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paddlepaddle--paddle/python/paddle/tensor/linalg.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import math
from typing import TYPE_CHECKING, Literal, NamedTuple, TypeAlias
import numpy as np
from typing_extensions import overload
import paddle
from paddle import _C_ops
from paddle._C_ops import ( # noqa: F401
bincount,
bmm,
cholesky,
cross,
det,
diagonal,
dist,
dot,
eigh,
matmul,
mv,
)
from paddle.common_ops_import import VarDesc
from paddle.tensor.math import broadcast_shape
from paddle.utils.decorator_utils import (
ParamAliasDecorator,
VariableArgsDecorator,
param_one_alias,
param_two_alias,
qr_decorator,
transpose_decorator,
)
from paddle.utils.inplace_utils import inplace_apis_in_dygraph_only
from ..base.data_feeder import (
check_dtype,
check_type,
check_variable_and_dtype,
convert_dtype,
)
from ..common_ops_import import Variable
from ..framework import (
LayerHelper,
in_dynamic_mode,
in_dynamic_or_pir_mode,
in_pir_mode,
)
from .creation import full
from .manipulation import cast
from .math import _get_reduce_axis
if TYPE_CHECKING:
from collections.abc import Sequence
from paddle import Tensor
_POrder: TypeAlias = Literal['fro', 'nuc']
# Consistent with kDefaultDim from C++ Backend
K_DEFAULT_DIM = 9
@overload
def transpose(
x: Tensor, perm: Sequence[int], name: str | None = None
) -> Tensor: ...
@overload
def transpose(
x: Tensor,
axis0: int,
axis1: int,
) -> Tensor: ...
@overload
def transpose(
input: Tensor,
dim0: int,
dim1: int,
) -> Tensor: ...
@transpose_decorator
def transpose(
x: Tensor, perm: Sequence[int], name: str | None = None
) -> Tensor:
"""
Permute the data dimensions of `input` according to `perm`.
The `i`-th dimension of the returned tensor will correspond to the
perm[i]-th dimension of `input`.
.. note::
Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim0`` & ``dim1`` can replace ``perm``.
For example, ``transpose(input=x, dim0=0, dim1=1)`` is equivalent to ``transpose(x=x, perm=[1, 0, 2])``.
Args:
x (Tensor): The input Tensor. It is a N-D Tensor of data types bool, float16, bfloat16, float32, float64, int8, int16, int32, int64, uint8, uint16, complex64, complex128.
alias: ``input``.
perm (list|tuple): Permute the input according to the data of perm.
name (str|None, optional): The name of this layer. For more information, please refer to :ref:`api_guide_Name`. Default is None.
Returns:
Tensor: A transposed n-D Tensor, with data type being bool, float32, float64, int32, int64.
Examples:
.. code-block:: text
# The following codes in this code block are pseudocode, designed to show the execution logic and results of the function.
x = to_tensor([[[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]]
[[13 14 15 16] [17 18 19 20] [21 22 23 24]]])
shape(x): return [2,3,4]
# Example 1
perm0 = [1,0,2]
y_perm0 = transpose(x, perm0) # Permute x by perm0
# dim:0 of y_perm0 is dim:1 of x
# dim:1 of y_perm0 is dim:0 of x
# dim:2 of y_perm0 is dim:2 of x
# The above two lines can also be understood as exchanging the zeroth and first dimensions of x
y_perm0.data = [[[ 1 2 3 4] [13 14 15 16]]
[[ 5 6 7 8] [17 18 19 20]]
[[ 9 10 11 12] [21 22 23 24]]]
shape(y_perm0): return [3,2,4]
# Example 2
perm1 = [2,1,0]
y_perm1 = transpose(x, perm1) # Permute x by perm1
# dim:0 of y_perm1 is dim:2 of x
# dim:1 of y_perm1 is dim:1 of x
# dim:2 of y_perm1 is dim:0 of x
# The above two lines can also be understood as exchanging the zeroth and second dimensions of x
y_perm1.data = [[[ 1 13] [ 5 17] [ 9 21]]
[[ 2 14] [ 6 18] [10 22]]
[[ 3 15] [ 7 19] [11 23]]
[[ 4 16] [ 8 20] [12 24]]]
shape(y_perm1): return [4,3,2]
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.randn([2, 3, 4])
>>> x_transposed = paddle.transpose(x, perm=[1, 0, 2])
>>> print(x_transposed.shape)
paddle.Size([3, 2, 4])
"""
if in_dynamic_or_pir_mode():
return _C_ops.transpose(x, perm)
else:
check_variable_and_dtype(
x,
'x',
[
'bool',
'float16',
'bfloat16',
'float32',
'float64',
'int8',
'uint8',
'int16',
'int32',
'int64',
'uint16',
'complex64',
'complex128',
'float8_e5m2',
'float8_e4m3fn',
],
'transpose',
)
check_type(perm, 'perm', (list, tuple), 'transpose')
if isinstance(perm, tuple):
perm = list(perm)
if len(perm) != len(x.shape):
raise ValueError(
"Input(perm) is the permutation of dimensions of Input(x), "
"its length should be equal to dimensions of Input(x), "
f"but received dimension of Input(x) is {len(x.shape)}, "
f"the length of Input(perm) is {len(perm)}."
)
for idx, dim in enumerate(perm):
if dim >= len(x.shape):
raise ValueError(
"Each element in Input(perm) should be less than Input(x)'s dimension, "
f"but {idx}-th element in Input(perm) is {perm[idx]} which exceeds Input(x)'s "
f"dimension {len(x.shape)}."
)
helper = LayerHelper('transpose', **locals())
out = helper.create_variable_for_type_inference(x.dtype)
x_shape = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='transpose2',
inputs={'X': [x]},
outputs={'Out': [out], 'XShape': [x_shape]},
attrs={'axis': perm},
)
return out
@transpose_decorator
@inplace_apis_in_dygraph_only
def transpose_(x, perm, name=None):
r"""
Inplace version of ``transpose`` API, the output Tensor will be inplaced with input ``x``.
Please refer to :ref:`api_paddle_transpose`.
"""
if in_dynamic_mode():
return _C_ops.transpose_(x, perm)
@VariableArgsDecorator('dims')
def permute(input: Tensor, dims: Sequence[int]) -> Tensor:
"""
Permute the dimensions of a tensor.
Args:
input (Tensor): the input tensor.
*dims (tuple|list|int): The desired ordering of dimensions. Supports passing as variable-length
arguments (e.g., permute(x, 1, 0, 2)) or as a single list/tuple (e.g., permute(x, [1, 0, 2])).
Returns:
Tensor: A tensor with permuted dimensions.
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.randn([2, 3, 4])
>>> y = paddle.permute(x, (1, 0, 2))
>>> print(y.shape)
paddle.Size([3, 2, 4])
>>> y = x.permute([1, 0, 2])
>>> print(y.shape)
paddle.Size([3, 2, 4])
"""
return transpose(x=input, perm=dims)
def matrix_transpose(
x: paddle.Tensor,
name: str | None = None,
) -> paddle.Tensor:
"""
Transpose the last two dimensions of the input tensor `x`.
Note:
If `n` is the number of dimensions of `x`, `paddle.matrix_transpose(x)` is equivalent to `x.transpose([0, 1, ..., n-2, n-1])`.
Args:
x (Tensor): The input tensor to be transposed. `x` must be an N-dimensional tensor (N >= 2) of any data type supported by Paddle.
name (str|None, optional): The name of this layer. For more information, please refer to :ref:`api_guide_Name`. Default is None.
Returns:
Tensor: A new tensor with the same shape as `x`, except that the last two dimensions are transposed.
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.ones(shape=[2, 3, 5])
>>> x_transposed = paddle.matrix_transpose(x)
>>> print(x_transposed.shape)
paddle.Size([2, 5, 3])
"""
return x.mT
def fp8_fp8_half_gemm_fused(
x,
y,
transpose_x=False,
transpose_y=False,
bias=None,
scale=1.0,
output_dtype="float16",
act="identity",
name=None,
):
if in_dynamic_or_pir_mode():
return _C_ops.fp8_fp8_half_gemm_fused(
x, y, bias, transpose_x, transpose_y, scale, output_dtype, act
)
else:
attrs = {
'transpose_x': transpose_x,
'transpose_y': transpose_y,
'scale': scale,
'output_dtype': output_dtype,
'act': act,
}
if bias is None:
def __check_input(x, y):
var_names = {'x': x, 'y': y}
for name, val in var_names.items():
check_variable_and_dtype(
val,
name,
[
'float8_e5m2',
'float8_e4m3fn',
],
'fp8_fp8_half_gemm_fused',
)
__check_input(x, y)
helper = LayerHelper('fp8_fp8_half_gemm_fused', **locals())
if output_dtype == 'float16':
out = helper.create_variable_for_type_inference(dtype='float16')
elif output_dtype == 'bfloat16':
out = helper.create_variable_for_type_inference(
dtype='bfloat16'
)
else:
raise ValueError("The output_dtype must be float16 or bfloat16")
helper.append_op(
type='fp8_fp8_half_gemm_fused',
inputs={'x': x, 'y': y},
outputs={'out': out},
attrs=attrs,
)
return out
else:
def __check_input(x, y):
var_names = {'x': x, 'y': y}
for name, val in var_names.items():
check_variable_and_dtype(
val,
name,
[
'float8_e5m2',
'float8_e4m3fn',
],
'fp8_fp8_half_gemm_fused',
)
__check_input(x, y)
if output_dtype == 'float16':
check_variable_and_dtype(
bias, 'bias', ['float16'], 'fp8_fp8_half_gemm_fused'
)
elif output_dtype == 'bfloat16':
check_variable_and_dtype(
bias, 'bias', ['bfloat16'], 'fp8_fp8_half_gemm_fused'
)
else:
raise ValueError("The output_dtype must be float16 or bfloat16")
helper = LayerHelper('fp8_fp8_half_gemm_fused', **locals())
if output_dtype == 'float16':
out = helper.create_variable_for_type_inference(dtype='float16')
elif output_dtype == 'bfloat16':
out = helper.create_variable_for_type_inference(
dtype='bfloat16'
)
else:
raise ValueError("The output_dtype must be float16 or bfloat16")
helper.append_op(
type='fp8_fp8_half_gemm_fused',
inputs={'x': x, 'y': y, 'bias': bias},
outputs={'out': out},
attrs=attrs,
)
return out
@param_two_alias(["p", "ord"], ["axis", "dim"])
def vector_norm(
x: Tensor,
p: float = 2.0,
axis: int | Sequence[int] | None = None,
keepdim: bool = False,
name: str | None = None,
*,
dtype: paddle._typing.DTypeLike | None = None,
out: Tensor | None = None,
) -> Tensor:
"""
Calculate the p-order vector norm for certain dimension of Tensor `input`.
Returns the vector norm (the 1-norm, the Euclidean or 2-norm, and in general the p-norm)
of a given tensor.
.. note::
Alias Support: The parameter name ``ord`` can be used as an alias for ``p``, and ``dim`` can be used as an alias for ``axis``.
Args:
x (Tensor): Tensor, data type float32, float64.
p (int|float, optional): None for porder=2.0. Default None.
axis (int|list|tuple, optional): None for last dimension. Default None.
keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.
name (str|None, 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`.
dtype (paddle._typing.DTypeLike, optional): It may be used to perform the computation in a more precise dtype. It is semantically equivalent to calling linalg.vector_norm(x.to(dtype)) but it is faster in some cases. Default None.
out (Tensor| None, optional): output tensor. Ignored if None. Default: None.
Returns:
Tensor: results of vector_norm operation on the specified axis of input tensor,
it's data type is the same as input's Tensor.
Examples:
.. code-block:: pycon
>>> import paddle
>>> import numpy as np
>>> x = paddle.arange(24, dtype="float32").reshape([2, 3, 4]) - 12
>>> print(x)
Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[-12., -11., -10., -9. ],
[-8. , -7. , -6. , -5. ],
[-4. , -3. , -2. , -1. ]],
[[ 0. , 1. , 2. , 3. ],
[ 4. , 5. , 6. , 7. ],
[ 8. , 9. , 10., 11.]]])
>>> out_vector_norm = paddle.linalg.vector_norm(x=x, p=2, axis=None, keepdim=False)
>>> print(out_vector_norm)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
34.)
>>> out_vector_norm = paddle.linalg.vector_norm(x=x, p=0, axis=[0, 1], keepdim=False)
>>> print(out_vector_norm)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[5., 6., 6., 6.])
>>> out_vector_norm = paddle.linalg.vector_norm(x=x, p=float("inf"), axis=[1, 2], keepdim=False)
>>> print(out_vector_norm)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[12., 11.])
>>> out_vector_norm = paddle.linalg.vector_norm(x=x, p=1, axis=1, keepdim=False)
>>> print(out_vector_norm)
Tensor(shape=[2, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[24., 21., 18., 15.],
[12., 15., 18., 21.]])
"""
def zero_norm(
input, porder=None, axis=axis, keepdim=False, asvector=False, name=None
):
return paddle.count_nonzero(
input, axis=axis, keepdim=keepdim, name=name
).astype(input.dtype)
def inf_norm(
input, porder=None, axis=axis, keepdim=False, asvector=False, name=None
):
if in_dynamic_or_pir_mode():
out = _C_ops.abs(input)
if porder == np.float64('inf'):
return _C_ops.max(out, axis, keepdim)
else:
return _C_ops.min(out, axis, keepdim)
else:
helper = LayerHelper('inf_norm', **locals())
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
helper.append_op(
type='abs', inputs={'X': input}, outputs={'Out': out}
)
reduce_out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
reduce_all, axis = _get_reduce_axis(axis, x)
reduce_type = (
'reduce_max' if porder == np.float64('inf') else 'reduce_min'
)
helper.append_op(
type=reduce_type,
inputs={'X': out},
outputs={'Out': reduce_out},
attrs={
'dim': axis,
'keep_dim': keepdim,
'reduce_all': reduce_all,
},
)
return reduce_out
def vector_norm_axis_tuple(
input, porder=2, axis=None, keepdim=False, asvector=False, name=None
):
"""
NOTE:
This function calculates the vector norm for dim >= 2.
"""
if in_dynamic_or_pir_mode():
abs_out = _C_ops.abs(input)
pow_out = _C_ops.pow(abs_out, porder)
sum_out = _C_ops.sum(pow_out, axis, None, keepdim)
out = _C_ops.pow(sum_out, float(1.0 / porder))
return out
block = LayerHelper('norm', **locals())
out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
abs_out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
block.append_op(
type='abs', inputs={'X': input}, outputs={'Out': abs_out}
)
pow_out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
block.append_op(
type='pow',
inputs={'X': abs_out},
outputs={'Out': pow_out},
attrs={'factor': porder},
)
sum_out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
reduce_all, axis = _get_reduce_axis(axis, x)
block.append_op(
type='reduce_sum',
inputs={'X': pow_out},
outputs={'Out': sum_out},
attrs={
'dim': axis,
'keep_dim': keepdim,
'reduce_all': reduce_all,
},
)
block.append_op(
type='pow',
inputs={'X': sum_out},
outputs={'Out': out},
attrs={'factor': float(1.0 / porder)},
)
return out
def vector_norm_axis_int(
input, porder=2, axis=None, keepdim=False, asvector=False, name=None
):
"""
NOTE:
This function calculates the vector norm for len(axis) == 1.
"""
if in_dynamic_or_pir_mode():
if axis is None:
axis = -1
return _C_ops.p_norm(input, porder, axis, 1e-12, keepdim, asvector)
else:
if porder is not None:
check_type(porder, 'porder', (float, int), 'p_norm')
if axis is not None:
check_type(axis, 'axis', (int), 'p_norm')
check_variable_and_dtype(
input,
'input',
['float16', 'uint16', 'float32', 'float64'],
'p_norm',
)
attrs = {
'axis': axis if axis is not None else -1,
'porder': float(porder) if porder is not None else 2.0,
'keepdim': keepdim,
'asvector': asvector,
'epsilon': 1e-12,
}
helper = LayerHelper('p_norm', **locals())
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
helper.append_op(
type='p_norm',
inputs={'X': input},
outputs={'Out': out},
attrs=attrs,
)
return out
if not isinstance(p, (int, float)):
raise ValueError(f"only valid p type is int and float, found {type(p)}")
if dtype is not None:
x = x.astype(dtype)
asvector = False
if axis is None:
axis = -1
asvector = True
if isinstance(axis, tuple):
axis = list(axis)
if isinstance(axis, list) and len(axis) == 1:
axis = axis[0]
if paddle.is_complex(x):
abs_x = paddle.abs(x)
else:
abs_x = x
# when len(axis) == 1, use the original op to calculate
if isinstance(axis, int):
tensor = vector_norm_axis_int(
abs_x,
axis=axis,
porder=p,
keepdim=keepdim,
asvector=asvector,
name=name,
)
# when len(axis) >= 1, calculate by combining other Python apis
elif isinstance(axis, list):
if p == np.inf or p == -np.inf:
tensor = inf_norm(
abs_x, porder=p, axis=axis, keepdim=keepdim, name=name
)
elif p == 0:
tensor = zero_norm(
abs_x, porder=p, axis=axis, keepdim=keepdim, name=name
)
else:
tensor = vector_norm_axis_tuple(
abs_x, porder=p, axis=axis, keepdim=keepdim, name=name
)
if out is not None:
paddle.assign(tensor, output=out)
return tensor
def matrix_norm(
x: Tensor,
p: float | _POrder = 'fro',
axis: int | list[int] | tuple[int, int] = [-2, -1],
keepdim: bool = False,
name: str | None = None,
) -> Tensor:
"""
Calculate the p-order matrix norm for certain dimension of Tensor `input`.
Args:
x (Tensor): Tensor, data type float32, float64.
p (int|float|string, optional): Default 'fro'.
axis (int|list|tuple, optional): The axis is a list(int)/tuple(int) with two elements. Default last two dimensions.
keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.
name (str|None, 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: results of matrix_norm operation on the specified axis of input tensor,
it's data type is the same as input's Tensor.
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.arange(24, dtype="float32").reshape([2, 3, 4]) - 12
>>> print(x)
Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[-12., -11., -10., -9. ],
[-8. , -7. , -6. , -5. ],
[-4. , -3. , -2. , -1. ]],
[[ 0. , 1. , 2. , 3. ],
[ 4. , 5. , 6. , 7. ],
[ 8. , 9. , 10., 11.]]])
>>> out_matrix_norm = paddle.linalg.matrix_norm(x=x, p=2, axis=[0, 1], keepdim=False)
>>> print(out_matrix_norm)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[15.75857544, 14.97978878, 14.69693947, 14.97978973])
>>> out_matrix_norm = paddle.linalg.matrix_norm(x=x, p='fro', axis=[0, 1], keepdim=False)
>>> print(out_matrix_norm)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[17.43559647, 16.91153526, 16.73320007, 16.91153526])
>>> out_matrix_norm = paddle.linalg.matrix_norm(x=x, p=float('inf'), axis=[1, 2], keepdim=False)
>>> print(out_matrix_norm)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[42., 38.])
>>> out_matrix_norm = paddle.linalg.matrix_norm(x=x, p=-1, axis=[0, 1], keepdim=False)
>>> print(out_matrix_norm)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[12., 12., 12., 12.])
>>> out_matrix_norm = paddle.linalg.matrix_norm(x=x, p='nuc', axis=[0, 1], keepdim=False)
>>> print(out_matrix_norm)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[23.21962357, 22.82873154, 22.69693947, 22.82873154])
"""
def _backshift_permutation(dim0, dim1, dimn):
"""
Auxiliary function for matrix_norm
Computes the permutation that moves the two given dimensions to the back
"""
pos_dim0 = dim0 % dimn
pos_dim1 = dim1 % dimn
ret = [i for i in range(dimn) if i != pos_dim0 and i != pos_dim1]
ret.extend((pos_dim0, pos_dim1))
return ret
def _inverse_permutation(perm):
"""
Given a permutation, returns its inverse. It's equivalent to argsort on an array
"""
return [i for i, j in sorted(enumerate(perm), key=lambda ij: ij[1])]
def frobenius_norm(
input: Tensor,
dim: list[int] | None = None,
keepdim: bool = False,
name: str | None = None,
) -> Tensor:
"""
The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.
Args:
input (Variable): Tensor, data type float32, float64, complex64, complex128.
dim (list, optional): None for last two dimensions. Default None.
keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.
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`.
"""
if dim is not None and not (isinstance(dim, list) and len(dim) == 2):
raise ValueError(
"The dim of frobenius norm op should be None or two elements list!"
)
if in_dynamic_or_pir_mode():
if dim is None:
return _C_ops.frobenius_norm(input, [], keepdim, True)
return _C_ops.frobenius_norm(input, dim, keepdim, False)
else:
attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}
if dim is None:
attrs['reduce_all'] = True
check_variable_and_dtype(
input, 'input', ['float32', 'float64'], 'frobenius_norm'
)
helper = LayerHelper('frobenius_norm', **locals())
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
helper.append_op(
type='frobenius_norm',
inputs={'X': input},
outputs={'Out': out},
attrs=attrs,
)
return out
def nuclear_norm(
input: Tensor,
axis: int | list[int] | tuple[int, int] = axis,
keepdim: bool = False,
name: str | None = None,
) -> Tensor:
"""
The nuclear norm OP is to calculate the nuclear norm of certain two dimensions of Tensor `input`.
Args:
input (Variable): Tensor, data type float32, float64.
axis (list): Two dimensions.
keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.
name (str|None, 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`.
"""
perm = _backshift_permutation(axis[0], axis[1], len(input.shape))
inv_perm = _inverse_permutation(perm)
if in_dynamic_or_pir_mode():
transposed = _C_ops.transpose(input, perm)
u, s, vh = _C_ops.svd(transposed, False)
result = _C_ops.sum(s, -1, None, keepdim)
if keepdim:
result = _C_ops.transpose(
_C_ops.unsqueeze(result, -1), inv_perm
)
return result
attrs = {'axis': axis, 'keepdim': keepdim}
check_variable_and_dtype(
input, 'input', ['float32', 'float64'], 'nuclear_norm'
)
block = LayerHelper('nuclear_norm', **locals())
out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
transpose_out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
input_shape = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
block.append_op(
type='transpose2',
inputs={'X': [input]},
outputs={'Out': [transpose_out], 'XShape': [input_shape]},
attrs={'axis': perm},
)
u = block.create_variable_for_type_inference(dtype=block.input_dtype())
s = block.create_variable_for_type_inference(dtype=block.input_dtype())
vt = block.create_variable_for_type_inference(dtype=block.input_dtype())
block.append_op(
type='svd',
inputs={'X': [transpose_out]},
outputs={'U': u, 'VH': vt, 'S': s},
attrs={'full_matrices': False},
)
reduce_all, sum_axis = _get_reduce_axis(-1, s)
block.append_op(
type='reduce_sum',
inputs={'X': s},
outputs={'Out': out},
attrs={
'dim': sum_axis,
'keep_dim': keepdim,
'reduce_all': reduce_all,
},
)
if keepdim:
unsqueeze_out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
block.append_op(
type='unsqueeze2',
inputs={'X': [out]},
outputs={'Out': [unsqueeze_out], 'XShape': [input_shape]},
attrs={'axes': [-1]},
)
block.append_op(
type='transpose2',
inputs={'X': [unsqueeze_out]},
outputs={'Out': [out], 'XShape': [input_shape]},
attrs={'axis': inv_perm},
)
return out
def p_matrix_norm(
input: Tensor,
porder: float | _POrder = 1.0,
axis: int | list[int] | tuple[int, int] = axis,
keepdim: bool = False,
name: str | None = None,
) -> Tensor:
"""
Calculate the p-order matrix norm for certain dimension of Tensor `input`.
Args:
input (Variable): Tensor, data type float32, float64.
porder (int|float,str): p in ['fro', 'nuc', ±1, ±2, ±inf] Default 1.
axis (list): Two dimensions.
keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.
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`.
"""
perm = _backshift_permutation(axis[0], axis[1], len(input.shape))
inv_perm = _inverse_permutation(perm)
if in_dynamic_or_pir_mode():
abs_ord = abs(porder)
max_min = _C_ops.max if porder > 0.0 else _C_ops.min
if abs_ord == 2.0:
transpose_out = _C_ops.transpose(input, perm)
u, s, vh = _C_ops.svd(transpose_out, False)
result = max_min(s, -1, keepdim)
if keepdim:
result = _C_ops.transpose(
_C_ops.unsqueeze(result, -1), inv_perm
)
return result
else: # 1,-1,inf,-inf
rank = len(x.shape)
dim0, dim1 = (d % rank for d in axis)
if abs_ord == np.float64("inf"):
dim0, dim1 = dim1, dim0
if not keepdim and (dim0 < dim1):
dim1 -= 1
return max_min(
vector_norm(input, 1.0, axis=dim0, keepdim=keepdim),
dim1,
keepdim,
)
check_variable_and_dtype(
input,
'input',
['float16', 'uint16', 'float32', 'float64'],
'p_matrix_norm',
)
block = LayerHelper('p_matrix_norm', **locals())
out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
abs_ord = abs(porder)
if abs_ord == 2.0:
transpose_out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
input_shape = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
block.append_op(
type='transpose2',
inputs={'X': [input]},
outputs={'Out': [transpose_out], 'XShape': [input_shape]},
attrs={'axis': perm},
)
u = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
s = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
vt = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
block.append_op(
type='svd',
inputs={'X': [transpose_out]},
outputs={'U': u, 'VH': vt, 'S': s},
attrs={'full_matrices': False},
)
reduce_type = 'reduce_max' if porder > 0 else 'reduce_min'
reduce_out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
reduce_all, max_min_axis = _get_reduce_axis(-1, s)
block.append_op(
type=reduce_type,
inputs={'X': s},
outputs={'Out': reduce_out},
attrs={
'dim': max_min_axis,
'keep_dim': keepdim,
'reduce_all': reduce_all,
},
)
if keepdim:
unsqueeze_out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
block.append_op(
type='unsqueeze2',
inputs={'X': [reduce_out]},
outputs={'Out': [unsqueeze_out], 'XShape': [input_shape]},
attrs={'axes': [-1]},
)
block.append_op(
type='transpose2',
inputs={'X': [unsqueeze_out]},
outputs={'Out': [out], 'XShape': [input_shape]},
attrs={'axis': inv_perm},
)
return out
return reduce_out
else:
rank = len(x.shape)
dim0, dim1 = (d % rank for d in axis)
if abs_ord == np.float64("inf"):
dim0, dim1 = dim1, dim0
if not keepdim and (dim0 < dim1):
dim1 -= 1
vector_out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
attrs = {
'axis': dim0,
'porder': 1,
'keepdim': keepdim,
'asvector': False,
'epsilon': 1e-12,
}
block.append_op(
type='p_norm',
inputs={'X': input},
outputs={'Out': vector_out},
attrs=attrs,
)
reduce_type = 'reduce_max' if porder > 0 else 'reduce_min'
reduce_out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
reduce_all, max_min_axis = _get_reduce_axis(dim1, vector_out)
block.append_op(
type=reduce_type,
inputs={'X': vector_out},
outputs={'Out': reduce_out},
attrs={
'dim': max_min_axis,
'keep_dim': keepdim,
'reduce_all': reduce_all,
},
)
return reduce_out
if isinstance(axis, tuple):
axis = list(axis)
if isinstance(axis, list) and len(axis) == 2:
if p == "fro":
return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)
elif p == "nuc":
return nuclear_norm(x, axis=axis, keepdim=keepdim, name=name)
elif (
p == np.inf
or p == -np.inf
or p == 1
or p == -1
or p == 2
or p == -2
):
return p_matrix_norm(
x, porder=p, axis=axis, keepdim=keepdim, name=name
)
else:
raise ValueError(
f"just support p value 'fro','nuc',1,-1,inf,-inf,2,-2 if axis is 2D, found {p}"
)
else:
raise ValueError(
f"except axis type int or list (length of list == 2), found {len(axis)}"
)
@ParamAliasDecorator({"x": ["input", "A"], "p": ["ord"], "axis": ["dim"]})
def norm(
x: Tensor,
p: float | _POrder | None = None,
axis: int | list[int] | tuple[int, int] | None = None,
keepdim: bool = False,
out: paddle.Tensor | None = None,
dtype: paddle._typing.DTypeLike | None = None,
name: str | None = None,
) -> Tensor:
"""
Returns the matrix norm (the Frobenius norm, the nuclear norm and p-norm) or vector norm (the 1-norm, the Euclidean
or 2-norm, and in general the p-norm) of a given tensor.
Whether the function calculates the vector norm or the matrix norm is determined as follows:
- If axis is of type int, calculate the vector norm.
- If axis is a two-dimensional array, calculate the matrix norm.
- If axis is None, x is compressed into a one-dimensional vector and the vector norm is calculated.
Paddle supports the following norms:
+----------------+--------------------------------+--------------------------------+
| porder | norm for matrices | norm for vectors |
+================+================================+================================+
| None(default) | frobenius norm | 2_norm |
+----------------+--------------------------------+--------------------------------+
| fro | frobenius norm | not support |
+----------------+--------------------------------+--------------------------------+
| nuc | nuclear norm | not support |
+----------------+--------------------------------+--------------------------------+
| inf | max(sum(abs(x), dim=1)) | max(abs(x)) |
+----------------+--------------------------------+--------------------------------+
| -inf | min(sum(abs(x), dim=1)) | min(abs(x)) |
+----------------+--------------------------------+--------------------------------+
| 0 | not support | sum(x != 0) |
+----------------+--------------------------------+--------------------------------+
| 1 | max(sum(abs(x), dim=0)) | as below |
+----------------+--------------------------------+--------------------------------+
| -1 | min(sum(abs(x), dim=0)) | as below |
+----------------+--------------------------------+--------------------------------+
| 2 |The maximum singular value | as below |
| |of a matrix consisting of axis. | |
+----------------+--------------------------------+--------------------------------+
| -2 |The minimum singular value | as below |
| |of a matrix consisting of axis. | |
+----------------+--------------------------------+--------------------------------+
| other int | not support | sum(abs(x)^{porder})^ |
| or float | | {(1 / porder)} |
+----------------+--------------------------------+--------------------------------+
.. note::
Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for ``axis``.
For example, ``norm(input=tensor_x, dim=1, ...)`` is equivalent to ``norm(x=tensor_x, axis=1, ...)``.
Args:
x (Tensor): The input tensor could be N-D tensor, and the input data
type could be float32 or float64.
alias: ``input``.
p (int|float|string|None, optional): Order of the norm. Supported values are `fro`, `nuc`, `0`, `±1`, `±2`,
`±inf` and any real number yielding the corresponding p-norm.
Default value is None.
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.
If `axis < 0`, the dimension to norm operation is rank(input) + axis.
If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.
Default value is `None`.
alias: ``dim``.
keepdim (bool, optional): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have fewer dimension
than the :attr:`input` unless :attr:`keepdim` is true, default
value is False.
out (Tensor, optional): The output tensor. Ignored out = None.
dtype (DTypeLike | None, optional): The data type of the output tensor. If specified, the input tensor is casted to `dtype` while performing the operation. Default value is None.
name (str|None, 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: 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:: pycon
>>> import paddle
>>> x = paddle.arange(24, dtype="float32").reshape([2, 3, 4]) - 12
>>> print(x)
Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[-12., -11., -10., -9. ],
[-8. , -7. , -6. , -5. ],
[-4. , -3. , -2. , -1. ]],
[[ 0. , 1. , 2. , 3. ],
[ 4. , 5. , 6. , 7. ],
[ 8. , 9. , 10., 11.]]])
>>> # compute frobenius norm along last two dimensions.
>>> out_fro = paddle.linalg.norm(x, p='fro', axis=[0, 1])
>>> print(out_fro)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[17.43559647, 16.91153526, 16.73320007, 16.91153526])
>>> # compute 2-order vector norm along last dimension.
>>> out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)
>>> print(out_pnorm)
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[21.11871147, 13.19090557, 5.47722578 ],
[3.74165750 , 11.22497177, 19.13112640]])
>>> # compute 2-order norm along [0,1] dimension.
>>> out_pnorm = paddle.linalg.norm(x, p=2, axis=[0, 1])
>>> print(out_pnorm)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[15.75857544, 14.97978878, 14.69693947, 14.97978973])
>>> # compute inf-order norm
>>> out_pnorm = paddle.linalg.norm(x, p=float("inf"))
>>> print(out_pnorm)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
12.)
>>> out_pnorm = paddle.linalg.norm(x, p=float("inf"), axis=0)
>>> print(out_pnorm)
Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[12., 11., 10., 9. ],
[8. , 7. , 6. , 7. ],
[8. , 9. , 10., 11.]])
>>> # compute -inf-order norm
>>> out_pnorm = paddle.linalg.norm(x, p=-float("inf"))
>>> print(out_pnorm)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.)
>>> out_pnorm = paddle.linalg.norm(x, p=-float("inf"), axis=0)
>>> print(out_pnorm)
Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0., 1., 2., 3.],
[4., 5., 6., 5.],
[4., 3., 2., 1.]])
"""
if isinstance(axis, tuple):
axis = list(axis)
elif isinstance(axis, list) and len(axis) == 1:
axis = axis[0]
if dtype is not None:
x = x.astype(dtype)
if isinstance(p, str):
if p == "fro" and (axis is None or isinstance(axis, int)):
output = vector_norm(
x,
p=2,
axis=axis,
keepdim=keepdim,
name=name,
)
else:
if axis is None:
axis = list(range(x.ndim))
output = matrix_norm(
x=x, p=p, axis=axis, keepdim=keepdim, name=name
)
else:
if isinstance(axis, list) and len(axis) == 2:
p = "fro" if p is None else p
output = matrix_norm(
x=x, p=p, axis=axis, keepdim=keepdim, name=name
)
else:
p = 2.0 if p is None else p
output = vector_norm(
x,
p=p,
axis=axis,
keepdim=keepdim,
name=name,
)
if out is not None:
paddle.assign(output, output=out)
return output
def cond(
x: Tensor,
p: float | _POrder | None = None,
name: str | None = None,
) -> Tensor:
"""
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``.
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`.
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: computing results of condition number, its data type is the same as input Tensor ``x``.
Examples:
.. code-block:: pycon
>>> import paddle
>>> paddle.seed(2023)
>>> x = paddle.to_tensor([[1.0, 0.0, -1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 1.0]])
>>> # compute conditional number when p is None
>>> out = paddle.linalg.cond(x)
>>> print(out)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.41421378)
>>> # compute conditional number when order of the norm is 'fro'
>>> out_fro = paddle.linalg.cond(x, p='fro')
>>> print(out_fro)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
3.16227770)
>>> # compute conditional number when order of the norm is 'nuc'
>>> out_nuc = paddle.linalg.cond(x, p='nuc')
>>> print(out_nuc)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
9.24264145)
>>> # compute conditional number when order of the norm is 1
>>> out_1 = paddle.linalg.cond(x, p=1)
>>> print(out_1)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
2.)
>>> # compute conditional number when order of the norm is -1
>>> out_minus_1 = paddle.linalg.cond(x, p=-1)
>>> print(out_minus_1)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.)
>>> # compute conditional number when order of the norm is 2
>>> out_2 = paddle.linalg.cond(x, p=2)
>>> print(out_2)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.41421378)
>>> # compute conditional number when order of the norm is -1
>>> out_minus_2 = paddle.linalg.cond(x, p=-2)
>>> print(out_minus_2)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.70710671)
>>> # compute conditional number when order of the norm is inf
>>> out_inf = paddle.linalg.cond(x, p=float("inf"))
>>> print(out_inf)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
2.)
>>> # compute conditional number when order of the norm is -inf
>>> out_minus_inf = paddle.linalg.cond(x, p=-float("inf"))
>>> print(out_minus_inf)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.)
>>> a = paddle.randn([2, 4, 4])
>>> print(a)
Tensor(shape=[2, 4, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[ 0.06132207, 1.11349595, 0.41906244, -0.24858207],
[-1.85169315, -1.50370061, 1.73954511, 0.13331604],
[ 1.66359663, -0.55764782, -0.59911072, -0.57773495],
[-1.03176904, -0.33741450, -0.29695082, -1.50258386]],
[[ 0.67233968, -1.07747352, 0.80170447, -0.06695852],
[-1.85003340, -0.23008066, 0.65083790, 0.75387722],
[ 0.61212337, -0.52664012, 0.19209850, -0.18707688],
[-0.00711021, 0.35236868, -0.40404350, 1.28656745]]])
>>> a_cond_fro = paddle.linalg.cond(a, p='fro')
>>> print(a_cond_fro)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[6.37173700 , 35.15111160])
>>> b = paddle.randn([2, 3, 4])
>>> print(b)
Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[ 0.03306439, 0.70149767, 0.77064633, -0.55978841],
[-0.84461296, 0.99335045, -1.23486686, 0.59551388],
[-0.63035583, -0.98797107, 0.09410731, 0.47007179]],
[[ 0.85850012, -0.98949534, -1.63086998, 1.07340240],
[-0.05492966, 1.04750192, -2.33754158, 1.16518629],
[ 0.66847134, -1.05326962, -0.05703246, -0.48190674]]])
>>> b_cond_2 = paddle.linalg.cond(b, p=2)
>>> print(b_cond_2)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[2.86566353, 6.85834646])
"""
def mat_norm(
input: Tensor, porder: float = 1.0, axis: list[int] | None = None
) -> Tensor:
"""
NOTE:
Calculate the matrix norm of a square matrix or batches of square matrices,
when porder is in (1, -1, inf, -inf)
"""
if in_dynamic_or_pir_mode():
abs_out = _C_ops.abs(input)
sum_out = _C_ops.sum(abs_out, axis, None, False)
if porder == 1 or porder == np.inf:
return _C_ops.max(sum_out, [-1], False)
if porder == -1 or porder == -np.inf:
return _C_ops.min(sum_out, [-1], False)
else:
block = LayerHelper('norm', **locals())
abs_out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
sum_out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
block.append_op(
type='abs', inputs={'X': input}, outputs={'Out': abs_out}
)
reduce_all, axis = _get_reduce_axis(axis, x)
block.append_op(
type='reduce_sum',
inputs={'X': abs_out},
outputs={'Out': sum_out},
attrs={
'dim': axis,
'keep_dim': False,
'reduce_all': reduce_all,
},
)
if porder == 1 or porder == np.inf:
block.append_op(
type='reduce_max',
inputs={'X': sum_out},
outputs={'Out': out},
attrs={
'dim': [-1],
'keep_dim': False,
'reduce_all': reduce_all,
},
)
if porder == -1 or porder == -np.inf:
block.append_op(
type='reduce_min',
inputs={'X': sum_out},
outputs={'Out': out},
attrs={
'dim': [-1],
'keep_dim': False,
'reduce_all': reduce_all,
},
)
return out
def fro_norm(
input: Tensor, porder: float = 2, axis: list[int] = [-1]
) -> Tensor:
"""
NOTE:
Calculate the frobenius norm of a square matrix or batches of square matrices.
"""
if in_dynamic_or_pir_mode():
pow_out = _C_ops.pow(input, porder)
sum_out_1 = _C_ops.sum(pow_out, axis, None, False)
sum_out_2 = _C_ops.sum(sum_out_1, axis, None, False)
return _C_ops.pow(sum_out_2, float(1.0 / porder))
else:
block = LayerHelper('norm', **locals())
pow_out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
sum_out_1 = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
sum_out_2 = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
block.append_op(
type='pow',
inputs={'X': input},
outputs={'Out': pow_out},
attrs={'factor': porder},
)
reduce_all, axis = _get_reduce_axis(axis, x)
block.append_op(
type='reduce_sum',
inputs={'X': pow_out},
outputs={'Out': sum_out_1},
attrs={
'dim': axis,
'keep_dim': False,
'reduce_all': reduce_all,
},
)
block.append_op(
type='reduce_sum',
inputs={'X': sum_out_1},
outputs={'Out': sum_out_2},
attrs={
'dim': axis,
'keep_dim': False,
'reduce_all': reduce_all,
},
)
block.append_op(
type='pow',
inputs={'X': sum_out_2},
outputs={'Out': out},
attrs={'factor': float(1.0 / porder)},
)
return out
def svd_norm(
input: Tensor, porder: float, axis: list[int] = [-1]
) -> Tensor:
"""
NOTE:
Calculate the matrix norm, which is related to singular values, of a matrix
or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.
"""
u, s, vh = svd(input, full_matrices=False)
if in_dynamic_or_pir_mode():
if porder == "nuc":
return _C_ops.sum(s, axis, None, False)
max_out = _C_ops.max(s, axis, False)
min_out = _C_ops.min(s, axis, False)
if porder == 2:
return _C_ops.divide(max_out, min_out)
if porder == -2:
return _C_ops.divide(min_out, max_out)
else:
reduce_all, axis = _get_reduce_axis(axis, x)
block = LayerHelper('norm', **locals())
out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
if porder == "nuc":
block.append_op(
type='reduce_sum',
inputs={'X': s},
outputs={'Out': out},
attrs={
'dim': axis,
'keep_dim': False,
'reduce_all': reduce_all,
},
)
return out
max_out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
min_out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
)
block.append_op(
type='reduce_max',
inputs={'X': s},
outputs={'Out': max_out},
attrs={
'dim': axis,
'keep_dim': False,
'reduce_all': reduce_all,
},
)
block.append_op(
type='reduce_min',
inputs={'X': s},
outputs={'Out': min_out},
attrs={
'dim': axis,
'keep_dim': False,
'reduce_all': reduce_all,
},
)
if porder == 2:
block.append_op(
type='elementwise_div',
inputs={'X': max_out, 'Y': min_out},
outputs={'Out': out},
attrs={'axis': -1},
)
return out
if porder == -2:
block.append_op(
type='elementwise_div',
inputs={'X': min_out, 'Y': max_out},
outputs={'Out': out},
attrs={'axis': -1},
)
return out
def empty_tensor(input, shape):
if in_dynamic_or_pir_mode():
if in_pir_mode():
raise ValueError(
"only support x is nonempty tensor in static graph mode"
)
# reshape([]) is invalid,
# so use reshae([0]) and sum to get a scalar when shape is []
old_size = input.numel()
if len(shape) == 0 and old_size == 0:
return input.reshape([0]).sum()
new_size = math.prod(shape)
# 0-size Tensor cannot be reshaped to non 0-size Tensor
if new_size > 0 and old_size == 0:
tmp = paddle.concat(
[
input.flatten(),
paddle.zeros([new_size], dtype=input.dtype),
]
)
return tmp.reshape(shape)
return input.reshape(shape)
raise ValueError(
"only support x is nonempty tensor in static graph mode"
)
x_shape = list(x.shape)
if not len(x_shape) >= 2:
raise ValueError(
"input should be a matrix or batches of matrices, "
+ f"but the dimension of received input is {len(x_shape)}"
)
if p is None:
p = 2
x_size = 0 if (0 in x_shape) else 1
if p in ("fro", "nuc", 1, -1, np.inf, -np.inf):
if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:
if x_size == 0:
return empty_tensor(x, x_shape[:-2])
x_inv = x.inverse()
if p == "fro":
return fro_norm(x) * fro_norm(x_inv)
if p == "nuc":
return svd_norm(x, p) * svd_norm(x_inv, p)
if p in (1, -1):
return mat_norm(x, porder=p, axis=[-2]) * mat_norm(
x_inv, porder=p, axis=[-2]
)
if p in (np.inf, -np.inf):
return mat_norm(x, porder=p, axis=[-1]) * mat_norm(
x_inv, porder=p, axis=[-1]
)
else:
raise ValueError(
f"only support p is {p} when input is a "
+ "square matrix or batches of square matrices"
)
elif p in (2, -2):
if x_size == 0:
return empty_tensor(x, x_shape[:-2])
return svd_norm(x, porder=p)
else:
raise ValueError(
f"unsupported {p} for p, only supporting ('fro', 'nuc', "
+ "1, -1, 2, -2, inf, -inf) or none"
)
def vecdot(
x: Tensor,
y: Tensor,
axis: int = -1,
name: str | None = None,
) -> Tensor:
"""
Computes the dot product of two tensors along a specified axis.
This function multiplies two tensors element-wise and sums them along a specified axis to compute their dot product. It supports tensors of any dimensionality, including 0-D tensors, as long as the shapes of `x` and `y` are broadcastable along the specified axis.
Args:
x (Tensor): The first input tensor. It should be a tensor with dtype of float32, float64, int32, int64, complex64, or complex128.
y (Tensor): The second input tensor. Its shape must be broadcastable with `x` along the specified `axis`, and it must have the same dtype as `x`.
axis (int, optional): The axis along which to compute the dot product. Default is -1, which indicates the last axis.
name (str|None, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`
Returns:
Tensor: A tensor containing the dot product of `x` and `y` along the specified axis.
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.to_tensor([[1, 2, 3], [4, 5, 6]], dtype='float32')
>>> y = paddle.to_tensor([[1, 2, 3], [4, 5, 6]], dtype='float32')
>>> result = paddle.linalg.vecdot(x, y, axis=1)
>>> print(result)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[14., 77.])
"""
out = (x.conj() * y).sum(axis=axis)
return out
def cov(
x: Tensor,
rowvar: bool = True,
ddof: bool = True,
fweights: Tensor | None = None,
aweights: Tensor | None = None,
name: str | None = None,
) -> Tensor:
"""
Estimate the covariance matrix of the input variables, given data and weights.
A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.
For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix
element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.
Parameters:
x (Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.
rowvar (bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True.
ddof (bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True.
fweights (Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None.
aweights (Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None.
name (str|None, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name` .
Returns:
Tensor: The covariance matrix Tensor of the variables.
Examples:
.. code-block:: pycon
>>> import paddle
>>> paddle.seed(2023)
>>> xt = paddle.rand((3, 4))
>>> paddle.linalg.cov(xt)
>>> print(xt)
Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0.86583614, 0.52014720, 0.25960937, 0.90525323],
[0.42400089, 0.40641287, 0.97020894, 0.74437362],
[0.51785129, 0.73292869, 0.97786582, 0.04315904]])
"""
op_type = 'cov'
if len(x.shape) > 2 or len(x.shape) < 1:
raise ValueError(
"Input(x) only support N-D (1<=N<=2) tensor in cov, but received "
f"length of Input(input) is {len(x.shape)}."
)
check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')
nx = x
if len(x.shape) == 1:
nx = x.reshape((1, -1))
if not rowvar and nx.shape[0] != 1:
nx = nx.t()
w = None
observation_num = nx.shape[1]
if fweights is not None:
w = fweights.astype(nx.dtype)
if len(w.shape) > 1:
raise ValueError(
"Input(fweights) only support N-D (N<=1) tensor in cov, but received "
f"shape of Input(input) is {len(fweights.shape)}."
)
if fweights.shape[0] != observation_num:
raise ValueError(
f"The number of Input(fweights) should equal to x's dim[1]: {observation_num}, but received "
f"size of Input(fweights) is {fweights.shape[0]}."
)
if fweights.min() < 0:
raise ValueError(
"The value of Input(fweights) cannot be negative, but received "
f"min of Input(fweights) is {fweights.min()}."
)
if not paddle.all(
fweights
== paddle.round(fweights.astype('float64')).astype(fweights.dtype)
):
raise ValueError("Input(fweights) must be integer ")
if aweights is not None:
aw = aweights.astype(nx.dtype)
if len(aw.shape) > 1:
raise ValueError(
"Input(aweights) only support N-D (N<=1) tensor in cov, but received "
f"length of Input(input) is {len(aweights.shape)}."
)
check_variable_and_dtype(
aweights, 'dtype', ['float32', 'float64'], 'cov'
)
if aweights.shape[0] != observation_num:
raise ValueError(
f"The number of Input(aweights) should equal to x's dim[1]: {observation_num}, but received "
f"size of Input(aweights) is {aweights.shape[0]}."
)
if aweights.min() < 0:
raise ValueError(
"The value of Input(aweights) cannot be negative, but received "
f"min of Input(aweights) is {aweights.min()}."
)
if w is not None:
w = w * aw
else:
w = aw
w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)
if fweights is not None or aweights is not None:
w_sum = w.sum()
if w_sum.item() == 0:
raise ValueError("The sum of weights is zero, can't be normalized.")
if w is not None:
nx_w = nx * w
avg = (nx_w).sum(axis=1) / w_sum
else:
avg = nx.sum(axis=1) / w_sum
nx_w = nx
if w is not None and aweights is not None and ddof:
norm_factor = w_sum - (w * aweights.astype(w.dtype)).sum() / w_sum
else:
norm_factor = w_sum - ddof
norm_factor = paddle.clip(norm_factor, min=0)
nx = nx - avg.unsqueeze(1)
xxt = paddle.mm(nx, nx_w.t().conj())
cov = paddle.divide(xxt, norm_factor).squeeze()
return cov
def t(input: Tensor, name: str | None = None) -> Tensor:
"""
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 float32, float64, int32, int64.
name (str|None, 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.
Examples:
.. code-block:: pycon
:name: code-example
>>> import paddle
>>> # Example 1 (0-D tensor)
>>> x = paddle.to_tensor([0.79])
>>> out = paddle.t(x)
>>> print(out)
Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.79000002])
>>> # Example 2 (1-D tensor)
>>> x = paddle.to_tensor([0.79, 0.84, 0.32])
>>> out2 = paddle.t(x)
>>> print(out2)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.79000002, 0.83999997, 0.31999999])
>>> print(paddle.t(x).shape)
paddle.Size([3])
>>> # Example 3 (2-D tensor)
>>> x = paddle.to_tensor([[0.79, 0.84, 0.32], [0.64, 0.14, 0.57]])
>>> print(x.shape)
paddle.Size([2, 3])
>>> out3 = paddle.t(x)
>>> print(out3)
Tensor(shape=[3, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0.79000002, 0.63999999],
[0.83999997, 0.14000000],
[0.31999999, 0.56999999]])
>>> print(paddle.t(x).shape)
paddle.Size([3, 2])
"""
if len(input.shape) > 2:
raise ValueError(
"Input(input) only support N-D (N<=2) tensor, but received "
f"length of Input(input) is {len(input.shape)}. Perhaps you can use paddle."
"tensor.transpose() instead."
)
if in_dynamic_or_pir_mode():
if len(input.shape) <= 1:
return input
# 2-D tensor
perm = [1, 0]
out = _C_ops.transpose(input, perm)
return out
else:
check_variable_and_dtype(
input,
'input',
['float16', 'float32', 'float64', 'int32', 'int64', 'uint16'],
'transpose',
)
helper = LayerHelper('t', **locals())
out = helper.create_variable_for_type_inference(input.dtype)
input_shape = helper.create_variable_for_type_inference(input.dtype)
if len(input.shape) <= 1:
out = input
else:
helper.append_op(
type='transpose2',
inputs={'X': [input]},
outputs={'Out': [out], 'XShape': [input_shape]},
attrs={'axis': [1, 0]},
)
return out
@inplace_apis_in_dygraph_only
def t_(input, name=None):
r"""
Inplace version of ``t`` API, the output Tensor will be inplaced with input ``input``.
Please refer to :ref:`api_paddle_t`.
"""
if len(input.shape) > 2:
raise ValueError(
"Input(input) only support N-D (N<=2) tensor, but received "
f"length of Input(input) is {len(input.shape)}. Perhaps you can use paddle."
"tensor.transpose() instead."
)
if in_dynamic_mode():
if len(input.shape) <= 1:
return input
# 2-D tensor
perm = [1, 0]
out = _C_ops.transpose_(input, perm)
return out
def matrix_rank(
x: Tensor,
tol: float | Tensor | None = None,
hermitian: bool = False,
atol: float | Tensor | None = None,
rtol: float | Tensor | None = None,
name: str | None = None,
) -> Tensor:
r"""
Computes the rank of a matrix.
Notes:
1. Support the use of attribute `tol` alone or the use of attributes `atol` and `rtol` together without `tol`.
2. When `tol` is used alone, it will return 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. It is compatible with numpy API.
3. When `atol` and `rtol` are used, the tolerance value is computed as `max(atol, sigma_1 * rtol)`, where sigma_1 is largest
singular value (or eigenvalues in absolute value).
4. When `atol` and `rtol` are used: If `rtol` is not specified, then it is set to be `max(m,n) * eps`, where `x` has dimension(m, n) and
`eps` is the epsilon value for the dtype of `x`; If `rtol` is not specified and `atol` is specified to be greater than 0, then it
is set to be 0.
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, float64, complex64 or complex128.
tol (float|Tensor, optional): The tolerance value. 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. Default: None.
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. Default: False.
atol (float|Tensor, optional): The absolute tolerance value. When None it is considered to be 0. Default: None.
rtol (float|Tensor, optional): The relative tolerance value. See above Notes for the value it takes when None. Default: None.
name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: Rank of tensor x.
Examples:
.. code-block:: pycon
>>> import paddle
>>> a = paddle.eye(10)
>>> b = paddle.linalg.matrix_rank(a)
>>> print(b)
Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True,
10)
>>> c = paddle.ones(shape=[3, 4, 5, 5])
>>> d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)
>>> print(d)
Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
[[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]])
"""
target_dtype = (
paddle.float32
if x.dtype == paddle.complex64
else (paddle.float64 if x.dtype == paddle.complex128 else x.dtype)
)
use_atol_rtol = False
if (atol is not None) or (rtol is not None):
if tol is not None:
raise ValueError(
"Only support to use tol alone or use atol and rtol without tol."
)
use_atol_rtol = True
if use_atol_rtol:
if atol is None:
atol = full([], 0.0, target_dtype)
if isinstance(atol, (float, int)):
atol = full([], atol, target_dtype)
if atol.dtype != target_dtype:
atol = cast(atol, target_dtype)
if rtol is not None:
if isinstance(rtol, (float, int)):
rtol = full([], rtol, target_dtype)
if rtol.dtype != target_dtype:
rtol = cast(rtol, target_dtype)
atol, rtol = paddle.broadcast_tensors([atol, rtol])
if in_dynamic_or_pir_mode():
return _C_ops.matrix_rank_atol_rtol(x, atol, rtol, hermitian)
else:
inputs = {}
attrs = {}
check_variable_and_dtype(
x,
'x',
['float32', 'float64', 'complex64', 'complex128'],
'matrix_rank_atol_rtol',
)
inputs['x'] = x
inputs['atol'] = atol
inputs['rtol'] = rtol
check_type(hermitian, 'hermitian', bool, 'matrix_rank_atol_rtol')
attrs['hermitian'] = hermitian
helper = LayerHelper('matrix_rank_atol_rtol', **locals())
out = helper.create_variable_for_type_inference(dtype='int32')
helper.append_op(
type='matrix_rank_atol_rtol',
inputs=inputs,
outputs={'out': out},
attrs=attrs,
)
return out
else:
if in_dynamic_or_pir_mode():
if isinstance(tol, (Variable, paddle.pir.Value)):
if tol.dtype != target_dtype:
tol_tensor = cast(tol, target_dtype)
else:
tol_tensor = tol
use_default_tol = False
return _C_ops.matrix_rank_tol(
x, tol_tensor, use_default_tol, hermitian
)
if tol is None:
tol_attr = 0.0
use_default_tol = True
else:
tol_attr = float(tol)
use_default_tol = False
return _C_ops.matrix_rank(x, tol_attr, use_default_tol, hermitian)
else:
inputs = {}
attrs = {}
check_variable_and_dtype(
x,
'x',
['float32', 'float64', 'complex64', 'complex128'],
'matrix_rank',
)
inputs['X'] = x
if tol is None:
attrs['use_default_tol'] = True
elif isinstance(tol, Variable):
attrs['use_default_tol'] = False
if tol.dtype != target_dtype:
inputs['TolTensor'] = cast(tol, target_dtype)
else:
inputs['TolTensor'] = tol
else:
check_type(tol, 'tol', float, 'matrix_rank')
attrs['use_default_tol'] = False
attrs['tol'] = tol
check_type(hermitian, 'hermitian', bool, 'matrix_rank')
attrs['hermitian'] = hermitian
helper = LayerHelper('matrix_rank', **locals())
out = helper.create_variable_for_type_inference(dtype='int32')
helper.append_op(
type='matrix_rank',
inputs=inputs,
outputs={'Out': out},
attrs=attrs,
)
return out
def histogram(
input: Tensor,
bins: int = 100,
min: float = 0.0,
max: float = 0.0,
weight: Tensor | None = None,
density: bool = False,
name: str | None = None,
) -> Tensor:
"""
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:
input (Tensor): A Tensor with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor
should be float32, float64, int32, int64.
bins (int, optional): number of histogram bins. Default: 100.
min (float, optional): lower end of the range (inclusive). Default: 0.0.
max (float, optional): upper end of the range (inclusive). Default: 0.0.
weight (Tensor, optional): If provided, it must have the same shape as input. Each value in input contributes its associated
weight towards the bin count (instead of 1). Default: None.
density (bool, optional): If False, the result will contain the count (or total weight) in each bin. If True, the result is the
value of the probability density function over the bins, normalized such that the integral over the range of the bins is 1.
name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
Returns:
Tensor, shape is (nbins,), the counts or density of the histogram.
Examples:
.. code-block:: pycon
>>> import paddle
>>> inputs = paddle.to_tensor([1, 2, 1])
>>> result = paddle.histogram(inputs, bins=4, min=0, max=3)
>>> print(result)
Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True,
[0, 2, 1, 0])
"""
if isinstance(min, int):
min = float(min)
if isinstance(max, int):
max = float(max)
if in_dynamic_or_pir_mode():
return _C_ops.histogram(input, weight, bins, min, max, density)
else:
helper = LayerHelper('histogram', **locals())
check_variable_and_dtype(
input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram'
)
if weight or density:
if weight:
check_variable_and_dtype(
weight,
'Weight',
['int32', 'int64', 'float32', 'float64'],
'histogram',
)
out = helper.create_variable_for_type_inference(
dtype=VarDesc.VarType.FP32
)
else:
out = helper.create_variable_for_type_inference(
dtype=VarDesc.VarType.INT64
)
helper.append_op(
type='histogram',
inputs={'X': input, 'Weight': weight},
outputs={'Out': out},
attrs={
'bins': bins,
'min': min,
'max': max,
'density': density,
},
)
return out
def histogram_bin_edges(
input: Tensor,
bins: int = 100,
min: float = 0.0,
max: float = 0.0,
name: str | None = None,
) -> Tensor:
"""
Computes only the edges of the bins used by the histogram function.
If min and max are both zero, the minimum and maximum values of the data are used.
Args:
input (Tensor): The data type of the input Tensor should be float32, float64, int32, int64.
bins (int, optional): number of histogram bins.
min (float, optional): lower end of the range (inclusive). Default: 0.0.
max (float, optional): upper end of the range (inclusive). Default: 0.0.
name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
Returns:
Tensor, the values of the bin edges. The output data type will be float32.
Examples:
.. code-block:: pycon
>>> import paddle
>>> inputs = paddle.to_tensor([1, 2, 1])
>>> result = paddle.histogram_bin_edges(inputs, bins=4, min=0, max=3)
>>> print(result)
Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True,
[0. , 0.75000000, 1.50000000, 2.25000000, 3. ])
"""
if isinstance(min, int):
min = float(min)
if isinstance(max, int):
max = float(max)
check_type(input, 'input', (Variable), 'histogram_bin_edges')
check_dtype(
input.dtype,
'input',
['float32', 'float64', 'int32', 'int64'],
'histogram_bin_edges',
)
check_type(bins, 'bins', int, 'histogram_bin_edges')
if max == 0.0 and min == 0.0:
min = paddle.min(input)
max = paddle.max(input)
else:
if max < min:
raise ValueError("max must be larger than min in range parameter")
if (min - max) == 0.0:
max = max + 0.5
min = min - 0.5
return paddle.linspace(min, max, bins + 1, name=name)
def slogdet(x: Tensor, name: str | None = None) -> Tensor:
"""
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, complex64, complex128.
Notes:
1. For matrices that have zero determinant, this returns ``(0, -inf)``.
2. For matrices with complex value, the :math:`abs(det)` is the modulus of the determinant,
and therefore :math:`sign = det / abs(det)`.
Args:
x (Tensor): the batch of matrices of size :math:`(*, n, n)`
where math:`*` is one or more batch dimensions.
name (str|None, optional): Name of the output.It's used to print debug info for
developers. Details: :ref:`api_guide_Name`. Default is None.
Returns:
y (Tensor), A tensor containing the sign of the determinant and the natural logarithm
of the absolute value of determinant, respectively. The output shape is :math:`(2, *)`,
where math:`*` is one or more batch dimensions of the input `x`.
Examples:
.. code-block:: pycon
>>> import paddle
>>> paddle.seed(2023)
>>> x = paddle.randn([3, 3, 3])
>>> A = paddle.linalg.slogdet(x)
>>> print(A)
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-1. , 1. , 1. ],
[ 0.25681755, -0.25061053, -0.10809582]])
"""
if in_dynamic_or_pir_mode():
return _C_ops.slogdet(x)
else:
check_dtype(
x.dtype,
'Input',
['float32', 'float64', 'complex64', 'complex128'],
'slogdet',
)
input_shape = list(x.shape)
assert len(input_shape) >= 2, (
"The x must be at least 2-dimensional, "
f"but received Input x's dimensional: {len(input_shape)}.\n"
)
assert input_shape[-1] == input_shape[-2], (
"Expect squared input,"
f"but received {input_shape[-2]} by {input_shape[-1]} matrix.\n"
)
helper = LayerHelper('slogdeterminant', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='slogdeterminant',
inputs={'Input': [x]},
outputs={'Out': [out]},
)
return out
def logdet(input: Tensor, name: str | None = None) -> Tensor:
"""
Computes the natural logarithm of the determinant of a square matrix or
batches of square matrices.
For matrices with negative determinant, returns ``nan``.
For matrices with zero determinant, returns ``-inf``.
Args:
input (Tensor): The input tensor of shape ``[*, n, n]`` where ``*``
is zero or more batch dimensions.
name (str|None, optional): Name for the operation. Default: None.
Returns:
Tensor: The log-determinant of ``input``, with shape ``[*]``.
"""
return det(input).log()
def svd(
x: Tensor,
full_matrices: bool = False,
name: str | None = None,
*,
out: tuple[Tensor, Tensor, Tensor] | None = None,
) -> tuple[Tensor, Tensor, Tensor]:
r"""
Computes the singular value decomposition of one matrix or a batch of regular matrices.
Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:
.. math::
X = U * diag(S) * V^{H}
Args:
x (Tensor): The input tensor. Its shape should be `[..., N, M]`,
where `...` is zero or more batch dimensions. N and M can be arbitrary
positive number. Note that if x is singular matrices, the grad is numerical
instable. The data type of x should be float32, float64, complex64 or complex128.
full_matrices (bool, optional): A flag to control the behavior of svd.
If full_matrices = True, svd op will compute full U and V matrices,
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.
which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).
Default value is False.
name (str|None, optional): Name for the operation. For more information,
please refer to :ref:`api_guide_Name`. Default value is None.
Returns:
- U (Tensor), is the singular value decomposition result U.
- S (Tensor), is the singular value decomposition result S.
- VH (Tensor), VH is the conjugate transpose of V, which is the singular value decomposition result V.
Tuple of 3 tensors(U, S, VH): VH is the conjugate transpose of V. S is the singular value vectors of matrices with shape `[..., K]`
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')
>>> x = x.reshape([3, 2])
>>> u, s, vh = paddle.linalg.svd(x)
>>> print(u)
Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[-0.27364809, -0.21695147],
[-0.37892198, -0.87112408],
[-0.88404460, 0.44053933]])
>>> print(s)
Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True,
[8.14753743, 0.78589688])
>>> print(vh)
Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[-0.51411221, -0.85772294],
[ 0.85772294, -0.51411221]])
>>> # one can verify : U * S * VT == X
>>> # U * UH == I
>>> # V * VH == I
"""
if in_dynamic_or_pir_mode():
return _C_ops.svd(x, full_matrices, out=out)
else:
check_variable_and_dtype(
x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'svd'
)
check_type(full_matrices, 'full_matrices', bool, 'svd')
helper = LayerHelper('svd', **locals())
u = helper.create_variable_for_type_inference(dtype=x.dtype)
vh = helper.create_variable_for_type_inference(dtype=x.dtype)
s = helper.create_variable_for_type_inference(dtype=x.dtype)
attrs = {}
attrs['full_matrices'] = full_matrices
helper.append_op(
type='svd',
inputs={'X': [x]},
outputs={'U': u, 'VH': vh, 'S': s},
attrs=attrs,
)
return u, s, vh
def svdvals(x: Tensor, name: str | None = None) -> Tensor:
r"""
Computes the singular values of one matrix or a batch of matrices.
Let :math:`X` be the input matrix or a batch of input matrices,
the output singular values :math:`S` are the diagonal elements of the matrix
produced by singular value decomposition:
.. math::
X = U * diag(S) * V^{H}
Args:
x (Tensor): The input tensor. Its shape should be `[..., M, N]`, where
`...` is zero or more batch dimensions. The data type of x should
be float32 or float64.
name (str|None, optional): Name for the operation. For more
information, please refer to :ref:`api_guide_Name`.
Default: None.
Returns:
Tensor: Singular values of x. The shape is `[..., K]`, where `K = min(M, N)`.
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]])
>>> s = paddle.linalg.svdvals(x)
>>> print(s)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[8.14753819, 0.78589684])
"""
return _C_ops.svdvals(x)
def _conjugate(x):
if x.is_complex():
return x.conj()
return x
def _transpose(x):
shape = x.shape
perm = list(range(0, len(shape)))
perm = [*perm[:-2], perm[-1], perm[-2]]
return paddle.transpose(x, perm)
def _transjugate(x):
return _conjugate(_transpose(x))
def _get_approximate_basis(x, q, niter=2, M=None):
niter = 2 if niter is None else niter
m, n = x.shape[-2:]
qr = paddle.linalg.qr
R = paddle.randn((n, q), dtype=x.dtype)
A_t = _transpose(x)
A_H = _conjugate(A_t)
if M is None:
Q = qr(paddle.matmul(x, R))[0]
for i in range(niter):
Q = qr(paddle.matmul(A_H, Q))[0]
Q = qr(paddle.matmul(x, Q))[0]
else:
M_H = _transjugate(M)
Q = qr(paddle.matmul(x, R) - paddle.matmul(M, R))[0]
for i in range(niter):
Q = qr(paddle.matmul(A_H, Q) - paddle.matmul(M_H, Q))[0]
Q = qr(paddle.matmul(x, Q) - paddle.matmul(M, Q))[0]
return Q
def svd_lowrank(
x: Tensor,
q: int | None = None,
niter: int = 2,
M: Tensor | None = None,
name: str | None = None,
) -> tuple[Tensor, Tensor, Tensor]:
r"""
Return the singular value decomposition (SVD) on a low-rank matrix or batches of such matrices.
If :math:`X` is the input matrix or a batch of input matrices, the output should satisfies:
.. math::
X \approx U * diag(S) * V^{H}
When :math:`M` is given, the output should satisfies:
.. math::
X - M \approx U * diag(S) * V^{H}
Args:
x (Tensor): The input tensor. Its shape should be `[..., N, M]`, where `...` is
zero or more batch dimensions. N and M can be arbitrary positive number.
The data type of ``x`` should be float32 or float64.
q (int, optional): A slightly overestimated rank of :math:`X`.
Default value is None, which means the overestimated rank is 6.
niter (int, optional): The number of iterations to perform. Default: 2.
M (Tensor, optional): The input tensor's mean. Its shape should be `[..., 1, M]`.
Default value is None.
name (str|None, optional): Name for the operation. For more information, please
refer to :ref:`api_guide_Name`. Default: None.
Returns:
- Tensor U, is N x q matrix.
- Tensor S, is a vector with length q.
- Tensor V, is M x q matrix.
tuple (U, S, V): which is the nearly optimal approximation of a singular value decomposition of the matrix :math:`X` or :math:`X - M`.
Examples:
.. code-block:: pycon
>>> import paddle
>>> paddle.seed(2024)
>>> x = paddle.randn((5, 5), dtype='float64')
>>> U, S, V = paddle.linalg.svd_lowrank(x)
>>> print(U)
Tensor(shape=[5, 5], dtype=float64, place=Place(cpu), stop_gradient=True,
[[-0.03586982, -0.17211503, 0.31536566, -0.38225676, -0.85059629],
[-0.38386839, 0.67754925, 0.23222694, 0.51777188, -0.26749766],
[-0.85977150, -0.28442378, -0.41412094, -0.08955629, -0.01948348],
[ 0.18611503, 0.56047358, -0.67717019, -0.39286761, -0.19577062],
[ 0.27841082, -0.34099254, -0.46535957, 0.65071250, -0.40770727]])
>>> print(S)
Tensor(shape=[5], dtype=float64, place=Place(cpu), stop_gradient=True,
[4.11253399, 3.03227120, 2.45499752, 1.25602436, 0.45825337])
>>> print(V)
Tensor(shape=[5, 5], dtype=float64, place=Place(cpu), stop_gradient=True,
[[ 0.46401347, 0.50977695, -0.08742316, -0.11140428, -0.71046833],
[-0.48927226, -0.35047624, 0.07918771, 0.45431083, -0.65200463],
[-0.20494730, 0.67097011, -0.05427719, 0.66510472, 0.24997083],
[-0.69645001, 0.40237917, 0.09360970, -0.58032322, -0.08666357],
[ 0.13512270, 0.07199989, 0.98710572, 0.04529277, 0.01134594]])
"""
if not paddle.is_tensor(x):
raise ValueError(f'Input must be tensor, but got {type(x)}')
m, n = x.shape[-2:]
if q is None:
q = min(6, m, n)
elif min(m, n) != 0 and not (q >= 0 and q <= min(m, n)):
raise ValueError(
f'q(={q}) must be non-negative integer'
f' and not greater than min(m, n)={min(m, n)}'
)
if not (niter >= 0):
raise ValueError(f'niter(={niter}) must be non-negative integer')
if M is None:
M_t = None
else:
M = M.broadcast_to(x.shape)
M_t = _transpose(M)
A_t = _transpose(x)
if m < n or n > q:
Q = _get_approximate_basis(A_t, q, niter=niter, M=M_t)
Q_c = _conjugate(Q)
if M is None:
B_t = paddle.matmul(x, Q_c)
else:
B_t = paddle.matmul(x, Q_c) - paddle.matmul(M, Q_c)
assert B_t.shape[-2] == m, (B_t.shape, m)
if B_t.shape[-1] != 0:
assert B_t.shape[-1] == q, (B_t.shape, q)
assert B_t.shape[-1] <= B_t.shape[-2], B_t.shape
U, S, Vh = paddle.linalg.svd(B_t, full_matrices=False)
V = _transjugate(Vh)
V = Q.matmul(V)
else:
Q = _get_approximate_basis(x, q, niter=niter, M=M)
Q_c = _conjugate(Q)
if M is None:
B = paddle.matmul(A_t, Q_c)
else:
B = paddle.matmul(A_t, Q_c) - paddle.matmul(M_t, Q_c)
B_t = _transpose(B)
if B_t.shape[-2] != 0:
assert B_t.shape[-2] == q, (B_t.shape, q)
assert B_t.shape[-1] == n, (B_t.shape, n)
assert B_t.shape[-1] <= B_t.shape[-2], B_t.shape
U, S, Vh = paddle.linalg.svd(B_t, full_matrices=False)
V = _transjugate(Vh)
U = Q.matmul(U)
return U, S, V
def pca_lowrank(
x: Tensor,
q: int | None = None,
center: bool = True,
niter: int = 2,
name: str | None = None,
) -> tuple[Tensor, Tensor, Tensor]:
r"""
Performs linear Principal Component Analysis (PCA) on a low-rank matrix or batches of such matrices.
Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:
.. math::
X = U * diag(S) * V^{T}
Args:
x (Tensor): The input tensor. Its shape should be `[..., N, M]`,
where `...` is zero or more batch dimensions. N and M can be arbitrary
positive number. The data type of x should be float32 or float64.
q (int, optional): a slightly overestimated rank of :math:`X`.
Default value is :math:`q=min(6,N,M)`.
center (bool, optional): if True, center the input tensor.
Default value is True.
niter (int, optional): number of iterations to perform. Default: 2.
name (str|None, optional): Name for the operation. For more information,
please refer to :ref:`api_guide_Name`. Default: None.
Returns:
- Tensor U, is N x q matrix.
- Tensor S, is a vector with length q.
- Tensor V, is M x q matrix.
tuple (U, S, V): which is the nearly optimal approximation of a singular value decomposition of a centered matrix :math:`X`.
Examples:
.. code-block:: pycon
>>> import paddle
>>> paddle.seed(2023)
>>> x = paddle.randn((5, 5), dtype='float64')
>>> U, S, V = paddle.linalg.pca_lowrank(x)
>>> print(U)
Tensor(shape=[5, 5], dtype=float64, place=Place(cpu), stop_gradient=True,
[[ 0.80131563, 0.11962647, 0.27667179, -0.25891214, 0.44721360],
[-0.12642301, 0.69917551, -0.17899393, 0.51296394, 0.44721360],
[ 0.08997135, -0.69821706, -0.20059228, 0.51396579, 0.44721360],
[-0.23871837, -0.02815453, -0.59888153, -0.61932365, 0.44721360],
[-0.52614559, -0.09243040, 0.70179595, -0.14869394, 0.44721360]])
>>> print(S)
Tensor(shape=[5], dtype=float64, place=Place(cpu), stop_gradient=True,
[2.60101614, 2.40554940, 1.49768346, 0.19064830, 0.00000000])
>>> print(V)
Tensor(shape=[5, 5], dtype=float64, place=Place(cpu), stop_gradient=True,
[[ 0.58339481, -0.17143771, 0.00522143, 0.57976310, 0.54231640],
[ 0.22334335, 0.72963474, -0.30148399, -0.39388750, 0.41438019],
[ 0.05416913, 0.34666487, 0.93549758, 0.00063507, 0.04162998],
[-0.39519094, 0.53074980, -0.16687419, 0.71175586, -0.16638919],
[-0.67131070, -0.19071018, 0.07795789, -0.04615811, 0.71046714]])
"""
if not paddle.is_tensor(x):
raise ValueError(f'Input must be tensor, but got {type(x)}')
(m, n) = x.shape[-2:]
if q is None:
q = min(6, m, n)
elif not (q >= 0 and q <= min(m, n)):
raise ValueError(
f'q(={q}) must be non-negative integer'
f' and not greater than min(m, n)={min(m, n)}'
)
if not (niter >= 0):
raise ValueError(f'niter(={niter}) must be non-negative integer')
if not center:
return svd_lowrank(x, q, niter=niter, M=None)
C = x.mean(axis=-2, keepdim=True)
return svd_lowrank(x - C, q, niter=niter, M=None)
def matrix_power(
x: Tensor, n: int, name: str | None = None
) -> tuple[Tensor, int]:
r"""
Computes the n-th power of a square matrix or a batch of square matrices.
Let :math:`X` be a square matrix or a batch of square matrices, :math:`n` be
an exponent, the equation should be:
.. math::
Out = X ^ {n}
Specifically,
- If `n > 0`, it returns the matrix or a batch of matrices raised to the power of `n`.
- If `n = 0`, it returns the identity matrix or a batch of identity matrices.
- If `n < 0`, it returns the inverse of each matrix (if invertible) raised to the power of `abs(n)`.
Args:
x (Tensor): A square matrix or a batch of square matrices to be raised
to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or
more batch dimensions. Its data type should be float32 or float64.
n (int): The exponent. It can be any positive, negative integer or zero.
name (str|None, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
- Tensor, The n-th power of the matrix (or the batch of matrices) `x`. Its
data type should be the same as that of `x`.
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.to_tensor(
... [
... [1, 2, 3],
... [1, 4, 9],
... [1, 8, 27],
... ],
... dtype='float64',
... )
>>> print(paddle.linalg.matrix_power(x, 2))
Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
[[6. , 34. , 102.],
[14. , 90. , 282.],
[36. , 250., 804.]])
>>> print(paddle.linalg.matrix_power(x, 0))
Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
>>> print(paddle.linalg.matrix_power(x, -2))
Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
[[ 12.91666667, -12.75000000, 2.83333333 ],
[-7.66666667 , 8. , -1.83333333 ],
[ 1.80555556 , -1.91666667 , 0.44444444 ]])
"""
if in_dynamic_or_pir_mode():
return _C_ops.matrix_power(x, n)
else:
check_variable_and_dtype(
x, 'dtype', ['float32', 'float64'], 'matrix_power'
)
check_type(n, 'n', int, 'matrix_power')
helper = LayerHelper('matrix_power', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='matrix_power',
inputs={'X': x},
outputs={'Out': out},
attrs={'n': n},
)
return out
class QrRetType(NamedTuple):
Q: Tensor
R: Tensor
@overload
def qr(
x: Tensor,
mode: Literal['reduced', 'complete'] = ...,
name: str | None = ...,
*,
out: tuple[Tensor, Tensor] | None = ...,
) -> QrRetType: ...
@overload
def qr(
x: Tensor,
mode: Literal['r'] = ...,
name: str | None = ...,
*,
out: Tensor | None = ...,
) -> Tensor: ...
@overload
def qr(
input: Tensor,
some: bool = ...,
*,
out: tuple[Tensor, Tensor] | None = ...,
) -> QrRetType: ...
@qr_decorator
def qr(
x,
mode="reduced",
name=None,
*,
out=None,
) -> QrRetType | Tensor:
r"""
Note:
This API supports two signatures:
1. ``paddle.linalg.qr(x, mode='reduced', name=None, *, out=None)`` (Paddle-style):
Computes the QR decomposition with a ``mode`` string parameter.
2. ``paddle.linalg.qr(input, some=True, *, out=None)`` (PyTorch-style):
Computes the QR decomposition with a ``some`` boolean parameter.
Computes the QR decomposition of one matrix or batches of matrices (backward is unsupported now).
Args:
x (Tensor): The input tensor. Its shape should be `[..., M, N]`,
where ... is zero or more batch dimensions. M and N can be arbitrary
positive number. The data type of x supports float, double, complex64, complex128.
Alias: ``input``, ``A``.
mode (str, optional): A flag to control the behavior of qr.
Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:
If mode = "reduced", qr op will return reduced Q and R matrices,
which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.
If mode = "complete", qr op will return complete Q and R matrices,
which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.
If mode = "r", qr op will only compute reduced R matrix, which means
R's shape is `[..., K, N]` and will not return Q. Default: "reduced".
name (str|None, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Keyword Args:
out (tuple[Tensor, Tensor]|Tensor|None, optional): The output tensor(s).
If mode is "r", out must be a single Tensor to store R.
Otherwise, out must be a tuple of (Q, R) tensors.
If set, the result will be stored in these Tensors. Default: None.
Returns:
QrRetType | Tensor: If mode="r", returns a single Tensor R.
Otherwise, returns a QrRetType named tuple (Q, R).
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')
>>> q, r = paddle.linalg.qr(x)
>>> print(q)
Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[-0.16903085, 0.89708523],
[-0.50709255, 0.27602622],
[-0.84515425, -0.34503278]])
>>> print(r)
Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[-5.91607978, -7.43735744],
[ 0. , 0.82807867]])
>>> # one can verify : X = Q * R ;
"""
if in_dynamic_or_pir_mode():
q, r = _C_ops.qr(x, mode)
else:
check_variable_and_dtype(
x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'qr'
)
check_type(mode, 'mode', str, 'qr')
helper = LayerHelper('qr', **locals())
q = helper.create_variable_for_type_inference(dtype=x.dtype)
r = helper.create_variable_for_type_inference(dtype=x.dtype)
attrs = {}
attrs['mode'] = mode
helper.append_op(
type='qr', inputs={'X': [x]}, outputs={'Q': q, 'R': r}, attrs=attrs
)
if mode == "r":
if out is not None:
paddle.assign(r, out)
return out
return r
if out is not None:
paddle.assign(q, out[0])
paddle.assign(r, out[1])
return QrRetType(Q=out[0], R=out[1])
return QrRetType(Q=q, R=r)
@overload
def lu(
x: Tensor,
pivot: bool = ...,
get_infos: Literal[False] = ...,
name: str | None = ...,
) -> tuple[Tensor, Tensor]: ...
@overload
def lu(
x: Tensor,
pivot: bool = ...,
get_infos: Literal[True] = ...,
name: str | None = ...,
) -> tuple[Tensor, Tensor, Tensor]: ...
@overload
def lu(
x: Tensor, pivot: bool = ..., get_infos: bool = ..., name: str | None = ...
) -> tuple[Tensor, Tensor] | tuple[Tensor, Tensor, Tensor]: ...
def lu(
x, pivot=True, get_infos=False, name=None
) -> tuple[Tensor, Tensor] | tuple[Tensor, Tensor, Tensor]:
r"""
Computes the LU factorization of an N-D(N>=2) matrix x.
Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and
upper triangular matrix U are combined to a single LU matrix.
Pivoting is done if pivot is set to True.
P mat can be get by pivots:
.. code-block:: text
ones = eye(rows) #eye matrix of rank rows
for i in range(cols):
swap(ones[i], ones[pivots[i]])
return ones
Args:
X (Tensor): the tensor to factor of N-dimensions(N>=2). Its data type should be float32, float64, complex64, or complex128.
pivot (bool, optional): controls whether pivoting is done. Default: True.
get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.
name (str|None, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
factorization (Tensor), LU matrix, the factorization of input X.
pivots (IntTensor), the pivots of size(\*(N-2), min(m,n)). `pivots` stores all the
intermediate transpositions of rows. The final permutation `perm` could be
reconstructed by this, details refer to upper example.
infos (IntTensor, optional), if `get_infos` is `True`, this is a tensor of size (\*(N-2))
where non-zero values indicate whether factorization for the matrix or each minibatch
has succeeded or failed.
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')
>>> lu, p, info = paddle.linalg.lu(x, get_infos=True)
>>> print(lu)
Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[5. , 6. ],
[0.20000000, 0.80000000],
[0.60000000, 0.50000000]])
>>> print(p)
Tensor(shape=[2], dtype=int32, place=Place(cpu), stop_gradient=True,
[3, 3])
>>> print(info)
Tensor(shape=[], dtype=int32, place=Place(cpu), stop_gradient=True,
0)
>>> P, L, U = paddle.linalg.lu_unpack(lu, p)
>>> print(P)
Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
[[0., 1., 0.],
[0., 0., 1.],
[1., 0., 0.]])
>>> print(L)
Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[1. , 0. ],
[0.20000000, 1. ],
[0.60000000, 0.50000000]])
>>> print(U)
Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[5. , 6. ],
[0. , 0.80000000]])
>>> # one can verify : X = P @ L @ U ;
"""
if in_dynamic_or_pir_mode():
lu, p, info = _C_ops.lu(x, pivot)
else:
check_variable_and_dtype(
x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lu'
)
helper = LayerHelper('lu', **locals())
lu = helper.create_variable_for_type_inference(dtype=x.dtype)
p = helper.create_variable_for_type_inference(dtype='int')
info = helper.create_variable_for_type_inference(dtype='int')
attrs = {}
attrs['pivot'] = pivot
helper.append_op(
type='lu',
inputs={'X': x},
outputs={'Out': lu, 'Pivots': p, 'Infos': info},
attrs=attrs,
)
if get_infos:
return lu, p, info
else:
return lu, p
def lu_solve(
b: Tensor,
lu: Tensor,
pivots: Tensor,
trans: Literal['N', 'T', 'C'] = 'N',
name: str | None = None,
):
r"""
Computes the solution x to the system of linear equations :math:`Ax = b` ,
given LU decomposition :math:`A` and column vector :math:`b`.
Args:
b (Tensor): Column vector `b` in the above equation. It has shape :math:`(*, m, k)`, where :math:`*` is batch dimensions,
with data type float32, float64, complex64, or complex128.
lu (Tensor): LU decomposition. It has shape :math:`(*, m, m)`, where :math:`*` is batch dimensions, that can be decomposed into an upper triangular matrix U and a lower triangular matrix L,
with data type float32, float64, complex64, or complex128.
pivots (Tensor): Permutation matrix P of LU decomposition. It has shape :math:`(*, m)`, where :math:`*` is batch dimensions, that can be converted to a permutation matrix P, with data type int32.
trans (str, optional): The transpose of the matrix A. It can be "N" , "T" or "C", "N" means :math:`Ax=b`, "T" means :math:`A^Tx=b`, "C" means :math:`A^Hx=b`, default is "N".
name (str|None, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor, the same data type as the `b` and `lu`.
Examples:
.. code-block:: pycon
>>> import paddle
>>> import numpy as np
>>> A = paddle.to_tensor([[3, 1], [1, 2]], dtype="float64")
>>> b = paddle.to_tensor([[9, 8], [9, 8]], dtype="float64")
>>> lu, p = paddle.linalg.lu(A)
>>> x = paddle.linalg.lu_solve(b, lu, p)
>>> paddle.allclose(A @ x, b)
>>> print(x)
Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[1.80000000, 1.60000000],
[3.60000000, 3.20000000]])
"""
if b.ndim < 2:
raise ValueError(
f'`b` dimension must be gather than 2, but got {len(b.shape)}'
)
if lu.ndim < 2:
raise ValueError(
f'`lu` dimension must be gather than 2, but got {len(lu.shape)}'
)
if pivots.ndim < 1:
raise ValueError(
f'`pivots` dimension must be gather than 1, but got {len(pivots.shape)}'
)
if b.shape[-2] != lu.shape[-2]:
raise ValueError(
f'the rows of `b` must be equal to the rows of `lu`, but got {b.shape[-2]} and {lu.shape[-2]}'
)
if lu.shape[-1] != lu.shape[-2]:
raise ValueError(
f'`lu` shape[-1] must be equal to `lu` shape[-2], but got {lu.shape[-1]} and {lu.shape[-2]}'
)
if pivots.shape[-1] != lu.shape[-1]:
raise ValueError(
f'`pivots` shape[-1] must be equal to `lu` shape[-1], but got {pivots.shape[-1]} and {lu.shape[-1]}'
)
temp_shape = broadcast_shape(b.shape[:-2], lu.shape[:-2])
batch_shape = broadcast_shape(temp_shape, pivots.shape[:-1])
b = (
b
if b.shape[:-2] == batch_shape
else paddle.broadcast_to(b, batch_shape + list(b.shape[-2:]))
)
trans = trans if trans == "N" else "T"
pivots = (
pivots
if pivots.shape[:-1] == batch_shape
else paddle.broadcast_to(pivots, batch_shape + list(pivots.shape[-1:]))
)
lu = (
lu
if lu.shape[:-2] == batch_shape
else paddle.broadcast_to(lu, batch_shape + list(lu.shape[-2:]))
)
pivots.stop_gradient = True
out = _C_ops.lu_solve(b, lu, pivots, trans)
return out
def lu_unpack(
x: Tensor,
y: Tensor,
unpack_ludata: bool = True,
unpack_pivots: bool = True,
name: str | None = None,
) -> tuple[Tensor, Tensor, Tensor]:
r"""
Unpack L U and P to single matrix tensor .
unpack L and U matrix from LU, unpack permutation matrix P from Pivots .
P mat can be get by pivots:
.. code-block:: text
ones = eye(rows) #eye matrix of rank rows
for i in range(cols):
swap(ones[i], ones[pivots[i]])
Args:
x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.
Its data type should be float32, float64, complex64, or complex128.
y (Tensor): Pivots get from paddle.linalg.lu. Its data type should be int32.
unpack_ludata (bool, optional): whether to unpack L and U from x. Default: True.
unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivots. Default: True.
name (str|None, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
P (Tensor), Permutation matrix P of lu factorization.
L (Tensor), The lower triangular matrix tensor of lu factorization.
U (Tensor), The upper triangular matrix tensor of lu factorization.
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')
>>> lu, p, info = paddle.linalg.lu(x, get_infos=True)
>>> print(lu)
Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[5. , 6. ],
[0.20000000, 0.80000000],
[0.60000000, 0.50000000]])
>>> print(p)
Tensor(shape=[2], dtype=int32, place=Place(cpu), stop_gradient=True,
[3, 3])
>>> print(info)
Tensor(shape=[], dtype=int32, place=Place(cpu), stop_gradient=True,
0)
>>> P, L, U = paddle.linalg.lu_unpack(lu, p)
>>> print(P)
Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
[[0., 1., 0.],
[0., 0., 1.],
[1., 0., 0.]])
>>> print(L)
Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[1. , 0. ],
[0.20000000, 1. ],
[0.60000000, 0.50000000]])
>>> print(U)
Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[5. , 6. ],
[0. , 0.80000000]])
>>> # one can verify : X = P @ L @ U ;
"""
if x.ndim < 2:
raise ValueError(
f"The shape of x should be (*, M, N), but received ndim is [{x.ndim} < 2]"
)
if y.ndim < 1:
raise ValueError(
f"The shape of Pivots should be (*, K), but received ndim is [{y.ndim} < 1]"
)
if in_dynamic_or_pir_mode():
P, L, U = _C_ops.lu_unpack(x, y, unpack_ludata, unpack_pivots)
return P, L, U
else:
check_variable_and_dtype(
x,
'dtype',
['float32', 'float64', 'complex64', 'complex128'],
'lu_unpack',
)
helper = LayerHelper('lu_unpack', **locals())
p = helper.create_variable_for_type_inference(dtype=x.dtype)
l = helper.create_variable_for_type_inference(dtype=x.dtype)
u = helper.create_variable_for_type_inference(dtype=x.dtype)
attrs = {}
attrs['unpack_ludata'] = unpack_ludata
attrs['unpack_pivots'] = unpack_pivots
helper.append_op(
type='lu_unpack',
inputs={'X': x, 'Pivots': y},
outputs={'Pmat': p, 'L': l, 'U': u},
attrs=attrs,
)
return p, l, u
def eig(x: Tensor, name: str | None = None) -> tuple[Tensor, Tensor]:
"""
Performs the eigenvalue decomposition of a square matrix or a batch of square matrices.
Note:
- If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`api_paddle_linalg_eigh` instead, which is much faster.
- If only eigenvalues is needed, please use :ref:`api_paddle_linalg_eigvals` instead.
- If the matrix is of any shape, please use :ref:`api_paddle_linalg_svd`.
- This API is only supported on CPU device.
- The output datatype is always complex for both real and complex input.
Args:
x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,
``float64``, ``complex64`` or ``complex128``.
name (str|None, 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:
Eigenvalues(Tensor): A tensor with shape math:`[*, N]` refers to the eigen values.
Eigenvectors(Tensor): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.to_tensor(
... [
... [1.6707249, 7.2249975, 6.5045543],
... [9.956216, 8.749598, 6.066444],
... [4.4251957, 1.7983172, 0.370647],
... ]
... )
>>> w, v = paddle.linalg.eig(x)
>>> print(v)
Tensor(shape=[3, 3], dtype=complex64, place=Place(cpu), stop_gradient=True,
[[ (0.50613654+0.00000000j), (0.79717612+0.00000000j),
(0.18518065+0.00000000j)],
[ (0.83082366+0.00000000j), (-0.34638131+0.00000000j),
(-0.68370056+0.00000000j)],
[ (0.23142573+0.00000000j), (-0.49449989+0.00000000j),
(0.70587653+0.00000000j)]])
>>> print(w)
Tensor(shape=[3], dtype=complex64, place=Place(cpu), stop_gradient=True,
[(16.50470734+0.00000000j), (-5.50348139+0.00000000j),
(-0.21026139+0.00000000j)])
"""
if in_dynamic_or_pir_mode():
return _C_ops.eig(x)
else:
check_variable_and_dtype(
x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig'
)
helper = LayerHelper('eig', **locals())
w = helper.create_variable_for_type_inference(x.dtype)
v = helper.create_variable_for_type_inference(x.dtype)
inputs = {'X': x}
outputs = {'Eigenvalues': w, 'Eigenvectors': v}
helper.append_op(type='eig', inputs=inputs, outputs=outputs)
return w, v
def eigvals(x: Tensor, name: str | None = None) -> Tensor:
"""
Compute the eigenvalues of one or more general matrices.
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 data type should be float32, float64, complex64, or complex128.
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 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:: pycon
>>> import paddle
>>> paddle.seed(2023)
>>> x = paddle.rand(shape=[3, 3], dtype='float64')
>>> print(x)
Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
[[0.86583615, 0.52014721, 0.25960938],
[0.90525323, 0.42400090, 0.40641288],
[0.97020893, 0.74437359, 0.51785128]])
>>> print(paddle.linalg.eigvals(x))
Tensor(shape=[3], dtype=complex128, place=Place(cpu), stop_gradient=True,
[ (1.78895669+0.00000000j), (0.16364485+0.00000000j),
(-0.14491322+0.00000000j)])
"""
x_shape = list(x.shape)
if len(x_shape) < 2:
raise ValueError(
f"The dimension of Input(x) should be at least 2, but received x's dimension = {len(x_shape)}, x's shape = {x_shape}"
)
if x_shape[-1] != x_shape[-2]:
raise ValueError(
f"The last two dimensions of Input(x) should be equal, but received x's shape = {x_shape}"
)
if in_dynamic_or_pir_mode():
return _C_ops.eigvals(x)
else:
check_variable_and_dtype(
x,
'dtype',
['float32', 'float64', 'complex64', 'complex128'],
'eigvals',
)
helper = LayerHelper('eigvals', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})
return out
def multi_dot(x: list[Tensor], name: str | None = None) -> Tensor:
"""
Multi_dot is an operator that calculates multiple matrix multiplications.
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.
If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector
of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it
is treated as a column vector of shape(n, 1).
If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,
otherwise the output is a 1-D vector.
Multi_dot will select the lowest cost multiplication order for calculation. The
cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.
Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,
we can calculate the cost of different multiplication orders as follows:
- Cost((AB)C) = 20x5x100 + 20x100x10 = 30000
- Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000
In this case, multiplying B and C first, then multiply A, which is 5 times faster
than sequential calculation.
Args:
x (list[Tensor]): The input tensors which is a list Tensor.
name (str|None, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: The output Tensor.
Examples:
.. code-block:: pycon
>>> import paddle
>>> # A * B
>>> A = paddle.rand([3, 4])
>>> B = paddle.rand([4, 5])
>>> out = paddle.linalg.multi_dot([A, B])
>>> print(out.shape)
paddle.Size([3, 5])
>>> # A * B * C
>>> A = paddle.rand([10, 5])
>>> B = paddle.rand([5, 8])
>>> C = paddle.rand([8, 7])
>>> out = paddle.linalg.multi_dot([A, B, C])
>>> print(out.shape)
paddle.Size([10, 7])
"""
if in_dynamic_or_pir_mode():
return _C_ops.multi_dot(x)
else:
check_type(x, 'x', (list, tuple), 'multi_dot')
for id, item in enumerate(x):
check_variable_and_dtype(
item,
'x[' + str(id) + ']',
['float16', 'float32', 'float64', 'uint16'],
'multi_dot',
)
if item.dtype != x[0].dtype:
raise TypeError(
"All the Tensors in the input must have the same data type."
)
helper = LayerHelper('multi_dot', **locals())
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='multi_dot', inputs={"X": x}, outputs={"Out": out}
)
return out
@param_one_alias(["x", "input", "A"])
def pinv(
x: Tensor,
rcond: float | Tensor = 1e-15,
hermitian: bool = False,
name: str | None = None,
*,
atol: float | Tensor | None = None,
rtol: float | Tensor | None = None,
out: Tensor | None = None,
) -> Tensor:
r"""
Calculate pseudo inverse via SVD(singular value decomposition)
of one matrix or batches of regular matrix.
.. math::
if hermitian == False:
x = u * s * vt (SVD)
out = v * 1/s * ut
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
arbitrary positive number. The data type of x should be
float32 or float64 or complex64 or complex128. When data
type is complex64 or complex128, hermitian should be set
True. Alias: ``input``.
rcond (Tensor|float, optional): the tolerance value to determine
when is a singular value zero. This is used for backward
compatibility with NumPy. Prefer using ``rtol`` instead.
If ``rtol`` is specified, ``rcond`` is ignored.
Default:1e-15.
hermitian (bool, optional): indicates whether x is Hermitian
if complex or symmetric if real. Default: False.
name (str|None, 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`.
Keyword Args:
atol (float|Tensor|None, optional): the absolute tolerance value.
When None, it is considered to be zero. Default: None.
rtol (float|Tensor|None, optional): the relative tolerance value.
When both ``atol`` and ``rtol`` are None, ``rcond`` is used.
If ``rtol`` is specified, ``rcond`` is ignored.
Default: None.
out (Tensor|None, optional): output tensor. If provided, the result
will be assigned to this tensor. Default: None.
Returns:
Tensor: The tensor with same data type with x. it represents
pseudo inverse of x. Its shape should be (*, n, m).
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.arange(15).reshape((3, 5)).astype('float64')
>>> input = paddle.to_tensor(x)
>>> out = paddle.linalg.pinv(input)
>>> print(input)
Tensor(shape=[3, 5], dtype=float64, place=Place(cpu), stop_gradient=True,
[[0. , 1. , 2. , 3. , 4. ],
[5. , 6. , 7. , 8. , 9. ],
[10., 11., 12., 13., 14.]])
>>> print(out)
Tensor(shape=[5, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
[[-0.22666667, -0.06666667, 0.09333333],
[-0.12333333, -0.03333333, 0.05666667],
[-0.02000000, -0.00000000, 0.02000000],
[ 0.08333333, 0.03333333, -0.01666667],
[ 0.18666667, 0.06666667, -0.05333333]])
# one can verify : x * out * x = x ;
# or out * x * out = x ;
"""
use_atol_rtol = atol is not None or rtol is not None
if in_dynamic_or_pir_mode():
if not hermitian:
# combine svd and matmul op
u, s, vt = _C_ops.svd(x, False)
if s.shape[-1] == 0:
max_singular_val = s
else:
max_singular_val = _C_ops.max(s, [-1], True)
if use_atol_rtol:
# Compute cutoff using atol and rtol
atol_val = atol if atol is not None else 0.0
if not isinstance(atol_val, paddle.Tensor):
atol_val = paddle.to_tensor(atol_val, dtype=x.dtype)
elif atol_val.dtype != x.dtype:
atol_val = paddle.cast(atol_val, x.dtype)
if rtol is not None:
rtol_val = rtol
if not isinstance(rtol_val, paddle.Tensor):
rtol_val = paddle.to_tensor(rtol_val, dtype=x.dtype)
elif rtol_val.dtype != x.dtype:
rtol_val = paddle.cast(rtol_val, x.dtype)
cutoff = paddle.maximum(
atol_val, max_singular_val * rtol_val
)
else:
cutoff = atol_val
else:
# Use rcond for backward compatibility
rcond = paddle.to_tensor(rcond, dtype=x.dtype)
cutoff = rcond * max_singular_val
y = float('inf')
y = paddle.to_tensor(y, dtype=x.dtype)
singular = paddle.where(s > cutoff, 1 / s, 1 / y)
st = _C_ops.unsqueeze(singular, [-2])
dims = list(range(len(vt.shape)))
perm = [*dims[:-2], dims[-1], dims[-2]]
v = _C_ops.transpose(vt, perm)
out_1 = v * st
out_2 = _C_ops.matmul(out_1, u, False, True)
if out is not None:
paddle.assign(out_2, out)
return out
return out_2
else:
if in_dynamic_mode() and x.size == 0:
dims = list(range(len(x.shape)))
perm = [*dims[:-2], dims[-1], dims[-2]]
return _C_ops.transpose(x, perm)
# combine eigh and matmul op
s, u = _C_ops.eigh(x, 'L')
s_abs = paddle.abs(s)
max_singular_val = _C_ops.max(s_abs, [-1], True)
if use_atol_rtol:
# Compute cutoff using atol and rtol
atol_val = atol if atol is not None else 0.0
if not isinstance(atol_val, paddle.Tensor):
atol_val = paddle.to_tensor(atol_val, dtype=s.dtype)
elif atol_val.dtype != s.dtype:
atol_val = paddle.cast(atol_val, s.dtype)
if rtol is not None:
rtol_val = rtol
if not isinstance(rtol_val, paddle.Tensor):
rtol_val = paddle.to_tensor(rtol_val, dtype=s.dtype)
elif rtol_val.dtype != s.dtype:
rtol_val = paddle.cast(rtol_val, s.dtype)
cutoff = paddle.maximum(
atol_val, max_singular_val * rtol_val
)
else:
cutoff = atol_val
else:
# Use rcond for backward compatibility
rcond = paddle.to_tensor(rcond, dtype=s.dtype)
cutoff = rcond * max_singular_val
y = float('inf')
y = paddle.to_tensor(y, dtype=s.dtype)
singular = paddle.where(s_abs > cutoff, 1 / s, 1 / y)
st = _C_ops.unsqueeze(singular, [-2])
out_1 = u * st
u_conj = _C_ops.conj(u)
out_2 = _C_ops.matmul(out_1, u_conj, False, True)
if out is not None:
paddle.assign(out_2, out)
return out
return out_2
else:
if not hermitian:
helper = LayerHelper('pinv', **locals())
dtype = x.dtype
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')
u = helper.create_variable_for_type_inference(dtype)
s = helper.create_variable_for_type_inference(dtype)
vt = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='svd',
inputs={'X': [x]},
outputs={'U': u, 'VH': vt, 'S': s},
attrs={'full_matrices': False},
)
max_singular_val = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='reduce_max',
inputs={'X': s},
outputs={'Out': max_singular_val},
attrs={'dim': [-1], 'keep_dim': True, 'reduce_all': False},
)
rcond = full(shape=[1], fill_value=rcond, dtype=dtype)
cutoff = rcond * max_singular_val
y = float('inf')
y = full(shape=[1], fill_value=y, dtype=dtype)
singular = paddle.where(s > cutoff, 1 / s, 1 / y)
st = helper.create_variable_for_type_inference(dtype=dtype)
st_shape = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='unsqueeze2',
inputs={'X': singular},
attrs={'axes': [-2]},
outputs={'Out': st, 'XShape': st_shape},
)
dims = list(range(len(vt.shape)))
perm = [*dims[:-2], dims[-1], dims[-2]]
v = helper.create_variable_for_type_inference(dtype)
v_shape = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='transpose2',
inputs={'X': [vt]},
outputs={'Out': [v], 'XShape': [v_shape]},
attrs={'axis': perm},
)
out_1 = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='elementwise_mul',
inputs={'X': v, 'Y': st},
outputs={'Out': out_1},
attrs={'axis': -1},
)
out_1 = helper.append_activation(out_1)
out_2 = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='matmul_v2',
inputs={'X': out_1, 'Y': u},
outputs={'Out': out_2},
attrs={'trans_x': False, 'trans_y': True},
)
return out_2
else:
helper = LayerHelper('pinv', **locals())
dtype = x.dtype
check_variable_and_dtype(
x,
'dtype',
['float32', 'float64', 'complex64', 'complex128'],
'pinv',
)
if dtype == paddle.complex128:
s_type = 'float64'
elif dtype == paddle.complex64:
s_type = 'float32'
else:
s_type = dtype
u = helper.create_variable_for_type_inference(dtype)
s = helper.create_variable_for_type_inference(s_type)
helper.append_op(
type='eigh',
inputs={'X': x},
outputs={'Eigenvalues': s, 'Eigenvectors': u},
attrs={'UPLO': 'L'},
)
s_abs = helper.create_variable_for_type_inference(s_type)
helper.append_op(
type='abs', inputs={'X': s}, outputs={'Out': s_abs}
)
max_singular_val = helper.create_variable_for_type_inference(s_type)
helper.append_op(
type='reduce_max',
inputs={'X': s_abs},
outputs={'Out': max_singular_val},
attrs={'dim': [-1], 'keep_dim': True, 'reduce_all': False},
)
rcond = full(shape=[1], fill_value=rcond, dtype=s_type)
cutoff = rcond * max_singular_val
y = float('inf')
y = full(shape=[1], fill_value=y, dtype=s_type)
singular = paddle.where(s_abs > cutoff, 1 / s, 1 / y)
st = helper.create_variable_for_type_inference(dtype=s_type)
st_shape = helper.create_variable_for_type_inference(dtype=s_type)
helper.append_op(
type='unsqueeze2',
inputs={'X': singular},
attrs={'axes': [-2]},
outputs={'Out': st, 'XShape': st_shape},
)
out_1 = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='elementwise_mul',
inputs={'X': u, 'Y': st},
outputs={'Out': out_1},
attrs={'axis': -1},
)
out_1 = helper.append_activation(out_1)
u_conj = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='conj', inputs={'X': u}, outputs={'Out': [u_conj]}
)
out_2 = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='matmul_v2',
inputs={'X': out_1, 'Y': u_conj},
outputs={'Out': out_2},
attrs={'trans_x': False, 'trans_y': True},
)
return out_2
def _check_right_solve_shape(x, y):
"""check the input shape of x and y for solve when left is False"""
x_shape = x.shape[-2:]
if len(y.shape) == 1:
raise ValueError(
"Incompatible shapes of X and Y for the equation Out * X = Y, "
f"where input X's matrix shape is {x_shape} and"
f"input Y's matrix shape is {list(y.shape).append(1)}"
)
else:
y_shape = y.shape[-2:]
if x_shape[0] != y_shape[1]:
raise ValueError(
"Incompatible shapes of X and Y for the equation Out * X = Y, "
f"where input X's matrix shape is {x_shape} and"
f"input Y's matrix shape is {y_shape}"
)
def _transpose_last_2dim(x):
"""transpose the last 2 dimension of a tensor"""
x_new_dims = list(range(len(x.shape)))
x_new_dims[-1], x_new_dims[-2] = x_new_dims[-2], x_new_dims[-1]
x = transpose(x, x_new_dims)
return x
@param_two_alias(["x", "A"], ["y", "B"])
def solve(
x: Tensor,
y: Tensor,
left: bool = True,
name: str | None = None,
*,
out: Tensor | None = None,
) -> Tensor:
r"""
Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.
Let :math:`X` be a square matrix or a batch of square matrices, :math:`Y` be
a vector/matrix or a batch of vectors/matrices. When `left` is True, the equation should be:
.. math::
Out = X^-1 * Y
When `left` is False, the equation should be:
.. math::
Out = Y * X^-1
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. Alias: ``A``.
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. Alias: ``B``.
left (bool, optional): Whether to solve the system :math:`X * Out = Y` or :math:`Out * X = Y`. Default: True.
name (str|None, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
out (Tensor|None, optional): The output tensor. Default: None.
Returns:
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:: pycon
>>> # a square system of linear equations:
>>> # 3*X0 + X1 = 9
>>> # X0 + 2*X1 = 8
>>> import paddle
>>> x = paddle.to_tensor([[3, 1], [1, 2]], dtype="float64")
>>> y = paddle.to_tensor([9, 8], dtype="float64")
>>> out = paddle.linalg.solve(x, y)
>>> print(out)
Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True,
[2., 3.])
"""
if not left:
_check_right_solve_shape(x, y)
x = _transpose_last_2dim(x)
y = _transpose_last_2dim(y)
if in_dynamic_or_pir_mode():
ret = _C_ops.solve(x, y)
else:
inputs = {"X": [x], "Y": [y]}
helper = LayerHelper("solve", **locals())
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')
check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="solve", inputs={"X": x, "Y": y}, outputs={"Out": out}
)
if not left:
ret = _transpose_last_2dim(ret)
if out is not None:
paddle.assign(ret, out)
return ret
def triangular_solve(
x: Tensor,
y: Tensor,
upper: bool = True,
transpose: bool = False,
unitriangular: bool = False,
name: str | None = None,
) -> Tensor:
r"""
Computes the solution of a system of equations with a triangular coefficient. `x` is coefficient matrix
`y` is multiple right-hand sides of equations.
Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs is also
batches.
Equations can be described as:
.. math::
x * Out = y
Solution of Equations is:
.. math::
Out = x ^ {-1} * y
Args:
x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or
more batch dimensions. Its data type should be float32, float64, complex64, complex128.
y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is
zero or more batch dimensions. Its data type should be float32, float64, complex64, complex128.
upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular
system of equations. Default: True.
transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.
unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed
to be 1 and not referenced from `x` . Default: 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: The solution of the system of equations. Its data type should be the same as that of `x`.
Examples:
.. code-block:: pycon
>>> # a square system of linear equations:
>>> # x1 + x2 + x3 = 0
>>> # 2*x2 + x3 = -9
>>> # -x3 = 5
>>> import paddle
>>> x = paddle.to_tensor(
... [
... [1, 1, 1],
... [0, 2, 1],
... [0, 0, -1],
... ],
... dtype="float64",
... )
>>> y = paddle.to_tensor([[0], [-9], [5]], dtype="float64")
>>> out = paddle.linalg.triangular_solve(x, y, upper=True)
>>> print(out)
Tensor(shape=[3, 1], dtype=float64, place=Place(cpu), stop_gradient=True,
[[ 7.],
[-2.],
[-5.]])
"""
if in_dynamic_or_pir_mode():
return _C_ops.triangular_solve(x, y, upper, transpose, unitriangular)
else:
inputs = {"X": [x], "Y": [y]}
helper = LayerHelper("triangular_solve", **locals())
check_variable_and_dtype(
x,
'x',
['float32', 'float64', 'complex64', 'complex128'],
'triangular_solve',
)
check_variable_and_dtype(
y,
'y',
['float32', 'float64', 'complex64', 'complex128'],
'triangular_solve',
)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='triangular_solve',
inputs={'X': x, 'Y': y},
outputs={'Out': out},
attrs={
'upper': upper,
'transpose': transpose,
'unitriangular': unitriangular,
},
)
return out
def cholesky_solve(
x: Tensor, y: Tensor, upper: bool = False, name: str | None = None
) -> Tensor:
r"""
Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.
Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs
is also batches.
Args:
x (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is
zero or more batch dimensions. Its data type should be float32 or float64.
y (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or
more batch dimensions. Its data type should be float32 or float64.
upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: 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: The solution of the system of equations. Its data type is the same as that of `x`.
Examples:
.. code-block:: pycon
>>> import paddle
>>> u = paddle.to_tensor(
... [
... [1, 1, 1],
... [0, 2, 1],
... [0, 0, -1],
... ],
... dtype="float64",
... )
>>> b = paddle.to_tensor([[0], [-9], [5]], dtype="float64")
>>> out = paddle.linalg.cholesky_solve(b, u, upper=True)
>>> print(out)
Tensor(shape=[3, 1], dtype=float64, place=Place(cpu), stop_gradient=True,
[[-2.50000000],
[-7. ],
[ 9.50000000]])
"""
if in_dynamic_or_pir_mode():
return _C_ops.cholesky_solve(x, y, upper)
else:
helper = LayerHelper("cholesky_solve", **locals())
check_variable_and_dtype(
x, 'x', ['float32', 'float64'], 'cholesky_solve'
)
check_variable_and_dtype(
y, 'y', ['float32', 'float64'], 'cholesky_solve'
)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='cholesky_solve',
inputs={'X': x, 'Y': y},
outputs={'Out': out},
attrs={'upper': upper},
)
return out
def eigvalsh(
x: Tensor, UPLO: Literal['L', 'U'] = 'L', name: str | None = None
) -> Tensor:
"""
Computes the eigenvalues of a
complex Hermitian (conjugate symmetric) or a real symmetric matrix.
Args:
x (Tensor): A tensor with shape :math:`[*, M, M]` , where * is zero or greater batch dimension. The data type of the input Tensor x
should be one of float32, float64, complex64, complex128.
UPLO(str, optional): Lower triangular part of a ('L', default) or the upper triangular part ('U').
name(str|None, 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: The tensor eigenvalues in ascending order.
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.to_tensor([[1, -2j], [2j, 5]])
>>> out_value = paddle.eigvalsh(x, UPLO='L')
>>> print(out_value)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.17157286, 5.82842731])
"""
if in_dynamic_mode():
values, _ = _C_ops.eigvalsh(x, UPLO, x.stop_gradient)
return values
def __check_input(x, UPLO):
x_shape = list(x.shape)
if len(x.shape) < 2:
raise ValueError(
"Input(input) only support >=2 tensor, but received "
f"length of Input(input) is {len(x.shape)}."
)
if x_shape[-1] != x_shape[-2]:
raise ValueError(
f"The input matrix must be batches of square matrices. But received x's dimension: {x_shape}"
)
if UPLO != 'L' and UPLO != 'U':
raise ValueError(
f"UPLO must be L or U. But received UPLO is: {UPLO}"
)
if in_pir_mode():
__check_input(x, UPLO)
values, _ = _C_ops.eigvalsh(x, UPLO, x.stop_gradient)
return values
else:
__check_input(x, UPLO)
helper = LayerHelper('eigvalsh', **locals())
check_variable_and_dtype(
x,
'dtype',
['float32', 'float64', 'complex64', 'complex128'],
'eigvalsh',
)
out_value = helper.create_variable_for_type_inference(dtype=x.dtype)
out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)
is_test = x.stop_gradient
helper.append_op(
type='eigvalsh',
inputs={'X': x},
outputs={'Eigenvalues': out_value, 'Eigenvectors': out_vector},
attrs={'UPLO': UPLO, 'is_test': is_test},
)
return out_value
def lstsq(
x: Tensor,
y: Tensor,
rcond: float | None = None,
driver: Literal['gels', 'gelsy', 'gelsd', 'gelss'] | None = None,
name: str | None = None,
) -> tuple[Tensor, Tensor, Tensor, Tensor]:
"""
Computes a solution to
the least squares problem of a system of linear equations.
Args:
x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``
should be one of float32, float64.
y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y``
should be one of float32, float64.
rcond(float, optional): The default value is None. A float pointing number used to determine
the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the
machine precision of x_dtype.
driver(str, optional): The default value is None. The name of LAPACK method to be used. For
CPU inputs the valid values are 'gels', 'gelsy', 'gelsd, 'gelss'. For CUDA input, the only
valid driver is 'gels'. If ``driver`` is None, 'gelsy' is used for CPU inputs and 'gels'
for CUDA inputs.
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:
Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``).
``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals``
is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed
when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor
with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in
('gelsy', 'gelsd', 'gelss'), otherwise return an empty tensor. ``singular_values`` is a tensor with
shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when
``driver`` in ('gelsd', 'gelss'), otherwise return an empty tensor.
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.0]])
>>> y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.0]])
>>> results = paddle.linalg.lstsq(x, y, driver="gelsd")
>>> print(results[0])
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.78350395, -0.22165027, -0.62371236],
[-0.11340097, 0.78866047, 1.14948535]])
>>> print(results[1])
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[19.81443405, 10.43814373, 30.56185532])
>>> print(results[2])
Tensor(shape=[], dtype=int32, place=Place(cpu), stop_gradient=True,
2)
>>> print(results[3])
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[9.03455734, 1.54167950])
>>> x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.0]])
>>> y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.0]])
>>> results = paddle.linalg.lstsq(x, y, driver="gels")
>>> print(results[0])
Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.39386186, 0.10230169, 0.93606132],
[ 0.10741688, -0.29028130, 0.11892584],
[-0.05115093, 0.51918161, -0.19948851]])
>>> print(results[1])
Tensor(shape=[0], dtype=float32, place=Place(cpu), stop_gradient=True,
[])
"""
device = paddle.get_device()
if device == "cpu":
if driver not in (None, "gels", "gelss", "gelsd", "gelsy"):
raise ValueError(
f"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {driver}"
)
driver = "gelsy" if driver is None else driver
elif device.startswith('gpu'):
if driver not in (None, "gels"):
raise ValueError(
f"Only support valid driver is 'gels' or None for CUDA inputs. But got {driver}"
)
driver = "gels" if driver is None else driver
else:
raise RuntimeError("Only support lstsq api for CPU or CUDA device.")
if not (
x.dtype == y.dtype
and x.dtype
in (
paddle.float32,
paddle.float64,
paddle.base.core.DataType.FLOAT32,
paddle.base.core.DataType.FLOAT64,
)
):
raise ValueError(
"Only support x and y have the same dtype such as 'float32' and 'float64'."
)
if x.ndim < 2:
raise ValueError(
f"The shape of x should be (*, M, N), but received ndim is [{x.ndim} < 2]"
)
if y.ndim < 2:
raise ValueError(
f"The shape of y should be (*, M, K), but received ndim is [{y.ndim} < 2]"
)
if x.shape[-2] != y.shape[-2]:
raise ValueError(
f"x with shape (*, M = {x.shape[-2]}, N) and y with shape (*, M = {y.shape[-2]}, K) should have same M."
)
if rcond is None:
if (
x.dtype == paddle.float32
or x.dtype == paddle.base.core.DataType.FLOAT32
):
rcond = 1e-7 * max(x.shape[-2], x.shape[-1])
elif (
x.dtype == paddle.float64
or x.dtype == paddle.base.core.DataType.FLOAT64
):
rcond = 1e-15 * max(x.shape[-2], x.shape[-1])
if in_dynamic_or_pir_mode():
solution, residuals, rank, singular_values = _C_ops.lstsq(
x, y, rcond, driver
)
if driver == "gels":
rank = paddle.empty(shape=[0], dtype="int64")
singular_values = paddle.empty(shape=[0], dtype=x.dtype)
elif driver == "gelsy":
singular_values = paddle.empty(shape=[0], dtype=x.dtype)
return solution, residuals, rank, singular_values
else:
helper = LayerHelper('lstsq', **locals())
check_variable_and_dtype(
x,
'dtype',
['float32', 'float64', 'complex64', 'complex128'],
'lstsq',
)
check_variable_and_dtype(
y,
'dtype',
['float32', 'float64', 'complex64', 'complex128'],
'lstsq',
)
solution = helper.create_variable_for_type_inference(dtype=x.dtype)
residuals = helper.create_variable_for_type_inference(dtype=x.dtype)
rank = helper.create_variable_for_type_inference(dtype=paddle.int32)
singular_values = helper.create_variable_for_type_inference(
dtype=x.dtype
)
helper.append_op(
type='lstsq',
inputs={'X': x, 'Y': y},
outputs={
'Solution': solution,
'Residuals': residuals,
'Rank': rank,
'SingularValues': singular_values,
},
attrs={'rcond': rcond, 'driver': driver},
)
if driver == "gels":
rank = paddle.static.data(name='rank', shape=[0])
singular_values = paddle.static.data(
name='singular_values', shape=[0]
)
elif driver == "gelsy":
singular_values = paddle.static.data(
name='singular_values', shape=[0]
)
return solution, residuals, rank, singular_values
def corrcoef(x: Tensor, rowvar: bool = True, name: str | None = None) -> Tensor:
"""
A correlation coefficient matrix indicate the correlation of each pair variables in the input matrix.
For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the correlation coefficient matrix
element Rij is the correlation of xi and xj. The element Rii is the covariance of xi itself.
The relationship between the correlation coefficient matrix `R` and the
covariance matrix `C`, is
.. math:: R_{ij} = \\frac{ C_{ij} } { \\sqrt{ C_{ii} * C_{jj} } }
The values of `R` are between -1 and 1.
Args:
x (Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.
rowvar (bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True.
name (str|None, optional): Name of the output. It's used to print debug info for developers. Details: :ref:`api_guide_Name`. Default: None.
Returns:
The correlation coefficient matrix of the variables.
Examples:
.. code-block:: pycon
>>> import paddle
>>> paddle.seed(2023)
>>> xt = paddle.rand((3, 4))
>>> print(paddle.linalg.corrcoef(xt))
Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.99999988, -0.47689593, -0.89559376],
[-0.47689593, 1. , 0.16345492],
[-0.89559382, 0.16345486, 1. ]])
"""
if len(x.shape) > 2 or len(x.shape) < 1:
raise ValueError(
"Input(x) only support N-D (1<=N<=2) tensor in corrcoef, but received "
f"length of Input(input) is {len(x.shape)}."
)
check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'corrcoef')
c = cov(x, rowvar)
if c.ndim == 0:
# scalar covariance
# nan if incorrect value (nan, inf, 0), 1 otherwise
return c / c
d = paddle.diag(c)
if paddle.is_complex(d):
d = d.real()
stddev = paddle.sqrt(d)
c /= stddev[:, None]
c /= stddev[None, :]
# Clip to [-1, 1]. This does not guarantee
if paddle.is_complex(c):
return paddle.complex(
paddle.clip(c.real(), -1, 1), paddle.clip(c.imag(), -1, 1)
)
else:
c = paddle.clip(c, -1, 1)
return c
@param_two_alias(["x", "x1"], ["y", "x2"])
def cdist(
x: Tensor,
y: Tensor,
p: float = 2.0,
compute_mode: Literal[
'use_mm_for_euclid_dist_if_necessary',
'use_mm_for_euclid_dist',
'donot_use_mm_for_euclid_dist',
] = "use_mm_for_euclid_dist_if_necessary",
name: str | None = None,
) -> Tensor:
r"""
Compute the p-norm distance between each pair of the two collections of inputs.
This function is equivalent to `scipy.spatial.distance.cdist(input,'minkowski', p=p)`
if :math:`p \in (0, \infty)`. When :math:`p = 0` it is equivalent to `scipy.spatial.distance.cdist(input, 'hamming') * M`.
When :math:`p = \infty`, the closest scipy function is `scipy.spatial.distance.cdist(xn, lambda x, y: np.abs(x - y).max())`.
Args:
x (Tensor): A tensor with shape :math:`B \times P \times M`.
Alias: ``x1``.
y (Tensor): A tensor with shape :math:`B \times R \times M`.
Alias: ``x2``.
p (float, optional): The value for the p-norm distance to calculate between each vector pair. Default: :math:`2.0`.
compute_mode (str, optional): The mode for compute distance.
- ``use_mm_for_euclid_dist_if_necessary`` , for p = 2.0 and (P > 25 or R > 25), it will use matrix multiplication to calculate euclid distance if possible.
- ``use_mm_for_euclid_dist`` , for p = 2.0, it will use matrix multiplication to calculate euclid distance.
- ``donot_use_mm_for_euclid_dist`` , it will not use matrix multiplication to calculate euclid distance.
Default: ``use_mm_for_euclid_dist_if_necessary``.
name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
Returns:
Tensor, the dtype is same as input tensor.
If x has shape :math:`B \times P \times M` and y has shape :math:`B \times R \times M` then
the output will have shape :math:`B \times P \times R`.
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.to_tensor([[0.9041, 0.0196], [-0.3108, -2.4423], [-0.4821, 1.059]], dtype=paddle.float32)
>>> y = paddle.to_tensor([[-2.1763, -0.4713], [-0.6986, 1.3702]], dtype=paddle.float32)
>>> distance = paddle.cdist(x, y)
>>> print(distance)
Tensor(shape=[3, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[3.11927032, 2.09589314],
[2.71384072, 3.83217239],
[2.28300953, 0.37910119]])
"""
check_variable_and_dtype(x, 'x', ('float32', 'float64'), 'cdist')
check_variable_and_dtype(y, 'y', ('float32', 'float64'), 'cdist')
check_type(p, 'p', (float, int), 'cdist')
if compute_mode not in [
'use_mm_for_euclid_dist_if_necessary',
'use_mm_for_euclid_dist',
'donot_use_mm_for_euclid_dist',
]:
raise ValueError(
"The compute_mode should be 'use_mm_for_euclid_dist_if_necessary', "
"'use_mm_for_euclid_dist' or 'donot_use_mm_for_euclid_dist', "
f"but received compute_mode is {compute_mode}."
)
mode = 0
if compute_mode == 'use_mm_for_euclid_dist_if_necessary':
mode = 0
elif compute_mode == 'use_mm_for_euclid_dist':
mode = 1
elif compute_mode == 'donot_use_mm_for_euclid_dist':
mode = 2
x_shape = list(x.shape)
assert len(x_shape) >= 2, (
"The x must be at least 2-dimensional, "
f"But received Input x's dimensional is {len(x_shape)}.\n"
)
y_shape = list(y.shape)
assert len(y_shape) >= 2, (
"The y must be at least 2-dimensional, "
f"But received Input y's dimensional is {len(y_shape)}.\n"
)
assert x_shape[-1] == y_shape[-1], (
"The x and y must have same last dimension, "
f"But received Input x's last dimension is {x_shape[-1]}, "
f"Input y's last dimension is {y_shape[-1]}.\n"
)
assert p >= 0, (
f"The p must be greater than or equal to 0, But received p is {p}.\n"
)
r1 = x.shape[-2]
r2 = y.shape[-2]
c1 = x.shape[-1]
p = float(p)
if r1 == 0 or r2 == 0:
batch_shape = paddle.broadcast_shape(
list(x.shape[:-2]), list(y.shape[:-2])
)
res = paddle.empty([*batch_shape, r1, r2], dtype=x.dtype)
res.stop_gradient = x.stop_gradient and y.stop_gradient
return res
if c1 == 0:
batch_shape = paddle.broadcast_shape(
list(x.shape[:-2]), list(y.shape[:-2])
)
res = paddle.zeros([*batch_shape, r1, r2], dtype=x.dtype)
res.stop_gradient = x.stop_gradient and y.stop_gradient
return res
if p == 2.0 and (mode == 1 or (mode == 0 and (r1 > 25 or r2 > 25))):
x_norm = paddle.sum(x.pow(2), axis=-1, keepdim=True)
y_norm = paddle.sum(y.pow(2), axis=-1, keepdim=True)
y_transposed = paddle.transpose(
y, perm=[*range(y.ndim - 2), y.ndim - 1, y.ndim - 2]
)
y_norm_transposed = paddle.transpose(
y_norm,
perm=[*range(y_norm.ndim - 2), y_norm.ndim - 1, y_norm.ndim - 2],
)
res = paddle.matmul(x, y_transposed) * -2 + y_norm_transposed + x_norm
res = paddle.clip(res, min=0.0).sqrt()
return res
return paddle.linalg.norm(
x[..., None, :] - y[..., None, :, :], p=p, axis=-1
)
def householder_product(
x: Tensor, tau: Tensor, name: str | None = None
) -> Tensor:
r"""
Computes the first n columns of a product of Householder matrices.
This function can get the vector :math:`\omega_{i}` from matrix `x` (m x n), the :math:`i-1` elements are zeros, and the i-th is `1`, the rest of the elements are from i-th column of `x`.
And with the vector `tau` can calculate the first n columns of a product of Householder matrices.
:math:`H_i = I_m - \tau_i \omega_i \omega_i^H`
Args:
x (Tensor): A tensor with shape (*, m, n) where * is zero or more batch dimensions.
tau (Tensor): A tensor with shape (*, k) where * is zero or more batch dimensions.
name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
Returns:
Tensor, the dtype is same as input tensor, the Q in QR decomposition.
:math:`out = Q = H_1H_2H_3...H_k`
Examples:
.. code-block:: pycon
>>> import paddle
>>> x = paddle.to_tensor(
... [
... [-1.1280, 0.9012, -0.0190],
... [0.3699, 2.2133, -1.4792],
... [0.0308, 0.3361, -3.1761],
... [-0.0726, 0.8245, -0.3812],
... ]
... )
>>> tau = paddle.to_tensor([1.7497, 1.1156, 1.7462])
>>> Q = paddle.linalg.householder_product(x, tau)
>>> print(Q)
Tensor(shape=[4, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.74969995, -0.02181768, 0.31115776],
[-0.64721400, -0.12367040, -0.21738708],
[-0.05389076, -0.37562516, -0.84836429],
[ 0.12702821, -0.91822827, 0.36892807]])
"""
check_dtype(
x.dtype,
'x',
[
'float16',
'float32',
'float64',
'complex64',
'complex128',
],
'householder_product',
)
check_dtype(
tau.dtype,
'tau',
[
'float16',
'float32',
'float64',
'complex64',
'complex128',
],
'householder_product',
)
assert x.dtype == tau.dtype, (
"The input x must have the same dtype with input tau.\n"
)
assert (
len(x.shape) >= 2
and len(tau.shape) >= 1
and len(x.shape) == len(tau.shape) + 1
), (
"The input x must have more than 2 dimensions, and input tau must have more than 1 dimension,"
"and the dimension of x is 1 larger than the dimension of tau\n"
)
assert x.shape[-2] >= x.shape[-1], (
"The rows of input x must be greater than or equal to the columns of input x.\n"
)
assert x.shape[-1] >= tau.shape[-1], (
"The last dim of x must be greater than tau.\n"
)
for idx, _ in enumerate(x.shape[:-2]):
assert x.shape[idx] == tau.shape[idx], (
"The input x must have the same batch dimensions with input tau.\n"
)
def _householder_product(x, tau):
m, n = x.shape[-2:]
k = tau.shape[-1]
Q = paddle.eye(m).astype(x.dtype)
for i in range(min(k, n)):
w = x[i:, i]
if in_dynamic_mode():
w[0] = 1
else:
w = paddle.static.setitem(w, 0, 1)
w = w.reshape([-1, 1])
if in_dynamic_mode():
if x.dtype in [paddle.complex128, paddle.complex64]:
Q[:, i:] = Q[:, i:] - (
Q[:, i:] @ w @ paddle.conj(w).T * tau[i]
)
else:
Q[:, i:] = Q[:, i:] - (Q[:, i:] @ w @ w.T * tau[i])
else:
Q = paddle.static.setitem(
Q,
(slice(None), slice(i, None)),
(
Q[:, i:] - (Q[:, i:] @ w @ w.T * tau[i])
if x.dtype in [paddle.complex128, paddle.complex64]
else Q[:, i:] - (Q[:, i:] @ w @ w.T * tau[i])
),
)
return Q[:, :n]
if len(x.shape) == 2:
return _householder_product(x, tau)
m, n = x.shape[-2:]
org_x_shape = x.shape
org_tau_shape = tau.shape
x = x.reshape((-1, org_x_shape[-2], org_x_shape[-1]))
tau = tau.reshape((-1, org_tau_shape[-1]))
n_batch = x.shape[0]
out = paddle.zeros([n_batch, m, n], dtype=x.dtype)
for i in range(n_batch):
if in_dynamic_mode():
out[i] = _householder_product(x[i], tau[i])
else:
out = paddle.static.setitem(
out, i, _householder_product(x[i], tau[i])
)
out = out.reshape(org_x_shape)
return out
# Reference: MatrixExponential, https://eigen.tuxfamily.org/dox/unsupported/MatrixExponential_8h_source.html
def _matrix_exp_pade3(mat_a, mat_i=None, mat_a2=None, *, dtype=None):
"""3rd-order Pade approximant."""
b = [120.0, 60.0, 12.0]
if not paddle.framework.in_dynamic_mode():
b = [paddle.full((), x, dtype) for x in b]
if mat_a2 is None:
mat_a2, *_ = _matrix_mats(mat_a, 2, dtype)
tmp = mat_a2 + b[1] * mat_i
mat_u = paddle.matmul(mat_a, tmp)
mat_v = b[2] * mat_a2 + b[0] * mat_i
return mat_u, mat_v
def _matrix_exp_pade5(
mat_a, mat_i=None, mat_a2=None, mat_a4=None, *, dtype=None
):
"""5th-order Pade approximant."""
b = [30240.0, 15120.0, 3360.0, 420.0, 30.0]
if not paddle.framework.in_dynamic_mode():
b = [paddle.full((), x, dtype) for x in b]
if mat_a4 is None:
mat_a2, mat_a4, *_ = _matrix_mats(mat_a, 4, dtype)
tmp = mat_a4 + b[3] * mat_a2 + b[1] * mat_i
mat_u = paddle.matmul(mat_a, tmp)
mat_v = b[4] * mat_a4 + b[2] * mat_a2 + b[0] * mat_i
return mat_u, mat_v
def _matrix_exp_pade7(
mat_a, mat_i=None, mat_a2=None, mat_a4=None, mat_a6=None, *, dtype=None
):
"""7th-order Pade approximant."""
b = [17297280.0, 8648640.0, 1995840.0, 277200.0, 25200.0, 1512.0, 56.0]
if not paddle.framework.in_dynamic_mode():
b = [paddle.full((), x, dtype) for x in b]
if mat_a6 is None:
mat_a2, mat_a4, mat_a6, *_ = _matrix_mats(mat_a, 6, dtype)
tmp = mat_a6 + b[5] * mat_a4 + b[3] * mat_a2 + b[1] * mat_i
mat_u = paddle.matmul(mat_a, tmp)
mat_v = b[6] * mat_a6 + b[4] * mat_a4 + b[2] * mat_a2 + b[0] * mat_i
return mat_u, mat_v
def _matrix_exp_pade9(
mat_a,
mat_i=None,
mat_a2=None,
mat_a4=None,
mat_a6=None,
mat_a8=None,
*,
dtype=None,
):
"""9th-order Pade approximant."""
b = [
17643225600.0,
8821612800.0,
2075673600.0,
302702400.0,
30270240.0,
2162160.0,
110880.0,
3960.0,
90.0,
]
if not paddle.framework.in_dynamic_mode():
b = [paddle.full((), x, dtype) for x in b]
if mat_a8 is None:
mat_a2, mat_a4, mat_a6, mat_a8, *_ = _matrix_mats(mat_a, 8, dtype)
tmp = mat_a8 + b[7] * mat_a6 + b[5] * mat_a4 + b[3] * mat_a2 + b[1] * mat_i
mat_u = paddle.matmul(mat_a, tmp)
mat_v = (
b[8] * mat_a8
+ b[6] * mat_a6
+ b[4] * mat_a4
+ b[2] * mat_a2
+ b[0] * mat_i
)
return mat_u, mat_v
def _matrix_exp_pade13(
mat_a, mat_i=None, mat_a2=None, mat_a4=None, mat_a6=None, *, dtype=None
):
"""13th-order Pade approximant."""
b = [
64764752532480000.0,
32382376266240000.0,
7771770303897600.0,
1187353796428800.0,
129060195264000.0,
10559470521600.0,
670442572800.0,
33522128640.0,
1323241920.0,
40840800.0,
960960.0,
16380.0,
182.0,
]
if not paddle.framework.in_dynamic_mode():
b = [paddle.full((), x, dtype) for x in b]
if mat_a6 is None:
mat_a2, mat_a4, mat_a6, *_ = _matrix_mats(mat_a, 6, dtype)
tmp_u = (
paddle.matmul(mat_a6, mat_a6 + b[11] * mat_a4 + b[9] * mat_a2)
+ b[7] * mat_a6
+ b[5] * mat_a4
+ b[3] * mat_a2
+ b[1] * mat_i
)
mat_u = paddle.matmul(mat_a, tmp_u)
tmp_v = b[12] * mat_a6 + b[10] * mat_a4 + b[8] * mat_a2
mat_v = (
paddle.matmul(mat_a6, tmp_v)
+ b[6] * mat_a6
+ b[4] * mat_a4
+ b[2] * mat_a2
+ b[0] * mat_i
)
return mat_u, mat_v
def _matrix_uv_where(vals, cases, l1_norm):
if len(vals) == 1:
return paddle.where(
paddle.less_than(l1_norm, vals[0]), cases[0], cases[1]
)
else:
return paddle.where(
paddle.less_than(l1_norm, vals[0]),
cases[0],
_matrix_uv_where(vals[1:], cases[1:], l1_norm),
)
def _matrix_mats(mat_a, total, dtype):
mat_a2 = paddle.matmul(mat_a, mat_a)
mat_a4 = None
mat_a6 = None
mat_a8 = None
if total > 2:
mat_a4 = paddle.matmul(mat_a2, mat_a2)
if total > 4:
mat_a6 = paddle.matmul(mat_a4, mat_a2)
if total > 6:
mat_a8 = paddle.matmul(mat_a6, mat_a2)
return mat_a2, mat_a4, mat_a6, mat_a8
def _matrix_uv_float32(mat_a, l1_norm, squarings, dtype):
mat_i = paddle.eye(mat_a.shape[-1], dtype=dtype)
mat_a2, mat_a4, *_ = _matrix_mats(mat_a, 4, dtype)
u3, v3 = _matrix_exp_pade3(mat_a, mat_i, mat_a2, dtype=dtype)
u5, v5 = _matrix_exp_pade5(mat_a, mat_i, mat_a2, mat_a4, dtype=dtype)
u7, v7 = _matrix_exp_pade7(
mat_a
/ paddle.cast(
paddle.pow(paddle.full((), 2.0, dtype), squarings),
dtype,
),
mat_i,
dtype=dtype,
)
conds = (
paddle.full((), 4.258730016922831e-001, dtype),
paddle.full((), 1.880152677804762e000, dtype),
)
u = _matrix_uv_where(conds, (u3, u5, u7), l1_norm)
v = _matrix_uv_where(conds, (v3, v5, v7), l1_norm)
return u, v
def _matrix_uv_float64(mat_a, l1_norm, squarings, dtype):
mat_i = paddle.eye(mat_a.shape[-1], dtype=dtype)
mat_a2, mat_a4, mat_a6, mat_a8, *_ = _matrix_mats(mat_a, 8, dtype)
u3, v3 = _matrix_exp_pade3(mat_a, mat_i, mat_a2, dtype=dtype)
u5, v5 = _matrix_exp_pade5(mat_a, mat_i, mat_a2, mat_a4, dtype=dtype)
u7, v7 = _matrix_exp_pade7(
mat_a, mat_i, mat_a2, mat_a4, mat_a6, dtype=dtype
)
u9, v9 = _matrix_exp_pade9(
mat_a, mat_i, mat_a2, mat_a4, mat_a6, mat_a8, dtype=dtype
)
u13, v13 = _matrix_exp_pade13(
mat_a
/ paddle.cast(
paddle.pow(paddle.full((), 2.0, dtype), squarings),
dtype,
),
mat_i,
dtype=dtype,
)
conds = (
paddle.full((), 1.495585217958292e-002, dtype),
paddle.full((), 2.539398330063230e-001, dtype),
paddle.full((), 9.504178996162932e-001, dtype),
paddle.full((), 2.097847961257068e000, dtype),
)
u = _matrix_uv_where(conds, (u3, u5, u7, u9, u13), l1_norm)
v = _matrix_uv_where(conds, (v3, v5, v7, v9, v13), l1_norm)
return u, v
def matrix_exp(x: Tensor, name: str | None = None) -> Tensor:
r"""
Computes the matrix exponential of square matrices.
.. math::
exp(A) = \sum_{n=0}^\infty A^n/n!
The input tensor x should be of square matrices with shape like :math:`(*, M, M)`, and the
exponential output is computed by Pade approximation of the scaling and squaring method.
[1] Nicholas J. Higham, The scaling and squaring method for the matrix exponential revisited.
Args:
x (Tensor): A tensor with shape :math:`(*, M, M)` where :math:`*` is zero or more batch dimensions. The data type should be one of float32, float64.
name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
Returns:
Tensor, the shape and dtype are same as input tensor.
Examples:
.. code-block:: pycon
>>> import paddle
>>> mat_a = paddle.empty((2, 2, 2))
>>> mat_a[0, :, :] = paddle.eye(2, 2)
>>> mat_a[1, :, :] = 2 * paddle.eye(2, 2)
>>> print(mat_a)
Tensor(shape=[2, 2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[1., 0.],
[0., 1.]],
[[2., 0.],
[0., 2.]]])
>>> out = paddle.linalg.matrix_exp(mat_a)
>>> print(out)
Tensor(shape=[2, 2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[2.71828198, 0. ],
[0. , 2.71828198]],
[[7.38905621, 0. ],
[0. , 7.38905621]]])
>>> import math
>>> mat_a = paddle.to_tensor([[0, math.pi / 3], [-math.pi / 3, 0]])
>>> out = paddle.linalg.matrix_exp(mat_a)
>>> print(out)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.49999994, 0.86602545],
[-0.86602551, 0.50000000]])
"""
# convert to tensor if necessary
if not isinstance(
x,
(
paddle.Tensor,
paddle.base.framework.Variable,
paddle.base.libpaddle.pir.Value,
),
):
mat_a = paddle.to_tensor(x)
else:
mat_a = x
dtype = convert_dtype(mat_a.dtype)
# check dtype, shape
if dtype not in ['float32', 'float64']:
raise ValueError(
f"The input tensor's dtype must be float32 or float64, but got {dtype}"
)
# 0-dim
if mat_a.ndim == 0:
return paddle.exp(mat_a)
# check tensor dim
if mat_a.ndim < 2:
raise ValueError('The input tensor must be at least two-dimensional')
if mat_a.shape[-1] != mat_a.shape[-2]:
raise ValueError('Last 2 dimensions of the tensor must be square')
# scalar case
if list(mat_a.shape[-2:]) == [1, 1]:
return paddle.exp(mat_a)
# compute uv
l1_norm = paddle.unsqueeze(
paddle.max(paddle.sum(paddle.abs(mat_a), axis=mat_a.ndim - 2), axis=-1),
axis=[-1, -2],
)
squarings = paddle.full(mat_a.shape, 0, dtype)
_matrix_uv_func = None
# dtype already checked before, we use `if-elif` only
if dtype == 'float32':
maxnorm = paddle.full((), 3.925724783138660, dtype)
squarings = paddle.floor(
paddle.log(l1_norm / maxnorm)
/ paddle.log(paddle.full((), 2.0, dtype))
)
squarings = paddle.maximum(squarings, paddle.zeros_like(squarings))
_matrix_uv_func = _matrix_uv_float32
elif dtype == 'float64':
maxnorm = paddle.full((), 5.371920351148152, dtype)
squarings = paddle.floor(
paddle.log(l1_norm / maxnorm)
/ paddle.log(paddle.full((), 2.0, dtype))
)
squarings = paddle.maximum(squarings, paddle.zeros_like(squarings))
_matrix_uv_func = _matrix_uv_float64
u, v = _matrix_uv_func(mat_a, l1_norm, squarings, dtype)
# compute result
is_finite = paddle.isfinite(paddle.max(l1_norm))
result = paddle.static.nn.cond(
is_finite,
lambda: paddle.linalg.solve(-u + v, u + v),
lambda: paddle.full(mat_a.shape, np.nan, dtype),
)
max_squaring = paddle.max(squarings)
i = paddle.full((), 0, dtype)
def cond(i, _):
return paddle.static.nn.cond(
is_finite,
lambda: paddle.less_than(i, max_squaring),
lambda: paddle.full((), False, dtype=paddle.bool),
)
def body(i, result):
return i + 1, paddle.where(
paddle.less_than(i, squarings),
paddle.matmul(result, result),
result,
)
_, result = paddle.static.nn.while_loop(cond, body, [i, result])
return result
def histogramdd(
x: Tensor,
bins: Tensor | list[int] | int = 10,
ranges: Sequence[float] | None = None,
density: bool = False,
weights: Tensor | None = None,
name: str | None = None,
) -> tuple[Tensor, list[Tensor]]:
r"""
Computes a multi-dimensional histogram of the values in a tensor.
Interprets the elements of an input tensor whose innermost dimension has size `N` as a collection of N-dimensional points. Maps each of the points into a set of N-dimensional bins and returns the number of points (or total weight) in each bin.
input `x` must be a tensor with at least 2 dimensions. If input has shape `(M, N)`, each of its `M` rows defines a point in N-dimensional space. If input has three or more dimensions, all but the last dimension are flattened.
Each dimension is independently associated with its own strictly increasing sequence of bin edges. Bin edges may be specified explicitly by passing a sequence of 1D tensors. Alternatively, bin edges may be constructed automatically by passing a sequence of integers specifying the number of equal-width bins in each dimension.
Args:
x (Tensor): The input tensor.
bins (list[Tensor], list[int], or int): If list[Tensor], defines the sequences of bin edges. If list[int], defines the number of equal-width bins in each dimension. If int, defines the number of equal-width bins for all dimensions.
ranges (sequence[float]|None, optional): Defines the leftmost and rightmost bin edges in each dimension. If is None, set the minimum and maximum as leftmost and rightmost edges for each dimension.
density (bool, optional): If False (default), the result will contain the count (or total weight) in each bin. If True, each count (weight) is divided by the total count (total weight), then divided by the volume of its associated bin.
weights (Tensor, optional): By default, each value in the input has weight 1. If a weight tensor is passed, each N-dimensional coordinate in input contributes its associated weight towards its bin's result. The weight tensor should have the same shape as the input tensor excluding its innermost dimension N.
name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
Returns:
N-dimensional Tensor containing the values of the histogram. ``bin_edges(Tensor[])``, sequence of N 1D Tensors containing the bin edges.
Examples:
.. code-block:: pycon
:name: example
>>> import paddle
>>> x = paddle.to_tensor([[0.0, 1.0], [1.0, 0.0], [2.0, 0.0], [2.0, 2.0]])
>>> bins = [3, 3]
>>> weights = paddle.to_tensor([1.0, 2.0, 4.0, 8.0])
>>> paddle.histogramdd(x, bins=bins, weights=weights)
(Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0., 1., 0.],
[2., 0., 0.],
[4., 0., 8.]]), [Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[0. , 0.66666669, 1.33333325, 2. ]), Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[0. , 0.66666669, 1.33333325, 2. ])])
.. code-block:: pycon
:name: examp2
>>> import paddle
>>> y = paddle.to_tensor([[0.0, 0.0], [1.0, 1.0], [2.0, 2.0]])
>>> bins = [2, 2]
>>> ranges = [0.0, 1.0, 0.0, 1.0]
>>> density = True
>>> paddle.histogramdd(y, bins=bins, ranges=ranges, density=density)
(Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[2., 0.],
[0., 2.]]), [Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[0. , 0.50000000, 1. ]), Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[0. , 0.50000000, 1. ])])
"""
def __check_x(x):
assert len(x.shape) >= 2, (
"input x must be a tensor with at least 2 dimensions."
)
check_variable_and_dtype(
x,
'x',
[
'float32',
'float64',
],
'histogramdd',
)
def __check_bins(bins, x): # when Tensor[], check dtype
for bins_tensor in bins:
bins_tensor = paddle.to_tensor(bins_tensor)
check_variable_and_dtype(
bins_tensor,
'bins',
[
'float32',
'float64',
],
'histogramdd',
)
assert bins_tensor.dtype == x.dtype, (
"When bins is Tensor[], the dtype of bins must be the same as x.\n"
)
def __check_weights(x, weights):
if weights is None:
return
x_shape, weights_shape = x.shape, weights.shape
assert len(x_shape) == len(weights_shape) + 1, (
"if weight tensor is provided,"
"it should have the same shape as the input tensor excluding its innermost dimension.\n"
)
for i, _ in enumerate(weights_shape):
assert weights_shape[i] == x_shape[i], (
"if weight tensor is provided,"
"it should have the same shape as the input tensor excluding its innermost dimension.\n"
)
check_variable_and_dtype(
weights,
'weights',
[
'float32',
'float64',
],
'histogramdd',
)
assert weights.dtype == x.dtype, (
"The dtype of weights must be the same as x.\n"
)
def __check_ranges(D, ranges):
if ranges is None:
return
check_type(ranges, 'ranges', (list, tuple), 'histogramdd')
assert D * 2 == len(ranges), (
f"The length of ranges list must be {D * 2}\n"
)
def __compute_flattened_index(index_list, hist_shape):
strides = paddle.to_tensor(hist_shape[::-1]).cumprod(dim=0).flip(0)[1:]
strides = paddle.concat(
[strides, paddle.to_tensor([1], dtype=strides.dtype)]
)
stacked_indices = paddle.stack(index_list, axis=-1)
flattened_index = (stacked_indices * strides).sum(axis=-1)
return flattened_index
check_type(density, 'density', bool, 'histogramdd')
__check_x(x)
# weights
__check_weights(x, weights)
D = x.shape[-1]
reshaped_input = x.reshape([-1, D])
N = reshaped_input.shape[0]
reshaped_weights = None
if weights is not None:
weights = weights.astype(x.dtype)
reshaped_weights = weights.reshape([N])
assert reshaped_weights.shape[0] == N, f"The size of weight must be {N}"
# ranges
__check_ranges(D, ranges)
if ranges is None:
ranges = paddle.zeros([D, 2], dtype=x.dtype)
maxv = paddle.max(reshaped_input, axis=0).reshape([-1])
minv = paddle.min(reshaped_input, axis=0).reshape([-1])
if paddle.in_dynamic_mode():
ranges[:, 0] = minv
ranges[:, 1] = maxv
else:
ranges = paddle.static.setitem(ranges, (slice(None), 0), minv)
ranges = paddle.static.setitem(ranges, (slice(None), 1), maxv)
else:
ranges = paddle.to_tensor(ranges, dtype=x.dtype).reshape([D, 2])
# bins to edges
edges = []
hist_shape = []
dedges = []
if isinstance(bins, (int, list)): # int or int[]
if isinstance(bins, int):
bins = [bins] * D
assert len(bins) == D, (
f"The length of bins must be {D} when bins is a list.\n"
)
for idx, r in enumerate(ranges):
if not isinstance(bins[idx], int):
raise ValueError(
f"The type of {idx}-th element in bins list must be int."
)
e = paddle.linspace(r[0], r[1], bins[idx] + 1, x.dtype)
edges.append(e)
dedges.append(e.diff())
hist_shape.append(bins[idx] + 2)
elif isinstance(
bins, tuple
): # tuple with D tensors for each innermost dimension
__check_bins(bins, x)
for bin in bins:
bin = paddle.to_tensor(bin)
edges.append(bin)
dedges.append(bin.diff())
hist_shape.append(bin.shape[0] + 1)
else:
raise ValueError("Input bins must be Tensor[], int[], or int.")
index_list = []
# edges shape: [D, linspaced]
# index_list shape: [D, N]
for idx, edge in enumerate(edges):
edge = paddle.to_tensor(edge)
index_list.append(
paddle.searchsorted(edge, reshaped_input[:, idx], right=True)
)
index_list = paddle.to_tensor(index_list)
for i in range(D):
on_edge = reshaped_input[:, i] == edges[i][-1]
if paddle.in_dynamic_mode():
index_list[i] = paddle.where(
on_edge, index_list[i] - 1, index_list[i]
)
else:
index_list_i = paddle.where(
on_edge, index_list[i] - 1, index_list[i]
)
index_list = paddle.static.setitem(index_list, i, index_list_i)
index_list = tuple(index_list)
flattened_index = __compute_flattened_index(index_list, hist_shape)
hist = paddle.bincount(
flattened_index,
reshaped_weights,
minlength=paddle.to_tensor(hist_shape).prod(),
)
hist = hist.reshape(hist_shape)
hist = hist.astype(x.dtype)
core = D * (slice(1, -1),)
hist = hist[core]
if density:
s = hist.sum()
for i in range(D):
shape = D * [1]
shape[i] = hist_shape[i] - 2
hist = hist / dedges[i].reshape(shape)
hist /= s
return (hist, edges)
def ormqr(
x: Tensor,
tau: Tensor,
y: Tensor,
left: bool = True,
transpose: bool = False,
name: str | None = None,
) -> Tensor:
r'''
Calculate the product of a normal matrix and a householder matrix.
Compute the product of the matrix C (given by y) with dimensions (m, n) and a matrix Q,
where Q is generated by the Householder reflection coefficient (x, tau). Returns a Tensor.
Args:
x (Tensor): Shape(\*,mn, k), when left is True, the value of mn is equal to m, otherwise the value of mn is equal to n. \* indicates that the length of the tensor on axis 0 is 0 or greater.
tau (Tensor): Shape (\*, min(mn, k)), where \* indicates that the length of the Tensor on axis 0 is 0 or greater, and its type is the same as input.
y (Tensor): Shape (\*m,n), where \* indicates that the length of the Tensor on axis 0 is 0 or greater, and its type is the same as input.
left (bool, optional): Determines the order in which the matrix product operations are operated. If left is true, the order of evaluation is op(Q) \* y, otherwise, the order of evaluation is y \* op(Q). Default value: True.
transpose (bool, optional): If true, the matrix Q is conjugated and transposed, otherwise, the conjugate transpose transformation is not performed. Default value: False.
name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
Returns:
Tensor. Data type and dimension are equals with :attr:`y`.
Examples:
.. code-block:: pycon
>>> import paddle
>>> import numpy as np
>>> from paddle import linalg
>>> input = paddle.to_tensor(
... [
... [-114.6, 10.9, 1.1],
... [-0.304, 38.07, 69.38],
... [-0.45, -0.17, 62],
... ]
... )
>>> tau = paddle.to_tensor([1.55, 1.94, 3.0])
>>> y = paddle.to_tensor(
... [
... [-114.6, 10.9, 1.1],
... [-0.304, 38.07, 69.38],
... [-0.45, -0.17, 62],
... ]
... )
>>> output = linalg.ormqr(input, tau, y)
>>> print(output)
Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 63.82712936 , -13.82312393 , -116.28614044],
[-53.65926361 , -28.15783691 , -70.42700958 ],
[-79.54292297 , 24.00182915 , -41.34253311 ]])
'''
check_dtype(
y.dtype,
'y',
[
'float16',
'float32',
'float64',
'complex64',
'complex128',
],
'ormqr',
)
check_type(left, 'left', bool, 'ormqr')
check_type(transpose, 'transpose', bool, 'ormqr')
assert x.dtype == tau.dtype and x.dtype == y.dtype, (
"The input tau and y must have the same dtype with the x.\n"
)
assert len(x.shape) >= 2 and len(y.shape) >= 2 and len(tau.shape) >= 1, (
"The input x and y must have more than 2 dimensions, and input tau must have more than 1 dimension"
)
assert len(x.shape) == len(tau.shape) + 1 and len(x.shape) == len(
y.shape
), (
"the dimension of x is 1 larger than the dimension of tau\n and the dimension of x is equal to the dimension of input"
)
assert x.shape[-1] == tau.shape[-1], (
"The innermost dimension of x and tau should be the same"
)
if transpose and left:
assert x.shape[-2] == y.shape[-2], (
"The row dimensions of x and y should be the same"
)
elif not transpose and left:
assert x.shape[-1] == y.shape[-2], (
"The column dimension of x and the row dimension of y should be the same"
)
elif transpose and not left:
assert x.shape[-2] == y.shape[-1], (
"The row dimension of x and the column dimension of y should be the same"
)
else:
assert x.shape[-1] == y.shape[-1], (
"The column dimensions of Impt and Osser's should be the same"
)
if len(x.shape) == 3:
assert x.shape[0] == y.shape[0] and x.shape[0] == tau.shape[0], (
"The input and tau and y parameters should have the same batch"
)
Q = householder_product(x, tau)
if len(x.shape) == 2:
Q = Q.T if transpose else Q
else:
Q = paddle.transpose(Q, [0, 2, 1]) if transpose else Q
result = matmul(Q, y) if left else matmul(y, Q)
return result
def cholesky_inverse(
x: Tensor, upper: bool = False, name: str | None = None
) -> Tensor:
r"""
Using the Cholesky factor `U` to calculate the inverse matrix of a symmetric positive definite matrix, returns the matrix `inv`.
If `upper` is `False`, `U` is lower triangular matrix:
.. math::
inv = (UU^{T})^{-1}
If `upper` is `True`, `U` is upper triangular matrix:
.. math::
inv = (U^{T}U)^{-1}
Args:
x (Tensor): A tensor of lower or upper triangular Cholesky decompositions of symmetric matrix with shape `[N, N]`.
The data type of the `x` should be one of ``float32``, ``float64``.
upper (bool, optional): If `upper` is `False`, `x` is lower triangular matrix, or is upper triangular matrix. Default: `False`.
name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
Returns:
Tensor. Computes the inverse matrix.
Examples:
.. code-block:: pycon
>>> import paddle
>>> # lower triangular matrix
>>> x = paddle.to_tensor([[3.0, 0.0, 0.0], [5.0, 3.0, 0.0], [-1.0, 1.0, 2.0]])
>>> out = paddle.linalg.cholesky_inverse(x)
>>> print(out)
Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.61728376, -0.25925916, 0.22222215],
[-0.25925916, 0.13888885, -0.08333330],
[ 0.22222213, -0.08333331, 0.24999997]])
>>> # upper triangular matrix
>>> out = paddle.linalg.cholesky_inverse(x.T, upper=True)
>>> print(out)
Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.61728376, -0.25925916, 0.22222215],
[-0.25925916, 0.13888885, -0.08333330],
[ 0.22222213, -0.08333331, 0.24999997]])
"""
if x.ndim != 2:
raise ValueError('The input tensor must be 2-dimensional')
if x.shape[0] != x.shape[1]:
raise ValueError('The input tensor must be square matrix')
if upper:
A = x.T @ x
else:
A = x @ x.T
return paddle.linalg.inv(A)
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