diff --git a/python/paddle/tensor/linalg.py b/python/paddle/tensor/linalg.py index 99f5bf7ba0ad1..583290e431d63 100644 --- a/python/paddle/tensor/linalg.py +++ b/python/paddle/tensor/linalg.py @@ -39,8 +39,8 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None): """ - Applies matrix multiplication to two tensors. `matmul` follows - the complete broadcast rules, + Applies matrix multiplication to two tensors. `matmul` follows + the complete broadcast rules, and its behavior is consistent with `np.matmul`. Currently, the input tensors' number of dimensions can be any, `matmul` can be used to @@ -50,8 +50,8 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None): flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically: - If a transpose flag is specified, the last two dimensions of the tensor - are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor - is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas + are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor + is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`. The multiplication behavior depends on the dimensions of `x` and `y`. Specifically: @@ -60,22 +60,22 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None): - If both tensors are 2-dimensional, the matrix-matrix product is obtained. - - If the `x` is 1-dimensional and the `y` is 2-dimensional, - a `1` is prepended to its dimension in order to conduct the matrix multiply. + - If the `x` is 1-dimensional and the `y` is 2-dimensional, + a `1` is prepended to its dimension in order to conduct the matrix multiply. After the matrix multiply, the prepended dimension is removed. - - - If the `x` is 2-dimensional and `y` is 1-dimensional, + + - If the `x` is 2-dimensional and `y` is 1-dimensional, the matrix-vector product is obtained. - - If both arguments are at least 1-dimensional and at least one argument - is N-dimensional (where N > 2), then a batched matrix multiply is obtained. - If the first argument is 1-dimensional, a 1 is prepended to its dimension - in order to conduct the batched matrix multiply and removed after. - If the second argument is 1-dimensional, a 1 is appended to its - dimension for the purpose of the batched matrix multiple and removed after. - The non-matrix (exclude the last two dimensions) dimensions are - broadcasted according the broadcast rule. - For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor, + - If both arguments are at least 1-dimensional and at least one argument + is N-dimensional (where N > 2), then a batched matrix multiply is obtained. + If the first argument is 1-dimensional, a 1 is prepended to its dimension + in order to conduct the batched matrix multiply and removed after. + If the second argument is 1-dimensional, a 1 is appended to its + dimension for the purpose of the batched matrix multiple and removed after. + The non-matrix (exclude the last two dimensions) dimensions are + broadcasted according the broadcast rule. + For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor, out will be a (j, k, n, p) tensor. Args: @@ -177,11 +177,17 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None): Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean or 2-norm, and in general the p-norm for p > 0) of a given tensor. + .. note:: + This norm API is different from `numpy.linalg.norm`. + This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm. + But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor. + For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM. + Args: x (Tensor): The input tensor could be N-D tensor, and the input data type could be float32 or float64. p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`, - `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm. + `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm. Default value is `fro`. axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int or list(int)/tuple(int) with only one element, the vector norm is computed over the axis. @@ -198,10 +204,10 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None): Returns: Tensor: results of norm operation on the specified axis of input tensor, it's data type is the same as input's Tensor. - + Examples: .. code-block:: python - + import paddle import numpy as np shape=[2, 3, 4] @@ -344,6 +350,10 @@ def inf_norm(input, return reduce_out def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None): + """ + NOTE: + This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm. + """ block = LayerHelper('norm', **locals()) out = block.create_variable_for_type_inference( dtype=block.input_dtype()) @@ -548,10 +558,10 @@ def dist(x, y, p=2): def dot(x, y, name=None): """ This operator calculates inner product for vectors. - + .. note:: - Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix - is the batch dimension, which means that the vectors of multiple batches are dotted. + Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix + is the batch dimension, which means that the vectors of multiple batches are dotted. Parameters: x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64`` @@ -604,17 +614,17 @@ def dot(x, y, name=None): def t(input, name=None): """ - Transpose <=2-D tensor. - 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to + Transpose <=2-D tensor. + 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to the paddle.transpose function which perm dimensions set 0 and 1. - + Args: input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32. - name(str, optional): The default value is None. Normally there is no need for + name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` Returns: Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64. - + For Example: .. code-block:: text @@ -679,10 +689,10 @@ def t(input, name=None): def cross(x, y, axis=None, name=None): """ Computes the cross product between two tensors along an axis. - + Inputs must have the same shape, and the length of their axes should be equal to 3. If `axis` is not given, it defaults to the first axis found with the length 3. - + Args: x (Tensor): The first input tensor. y (Tensor): The second input tensor. @@ -691,7 +701,7 @@ def cross(x, y, axis=None, name=None): Returns: Tensor. A Tensor with same data type as `x`. - + Examples: .. code-block:: python @@ -737,8 +747,8 @@ def cross(x, y, axis=None, name=None): def cholesky(x, upper=False, name=None): r""" Computes the Cholesky decomposition of one symmetric positive-definite - matrix or batches of symmetric positive-definite matrice. - + matrix or batches of symmetric positive-definite matrice. + If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` , and the returned matrix :math:`U` is upper-triangular. Otherwise, the decomposition has the form :math:`A = LL^{T}` , and the returned matrix @@ -755,7 +765,7 @@ def cholesky(x, upper=False, name=None): Returns: Tensor: A Tensor with same shape and data type as `x`. It represents \ triangular matrices generated by Cholesky decomposition. - + Examples: .. code-block:: python @@ -845,7 +855,7 @@ def bmm(x, y, name=None): def histogram(input, bins=100, min=0, max=0): """ - Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max. + Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max. If min and max are both zero, the minimum and maximum values of the data are used. Args: