ёi&SSKJr SSKJr SSKrSSKrSSKJr SSKJ r J r SSK J r J r Jr SSKJr SSKJr SS KJr \(aSS KJr SS KJr SS KJrJr /r\ R8R:R<\ R8R:R>\ R8R:R@\ R8R:RB\ R8R:RD\ R8R:RF/r$S(S)Sjjr%S(S)Sjjr&S(S)Sjjr'S(S*Sjjr(S+S,Sjjr)S(S)Sjjr*S(S)Sjjr+S(S)Sjjr,S(S)Sjjr-S(S)Sjjr.S(S)Sjjr/S(S)Sjjr0S(S)Sjjr1S-S.Sjjr2S(S/Sjjr3S(S)Sjjr4S(S)Sjjr5S(S)Sjjr6S(S)S jjr7S(S)S!jjr8S(S)S"jjr9\"S S#/5S(S0S$jj5r:S(S)S%jjr;S(S1S&jjr<S2S3S'jjr=g)4) annotations) TYPE_CHECKINGN)_C_ops) check_typecheck_variable_and_dtype)convert_np_dtype_to_dtype_corein_dynamic_or_pir_mode)Variable) LayerHelper)param_one_alias)Sequence)Tensor) DTypeLike ShapeLikexcZ[5(dS5e[R"U5$)aZ Calculate elementwise sin of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = sin(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64, complex64, complex128. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.sin(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-0.90929741, 0.84147102]) >> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.tan(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[2.18503976, 1.55740774]) r)r r sparse_tanrs rtanrXrrcZ[5(dS5e[R"U5$)a[ Calculate elementwise asin of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = asin(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64, complex64, complex128. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.asin(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[nan , 1.57079625]) r)r r sparse_asinrs rasinr!{0: " # #F #   a  rcZ[5(dS5e[R"X5$)a0 Changes the perm order of ``x`` without changing its data, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = transpose(x, perm) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. perm (list|tuple): Permute the input according to the data of perm. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A transposed Sparse Tensor with the same data type as ``x``. Examples: .. code-block:: python >>> # doctest: +SKIP('indices overflow') >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> dense_x = paddle.to_tensor([[-2., 0.], [1., 2.]]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.transpose(sparse_x, [1, 0]) >>> out Tensor(shape=[2, 2], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True, indices=[[0, 0]], values=[[-2., 0.], [ 1., 2.]]) r)r rsparse_transpose)rpermrs r transposer&s1H " # #F #  " "1 ++rc SnUb Sn[U5n[5(a[R"XX#5$Uc/nOU/nXUS.nU(aUR UR US.5 [ US/SQS5 [US [[[[S5[4S5 Sn[U5nU(aURUS 9n OURUR S 9n URUSU0S U 0US 9 U $) ae Computes the sum of sparse tensor elements over the given dimension, requiring x to be a SparseCooTensor or SparseCsrTensor. Args: x (Tensor): An N-D Tensor, the data type is bool, float16, float32, float64, int32 or int64. axis (int|list|tuple|None, optional): The dimensions along which the sum is performed. If :attr:`None`, sum all elements of :attr:`x` and return a Tensor with a single element, otherwise must be in the range :math:`[-rank(x), rank(x))`. If :math:`axis[i] < 0`, the dimension to reduce is :math:`rank + axis[i]`. dtype (str|None, optional): The dtype of output Tensor. The default value is None, the dtype of output is the same as input Tensor `x`. keepdim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result Tensor will have one fewer dimension than the :attr:`x` unless :attr:`keepdim` is true, default value is False. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Results of summation operation on the specified axis of input Tensor `x`. if `x.dtype='bool'` or `x.dtype='int32'`, it's data type is `'int64'`, otherwise it's data type is the same as `x`. Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([[-2., 0.], [1., 2.]]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out1 = paddle.sparse.sum(sparse_x) >>> out1 Tensor(shape=[1], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[0], values=1.) >>> out2 = paddle.sparse.sum(sparse_x, axis=0) >>> out2 Tensor(shape=[1, 2], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0]], values=[[-1., 2.]]) >>> out3 = paddle.sparse.sum(sparse_x, axis=-1) >>> out3 Tensor(shape=[2], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 1]], values=[-2., 3.]) >>> out4 = paddle.sparse.sum(sparse_x, axis=1, keepdim=True) >>> out4 Tensor(shape=[2, 1], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 1]], values=[[-2.], [ 3.]]) FNT)axisdtypekeepdim)in_dtype out_dtyperboolfloat32float64int16int32int64 sparse_sumr(r)outtypeinputsoutputsattrs)rr rr4updater)rrintlisttupler8r r )create_sparse_variable_for_type_inference append_op) rr(r)r*r dtype_flagr;op_typehelperr6s rsumrEsvJ  *51  %99 <D6D'B  LLaggEB C      &3eT$ZBL W% BBBOCBBggCC #qE3>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.atan(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-1.10714877, 0.78539819]) r)r r sparse_atanrs ratanrH4r"rcZ[5(dS5e[R"U5$)a] Calculate elementwise sinh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = sinh(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64, complex64, complex128. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.sinh(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-3.62686038, 1.17520118]) r)r r sparse_sinhrs rsinhrKWr"rcZ[5(dS5e[R"U5$)a` Calculate elementwise asinh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = asinh(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64, complex64, complex128. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.asinh(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-1.44363546, 0.88137358]) r)r r sparse_asinhrs rasinhrNz0: " # #F #   q !!rcZ[5(dS5e[R"U5$)aR Calculate elementwise atanh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = atanh(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64, complex64, complex128. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.atanh(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[nan , inf.]) r)r r sparse_atanhrs ratanhrRrOrcZ[5(dS5e[R"U5$)a] Calculate elementwise tanh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = tanh(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64, complex64, complex128. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.tanh(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-0.96402758, 0.76159418]) r)r r sparse_tanhrs rtanhrUr"rcZ[5(dS5e[R"U5$)aQ Calculate elementwise square of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = square(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64, complex64, complex128. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.square(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[4., 1.]) r)r r sparse_squarers rsquarerXs0: " # #F #    ""rcZ[5(dS5e[R"U5$)a6 Calculate elementwise sqrt of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = sqrt(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.sqrt(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[nan, 1. ]) r)r r sparse_sqrtrs rsqrtr[r"rcZ[5(dS5e[R"U5$)ab Calculate the natural log of (1+x), requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = ln(1+x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64, complex64, complex128. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2, 0, 1], dtype='float32') >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.log1p(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[nan , 0.69314718]) r)r r sparse_log1prs rlog1pr^)rOrc[5(dS5eU(aD[U[RR[R 45(d [ U5nU(aD[U[RR[R 45(d [ U5n[R"XU5$)a cast non-zero-index of SparseTensor to `index_dtype`, non-zero-element of SparseTensor to `value_dtype` , requiring x to be a SparseCooTensor or SparseCsrTensor. Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. index_dtype (np.dtype|str, optional): Data type of the index of SparseCooTensor, or crows/cols of SparseCsrTensor. Can be uint8, int8, int16, int32, int64. value_dtype (np.dtype|str, optional): Data type of the value of SparseCooTensor, SparseCsrTensor. Can be bool, float16, float32, float64, int8, int32, int64, uint8. name (str|core.VarDesc.VarType|core.DataType|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2, 0, 1]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.cast(sparse_x, 'int32', 'float64') >>> out Tensor(shape=[3], dtype=paddle.float64, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-2., 1.]) r) r isinstancer VarDescVarTypeDataTyperr sparse_cast)r index_dtype value_dtypers rcastrgLsF " # #F #:dll**DMM:1= :dll**DMM:1=   ak ::rcn[5(dS5e[R"U[U55$)ab Calculate elementwise pow of x, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = x^{factor} Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. factor (float|int): factor of pow. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32') >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.pow(sparse_x, 2) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[4., 9.]) r)r r sparse_powfloat)rfactorrs rpowrl}s6< " # #F #   Qf ..rc`[5(dS5e[R"USSS5$)a7 Calculate elementwise negative of x, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = -x Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32') >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.neg(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[ 2., -3.]) rgT)r r sparse_scalers rnegrps6: " # #F #   q$T 22rcZ[5(dS5e[R"U5$)aS Calculate elementwise absolute value of x, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = |x| Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64, complex64, complex128. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32') >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.abs(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[2., 3.]) r)r r sparse_absrs rabsrsrrcZ[5(dS5e[R"U5$)a the coalesced operator include sorted and merge, after coalesced, the indices of x is sorted and unique. Parameters: x (Tensor): the input SparseCooTensor. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: return the SparseCooTensor after coalesced. Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 0, 1], [1, 1, 2]] >>> values = [1.0, 2.0, 3.0] >>> sp_x = paddle.sparse.sparse_coo_tensor(indices, values) >>> sp_x = paddle.sparse.coalesce(sp_x) >>> print(sp_x.indices()) Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 1], [1, 2]]) >>> print(sp_x.values()) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [3., 3.]) r)r rsparse_coalescers rcoalescervs0: " # #F #  ! !! $$rc[5(dS5eUR[;a:[R"US[ R RR5n[R"US[R- SS5$)a Convert each of the elements of input x from radian to degree, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: rad2deg(x) = 180/ \pi * x Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64, int32, int64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([3.142, 0., -3.142]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.rad2deg(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[ 180.02334595, -180.02334595]) rNf@rnT r r) _int_dtype_rrdr rarbFP32ronppirs rrad2degr~ sn< " # #F # ww+   q$ (<(<(A(A B   q%"%%-d ;;rc[5(dS5eUR[;a:[R"US[ R RR5n[R"U[RS- SS5$)ax Convert each of the elements of input x from degree to radian, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: deg2rad(x) = \pi * x / 180 Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64, int32, int64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-180, 0, 180]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.deg2rad(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-3.14159274, 3.14159274]) rNrxrnTryrs rdeg2radr0sn< " # #F # ww+   q$ (<(<(A(A B   q"%%%-d ;;rcZ[5(dS5e[R"U5$)aX Calculate elementwise `exp(x)-1` , requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = exp(x) - 1 Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64, complex64, complex128. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.expm1(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-0.86466473, 1.71828187]) r)r r sparse_expm1rs rexpm1rVrOrinputc$[5(a[R"X5$[US/SQS5 [ US[ [ 4S5 SU0nSU0n[S5nURUR5nURSUSU0US9 U$)aF Changes the shape of ``x`` without changing its value, requiring x to be a SparseCooTensor or SparseCsrTensor. Currently this function can only reshape the sparse dims of ``x`` , but ``shape`` argument must be specified as the shape of the reshaped tensor. Note that if x is a SparseCsrTensor, then len(shape) must be 2 or 3. There are some tricks when specifying the target shape. - 1. -1 means the value of this dimension is inferred from the total element number of x and remaining dimensions. Thus one and only one dimension can be set -1. - 2. 0 means the actual dimension value is going to be copied from the corresponding dimension of x. The indices of 0 in the target shape can not exceed the rank of x. Here are some examples to explain it. - 1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [6, 8], the reshape operator will transform x into a 2-D tensor with shape [6, 8] and leaving x's data unchanged. - 2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [2, 3, -1, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this case, one dimension of the target shape is set to -1, the value of this dimension is inferred from the total element number of x and remaining dimensions. - 3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 4, 3, 2] and leaving x's data unchanged. In this case, besides -1, 0 means the actual dimension value is going to be copied from the corresponding dimension of x. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``. For example, ``reshape(input=tensor_x, ...)`` is equivalent to ``reshape(x=tensor_x, ...)``. Args: x (Tensor): The input sparse tensor with data type ``float32``, ``float64``, ``int32``, ``int64`` or ``bool``. shape (list|tuple): Define the target shape. At most one dimension of the target shape can be -1. The data type is ``int32``. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A reshaped Tensor with the same data type as ``x``. Examples: .. code-block:: pycon >>> import paddle >>> x_shape = [6, 2, 3] >>> new_shape = [1, 0, 2, -1, 3] >>> format = "coo" >>> dense_x = paddle.randint(-100, 100, x_shape) * paddle.randint(0, 2, x_shape) >>> if format == "coo": ... sp_x = dense_x.to_sparse_coo(len(x_shape)) >>> else: ... sp_x = dense_x.to_sparse_csr() >>> sp_out = paddle.sparse.reshape(sp_x, new_shape) >>> print(sp_out.shape) paddle.Size([1, 2, 2, 3, 3]) r)float16r/r0r1r2r3r.uint16reshapeshapesparse_reshaper6r7) r rrrrr>r?r r@r)rA)rrrr9r;rDr6s rrryst$$Q..     5'D%=)<q% -.>>qwwG!CL   rc[5(a[R"U5$Sn[U5nUR UR 5nUR USU0SU00S9 U$)a+ Return whether every element of input tensor is `NaN` or not, requiring x to be a SparseCooTensor or SparseCsrTensor. Args: x (Tensor): The input tensor (SparseCooTensor or SparseCsrTensor), it's data type should be float16, float32, float64, int32, int64. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same shape as ``x``, the bool result which shows every element of `x` whether it is `NaN` or not. Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> format = "coo" >>> np_x = np.asarray([[[0., 0], [1., 2.]], [[0., 0], [3., float('nan')]]]) >>> dense_x = paddle.to_tensor(np_x) >>> if format == "coo": ... sparse_x = dense_x.to_sparse_coo(len(np_x.shape)) >>> else: ... sparse_x = dense_x.to_sparse_csr() ... >>> sparse_out = paddle.sparse.isnan(sparse_x) >>> print(sparse_out) Tensor(shape=[2, 2, 2], dtype=paddle.bool, place=Place(gpu:0), stop_gradient=True, indices=[[0, 0, 1, 1], [1, 1, 1, 1], [0, 1, 0, 1]], values=[False, False, False, True ]) sparse_isnanrr6r7)r rrr r@r)rA)rrrCrDr6s risnanrsmH""1%% W%>>qwwG#qE3>> import paddle >>> import numpy as np >>> format = 'coo' >>> np_x = np.asarray([[4, 0, 7, 0], [0, 0, 5, 0], [-4, 2, 0, 0]]) >>> dense_x = paddle.to_tensor(np_x) >>> if format == 'coo': ... sp_x = dense_x.to_sparse_coo(len(np_x.shape)) >>> else: ... sp_x = dense_x.to_sparse_csr() ... >>> axes = [0, 1] >>> starts = [1, 0] >>> ends = [3, -2] >>> sp_out = paddle.sparse.slice(sp_x, axes, starts, ends) >>> # sp_out is x[1:3, 0:-2] >>> print(sp_out) Tensor(shape=[2, 2], dtype=paddle.int64, place=Place(cpu), stop_gradient=True, indices=[[1, 1], [0, 1]], values=[-4, 2]) )axesstartsendsrr- sparse_slicerrrr5r6r7) r rrrrr>r?r r@r)rA) rrrrrr;rCrDr6s rslicersz""1F99>     4$?68dE]NC4$? W%>>QWW>M#qE3>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> format = "coo" >>> paddle.seed(2023) >>> dense_x = paddle.randn((5, 5), dtype='float64') >>> if format == "coo": ... sparse_x = dense_x.to_sparse_coo(len(dense_x.shape)) >>> else: ... sparse_x = dense_x.to_sparse_csr() >>> print("sparse.pca_lowrank API only support CUDA 11.x") >>> # U, S, V = None, None, None >>> # use code blow when your device CUDA version >= 11.0 >>> U, S, V = paddle.sparse.pca_lowrank(sparse_x) >>> print(U) Tensor(shape=[5, 5], dtype=float64, place=Place(gpu:0), stop_gradient=True, [[-0.31412600, 0.44814876, 0.18390454, -0.19967630, -0.79170452], [-0.31412600, 0.44814876, 0.18390454, -0.58579808, 0.56877700], [-0.31412600, 0.44814876, 0.18390454, 0.78547437, 0.22292751], [-0.38082462, 0.10982129, -0.91810233, 0.00000000, 0.00000000], [ 0.74762770, 0.62082796, -0.23585052, 0.00000000, -0.00000000]]) >>> print(S) Tensor(shape=[5], dtype=float64, place=Place(gpu:0), stop_gradient=True, [1.56031096, 1.12956227, 0.27922715, 0.00000000, 0.00000000]) >>> print(V) Tensor(shape=[5, 5], dtype=float64, place=Place(gpu:0), stop_gradient=True, [[ 0.88568469, -0.29081908, 0.06163676, 0.19597228, -0.29796422], [-0.26169364, -0.27616183, 0.43148760, -0.42522796, -0.69874939], [ 0.28587685, 0.30695344, -0.47790836, -0.76982533, -0.05501437], [-0.23958121, -0.62770647, -0.71141770, 0.11463224, -0.17125926], [ 0.08918713, -0.59238761, 0.27478686, -0.41833534, 0.62498824]]) cURnU[R[R[R4;aU$[R$N)r)paddlerr/r0)rr)s rget_floating_dtype'pca_lowrank..get_floating_dtypes5 V^^V^^V^^D DL~~rcPUR5(aUR5$U$r) is_complexconj)rs r conjugatepca_lowrank..conjugates <<>>668Orc URn[[S[U555n/USSQUSPUSPnUR 5(a[ R RX5$[ R"X5$)Nr)rr>rangelen is_sparsersparser&)rrr%s rr&pca_lowrank..transposessE!SZ()/cr/DH/d2h/ ;;====**13 3((rc >T"T"U55$r)rrr&s r transjugate pca_lowrank..transjugates1&&rNc>UcSOUnURSSupE[RRn[R"XQ4UR S9nT"U5nT "U5n UcU"[R RX55Sn [U5HSn U"[R RX55Sn U"[R RX 55Sn MU U $T"U5n U"[R RX5[R"X75- 5Sn [U5Hn U"[R RX5[R"X5- 5Sn U"[R RX 5[R"X:5- 5Sn M U $)Nrrr5r) rrlinalgqrrandnr)rmatmulr)rqniterMmnrRA_tA_HQiM_Hrrr&s rget_approximate_basis*pca_lowrank..get_approximate_basissn]wwrs| ]]   LL!qww /ln 96==''-.q1A5\v}}++C34Q7v}}++A1215" a.C6==''- a0CCDQGA5\v}}++C3fmmC6KKLQOv}}++A1FMM!4GGHK"rc>UcSOUnURSSupEUcSnOT"U5nT"U5nXE:dXQ:Ga!T"XqX&S9nT"U5n Uc [RRX 5n O6[RRX 5[R"X95- n U RSU:XdU RU45eU RSU:XdU RU45eU RSU RS::dU R5e[RR U SS9upn T"U 5nURU5nGO'T"XX#S9nT"U5n Uc [RRXy5nO6[RRXy5[R"Xi5- nT"U5n U RSU:XdU RU45eU RSU:XdU RU45eU RSU RS::dU R5e[RR U SS9upn T"U 5nURU 5n XU4$)NrrrrrF) full_matrices)rrrrrsvd)rrrrrrM_trrQ_cB_tUSVhVBrrrr&s r svd_lowrank pca_lowrank..svd_lowranks6Awwrs| 9CA,Cl 5AE%cEAAA,Cymm**12mm**12V]]15JJ99R=A% 5 1~ 5%99R=A% 5 1~ 5%99R=CIIbM1 <399 <1}}((E(BHA"BA A%a%=AA,CyMM((2MM((2V]]35LLA,C99R=A% 5 1~ 5%99R=A% 5 1~ 5%99R=CIIbM1 <399 <1}}((E(BHA"BA AQwrzInput must be tensor, but got z#Input must be sparse, but got denseFalse.r z-sparse.pca_lowrank API only support CUDA 11.xrzq(=z>) must be non-negative integer and not greater than min(m, n)=zniter(=z) must be non-negative integerrz,input is expected to be 2-dimensional tensor)r()r)placer5)rN)rrN)r is_tensor ValueErrorr8rversioncudar=splitrminrrrEvaluessparse_coo_tensorindicesr)rzerosonesrto_dense)rrcenterrrrr cuda_versionrrr)s_sums_valccolumn_indicesrC_t ones_m1_trrrrr&s @@@@r pca_lowrankr_s{V  )'.%%N   A  9$q'CDD ;;==>??>>&&(L 7 " |!!#&q) *R /HII WWRS\FQy 1L1fc!i!//21yk ;   QJ75')GHII q !E 1u55 177|qGHH MM  ab  )E LLNQ E '' kk ( AYY[^NllAs>23>;O;OPGAJ -- ) )aV5 * C 1aggcrl1A1q1?I&--  :;A q5A ..rr)rrr str | Nonereturnr)rrr%z Sequence[int]rrrr)NNFN) rrr(zint | Sequence[int] | Noner)DTypeLike | Noner*r.rrrr)NNN) rrrerrfrrrrr)rrrkrjrrrr)rrrrrrrr) rrr)Sequence[int] | Sequence[Tensor] | Tensorrrrrrrrr)NTrN) rrrz int | Nonerr.rr=rrrztuple[Tensor, Tensor, Tensor])> __future__rtypingrnumpyr|rrpaddle.base.data_feederrrpaddle.base.frameworkrr r paddle.common_ops_importr paddle.frameworkr paddle.utils.decorator_utilsr collections.abcrrpaddle._typingrr__all__rarbUINT8INT8INT16INT32INT64BOOLrzrrr!r&rErHrKrNrRrUrXr[r^rgrlrprsrvr~rrrrrrrrrrs#  H .((3  LLLLLLLLLLLL    F  F !H8<', ',"',*4', ',X(," i i $i i i  i  iX !F !F "F "F !F #F !F "J%)$( .; .;!.;".;  .;  .;b!/H 3F  F %F#