ёiP SSKJr SSKJr SSKJr SSKJrJrJ r J r J r SSK J r \(aSSKJr /r\R R"R$\R R"R&\R R"R(\R R"R*\R R"R,\R R"R.\R0R$\R0R&\R0R(\R0R*\R0R,\R0R./ r\R0R$S\R0R&S\R0R(S\R0R*S \R0R,S \R0R.S0rSSS jjrSSS jjrSSSjjrSSSjjrSSSjjrSSSjjr SSSjjr!SSjr"\SSSjj5r#g )) annotations) TYPE_CHECKING)_C_ops)core dygraph_onlyin_dygraph_modein_dynamic_or_pir_mode in_pir_mode) LayerHelper)TensorNcZ[5(dS5e[R"X5$)a] Applies matrix multiplication of two Tensors. The supported input/output Tensor type are as follows: Note: x[SparseCsrTensor] @ y[SparseCsrTensor] -> out[SparseCsrTensor] x[SparseCsrTensor] @ y[DenseTensor] -> out[DenseTensor] x[SparseCooTensor] @ y[SparseCooTensor] -> out[SparseCooTensor] x[SparseCooTensor] @ y[DenseTensor] -> out[DenseTensor] It supports backward propagation. Dimensions `x` and `y` must be >= 2D. Automatic broadcasting of Tensor is not supported. the shape of `x` should be `[*, M, K]` , and the shape of `y` should be `[*, K, N]` , where `*` is zero or more batch dimensions. Args: x (SparseTensor): The input tensor. It can be SparseCooTensor/SparseCsrTensor. The data type can be float32 or float64. y (SparseTensor|DenseTensor): The input tensor. It can be SparseCooTensor/SparseCsrTensor/DenseTensor. The data type can be float32 or float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: SparseTensor|DenseTensor: Determined by `x` and `y` . Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> # csr @ dense -> dense >>> crows = [0, 1, 2, 3] >>> cols = [1, 2, 0] >>> values = [1., 2., 3.] >>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, [3, 3]) >>> print(csr) Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True, crows=[0, 1, 2, 3], cols=[1, 2, 0], values=[1., 2., 3.]) >>> dense = paddle.ones([3, 2]) >>> out = paddle.sparse.matmul(csr, dense) >>> print(out) Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[1., 1.], [2., 2.], [3., 3.]]) >>> # coo @ dense -> dense >>> indices = [[0, 1, 2], [1, 2, 0]] >>> values = [1., 2., 3.] >>> coo = paddle.sparse.sparse_coo_tensor(indices, values, [3, 3]) >>> print(coo) Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True, indices=[[0, 1, 2], [1, 2, 0]], values=[1., 2., 3.]) >>> dense = paddle.ones([3, 2]) >>> out = paddle.sparse.matmul(coo, dense) >>> print(out) Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[1., 1.], [2., 2.], [3., 3.]]) out[SparseCooTensor] x[DenseTensor] @ y[DenseTensor] * mask[SparseCsrTensor] -> out[SparseCsrTensor] It supports backward propagation. Dimensions `x` and `y` must be >= 2D. Automatic broadcasting of Tensor is not supported. the shape of `x` should be `[*, M, K]` , and the shape of `y` should be `[*, K, N]` , and the shape of `mask` should be `[*, M, N]` , where `*` is zero or more batch dimensions. Args: x (DenseTensor): The input tensor. It is DenseTensor. The data type can be float32 or float64. y (DenseTensor): The input tensor. It is DenseTensor. The data type can be float32 or float64. mask (SparseTensor): The mask tensor, which can be SparseCooTensor/SparseCsrTensor. It specify sparse coordinates. The data type can be float32 or float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: SparseTensor: SparseCooTensor or SparseCsrTensor, which is same with `mask` . Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> paddle.seed(100) >>> # dense @ dense * csr_mask -> csr >>> crows = [0, 2, 3, 5] >>> cols = [1, 3, 2, 0, 1] >>> values = [1., 2., 3., 4., 5.] >>> dense_shape = [3, 4] >>> mask = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) >>> print(mask) Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True, crows=[0, 2, 3, 5], cols=[1, 3, 2, 0, 1], values=[1., 2., 3., 4., 5.]) >>> x = paddle.rand([3, 5]) >>> y = paddle.rand([5, 4]) >>> out = paddle.sparse.masked_matmul(x, y, mask) >>> print(out) Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True, crows=[0, 2, 3, 5], cols=[1, 3, 2, 0, 1], values=[0.98986477, 0.97800624, 1.14591956, 0.68561077, 0.94714981]) r)r rsparse_masked_matmul)rrmaskrs r masked_matmulrs3v " # #F #  & &qT 22rcZ[5(dS5e[R"X5$)a  Applies matrix-vector product of Sparse Matrix 'x' and Dense vector 'vec' . The supported input/output Tensor layout are as follows: Note: x[SparseCsrTensor] @ vec[DenseTensor] -> out[DenseTensor] x[SparseCooTensor] @ vec[DenseTensor] -> out[DenseTensor] It supports backward propagation. The shape of `x` should be `[M, N]` , and the shape of `vec` should be `[N]` , and the shape of `out` will be `[M]` . Args: x (SparseTensor): The input 2D tensor. It must be SparseCooTensor/SparseCsrTensor. The data type can be float32 or float64. vec (DenseTensor): The input 1D tensor. It must be DenseTensor vector. The data type can be float32 or float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: DenseTensor: 1D DenseTensor whose dtype is same with input. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> paddle.seed(100) >>> # csr @ dense -> dense >>> crows = [0, 2, 3, 5] >>> cols = [1, 3, 2, 0, 1] >>> values = [1., 2., 3., 4., 5.] >>> dense_shape = [3, 4] >>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) >>> print(csr) Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True, crows=[0, 2, 3, 5], cols=[1, 3, 2, 0, 1], values=[1., 2., 3., 4., 5.]) >>> vec = paddle.randn([4]) >>> out = paddle.sparse.mv(csr, vec) >>> print(out) Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True, [-3.85499096, -2.42975140, -1.75087738]) r)r r sparse_mv)rvecrs rmvr"s1f " # #F #   A ##rc[5(a[R"X5$SnXS.n[U5nUR UR 5nUR X4SU00S9 U$)u Add two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse type（SparseCooTensor or SparseCsrTensor）.If input is SparseCooTensor, x and y's sparse_dim should be identical. The equation is: .. math:: out = x + y Args: x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64, complex64, complex128. y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64, complex64, complex128. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: the result tensor. Examples: .. code-block:: python >>> import paddle >>> paddle.device.set_device("cpu") >>> x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32') >>> y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32') >>> sparse_x = x.to_sparse_csr() >>> sparse_y = y.to_sparse_csr() >>> sparse_z = paddle.sparse.add(sparse_x, sparse_y) >>> print(sparse_z.to_dense()) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0., -1., 0., 0.], [ 0., 2., -6., 0.], [ 6., 8., 4., 8.]]) sparse_addrrout)typeinputsoutputsattrs)r rr$r )create_sparse_variable_for_type_inferencedtype append_op)rrrop_typer(helperr&s raddr0soL  &&!W%>>qwwG%R   rc[5(a[R"X5$[5(a[R"X5$[ S5e)uO Subtract two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse type（SparseCooTensor or SparseCsrTensor）.If input is SparseCooTensor, x and y's sparse_dim should be identical. The equation is: .. math:: out = x - y Args: x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64, complex64, complex128. y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64, complex64, complex128. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: the result tensor. Examples: .. code-block:: python >>> import paddle >>> paddle.device.set_device("cpu") >>> x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32') >>> y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32') >>> sparse_x = x.to_sparse_csr() >>> sparse_y = y.to_sparse_csr() >>> sparse_z = paddle.sparse.subtract(sparse_x, sparse_y) >>> print(sparse_z.to_dense()) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0., -1., 0., 4.], [ 0., -2., 0., 0.], [ 2., 2., -4., -8.]]) @We currently only support dynamic graph mode or the new IR mode.)rrsparse_subtractr RuntimeErrorrs rsubtractr55sGL%%a++ %%a++ N  rc&[U[[45(a"[R"U[U5SS5$[ 5(a[R "X5$[5(a[R "X5$[S5e)uO Multiply two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse type（SparseCooTensor or SparseCsrTensor）.If input is SparseCooTensor, x and y's sparse_dim should be identical. The equation is: .. math:: out = x * y Args: x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64, complex64, complex128. y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64, complex64, complex128. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: the result tensor. Examples: .. code-block:: python >>> import paddle >>> paddle.device.set_device("cpu") >>> x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32') >>> y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32') >>> sparse_x = x.to_sparse_csr() >>> sparse_y = y.to_sparse_csr() >>> sparse_z = paddle.sparse.multiply(sparse_x, sparse_y) >>> print(sparse_z.to_dense()) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0., -0., 0., -4.], [ 0., 0., 9., 0.], [ 8., 15., 0., 0.]]) gTr2) isinstanceintfloatr sparse_scalersparse_multiplyr r4rs rmultiplyr<essL!c5\""""1eAhT::   ))!/ / ]]))!/ /R rc"[U[[45(a [R"U[U55$[ 5(a[R "X5$[5(a[R "X5$[S5e)u Divide two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse type（SparseCooTensor or SparseCsrTensor）.If input is SparseCooTensor, x and y's sparse_dim should be identical. The equation is: .. math:: out = x / y Args: x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64, complex64, complex128. y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64, complex64, complex128. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: the result tensor. Examples: .. code-block:: python >>> import paddle >>> paddle.device.set_device("cpu") >>> x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32') >>> y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32') >>> sparse_x = x.to_sparse_csr() >>> sparse_y = y.to_sparse_csr() >>> sparse_z = paddle.sparse.divide(sparse_x, sparse_y) >>> print(sparse_z.to_dense()) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[ nan , -inf. , nan , -1. ], [ nan , 0. , 1. , nan ], [ 2. , 1.66666663, 0. , 0. ]]) r2) r7r8r9rsparse_divide_scalarr sparse_divider r4rs rdivider@soL!c5\""**1eAh77   ''- - ]]''- -R rcP[5(dS5eURU5$)a  Return the results of shape comparison between two Tensors, check whether x.shape equal to y.shape. Any two type Tensor among DenseTensor/SparseCooTensor/SparseCsrTensor are supported. Args: x (Tensor): The input tensor. It can be DenseTensor/SparseCooTensor/SparseCsrTensor. y (Tensor): The input tensor. It can be DenseTensor/SparseCooTensor/SparseCsrTensor. Returns: bool: True for same shape and False for different shape. Examples: .. code-block:: python >>> import paddle >>> x = paddle.rand([2, 3, 8]) >>> y = paddle.rand([2, 3, 8]) >>> y = y.to_sparse_csr() >>> z = paddle.rand([2, 5]) >>> paddle.sparse.is_same_shape(x, y) True >>> paddle.sparse.is_same_shape(x, z) False r)r is_same_shaper%s rrBrBs.: " # #F # ??1 rc.[R"X5$)ak Filter the input dense tensor `x` using the `indices` of the sparse matrix `mask`, which in turn generates a sparse matrix of the corresponding format. The input `x` and `mask` must have the same shape, and the sparse tensor returned has the same indices as `mask` even `zero` values exist in the corresponding indices. Args: x (Tensor): The input tensor. It should be a DenseTensor. The data type can be float32, float64, int32, int64, complex64, complex128, int8, int16, float16. mask (Tensor): The input tensor. It can be SparseCooTensor or SparseCsrTensor. It should be 2D or 3D when the mask is SparseCsrTensor. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A sparse tensor. Examples: .. code-block:: python >>> import paddle >>> paddle.set_device('cpu') >>> # csr sparse tensor >>> crows = [0, 2, 3, 5] >>> cols = [1, 3, 2, 0, 1] >>> values = [1., 2., 3., 4., 5.] >>> dense_shape = [3, 4] >>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) >>> paddle.seed(2024) >>> x = paddle.rand(dense_shape).astype(csr.dtype) >>> out = paddle.sparse.mask_as(x, csr) >>> print(out) Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, crows=[0, 2, 3, 5], cols=[1, 3, 2, 0, 1], values=[0.23659813, 0.08467803, 0.64152628, 0.66596609, 0.90394485]) >>> # coo sparse tensor >>> indices = [[0, 1, 2], [1, 2, 0]] >>> values = [1.0, 2.0, 3.0] >>> dense_shape = [3, 3] >>> coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape) >>> paddle.seed(2024) >>> x = paddle.rand(dense_shape).astype(coo.dtype) >>> out = paddle.sparse.mask_as(x, coo) >>> print(out) Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 1, 2], [1, 2, 0]], values=[0.23659813, 0.40340215, 0.64152628]) )rsparse_mask_as)rrrs rmask_asrEsl   ))r)N)rr rr r str | Nonereturnr ) rr rr rr rrFrGr )rr r!r rrFrGr )rr rr rGbool)rr rr rrFrGr )$ __future__rtypingrpaddlerpaddle.base.frameworkrrrr r paddle.base.layer_helperr r __all__VarDescVarTypeUINT8INT8INT16INT32INT64BOOLDataType _int_dtype__pir_int_dtype_rrr"r0r5r<r@rBrErrr[s# 1  LLLLLLLLLLLLMMMMMMMMMMMM   MMMMMMMMMMMM J&\<@>3 >3>3 &>3.8>3 >3B6$r0f- `0f0f F5*5*r