From 632a006477867fa828362f9a1703f2e49efc6f78 Mon Sep 17 00:00:00 2001 From: zhangkaihuo Date: Tue, 28 Sep 2021 11:31:33 +0800 Subject: [PATCH] [cherry-pick] update multi_dot exposure rules (#36018) (#36131) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 根据线性代数库的API暴露规则修改multi_dot的API暴露规则： 1、在python/paddle/tensor/linalg.py 路径下实现 2、在python/paddle/linalg.py 下import并加入__all__列表 3、在python/paddle/tensor/init.py下引入并加入tensor_method_func列表 4、删除了pythonpaddle/init.py的import --- python/paddle/tensor/__init__.py | 1 + python/paddle/tensor/linalg.py | 134 +++++++++++++++---------------- 2 files changed, 68 insertions(+), 67 deletions(-) diff --git a/python/paddle/tensor/__init__.py b/python/paddle/tensor/__init__.py index 02b34bb21a..080a06455a 100755 --- a/python/paddle/tensor/__init__.py +++ b/python/paddle/tensor/__init__.py @@ -387,6 +387,7 @@ tensor_method_func = [ #noqa 'bitwise_not', 'broadcast_tensors', 'uniform_', + 'multi_dot', 'solve', ] diff --git a/python/paddle/tensor/linalg.py b/python/paddle/tensor/linalg.py index b6e3196265..9ba9370a43 100644 --- a/python/paddle/tensor/linalg.py +++ b/python/paddle/tensor/linalg.py @@ -551,8 +551,8 @@ def cond(x, p=None, name=None): Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``. Args: - x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions - for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``. + x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions + for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``. And the input data type could be ``float32`` or ``float64``. p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`, `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`. @@ -607,7 +607,7 @@ def cond(x, p=None, name=None): # out_minus_inf.numpy() [1.] a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32')) - # a.numpy() + # a.numpy() # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543] # [-0.16303636 1.5534962 -0.49919784 -0.04402903] # [-1.1341571 -0.6022629 0.5445269 0.29154757] @@ -975,8 +975,8 @@ def t(input, name=None): return out check_variable_and_dtype( - input, 'input', ['float16', 'float32', 'float64', 'int32', 'int64'], - 'transpose') + input, 'input', ['float16', 'float32', 'float64', 'int32', + 'int64'], 'transpose') helper = LayerHelper('t', **locals()) out = helper.create_variable_for_type_inference(input.dtype) @@ -1108,17 +1108,17 @@ def matrix_rank(x, tol=None, hermitian=False, name=None): r""" Computes the rank of a matrix. - The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False, + The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False, or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True. Args: - x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch - of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64. - tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest - singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed + x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch + of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64. + tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest + singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch. - hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian, - enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use + hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian, + enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use the lower triangular of the matrix to compute. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. @@ -1225,7 +1225,7 @@ def bmm(x, y, name=None): #output value: #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]] out_np = out.numpy() - + """ x_shape = x.shape y_shape = y.shape @@ -1360,7 +1360,7 @@ def det(x, name=None): Returns: y (Tensor):the determinant value of a square matrix or batches of square matrices. - Example: + Examples: .. code-block:: python import paddle @@ -1370,10 +1370,10 @@ def det(x, name=None): A = paddle.linalg.det(x) print(A) - + # [ 0.02547996, 2.52317095, -6.15900707]) - + """ if in_dygraph_mode(): return core.ops.determinant(x) @@ -1403,7 +1403,7 @@ def slogdet(x, name=None): """ Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant. The determinant can be computed with ``sign * exp(logabsdet) - + Supports input of float, double Note that for matrices that have zero determinant, this returns ``(0, -inf)`` @@ -1415,7 +1415,7 @@ def slogdet(x, name=None): y (Tensor): A tensor containing the sign of the determinant and the natural logarithm of the absolute value of determinant, respectively. - Example: + Examples: .. code-block:: python import paddle @@ -1425,7 +1425,7 @@ def slogdet(x, name=None): A = paddle.linalg.slogdet(x) print(A) - + # [[ 1. , 1. , -1. ], # [-0.98610914, -0.43010661, -0.10872950]]) @@ -1461,19 +1461,19 @@ def svd(x, full_matrices=False, name=None): Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies: .. math:: - X = U * diag(S) * VT - + X = U * diag(S) * VT + Args: x (Tensor): The input tensor. Its shape should be `[..., N, M]`, where `...` is zero or more batch dimensions. N and M can be arbitraty - positive number. Note that if x is sigular matrices, the grad is numerical - instable. The data type of x should be float32 or float64. - full_matrices (bool): A flag to control the behavor of svd. - If full_matrices = True, svd op will compute full U and V matrics, + positive number. Note that if x is sigular matrices, the grad is numerical + instable. The data type of x should be float32 or float64. + full_matrices (bool): A flag to control the behavor of svd. + If full_matrices = True, svd op will compute full U and V matrics, which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N). - If full_matrices = False, svd op will use a economic method to store U and V. + If full_matrices = False, svd op will use a economic method to store U and V. which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N). - name (str, optional): Name for the operation (optional, default is None). + name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: @@ -1497,9 +1497,9 @@ def svd(x, full_matrices=False, name=None): print (vh) #VT= [[ 0.51411221, 0.85772294], # [ 0.85772294, -0.51411221]] - + # one can verify : U * S * VT == X - # U * UH == I + # U * UH == I # V * VH == I """ @@ -1526,7 +1526,7 @@ def svd(x, full_matrices=False, name=None): def matrix_power(x, n, name=None): r""" Computes the n-th power of a square matrix or a batch of square matrices. - + Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be an exponent, the equation should be: @@ -1596,27 +1596,27 @@ def matrix_power(x, n, name=None): def eigvals(x, name=None): """ Compute the eigenvalues of one or more general matrices. - - Warning: - The gradient kernel of this operator does not yet developed. + + Warning: + The gradient kernel of this operator does not yet developed. If you need back propagation through this operator, please replace it with paddle.linalg.eig. Args: x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed. - Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions. + Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions. Its data type should be float32, float64, complex64, or complex128. - name (str, optional): Name for the operation (optional, default is None). + name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: - Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`. + Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`. The eigenvalues are complex-valued even when `x` is real. Examples: .. code-block:: python import paddle - + paddle.set_device("cpu") paddle.seed(1234) @@ -1630,8 +1630,8 @@ def eigvals(x, name=None): """ check_variable_and_dtype(x, 'dtype', - ['float32', 'float64', 'complex64', 'complex128'], - 'eigvals') + ['float32', 'float64', 'complex64', + 'complex128'], 'eigvals') x_shape = list(x.shape) if len(x_shape) < 2: @@ -1657,7 +1657,7 @@ def multi_dot(x, name=None): """ Multi_dot is an operator that calculates multiple matrix multiplications. - Supports inputs of float, double and float16 dtypes. This function does not + Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not support batched inputs. The input tensor in [x] must be 2-D except for the first and last can be 1-D. @@ -1735,7 +1735,7 @@ def multi_dot(x, name=None): def eigh(x, UPLO='L', name=None): """ - Compute the eigenvalues and eigenvectors of a + Compute the eigenvalues and eigenvectors of a complex Hermitian (conjugate symmetric) or a real symmetric matrix. Args: @@ -1804,7 +1804,7 @@ def eigh(x, UPLO='L', name=None): def pinv(x, rcond=1e-15, hermitian=False, name=None): r""" - Calculate pseudo inverse via SVD(singular value decomposition) + Calculate pseudo inverse via SVD(singular value decomposition) of one matrix or batches of regular matrix. .. math:: @@ -1815,30 +1815,30 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None): else: x = u * s * ut (eigh) out = u * 1/s * u.conj().transpose(-2,-1) - + If x is hermitian or symmetric matrix, svd will be replaced with eigh. Args: - x(Tensor): The input tensor. Its shape should be (*, m, n) - where * is zero or more batch dimensions. m and n can be - arbitraty positive number. The data type of x should be + x(Tensor): The input tensor. Its shape should be (*, m, n) + where * is zero or more batch dimensions. m and n can be + arbitraty positive number. The data type of x should be float32 or float64 or complex64 or complex128. When data type is complex64 or cpmplex128, hermitian should be set True. - rcond(Tensor, optional): the tolerance value to determine - when is a singular value zero. Defalut:1e-15. - - hermitian(bool, optional): indicates whether x is Hermitian + rcond(Tensor, optional): the tolerance value to determine + when is a singular value zero. Defalut:1e-15. + + hermitian(bool, optional): indicates whether x is Hermitian if complex or symmetric if real. Default: False. - - name(str|None): A name for this layer(optional). If set None, + + name(str|None): A name for this layer(optional). If set None, the layer will be named automatically. - + Returns: - Tensor: The tensor with same data type with x. it represents + Tensor: The tensor with same data type with x. it represents pseudo inverse of x. Its shape should be (*, n, m). - + Examples: .. code-block:: python @@ -1998,8 +1998,8 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None): helper = LayerHelper('pinv', **locals()) dtype = x.dtype check_variable_and_dtype( - x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], - 'pinv') + x, 'dtype', ['float32', 'float64', 'complex64', + 'complex128'], 'pinv') if dtype == paddle.complex128: s_type = 'float64' @@ -2079,40 +2079,40 @@ def solve(x, y, name=None): Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'. Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be a vector/matrix or a batch of vectors/matrices, the equation should be: - + .. math:: Out = X^-1 * Y Specifically, - This system of linear equations has one solution if and only if input 'X' is invertible. - + Args: x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions. Its data type should be float32 or float64. y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or more batch dimensions. Its data type should be float32 or float64. - name(str, optional): Name for the operation (optional, default is None). + name(str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. - + Returns: - Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'. + Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'. Its data type should be the same as that of `x`. - + Examples: .. code-block:: python - + # a square system of linear equations: # 2*X0 + X1 = 9 # X0 + 2*X1 = 8 - + import paddle import numpy as np - + np_x = np.array([[3, 1],[1, 2]]) np_y = np.array([9, 8]) x = paddle.to_tensor(np_x, dtype="float64") y = paddle.to_tensor(np_y, dtype="float64") out = paddle.linalg.solve(x, y) - + print(out) # [2., 3.]) """ -- GitLab