D].}||} td|jd|D]} | |} t||| | | fd} | ||<|d7}qwqh||jfS)a3 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). r$r%rr&Nr')r(r)r r*emptyr+r7squeeze) r,r-Z remainder_0Z remainder_1Z new_shaper/r4curr_idx row_startrow_end col_startcol_endr5rrr _reshape_2ds*""    rEc Cs~t||\}}|D]3}tt|||dk}ttj|dd||kdkr>> 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 rr&ZaxisFT)rEr r9r?r+sum)r,r1r-r/r)r5sub_maskrrrrs/  rcst|\}}t|d}tt|D]i}tt||}t||}t|} fdd| D} t } t } tt| dddD]5} | | }| |d|ks`| |d|kraqJd||d|df<| |dd7<| |dd7<qJqt |}d}td|dD]%}|}td|dD]}|}|||||||f<|d7}qq|d|j dd|j dfS)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 r'cs g|] }t||fqSr)r2).0rr-rr {sz&get_mask_2d_greedy..r&rg?N) rEr Z zeros_liker+r7r(r9r?r8 collectionsCounterr>r))r,r1r-r/r)Z mask_paddedidxr5rHZmin_order_1d_indicesZmin_order_2d_indicesZ row_counterZ col_counterr=Z matrix_entryr<r@rArBrCrDrrJrr Ns<%     r cCs|d|}|tvrt|St|}d|d|<ttt|}||}tttt||}|jdd|kjdd|k d d}t |j d||f}||dd|dd<t |t|<t |S)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. _r&NrFrr')_valid_2d_patternsr r*listsetrr:ZasarrayrGr!r+r>r)_valid_2d_patterns_lockacquirerelease)r1r-Z valid_keypatternsZvalidZvalid_patternsrrr_compute_valid_2d_patternss&  rWc Cst||}t||\}}t|d||}tjt|||jd||jdd}||dd|dd<t |}d} t d|d|D]%} | |} t d|d|D]} | |} || || | | | f<| d7} qTqF|d|jdd|jdfS)a 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 r'rr&rFN) rWrEr r6r+Zargmaxmatmulr)Tr>r7)r,r1r-rVr4r)r;Zpmaxr<r@rArBrCrDrrrr s$ "   r r$tensor func_namec CsZ|j}|j}|t}t|tsJdt|ttj t |j d}t |dkr2| d|d}nlt |dkrC| |d|d}n[t |dkrX| |d|d|d}nFt |dkr|gd |d|d|d|d}||||d }| |d|d|d|dggd|Std t |||||d }| ||S) aI Create `n:m` sparse pattern mask of the input tensor via function given by :attr:`func_name`. Currently only support tensor with dimension less than or equal to 4. Args: tensor (nparray): The input tensor. func_name (MaskAlgo, optional): The function name to generate sparse mask. Default is `MaskAlgo.MASK_1D`. All options please refer to `MaskAlgo`. n (int, optional): n of `n:m` sparse pattern. Default is 2. m (int, optional): m of `n:m` sparse pattern. Default is 4. Returns: nparray: The `n:m` sparse mask of :attr:`tensor` generated by :attr:`func_name`. Examples: .. code-block:: python >>> import numpy as np >>> import paddle.incubate.asp as sparsity >>> tensor = np.array([[2, 8, 9, 9], ... [9, 1, 3, 9], ... [5, 6, 3, 9], ... [2, 4, 6, 9]]) >>> mask_1d = sparsity.create_mask(tensor, func_name=sparsity.MaskAlgo.MASK_1D) >>> print(mask_1d) [[0 0 1 1] [1 0 0 1] [0 1 0 1] [0 0 1 1]] >>> mask_2d = sparsity.create_mask(tensor, func_name=sparsity.MaskAlgo.MASK_2D_BEST) >>> print(mask_2d) [[0 1 1 0] [1 0 0 1] [1 1 0 0] [0 0 1 1]] zMfunc_name argument of create_mask is only accepted as type MaskAlgo. But got Nr&rr$rZrr&r]r$r1r-gThe dimension of input tensor is not supported in create_mask, Only dimension < 4 is supported but got )r)dtypeastyperrrtypegetattrsysmodulesr valuer(r+ transpose ValueError) r[r\r1r-r)ratfuncr<rrr create_masks>(        rlcCs |j}|t}t|tksJdt|ttjt|j d}t |dkr0| d|d}nNt |dkrA| |d|d}n=t |dkrV| |d|d|d}n(t |dkru| gd |d|d|d|dg}n t d t |||||d S) a Check if input tensor is in `n:m` sparse pattern via function given by :attr:`func_name`. Currently only support tensor with dimension less than or equal to 4. Args: tensor (nparray): The input tensor. func_name (CheckMethod, optional): The function name to generate sparse mask. Default is `CheckMethod.CHECK_1D`. All options please refer to `CheckMethod`. n (int, optional): n of `n:m` sparse pattern. Default is 2. m (int, optional): m of `n:m` sparse pattern. Default is 4. Returns: bool: True if tensor pass checking of function given by :attr:`func_name`, else False. Examples: .. code-block:: python >>> import numpy as np >>> import paddle.incubate.asp as sparsity >>> tensor = np.array([[2, 8, 9, 9], ... [9, 1, 3, 9], ... [5, 6, 3, 9], ... [2, 4, 6, 9]]) >>> mask_1d = sparsity.create_mask(tensor, func_name=sparsity.MaskAlgo.MASK_1D) >>> print(mask_1d) [[0 0 1 1] [1 0 0 1] [0 1 0 1] [0 0 1 1]] >>> y = sparsity.check_sparsity(mask_1d, func_name=sparsity.CheckMethod.CHECK_1D) >>> print(y) True >>> y = sparsity.check_sparsity(mask_1d, func_name=sparsity.CheckMethod.CHECK_2D) >>> print(y) True zSfunc_name argument of check_sparsity is only accepted as type CheckMethod. But got Nr&rr$r]rZr^r`r_)r)rbrrcrrdrerfr rgr(r+rhri)r[r\r1r-r)rjrkrrrcheck_sparsityHs0(     rm)rrrr)r,rr1r2r-r2rr3)r,rr1r2r-r2rr) r[rr\rr1r2r-r2rr) r[rr\rr1r2r-r2rr3)$r __future__rrLre threadingenumr itertoolsrtypingrrnumpyr Z numpy.typingZnpt__all__rrr#r0rrrErr LockrSrPrWr rrlrrmrrrrsB     '  2* + 9I ): N