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pytorch/torch/_torch_docs.py
Choongwoo Han fae6c67121 Configurable flushing denormal numbers on CPU (#5294) 
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"""Adds docstrings to functions defined in the torch._C"""
import torch._C
from torch._C import _add_docstr as add_docstr
add_docstr(torch._C.abs,
r"""abs(input, out=None) -> Tensor
Computes the element-wise absolute value of the given :attr:`input` tensor.
Example::
>>> torch.abs(torch.FloatTensor([-1, -2, 3]))
FloatTensor([1, 2, 3])
""")
add_docstr(torch._C.acos,
r"""
acos(input, out=None) -> Tensor
Returns a new tensor with the arccosine of the elements of :attr:`input`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
-0.6366
0.2718
0.4469
1.3122
[torch.FloatTensor of size 4]
>>> torch.acos(a)
2.2608
1.2956
1.1075
nan
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.add,
r"""
.. function:: add(input, value, out=None)
Adds the scalar :attr:`value` to each element of the input :attr:`input`
and returns a new resulting tensor.
.. math::
out = input + value
If :attr:`input` is of type FloatTensor or DoubleTensor, :attr:`value` must be
a real number, otherwise it should be an integer.
Args:
input (Tensor): the input tensor
value (Number): the number to be added to each element of :attr:`input`
Keyword arguments:
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
0.4050
-1.2227
1.8688
-0.4185
[torch.FloatTensor of size 4]
>>> torch.add(a, 20)
20.4050
18.7773
21.8688
19.5815
[torch.FloatTensor of size 4]
.. function:: add(input, value=1, other, out=None)
Each element of the tensor :attr:`other` is multiplied by the scalar
:attr:`value` and added to each element of the tensor :attr:`input`.
The resulting tensor is returned.
The shapes of :attr:`input` and :attr:`other` must be
:ref:`broadcastable <broadcasting-semantics>`.
.. math::
out = input + value \times other
If :attr:`other` is of type FloatTensor or DoubleTensor, :attr:`value` must be
a real number, otherwise it should be an integer.
Args:
input (Tensor): the first input tensor
value (Number): the scalar multiplier for :attr:`other`
other (Tensor): the second input tensor
Keyword arguments:
out (Tensor, optional): the output tensor
Example::
>>> import torch
>>> a = torch.randn(4)
>>> a
-0.9310
2.0330
0.0852
-0.2941
[torch.FloatTensor of size 4]
>>> b = torch.randn(2, 2)
>>> b
1.0663 0.2544
-0.1513 0.0749
[torch.FloatTensor of size 2x2]
>>> torch.add(a, 10, b)
9.7322
4.5770
-1.4279
0.4552
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.addbmm,
r"""
addbmm(beta=1, mat, alpha=1, batch1, batch2, out=None) -> Tensor
Performs a batch matrix-matrix product of matrices stored
in :attr:`batch1` and :attr:`batch2`,
with a reduced add step (all matrix multiplications get accumulated
along the first dimension).
:attr:`mat` is added to the final result.
:attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the
same number of matrices.
If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a
:math:`(b \times m \times p)` tensor, ::attr:`mat` must be
:ref:`broadcastable <broadcasting-semantics>` with a :math:`(n \times p)` tensor
and attr:`out` will be a :math:`(n \times p)` tensor.
.. math::
out = \beta\ mat + \alpha\ (\sum_{i=0}^{b} batch1_i \mathbin{@} batch2_i)
For inputs of type `FloatTensor` or `DoubleTensor`, args `beta` and `alpha`
must be real numbers, otherwise they should be integers.
Args:
beta (Number, optional): multiplier for :attr:`mat`
mat (Tensor): matrix to be added
alpha (Number, optional): multiplier for `batch1 @ batch2`
batch1 (Tensor): the first batch of matrices to be multiplied
batch2 (Tensor): the second batch of matrices to be multiplied
out (Tensor, optional): the output tensor
Example::
>>> M = torch.randn(3, 5)
>>> batch1 = torch.randn(10, 3, 4)
>>> batch2 = torch.randn(10, 4, 5)
>>> torch.addbmm(M, batch1, batch2)
-3.1162 11.0071 7.3102 0.1824 -7.6892
1.8265 6.0739 0.4589 -0.5641 -5.4283
-9.3387 -0.1794 -1.2318 -6.8841 -4.7239
[torch.FloatTensor of size 3x5]
""")
add_docstr(torch._C.addcdiv,
r"""
addcdiv(tensor, value=1, tensor1, tensor2, out=None) -> Tensor
Performs the element-wise division of :attr:`tensor1` by :attr:`tensor2`,
multiply the result by the scalar :attr:`value` and add it to :attr:`tensor`.
.. math::
out_i = tensor_i + value \times \frac{tensor1_i}{tensor2_i}
The shapes of :attr:`tensor`, :attr:`tensor1`, and :attr:`tensor2` must be
:ref:`broadcastable <broadcasting-semantics>`.
For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be
a real number, otherwise an integer.
Args:
tensor (Tensor): the tensor to be added
value (Number, optional): multiplier for :math:`tensor1 ./ tensor2`
tensor1 (Tensor): the numerator tensor
tensor2 (Tensor): the denominator tensor
out (Tensor, optional): the output tensor
Example::
>>> t = torch.randn(2, 3)
>>> t1 = torch.randn(1, 6)
>>> t2 = torch.randn(6, 1)
>>> torch.addcdiv(t, 0.1, t1, t2)
0.0122 -0.0188 -0.2354
0.7396 -1.5721 1.2878
[torch.FloatTensor of size 2x3]
""")
add_docstr(torch._C.addcmul,
r"""
addcmul(tensor, value=1, tensor1, tensor2, out=None) -> Tensor
Performs the element-wise multiplication of :attr:`tensor1`
by :attr:`tensor2`, multiply the result by the scalar :attr:`value`
and add it to :attr:`tensor`.
.. math::
out_i = tensor_i + value \times tensor1_i \times tensor2_i
The shapes of :attr:`tensor`, :attr:`tensor1`, and :attr:`tensor2` must be
:ref:`broadcastable <broadcasting-semantics>`.
For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be
a real number, otherwise an integer.
Args:
tensor (Tensor): the tensor to be added
value (Number, optional): multiplier for :math:`tensor1 .* tensor2`
tensor1 (Tensor): the tensor to be multiplied
tensor2 (Tensor): the tensor to be multiplied
out (Tensor, optional): the output tensor
Example::
>>> t = torch.randn(2, 3)
>>> t1 = torch.randn(1, 6)
>>> t2 = torch.randn(6, 1)
>>> torch.addcmul(t, 0.1, t1, t2)
0.0122 -0.0188 -0.2354
0.7396 -1.5721 1.2878
[torch.FloatTensor of size 2x3]
""")
add_docstr(torch._C.addmm,
r"""
addmm(beta=1, mat, alpha=1, mat1, mat2, out=None) -> Tensor
Performs a matrix multiplication of the matrices :attr:`mat1` and :attr:`mat2`.
The matrix :attr:`mat` is added to the final result.
If :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a
:math:`(m \times p)` tensor, then :attr:`mat` must be
:ref:`broadcastable <broadcasting-semantics>` with a :math:`(n \times p)` tensor
and :attr:`out` will be a :math:`(n \times p)` tensor.
`alpha` and `beta` are scaling factors on `mat1 @ mat2` and `mat` respectively.
.. math::
out = \beta\ mat + \alpha\ (mat1_i \mathbin{@} mat2_i)
For inputs of type `FloatTensor` or `DoubleTensor`, args :attr:`beta` and
:attr:`alpha` must be real numbers, otherwise they should be integers.
Args:
beta (Number, optional): multiplier for :attr:`mat`
mat (Tensor): matrix to be added
alpha (Number, optional): multiplier for :math:`mat1 @ mat2`
mat1 (Tensor): the first matrix to be multiplied
mat2 (Tensor): the second matrix to be multiplied
out (Tensor, optional): the output tensor
Example::
>>> M = torch.randn(2, 3)
>>> mat1 = torch.randn(2, 3)
>>> mat2 = torch.randn(3, 3)
>>> torch.addmm(M, mat1, mat2)
-0.4095 -1.9703 1.3561
5.7674 -4.9760 2.7378
[torch.FloatTensor of size 2x3]
""")
add_docstr(torch._C.addmv,
r"""
addmv(beta=1, tensor, alpha=1, mat, vec, out=None) -> Tensor
Performs a matrix-vector product of the matrix :attr:`mat` and
the vector :attr:`vec`.
The vector :attr:`tensor` is added to the final result.
If :attr:`mat` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of
size `m`, then :attr:`tensor` must be
:ref:`broadcastable <broadcasting-semantics>` with a 1-D tensor of size `n` and
:attr:`out` will be 1-D tensor of size `n`.
`alpha` and `beta` are scaling factors on `mat * vec` and `tensor` respectively.
.. math::
out = \beta\ tensor + \alpha\ (mat \mathbin{@} vec)
For inputs of type `FloatTensor` or `DoubleTensor`, args :attr:`beta` and
:attr:`alpha` must be real numbers, otherwise they should be integers
Args:
beta (Number, optional): multiplier for :attr:`tensor`
tensor (Tensor): vector to be added
alpha (Number, optional): multiplier for :math:`mat @ vec`
mat (Tensor): matrix to be multiplied
vec (Tensor): vector to be multiplied
out (Tensor, optional): the output tensor
Example::
>>> M = torch.randn(2)
>>> mat = torch.randn(2, 3)
>>> vec = torch.randn(3)
>>> torch.addmv(M, mat, vec)
-2.0939
-2.2950
[torch.FloatTensor of size 2]
""")
add_docstr(torch._C.addr,
r"""
addr(beta=1, mat, alpha=1, vec1, vec2, out=None) -> Tensor
Performs the outer-product of vectors :attr:`vec1` and :attr:`vec2`
and adds it to the matrix :attr:`mat`.
Optional values :attr:`beta` and :attr:`alpha` are scalars that multiply
:attr:`mat` and :math:`(vec1 \otimes vec2)` respectively.
.. math::
out = \beta\ mat + \alpha\ (vec1 \otimes vec2)
If :attr:`vec1` is a vector of size `n` and :attr:`vec2` is a vector
of size `m`, then :attr:`mat` must be
:ref:`broadcastable <broadcasting-semantics>` with a matrix of size
:math:`(n \times m)` and :attr:`out` will be a matrix of size
:math:`(n \times m)`.
For inputs of type `FloatTensor` or `DoubleTensor`, args :attr:`beta` and
:attr:`alpha` must be real numbers, otherwise they should be integers
Args:
beta (Number, optional): multiplier for :attr:`mat`
mat (Tensor): matrix to be added
alpha (Number, optional): multiplier for :math:`vec1 \otimes vec2`
vec1 (Tensor): the first vector of the outer product
vec2 (Tensor): the second vector of the outer product
out (Tensor, optional): the output tensor
Example::
>>> vec1 = torch.arange(1, 4)
>>> vec2 = torch.arange(1, 3)
>>> M = torch.zeros(3, 2)
>>> torch.addr(M, vec1, vec2)
1 2
2 4
3 6
[torch.FloatTensor of size 3x2]
""")
add_docstr(torch._C.asin,
r"""
asin(input, out=None) -> Tensor
Returns a new tensor with the arcsine of the elements of :attr:`input`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
-0.6366
0.2718
0.4469
1.3122
[torch.FloatTensor of size 4]
>>> torch.asin(a)
-0.6900
0.2752
0.4633
nan
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.atan,
r"""
atan(input, out=None) -> Tensor
Returns a new tensor with the arctangent of the elements of :attr:`input`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
-0.6366
0.2718
0.4469
1.3122
[torch.FloatTensor of size 4]
>>> torch.atan(a)
-0.5669
0.2653
0.4203
0.9196
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.atan2,
r"""
atan2(input1, input2, out=None) -> Tensor
Returns a new tensor with the arctangent of the elements of :attr:`input1`
and :attr:`input2`.
The shapes of :attr:`input1` and :attr:`input2` must be
:ref:`broadcastable <broadcasting-semantics>`.
Args:
input1 (Tensor): the first input tensor
input2 (Tensor): the second input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
-0.6366
0.2718
0.4469
1.3122
[torch.FloatTensor of size 4]
>>> torch.atan2(a, torch.randn(4))
-2.4167
2.9755
0.9363
1.6613
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.baddbmm,
r"""
baddbmm(beta=1, mat, alpha=1, batch1, batch2, out=None) -> Tensor
Performs a batch matrix-matrix product of matrices in :attr:`batch1`
and :attr:`batch2`.
:attr:`mat` is added to the final result.
:attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the same
number of matrices.
If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a
:math:`(b \times m \times p)` tensor, then :attr:`mat` must be
:ref:`broadcastable <broadcasting-semantics>` with a
:math:`(b \times n \times p)` tensor and :attr:`out` will be a
:math:`(b \times n \times p)` tensor.
.. math::
out_i = \beta\ mat_i + \alpha\ (batch1_i \mathbin{@} batch2_i)
For inputs of type `FloatTensor` or `DoubleTensor`, args :attr:`beta` and
:attr:`alpha` must be real numbers, otherwise they should be integers.
Args:
beta (Number, optional): multiplier for :attr:`mat`
mat (Tensor): the tensor to be added
alpha (Number, optional): multiplier for `batch1 @ batch2`
batch1 (Tensor): the first batch of matrices to be multiplied
batch2 (Tensor): the second batch of matrices to be multiplied
out (Tensor, optional): the output tensor
Example::
>>> M = torch.randn(10, 3, 5)
>>> batch1 = torch.randn(10, 3, 4)
>>> batch2 = torch.randn(10, 4, 5)
>>> torch.baddbmm(M, batch1, batch2).size()
torch.Size([10, 3, 5])
""")
add_docstr(torch._C.bernoulli,
r"""
bernoulli(input, out=None) -> Tensor
Draws binary random numbers (0 or 1) from a Bernoulli distribution.
The :attr:`input` tensor should be a tensor containing probabilities
to be used for drawing the binary random number.
Hence, all values in :attr:`input` have to be in the range:
:math:`0 \leq input_i \leq 1`
The `i-th` element of the output tensor will draw a value `1` according
to the `i-th` probability value given in :attr:`input`.
The returned :attr:`out` tensor only has values 0 or 1 and is of the same
shape as :attr:`input`
Args:
input (Tensor): the input tensor of probability values for the Bernoulli distribution
out (Tensor, optional): the output tensor
Example::
>>> a = torch.Tensor(3, 3).uniform_(0, 1) # generate a uniform random matrix with range [0, 1]
>>> a
0.7544 0.8140 0.9842
0.5282 0.0595 0.6445
0.1925 0.9553 0.9732
[torch.FloatTensor of size 3x3]
>>> torch.bernoulli(a)
1 1 1
0 0 1
0 1 1
[torch.FloatTensor of size 3x3]
>>> a = torch.ones(3, 3) # probability of drawing "1" is 1
>>> torch.bernoulli(a)
1 1 1
1 1 1
1 1 1
[torch.FloatTensor of size 3x3]
>>> a = torch.zeros(3, 3) # probability of drawing "1" is 0
>>> torch.bernoulli(a)
0 0 0
0 0 0
0 0 0
[torch.FloatTensor of size 3x3]
""")
add_docstr(torch._C.bmm,
r"""
bmm(batch1, batch2, out=None) -> Tensor
Performs a batch matrix-matrix product of matrices stored in :attr:`batch1`
and :attr:`batch2`.
:attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing
the same number of matrices.
If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a
:math:`(b \times m \times p)` tensor, :attr:`out` will be a
:math:`(b \times n \times p)` tensor.
.. math::
out_i = batch1_i \mathbin{@} batch2_i
.. note:: This function does not :ref:`broadcast <broadcasting-semantics>`.
For broadcasting matrix products, see :func:`torch.matmul`.
Args:
batch1 (Tensor): the first batch of matrices to be multiplied
batch2 (Tensor): the second batch of matrices to be multiplied
out (Tensor, optional): the output tensor
Example::
>>> batch1 = torch.randn(10, 3, 4)
>>> batch2 = torch.randn(10, 4, 5)
>>> res = torch.bmm(batch1, batch2)
>>> res.size()
torch.Size([10, 3, 5])
""")
add_docstr(torch._C.cat,
r"""
cat(seq, dim=0, out=None) -> Tensor
Concatenates the given sequence of :attr:`seq` tensors in the given dimension.
All tensors must either have the same shape (except in the cat dimension) or be
empty.
:func:`torch.cat` can be seen as an inverse operation for :func:`torch.split`
and :func:`torch.chunk`
:func:`cat` can be best understood via examples.
Args:
seq (sequence of tensors): any python sequence of tensors of the same type.
Non-empty tensors provided must have the same shape, except in the
cat dimension.
dim (int, optional): the dimension over which the tensors are concatenated
out (Tensor, optional): the output tensor
Example::
>>> x = torch.randn(2, 3)
>>> x
0.5983 -0.0341 2.4918
1.5981 -0.5265 -0.8735
[torch.FloatTensor of size 2x3]
>>> torch.cat((x, x, x), 0)
0.5983 -0.0341 2.4918
1.5981 -0.5265 -0.8735
0.5983 -0.0341 2.4918
1.5981 -0.5265 -0.8735
0.5983 -0.0341 2.4918
1.5981 -0.5265 -0.8735
[torch.FloatTensor of size 6x3]
>>> torch.cat((x, x, x), 1)
0.5983 -0.0341 2.4918 0.5983 -0.0341 2.4918 0.5983 -0.0341 2.4918
1.5981 -0.5265 -0.8735 1.5981 -0.5265 -0.8735 1.5981 -0.5265 -0.8735
[torch.FloatTensor of size 2x9]
""")
add_docstr(torch._C.ceil,
r"""
ceil(input, out=None) -> Tensor
Returns a new tensor with the ceil of the elements of :attr:`input`,
the smallest integer greater than or equal to each element.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
1.3869
0.3912
-0.8634
-0.5468
[torch.FloatTensor of size 4]
>>> torch.ceil(a)
2
1
-0
-0
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.reciprocal,
r"""
reciprocal(input, out=None) -> Tensor
Returns a new tensor with the reciprocal of the elements of :attr:`input`,
i.e. :math:`x^{-1} = \frac{1}{x}`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
1.3869
0.3912
-0.8634
-0.5468
[torch.FloatTensor of size 4]
>>> torch.reciprocal(a)
0.7210
2.5565
-1.1583
-1.8289
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.clamp,
r"""
clamp(input, min, max, out=None) -> Tensor
Clamp all elements in :attr:`input` into the range `[min, max]` and return
a resulting tensor:
.. math::
y_i = \begin{cases}
min & \text{if } x_i < min \\
x_i & \text{if } min \leq x_i \leq max \\
max & \text{if } x_i > max
\end{cases}
If :attr:`input` is of type `FloatTensor` or `DoubleTensor`, args :attr:`min`
and :attr:`max` must be real numbers, otherwise they should be integers.
Args:
input (Tensor): the input tensor
min (Number): lower-bound of the range to be clamped to
max (Number): upper-bound of the range to be clamped to
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
1.3869
0.3912
-0.8634
-0.5468
[torch.FloatTensor of size 4]
>>> torch.clamp(a, min=-0.5, max=0.5)
0.5000
0.3912
-0.5000
-0.5000
[torch.FloatTensor of size 4]
.. function:: clamp(input, *, min, out=None) -> Tensor
Clamps all elements in :attr:`input` to be larger or equal :attr:`min`.
If :attr:`input` is of type `FloatTensor` or `DoubleTensor`, :attr:`value`
should be a real number, otherwise it should be an integer.
Args:
input (Tensor): the input tensor
value (Number): minimal value of each element in the output
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
1.3869
0.3912
-0.8634
-0.5468
[torch.FloatTensor of size 4]
>>> torch.clamp(a, min=0.5)
1.3869
0.5000
0.5000
0.5000
[torch.FloatTensor of size 4]
.. function:: clamp(input, *, max, out=None) -> Tensor
Clamps all elements in :attr:`input` to be smaller or equal :attr:`max`.
If :attr:`input` is of type `FloatTensor` or `DoubleTensor`, :attr:`value`
should be a real number, otherwise it should be an integer.
Args:
input (Tensor): the input tensor
value (Number): maximal value of each element in the output
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
1.3869
0.3912
-0.8634
-0.5468
[torch.FloatTensor of size 4]
>>> torch.clamp(a, max=0.5)
0.5000
0.3912
-0.8634
-0.5468
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.cos,
r"""
cos(input, out=None) -> Tensor
Returns a new tensor with the cosine of the elements of :attr:`input`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
-0.6366
0.2718
0.4469
1.3122
[torch.FloatTensor of size 4]
>>> torch.cos(a)
0.8041
0.9633
0.9018
0.2557
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.cosh,
r"""
cosh(input, out=None) -> Tensor
Returns a new tensor with the hyperbolic cosine of the elements of
:attr:`input`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
-0.6366
0.2718
0.4469
1.3122
[torch.FloatTensor of size 4]
>>> torch.cosh(a)
1.2095
1.0372
1.1015
1.9917
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.cross,
r"""
cross(input, other, dim=-1, out=None) -> Tensor
Returns the cross product of vectors in dimension :attr:`dim` of :attr:`input`
and :attr:`other`.
:attr:`input` and :attr:`other` must have the same size, and the size of their
:attr:`dim` dimension should be 3.
If :attr:`dim` is not given, it defaults to the first dimension found with the
size 3.
Args:
input (Tensor): the input tensor
other (Tensor): the second input tensor
dim (int, optional): the dimension to take the cross-product in.
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4, 3)
>>> a
-0.6652 -1.0116 -0.6857
0.2286 0.4446 -0.5272
0.0476 0.2321 1.9991
0.6199 1.1924 -0.9397
[torch.FloatTensor of size 4x3]
>>> b = torch.randn(4, 3)
>>> b
-0.1042 -1.1156 0.1947
0.9947 0.1149 0.4701
-1.0108 0.8319 -0.0750
0.9045 -1.3754 1.0976
[torch.FloatTensor of size 4x3]
>>> torch.cross(a, b, dim=1)
-0.9619 0.2009 0.6367
0.2696 -0.6318 -0.4160
-1.6805 -2.0171 0.2741
0.0163 -1.5304 -1.9311
[torch.FloatTensor of size 4x3]
>>> torch.cross(a, b)
-0.9619 0.2009 0.6367
0.2696 -0.6318 -0.4160
-1.6805 -2.0171 0.2741
0.0163 -1.5304 -1.9311
[torch.FloatTensor of size 4x3]
""")
add_docstr(torch._C.cumprod,
r"""
cumprod(input, dim, out=None) -> Tensor
Returns the cumulative product of elements of :attr:`input` in the dimension
:attr:`dim`.
For example, if :attr:`input` is a vector of size N, the result will also be
a vector of size N, with elements.
.. math::
y_i = x_1 \times x_2\times x_3\times \dots \times x_i
Args:
input (Tensor): the input tensor
dim (int): the dimension to do the operation over
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(10)
>>> a
1.1148
1.8423
1.4143
-0.4403
1.2859
-1.2514
-0.4748
1.1735
-1.6332
-0.4272
[torch.FloatTensor of size 10]
>>> torch.cumprod(a, dim=0)
1.1148
2.0537
2.9045
-1.2788
-1.6444
2.0578
-0.9770
-1.1466
1.8726
-0.8000
[torch.FloatTensor of size 10]
>>> a[5] = 0.0
>>> torch.cumprod(a, dim=0)
1.1148
2.0537
2.9045
-1.2788
-1.6444
-0.0000
0.0000
0.0000
-0.0000
0.0000
[torch.FloatTensor of size 10]
""")
add_docstr(torch._C.cumsum,
r"""
cumsum(input, dim, out=None) -> Tensor
Returns the cumulative sum of elements of :attr:`input` in the dimension
:attr:`dim`.
For example, if :attr:`input` is a vector of size N, the result will also be
a vector of size N, with elements.
.. math::
y_i = x_1 + x_2 + x_3 + \dots + x_i
Args:
input (Tensor): the input tensor
dim (int): the dimension to do the operation over
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(10)
>>> a
-0.6039
-0.2214
-0.3705
-0.0169
1.3415
-0.1230
0.9719
0.6081
-0.1286
1.0947
[torch.FloatTensor of size 10]
>>> torch.cumsum(a, dim=0)
-0.6039
-0.8253
-1.1958
-1.2127
0.1288
0.0058
0.9777
1.5858
1.4572
2.5519
[torch.FloatTensor of size 10]
""")
add_docstr(torch._C.diag,
r"""
diag(input, diagonal=0, out=None) -> Tensor
- If :attr:`input` is a vector (1-D tensor), then returns a 2-D square tensor
with the elements of :attr:`input` as the diagonal.
- If :attr:`input` is a matrix (2-D tensor), then returns a 1-D tensor with
the diagonal elements of :attr:`input`.
The argument :attr:`diagonal` controls which diagonal to consider:
- If :attr:`diagonal` = 0, it is the main diagonal.
- If :attr:`diagonal` > 0, it is above the main diagonal.
- If :attr:`diagonal` < 0, it is below the main diagonal.
Args:
input (Tensor): the input tensor
diagonal (int, optional): the diagonal to consider
out (Tensor, optional): the output tensor
Example:
Get the square matrix where the input vector is the diagonal::
>>> a = torch.randn(3)
>>> a
1.0480
-2.3405
-1.1138
[torch.FloatTensor of size 3]
>>> torch.diag(a)
1.0480 0.0000 0.0000
0.0000 -2.3405 0.0000
0.0000 0.0000 -1.1138
[torch.FloatTensor of size 3x3]
>>> torch.diag(a, 1)
0.0000 1.0480 0.0000 0.0000
0.0000 0.0000 -2.3405 0.0000
0.0000 0.0000 0.0000 -1.1138
0.0000 0.0000 0.0000 0.0000
[torch.FloatTensor of size 4x4]
Get the k-th diagonal of a given matrix::
>>> a = torch.randn(3, 3)
>>> a
-1.5328 -1.3210 -1.5204
0.8596 0.0471 -0.2239
-0.6617 0.0146 -1.0817
[torch.FloatTensor of size 3x3]
>>> torch.diag(a, 0)
-1.5328
0.0471
-1.0817
[torch.FloatTensor of size 3]
>>> torch.diag(a, 1)
-1.3210
-0.2239
[torch.FloatTensor of size 2]
""")
add_docstr(torch._C.dist,
r"""
dist(input, other, p=2) -> float
Returns the p-norm of (:attr:`input` - :attr:`other`)
The shapes of :attr:`input` and :attr:`other` must be
:ref:`broadcastable <broadcasting-semantics>`.
Args:
input (Tensor): the input tensor
other (Tensor): the Right-hand-side input tensor
p (float, optional): the norm to be computed
Example::
>>> x = torch.randn(4)
>>> x
0.2505
-0.4571
-0.3733
0.7807
[torch.FloatTensor of size 4]
>>> y = torch.randn(4)
>>> y
0.7782
-0.5185
1.4106
-2.4063
[torch.FloatTensor of size 4]
>>> torch.dist(x, y, 3.5)
3.302832063224223
>>> torch.dist(x, y, 3)
3.3677282206393286
>>> torch.dist(x, y, 0)
inf
>>> torch.dist(x, y, 1)
5.560028076171875
""")
add_docstr(torch._C.div,
r"""
.. function:: div(input, value, out=None)
Divides each element of the input :attr:`input` with the scalar :attr:`value`
and returns a new resulting tensor.
.. math::
out_i = \frac{input_i}{value}
If :attr:`input` is of type `FloatTensor` or `DoubleTensor`, :attr:`value`
should be a real number, otherwise it should be an integer
Args:
input (Tensor): the input tensor
value (Number): the number to be divided to each element of :attr:`input`
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(5)
>>> a
-0.6147
-1.1237
-0.1604
-0.6853
0.1063
[torch.FloatTensor of size 5]
>>> torch.div(a, 0.5)
-1.2294
-2.2474
-0.3208
-1.3706
0.2126
[torch.FloatTensor of size 5]
.. function:: div(input, other, out=None)
Each element of the tensor :attr:`input` is divided by each element
of the tensor :attr:`other`. The resulting tensor is returned. The shapes of
:attr:`input` and :attr:`other` must be
:ref:`broadcastable <broadcasting-semantics>`.
.. math::
out_i = \frac{input_i}{other_i}
Args:
input (Tensor): the numerator tensor
other (Tensor): the denominator tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4,4)
>>> a
-0.1810 0.4017 0.2863 -0.1013
0.6183 2.0696 0.9012 -1.5933
0.5679 0.4743 -0.0117 -0.1266
-0.1213 0.9629 0.2682 1.5968
[torch.FloatTensor of size 4x4]
>>> b = torch.randn(8, 2)
>>> b
0.8774 0.7650
0.8866 1.4805
-0.6490 1.1172
1.4259 -0.8146
1.4633 -0.1228
0.4643 -0.6029
0.3492 1.5270
1.6103 -0.6291
[torch.FloatTensor of size 8x2]
>>> torch.div(a, b)
-0.2062 0.5251 0.3229 -0.0684
-0.9528 1.8525 0.6320 1.9559
0.3881 -3.8625 -0.0253 0.2099
-0.3473 0.6306 0.1666 -2.5381
[torch.FloatTensor of size 4x4]
""")
add_docstr(torch._C.dot,
r"""
dot(tensor1, tensor2) -> float
Computes the dot product (inner product) of two tensors.
.. note:: This function does not :ref:`broadcast <broadcasting-semantics>`.
Example::
>>> torch.dot(torch.Tensor([2, 3]), torch.Tensor([2, 1]))
7.0
""")
add_docstr(torch._C.eig,
r"""
eig(a, eigenvectors=False, out=None) -> (Tensor, Tensor)
Computes the eigenvalues and eigenvectors of a real square matrix.
Args:
a (Tensor): the square matrix for which the eigenvalues and eigenvectors will be computed
eigenvectors (bool): ``True`` to compute both eigenvalues and eigenvectors;
otherwise, only eigenvalues will be computed
out (tuple, optional): the output tensors
Returns:
(Tensor, Tensor): A tuple containing
- **e** (*Tensor*): the right eigenvalues of ``a``
- **v** (*Tensor*): the eigenvectors of ``a`` if ``eigenvectors`` is ``True``; otherwise an empty tensor
""")
add_docstr(torch._C.eq,
r"""
eq(input, other, out=None) -> Tensor
Computes element-wise equality
The second argument can be a number or a tensor whose shape is
:ref:`broadcastable <broadcasting-semantics>` with the first argument.
Args:
input (Tensor): the tensor to compare
other (Tensor or float): the tensor or value to compare
out (Tensor, optional): the output tensor. Must be a `ByteTensor` or the same type as `input`.
Returns:
Tensor: A ``torch.ByteTensor`` containing a 1 at each location where comparison is true
Example::
>>> torch.eq(torch.Tensor([[1, 2], [3, 4]]), torch.Tensor([[1, 1], [4, 4]]))
1 0
0 1
[torch.ByteTensor of size 2x2]
""")
add_docstr(torch._C.equal,
r"""
equal(tensor1, tensor2) -> bool
``True`` if two tensors have the same size and elements, ``False`` otherwise.
Example::
>>> torch.equal(torch.Tensor([1, 2]), torch.Tensor([1, 2]))
True
""")
add_docstr(torch._C.erf,
r"""
erf(tensor, out=None) -> Tensor
Computes the error function of each element.
Example::
>>> torch.erf(torch.Tensor([0, -1., 10.]))
torch.FloatTensor([0., -0.8427, 1.])
""")
add_docstr(torch._C.erfinv,
r"""
erfinv(tensor, out=None) -> Tensor
Computes the inverse error function of each element.
Example::
>>> torch.erfinv(torch.Tensor([0, 0.5., -1.]))
torch.FloatTensor([0., 0.4769, -inf])
""")
add_docstr(torch._C.exp,
r"""
exp(tensor, out=None) -> Tensor
Computes the exponential of each element.
Example::
>>> torch.exp(torch.Tensor([0, math.log(2)]))
torch.FloatTensor([1, 2])
""")
add_docstr(torch._C.expm1,
r"""
expm1(tensor, out=None) -> Tensor
Computes the exponential of each element minus 1.
Example::
>>> torch.expm1(torch.Tensor([0, math.log(2)]))
torch.FloatTensor([0, 1])
""")
add_docstr(torch._C.eye,
r"""
eye(n, m=None, out=None)
Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.
Args:
n (int): the number of rows
m (int, optional): the number of columns with default being :attr:`n`
out (Tensor, optional): the output tensor
Returns:
Tensor: A 2-D tensor with ones on the diagonal and zeros elsewhere
Example::
>>> torch.eye(3)
1 0 0
0 1 0
0 0 1
[torch.FloatTensor of size 3x3]
""")
add_docstr(torch._C.floor,
r"""
floor(input, out=None) -> Tensor
Returns a new tensor with the floor of the elements of :attr:`input`,
the largest integer less than or equal to each element.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
1.3869
0.3912
-0.8634
-0.5468
[torch.FloatTensor of size 4]
>>> torch.floor(a)
1
0
-1
-1
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.fmod,
r"""
fmod(input, divisor, out=None) -> Tensor
Computes the element-wise remainder of division.
The dividend and divisor may contain both for integer and floating point
numbers. The remainder has the same sign as the dividend :attr:`input`.
When :attr:`divisor` is a tensor, the shapes of :attr:`input` and
:attr:`divisor` must be :ref:`broadcastable <broadcasting-semantics>`.
Args:
input (Tensor): the dividend
divisor (Tensor or float): the divisor, which may be either a number or a tensor of the same shape as the dividend
out (Tensor, optional): the output tensor
Example::
>>> torch.fmod(torch.Tensor([-3, -2, -1, 1, 2, 3]), 2)
torch.FloatTensor([-1, -0, -1, 1, 0, 1])
>>> torch.fmod(torch.Tensor([1, 2, 3, 4, 5]), 1.5)
torch.FloatTensor([1.0, 0.5, 0.0, 1.0, 0.5])
.. seealso::
:func:`torch.remainder`, which computes the element-wise remainder of
division equivalently to Python's `%` operator
""")
add_docstr(torch._C.frac,
r"""
frac(tensor, out=None) -> Tensor
Computes the fractional portion of each element in :attr:`tensor`.
Example::
>>> torch.frac(torch.Tensor([1, 2.5, -3.2])
torch.FloatTensor([0, 0.5, -0.2])
""")
add_docstr(torch._C.from_numpy,
r"""
from_numpy(ndarray) -> Tensor
Creates a :class:`Tensor` from a :class:`numpy.ndarray`.
The returned tensor and `ndarray` share the same memory. Modifications to the
tensor will be reflected in the `ndarray` and vice versa. The returned tensor
is not resizable.
Example::
>>> a = numpy.array([1, 2, 3])
>>> t = torch.from_numpy(a)
>>> t
torch.LongTensor([1, 2, 3])
>>> t[0] = -1
>>> a
array([-1, 2, 3])
""")
add_docstr(torch._C.gather,
r"""
gather(input, dim, index, out=None) -> Tensor
Gathers values along an axis specified by `dim`.
For a 3-D tensor the output is specified by::
out[i][j][k] = input[index[i][j][k]][j][k] # if dim == 0
out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1
out[i][j][k] = input[i][j][index[i][j][k]] # if dim == 2
If :attr:`input` is an n-dimensional tensor with size
:math:`(x_0, x_1..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})`
and :attr:`dim` = i, then :attr:`index` must be an n-dimensional tensor with
size :math:`(x_0, x_1, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})` where y >= 1 and
:attr:`out` will have the same size as :attr:`index`.
Args:
input (Tensor): the source tensor
dim (int): the axis along which to index
index (LongTensor): the indices of elements to gather
out (Tensor, optional): the destination tensor
Example::
>>> t = torch.Tensor([[1,2],[3,4]])
>>> torch.gather(t, 1, torch.LongTensor([[0,0],[1,0]]))
1 1
4 3
[torch.FloatTensor of size 2x2]
""")
add_docstr(torch._C.ge,
r"""
ge(input, other, out=None) -> Tensor
Computes `input >= other` element-wise.
The second argument can be a number or a tensor whose shape is
:ref:`broadcastable <broadcasting-semantics>` with the first argument.
Args:
input (Tensor): the tensor to compare
other (Tensor or float): the tensor or value to compare
out (Tensor, optional): the output tensor that must be a `ByteTensor` or the same type as :attr:`input`
Returns:
Tensor: A ``torch.ByteTensor`` containing a 1 at each location where comparison is true
Example::
>>> torch.ge(torch.Tensor([[1, 2], [3, 4]]), torch.Tensor([[1, 1], [4, 4]]))
1 1
0 1
[torch.ByteTensor of size 2x2]
""")
add_docstr(torch._C.gels,
r"""
gels(B, A, out=None) -> Tensor
Computes the solution to the least squares and least norm problems for a full
rank :math:`m` by :math:`n` matrix :math:`A`.
If :math:`m >= n`, :func:`gels` solves the least-squares problem:
.. math::
\begin{array}{ll}
\mbox{minimize} & \|AX-B\|_F.
\end{array}
If :math:`m < n`, :func:`gels` solves the least-norm problem:
.. math::
\begin{array}{ll}
\mbox{minimize} & \|X\|_F & \mbox{subject to} & AX = B.
\end{array}
The first :math:`n` rows of the returned matrix :math:`X` contains the
solution. The remaining rows contain residual information: the euclidean norm
of each column starting at row :math:`n` is the residual for the corresponding
column.
Args:
B (Tensor): the matrix :math:`B`
A (Tensor): the :math:`m` by :math:`n` matrix :math:`A`
out (tuple, optional): the optional destination tensor
Returns:
(Tensor, Tensor): A tuple containing:
- **X** (*Tensor*): the least squares solution
- **qr** (*Tensor*): the details of the QR factorization
.. note::
The returned matrices will always be transposed, irrespective of the strides
of the input matrices. That is, they will have stride `(1, m)` instead of
`(m, 1)`.
Example::
>>> A = torch.Tensor([[1, 1, 1],
... [2, 3, 4],
... [3, 5, 2],
... [4, 2, 5],
... [5, 4, 3]])
>>> B = torch.Tensor([[-10, -3],
[ 12, 14],
[ 14, 12],
[ 16, 16],
[ 18, 16]])
>>> X, _ = torch.gels(B, A)
>>> X
2.0000 1.0000
1.0000 1.0000
1.0000 2.0000
[torch.FloatTensor of size 3x2]
""")
add_docstr(torch._C.geqrf,
r"""
geqrf(input, out=None) -> (Tensor, Tensor)
This is a low-level function for calling LAPACK directly.
You'll generally want to use :func:`torch.qr` instead.
Computes a QR decomposition of :attr:`input`, but without constructing
`Q` and `R` as explicit separate matrices.
Rather, this directly calls the underlying LAPACK function `?geqrf`
which produces a sequence of 'elementary reflectors'.
See `LAPACK documentation`_ for further details.
Args:
input (Tensor): the input matrix
out (tuple, optional): the output tuple of (Tensor, Tensor)
.. _LAPACK documentation:
https://software.intel.com/en-us/node/521004
""")
add_docstr(torch._C.ger,
r"""
ger(vec1, vec2, out=None) -> Tensor
Outer product of :attr:`vec1` and :attr:`vec2`.
If :attr:`vec1` is a vector of size `n` and :attr:`vec2` is a vector of
size `m`, then :attr:`out` must be a matrix of size :math:`(n \times m)`.
.. note:: This function does not :ref:`broadcast <broadcasting-semantics>`.
Args:
vec1 (Tensor): 1-D input vector
vec2 (Tensor): 1-D input vector
out (Tensor, optional): optional output matrix
Example::
>>> v1 = torch.arange(1, 5)
>>> v2 = torch.arange(1, 4)
>>> torch.ger(v1, v2)
1 2 3
2 4 6
3 6 9
4 8 12
[torch.FloatTensor of size 4x3]
""")
add_docstr(torch._C.gesv,
r"""
gesv(B, A, out=None) -> (Tensor, Tensor)
`X, LU = torch.gesv(B, A)` returns the solution to the system of linear
equations represented by :math:`AX = B`
`LU` contains `L` and `U` factors for LU factorization of `A`.
:attr:`A` has to be a square and non-singular matrix (2-D tensor).
If `A` is an :math:`(m \times m)` matrix and `B` is :math:`(m \times k)`,
the result `LU` is :math:`(m \times m)` and `X` is :math:`(m \times k)`.
.. note::
Irrespective of the original strides, the returned matrices
`X` and `LU` will be transposed, i.e. with strides `(1, m)`
instead of `(m, 1)`.
Args:
B (Tensor): input matrix of :math:`(m \times k)` dimensions
A (Tensor): input square matrix of :math:`(m \times m)` dimensions
out (Tensor, optional): optional output matrix
Example::
>>> A = torch.Tensor([[6.80, -2.11, 5.66, 5.97, 8.23],
... [-6.05, -3.30, 5.36, -4.44, 1.08],
... [-0.45, 2.58, -2.70, 0.27, 9.04],
... [8.32, 2.71, 4.35, -7.17, 2.14],
... [-9.67, -5.14, -7.26, 6.08, -6.87]]).t()
>>> B = torch.Tensor([[4.02, 6.19, -8.22, -7.57, -3.03],
... [-1.56, 4.00, -8.67, 1.75, 2.86],
... [9.81, -4.09, -4.57, -8.61, 8.99]]).t()
>>> X, LU = torch.gesv(B, A)
>>> torch.dist(B, torch.mm(A, X))
9.250057093890353e-06
""")
add_docstr(torch._C.get_num_threads,
r"""
get_num_threads() -> int
Gets the number of OpenMP threads used for parallelizing CPU operations
""")
add_docstr(torch._C.gt,
r"""
gt(input, other, out=None) -> Tensor
Computes `input > other` element-wise.
The second argument can be a number or a tensor whose shape is
:ref:`broadcastable <broadcasting-semantics>` with the first argument.
Args:
input (Tensor): the tensor to compare
other (Tensor or float): the tensor or value to compare
out (Tensor, optional): the output tensor that must be a `ByteTensor` or the same type as :attr:`input`
Returns:
Tensor: A ``torch.ByteTensor`` containing a 1 at each location where comparison is true
Example::
>>> torch.gt(torch.Tensor([[1, 2], [3, 4]]), torch.Tensor([[1, 1], [4, 4]]))
0 1
0 0
[torch.ByteTensor of size 2x2]
""")
add_docstr(torch._C.histc,
r"""
histc(input, bins=100, min=0, max=0, out=None) -> Tensor
Computes the histogram of a tensor.
The elements are sorted into equal width bins between :attr:`min` and
:attr:`max`. If :attr:`min` and :attr:`max` are both zero, the minimum and
maximum values of the data are used.
Args:
input (Tensor): the input tensor
bins (int): number of histogram bins
min (int): lower end of the range (inclusive)
max (int): upper end of the range (inclusive)
out (Tensor, optional): the output tensor
Returns:
Tensor: Histogram represented as a tensor
Example::
>>> torch.histc(torch.FloatTensor([1, 2, 1]), bins=4, min=0, max=3)
FloatTensor([0, 2, 1, 0])
""")
add_docstr(torch._C.index_select,
r"""
index_select(input, dim, index, out=None) -> Tensor
Returns a new tensor which indexes the :attr:`input` tensor along dimension
:attr:`dim` using the entries in :attr:`index` which is a `LongTensor`.
The returned tensor has the same number of dimensions as the original tensor
(:attr:`input`). The :attr:`dim`\ th dimension has the same size as the length
of :attr:`index`; other dimensions have the same size as in the original tensor.
.. note:: The returned tensor does **not** use the same storage as the original
tensor. If :attr:`out` has a different shape than expected, we
silently change it to the correct shape, reallocating the underlying
storage if necessary.
Args:
input (Tensor): the input tensor
dim (int): the dimension in which we index
index (LongTensor): the 1-D tensor containing the indices to index
out (Tensor, optional): the output tensor
Example::
>>> x = torch.randn(3, 4)
>>> x
1.2045 2.4084 0.4001 1.1372
0.5596 1.5677 0.6219 -0.7954
1.3635 -1.2313 -0.5414 -1.8478
[torch.FloatTensor of size 3x4]
>>> indices = torch.LongTensor([0, 2])
>>> torch.index_select(x, 0, indices)
1.2045 2.4084 0.4001 1.1372
1.3635 -1.2313 -0.5414 -1.8478
[torch.FloatTensor of size 2x4]
>>> torch.index_select(x, 1, indices)
1.2045 0.4001
0.5596 0.6219
1.3635 -0.5414
[torch.FloatTensor of size 3x2]
""")
add_docstr(torch._C.inverse,
r"""
inverse(input, out=None) -> Tensor
Takes the inverse of the square matrix :attr:`input`.
.. note::
Irrespective of the original strides, the returned matrix will be
transposed, i.e. with strides `(1, m)` instead of `(m, 1)`
Args:
input (Tensor): the input 2-D square tensor
out (Tensor, optional): the optional output tensor
Example::
>>> x = torch.rand(10, 10)
>>> x
0.7800 0.2267 0.7855 0.9479 0.5914 0.7119 0.4437 0.9131 0.1289 0.1982
0.0045 0.0425 0.2229 0.4626 0.6210 0.0207 0.6338 0.7067 0.6381 0.8196
0.8350 0.7810 0.8526 0.9364 0.7504 0.2737 0.0694 0.5899 0.8516 0.3883
0.6280 0.6016 0.5357 0.2936 0.7827 0.2772 0.0744 0.2627 0.6326 0.9153
0.7897 0.0226 0.3102 0.0198 0.9415 0.9896 0.3528 0.9397 0.2074 0.6980
0.5235 0.6119 0.6522 0.3399 0.3205 0.5555 0.8454 0.3792 0.4927 0.6086
0.1048 0.0328 0.5734 0.6318 0.9802 0.4458 0.0979 0.3320 0.3701 0.0909
0.2616 0.3485 0.4370 0.5620 0.5291 0.8295 0.7693 0.1807 0.0650 0.8497
0.1655 0.2192 0.6913 0.0093 0.0178 0.3064 0.6715 0.5101 0.2561 0.3396
0.4370 0.4695 0.8333 0.1180 0.4266 0.4161 0.0699 0.4263 0.8865 0.2578
[torch.FloatTensor of size 10x10]
>>> x = torch.rand(10, 10)
>>> y = torch.inverse(x)
>>> z = torch.mm(x, y)
>>> z
1.0000 0.0000 0.0000 -0.0000 0.0000 0.0000 0.0000 0.0000 -0.0000 -0.0000
0.0000 1.0000 -0.0000 0.0000 0.0000 0.0000 -0.0000 -0.0000 -0.0000 -0.0000
0.0000 0.0000 1.0000 -0.0000 -0.0000 0.0000 0.0000 0.0000 -0.0000 -0.0000
0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000
0.0000 0.0000 -0.0000 -0.0000 1.0000 0.0000 0.0000 -0.0000 -0.0000 -0.0000
0.0000 0.0000 0.0000 -0.0000 0.0000 1.0000 -0.0000 -0.0000 -0.0000 -0.0000
0.0000 0.0000 0.0000 -0.0000 0.0000 0.0000 1.0000 0.0000 -0.0000 0.0000
0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -0.0000 1.0000 -0.0000 0.0000
-0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 1.0000 -0.0000
-0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 -0.0000 0.0000 1.0000
[torch.FloatTensor of size 10x10]
>>> torch.max(torch.abs(z - torch.eye(10))) # Max nonzero
5.096662789583206e-07
""")
add_docstr(torch._C.kthvalue,
r"""
kthvalue(input, k, dim=None, keepdim=False, out=None) -> (Tensor, LongTensor)
Returns the :attr:`k` th smallest element of the given :attr:`input` tensor
along a given dimension.
If :attr:`dim` is not given, the last dimension of the `input` is chosen.
A tuple of `(values, indices)` is returned, where the `indices` is the indices
of the kth-smallest element in the original `input` tensor in dimension `dim`.
If :attr:`keepdim` is ``True``, both the :attr:`values` and :attr:`indices` tensors
are the same size as :attr:`input`, except in the dimension :attr:`dim` where
they are of size 1. Otherwise, :attr:`dim` is squeezed
(see :func:`torch.squeeze`), resulting in both the :attr:`values` and
:attr:`indices` tensors having 1 fewer dimension than the :attr:`input` tensor.
Args:
input (Tensor): the input tensor
k (int): k for the k-th smallest element
dim (int, optional): the dimension to find the kth value along
keepdim (bool): whether the output tensors have :attr:`dim` retained or not
out (tuple, optional): the output tuple of (Tensor, LongTensor)
can be optionally given to be used as output buffers
Example::
>>> x = torch.arange(1, 6)
>>> x
1
2
3
4
5
[torch.FloatTensor of size 5]
>>> torch.kthvalue(x, 4)
(
4
[torch.FloatTensor of size 1]
,
3
[torch.LongTensor of size 1]
)
>>> x=torch.arange(1,7).resize_(2,3)
>>> x
1 2 3
4 5 6
[torch.FloatTensor of size 2x3]
>>> torch.kthvalue(x,2,0,True)
(
4 5 6
[torch.FloatTensor of size 1x3]
,
1 1 1
[torch.LongTensor of size 1x3]
)
""")
add_docstr(torch._C.le,
r"""
le(input, other, out=None) -> Tensor
Computes `input <= other` element-wise.
The second argument can be a number or a tensor whose shape is
:ref:`broadcastable <broadcasting-semantics>` with the first argument.
Args:
input (Tensor): the tensor to compare
other (Tensor or float): the tensor or value to compare
out (Tensor, optional): the output tensor that must be a `ByteTensor` or the same type as :attr:`input`
Returns:
Tensor: A ``torch.ByteTensor`` containing a 1 at each location where comparison is true
Example::
>>> torch.le(torch.Tensor([[1, 2], [3, 4]]), torch.Tensor([[1, 1], [4, 4]]))
1 0
1 1
[torch.ByteTensor of size 2x2]
""")
add_docstr(torch._C.lerp,
r"""
lerp(start, end, weight, out=None)
Does a linear interpolation of two tensors :attr:`start` and :attr:`end` based
on a scalar :attr:`weight` and returns the resulting :attr:`out` tensor.
.. math::
out_i = start_i + weight \times (end_i - start_i)
The shapes of :attr:`start` and :attr:`end` must be
:ref:`broadcastable <broadcasting-semantics>`.
Args:
start (Tensor): the tensor with the starting points
end (Tensor): the tensor with the ending points
weight (float): the weight for the interpolation formula
out (Tensor, optional): the output tensor
Example::
>>> start = torch.arange(1, 5)
>>> end = torch.Tensor(4).fill_(10)
>>> start
1
2
3
4
[torch.FloatTensor of size 4]
>>> end
10
10
10
10
[torch.FloatTensor of size 4]
>>> torch.lerp(start, end, 0.5)
5.5000
6.0000
6.5000
7.0000
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.linspace,
r"""
linspace(start, end, steps=100, out=None) -> Tensor
Returns a one-dimensional tensor of :attr:`steps`
equally spaced points between :attr:`start` and :attr:`end`
The output tensor is 1-D of size :attr:`steps`
Args:
start (float): the starting value for the set of points
end (float): the ending value for the set of points
steps (int): number of points to sample between :attr:`start`
and :attr:`end`
out (Tensor, optional): the output tensor
Example::
>>> torch.linspace(3, 10, steps=5)
3.0000
4.7500
6.5000
8.2500
10.0000
[torch.FloatTensor of size 5]
>>> torch.linspace(-10, 10, steps=5)
-10
-5
0
5
10
[torch.FloatTensor of size 5]
>>> torch.linspace(start=-10, end=10, steps=5)
-10
-5
0
5
10
[torch.FloatTensor of size 5]
""")
add_docstr(torch._C.log,
r"""
log(input, out=None) -> Tensor
Returns a new tensor with the natural logarithm of the elements
of :attr:`input`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(5)
>>> a
-0.4183
0.3722
-0.3091
0.4149
0.5857
[torch.FloatTensor of size 5]
>>> torch.log(a)
nan
-0.9883
nan
-0.8797
-0.5349
[torch.FloatTensor of size 5]
""")
add_docstr(torch._C.log1p,
r"""
log1p(input, out=None) -> Tensor
Returns a new tensor with the natural logarithm of (1 + :attr:`input`).
.. math::
y_i = \log (x_i + 1)
.. note:: This function is more accurate than :func:`torch.log` for small
values of :attr:`input`
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(5)
>>> a
-0.4183
0.3722
-0.3091
0.4149
0.5857
[torch.FloatTensor of size 5]
>>> torch.log1p(a)
-0.5418
0.3164
-0.3697
0.3471
0.4611
[torch.FloatTensor of size 5]
""")
add_docstr(torch._C.logspace,
r"""
logspace(start, end, steps=100, out=None) -> Tensor
Returns a one-dimensional tensor of :attr:`steps` points
logarithmically spaced between :math:`10^{start}` and :math:`10^{end}`.
The output is a 1-D tensor of size :attr:`steps`
Args:
start (float): the starting value for the set of points
end (float): the ending value for the set of points
steps (int): number of points to sample between
:attr:`start` and :attr:`end`
out (Tensor, optional): the output tensor
Example::
>>> torch.logspace(start=-10, end=10, steps=5)
1.0000e-10
1.0000e-05
1.0000e+00
1.0000e+05
1.0000e+10
[torch.FloatTensor of size 5]
>>> torch.logspace(start=0.1, end=1.0, steps=5)
1.2589
2.1135
3.5481
5.9566
10.0000
[torch.FloatTensor of size 5]
""")
add_docstr(torch._C.lt,
r"""
lt(input, other, out=None) -> Tensor
Computes `input < other` element-wise.
The second argument can be a number or a tensor whose shape is
:ref:`broadcastable <broadcasting-semantics>` with the first argument.
Args:
input (Tensor): the tensor to compare
other (Tensor or float): the tensor or value to compare
out (Tensor, optional): the output tensor that must be a `ByteTensor` or the same type as :attr:`input`
Returns:
Tensor: A `torch.ByteTensor` containing a 1 at each location where comparison is true
Example::
>>> torch.lt(torch.Tensor([[1, 2], [3, 4]]), torch.Tensor([[1, 1], [4, 4]]))
0 0
1 0
[torch.ByteTensor of size 2x2]
""")
add_docstr(torch._C.masked_select,
r"""
masked_select(input, mask, out=None) -> Tensor
Returns a new 1-D tensor which indexes the :attr:`input` tensor according to
the binary mask :attr:`mask` which is a `ByteTensor`.
The shapes of the :attr:`mask` tensor and the :attr:`input` tensor don't need
to match, but they must be :ref:`broadcastable <broadcasting-semantics>`.
.. note:: The returned tensor does **not** use the same storage
as the original tensor
Args:
input (Tensor): the input data
mask (ByteTensor): the tensor containing the binary mask to index with
out (Tensor, optional): the output tensor
Example::
>>> x = torch.randn(3, 4)
>>> x
1.2045 2.4084 0.4001 1.1372
0.5596 1.5677 0.6219 -0.7954
1.3635 -1.2313 -0.5414 -1.8478
[torch.FloatTensor of size 3x4]
>>> mask = x.ge(0.5)
>>> mask
1 1 0 1
1 1 1 0
1 0 0 0
[torch.ByteTensor of size 3x4]
>>> torch.masked_select(x, mask)
1.2045
2.4084
1.1372
0.5596
1.5677
0.6219
1.3635
[torch.FloatTensor of size 7]
""")
add_docstr(torch._C.max,
r"""
.. function:: max(input) -> float
Returns the maximum value of all elements in the :attr:`input` tensor.
Args:
input (Tensor): the input tensor
Example::
>>> a = torch.randn(1, 3)
>>> a
0.4729 -0.2266 -0.2085
[torch.FloatTensor of size 1x3]
>>> torch.max(a)
0.4729
.. function:: max(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor)
Returns the maximum value of each row of the :attr:`input` tensor in the given
dimension :attr:`dim`. The second return value is the index location of each
maximum value found (argmax).
If :attr:`keepdim` is ``True``, the output tensors are of the same size
as :attr:`input` except in the dimension :attr:`dim` where they are of size 1.
Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting
in the output tensors having 1 fewer dimension than :attr:`input`.
Args:
input (Tensor): the input tensor
dim (int): the dimension to reduce
keepdim (bool): whether the output tensors have :attr:`dim` retained or not
out (tuple, optional): the result tuple of two output tensors (max, max_indices)
Example::
>> a = torch.randn(4, 4)
>> a
0.0692 0.3142 1.2513 -0.5428
0.9288 0.8552 -0.2073 0.6409
1.0695 -0.0101 -2.4507 -1.2230
0.7426 -0.7666 0.4862 -0.6628
torch.FloatTensor of size 4x4]
>>> torch.max(a, 1)
(
1.2513
0.9288
1.0695
0.7426
[torch.FloatTensor of size 4]
,
2
0
0
0
[torch.LongTensor of size 4]
)
.. function:: max(input, other, out=None) -> Tensor
Each element of the tensor :attr:`input` is compared with the corresponding
element of the tensor :attr:`other` and an element-wise `max` is taken.
The shapes of :attr:`input` and :attr:`other` don't need to match,
but they must be :ref:`broadcastable <broadcasting-semantics>`.
.. math::
out_i = \max(tensor_i, other_i)
.. note:: When the shapes do not match, the shape of the returned output tensor
follows the :ref:`broadcasting rules <broadcasting-semantics>`.
Args:
input (Tensor): the input tensor
other (Tensor): the second input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
1.3869
0.3912
-0.8634
-0.5468
[torch.FloatTensor of size 4]
>>> b = torch.randn(4)
>>> b
1.0067
-0.8010
0.6258
0.3627
[torch.FloatTensor of size 4]
>>> torch.max(a, b)
1.3869
0.3912
0.6258
0.3627
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.mean,
r"""
.. function:: mean(input) -> float
Returns the mean value of all elements in the :attr:`input` tensor.
Args:
input (Tensor): the input tensor
Example::
>>> a = torch.randn(1, 3)
>>> a
-0.2946 -0.9143 2.1809
[torch.FloatTensor of size 1x3]
>>> torch.mean(a)
0.32398951053619385
.. function:: mean(input, dim, keepdim=False, out=None) -> Tensor
Returns the mean value of each row of the :attr:`input` tensor in the given
dimension :attr:`dim`.
If :attr:`keepdim` is ``True``, the output tensor is of the same size
as :attr:`input` except in the dimension :attr:`dim` where it is of size 1.
Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the
output tensor having 1 fewer dimension.
Args:
input (Tensor): the input tensor
dim (int): the dimension to reduce
keepdim (bool, optional): whether the output tensor has :attr:`dim` retained or not
out (Tensor): the output tensor
Example::
>>> a = torch.randn(4, 4)
>>> a
-1.2738 -0.3058 0.1230 -1.9615
0.8771 -0.5430 -0.9233 0.9879
1.4107 0.0317 -0.6823 0.2255
-1.3854 0.4953 -0.2160 0.2435
[torch.FloatTensor of size 4x4]
>>> torch.mean(a, 1)
-0.8545
0.0997
0.2464
-0.2157
[torch.FloatTensor of size 4]
>>> torch.mean(a, 1, True)
-0.8545
0.0997
0.2464
-0.2157
[torch.FloatTensor of size 4x1]
""")
add_docstr(torch._C.median,
r"""
.. function:: median(input) -> float
Returns the median value of all elements in the :attr:`input` tensor.
Args:
input (Tensor): the input tensor
Example::
>>> a = torch.randn(1, 3)
>>> a
0.4729 -0.2266 -0.2085
[torch.FloatTensor of size 1x3]
>>> torch.median(a)
-0.2085
.. function:: median(input, dim=-1, keepdim=False, values=None, indices=None) -> (Tensor, LongTensor)
Returns the median value of each row of the :attr:`input` tensor in the given
dimension :attr:`dim`. Also returns the index location of the median value
as a `LongTensor`.
By default, :attr:`dim` is the last dimension of the :attr:`input` tensor.
If :attr:`keepdim` is ``True``, the output tensors are of the same size
as :attr:`input` except in the dimension :attr:`dim` where they are of size 1.
Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in
the outputs tensor having 1 fewer dimension than :attr:`input`.
Args:
input (Tensor): the input tensor
dim (int): the dimension to reduce
keepdim (bool): whether the output tensors have :attr:`dim` retained or not
values (Tensor, optional): the output tensor
indices (Tensor, optional): the output index tensor
Example::
>>> a
-0.6891 -0.6662
0.2697 0.7412
0.5254 -0.7402
0.5528 -0.2399
[torch.FloatTensor of size 4x2]
>>> a = torch.randn(4, 5)
>>> a
0.4056 -0.3372 1.0973 -2.4884 0.4334
2.1336 0.3841 0.1404 -0.1821 -0.7646
-0.2403 1.3975 -2.0068 0.1298 0.0212
-1.5371 -0.7257 -0.4871 -0.2359 -1.1724
[torch.FloatTensor of size 4x5]
>>> torch.median(a, 1)
(
0.4056
0.1404
0.0212
-0.7257
[torch.FloatTensor of size 4]
,
0
2
4
1
[torch.LongTensor of size 4]
)
""")
add_docstr(torch._C.min,
r"""
.. function:: min(input) -> float
Returns the minimum value of all elements in the :attr:`input` tensor.
Args:
input (Tensor): the input tensor
Example::
>>> a = torch.randn(1, 3)
>>> a
0.4729 -0.2266 -0.2085
[torch.FloatTensor of size 1x3]
>>> torch.min(a)
-0.22663167119026184
.. function:: min(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor)
Returns the minimum value of each row of the :attr:`input` tensor in the given
dimension :attr:`dim`. The second return value is the index location of each
minimum value found (argmin).
If :attr:`keepdim` is ``True``, the output tensors are of the same size as
:attr:`input` except in the dimension :attr:`dim` where they are of size 1.
Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in
the output tensors having 1 fewer dimension than :attr:`input`.
Args:
input (Tensor): the input tensor
dim (int): the dimension to reduce
keepdim (bool): whether the output tensors have :attr:`dim` retained or not
out (tuple, optional): the tuple of two output tensors (min, min_indices)
Example::
>> a = torch.randn(4, 4)
>> a
0.0692 0.3142 1.2513 -0.5428
0.9288 0.8552 -0.2073 0.6409
1.0695 -0.0101 -2.4507 -1.2230
0.7426 -0.7666 0.4862 -0.6628
torch.FloatTensor of size 4x4]
>> torch.min(a, 1)
0.5428
0.2073
2.4507
0.7666
torch.FloatTensor of size 4]
3
2
2
1
torch.LongTensor of size 4]
.. function:: min(input, other, out=None) -> Tensor
Each element of the tensor :attr:`input` is compared with the corresponding
element of the tensor :attr:`other` and an element-wise `min` is taken.
The resulting tensor is returned.
The shapes of :attr:`input` and :attr:`other` don't need to match,
but they must be :ref:`broadcastable <broadcasting-semantics>`.
.. math::
out_i = \min(tensor_i, other_i)
.. note:: When the shapes do not match, the shape of the returned output tensor
follows the :ref:`broadcasting rules <broadcasting-semantics>`.
Args:
input (Tensor): the input tensor
other (Tensor): the second input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
1.3869
0.3912
-0.8634
-0.5468
[torch.FloatTensor of size 4]
>>> b = torch.randn(4)
>>> b
1.0067
-0.8010
0.6258
0.3627
[torch.FloatTensor of size 4]
>>> torch.min(a, b)
1.0067
-0.8010
-0.8634
-0.5468
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.mm,
r"""
mm(mat1, mat2, out=None) -> Tensor
Performs a matrix multiplication of the matrices :attr:`mat1` and :attr:`mat2`.
If :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a
:math:`(m \times p)` tensor, :attr:`out` will be a :math:`(n \times p)` tensor.
.. note:: This function does not :ref:`broadcast <broadcasting-semantics>`.
For broadcasting matrix products, see :func:`torch.matmul`.
Args:
mat1 (Tensor): the first matrix to be multiplied
mat2 (Tensor): the second matrix to be multiplied
out (Tensor, optional): the output tensor
Example::
>>> mat1 = torch.randn(2, 3)
>>> mat2 = torch.randn(3, 3)
>>> torch.mm(mat1, mat2)
0.0519 -0.3304 1.2232
4.3910 -5.1498 2.7571
[torch.FloatTensor of size 2x3]
""")
add_docstr(torch._C.mode,
r"""
mode(input, dim=-1, keepdim=False, values=None, indices=None) -> (Tensor, LongTensor)
Returns the mode value of each row of the :attr:`input` tensor in the given
dimension :attr:`dim`. Also returns the index location of the mode value
as a `LongTensor`.
By default, :attr:`dim` is the last dimension of the :attr:`input` tensor.
If :attr:`keepdim` is ``True``, the output tensors are of the same size as
:attr:`input` except in the dimension :attr:`dim` where they are of size 1.
Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting
in the output tensors having 1 fewer dimension than :attr:`input`.
.. note:: This function is not defined for ``torch.cuda.Tensor`` yet.
Args:
input (Tensor): the input tensor
dim (int): the dimension to reduce
keepdim (bool): whether the output tensors have :attr:`dim` retained or not
values (Tensor, optional): the output tensor
indices (Tensor, optional): the output index tensor
Example::
>>> a
-0.6891 -0.6662
0.2697 0.7412
0.5254 -0.7402
0.5528 -0.2399
[torch.FloatTensor of size 4x2]
>>> a = torch.randn(4, 5)
>>> a
0.4056 -0.3372 1.0973 -2.4884 0.4334
2.1336 0.3841 0.1404 -0.1821 -0.7646
-0.2403 1.3975 -2.0068 0.1298 0.0212
-1.5371 -0.7257 -0.4871 -0.2359 -1.1724
[torch.FloatTensor of size 4x5]
>>> torch.mode(a, 1)
(
-2.4884
-0.7646
-2.0068
-1.5371
[torch.FloatTensor of size 4]
,
3
4
2
0
[torch.LongTensor of size 4]
)
""")
add_docstr(torch._C.mul,
r"""
.. function:: mul(input, value, out=None)
Multiplies each element of the input :attr:`input` with the scalar
:attr:`value` and returns a new resulting tensor.
.. math::
out_i = value \times input_i
If :attr:`input` is of type `FloatTensor` or `DoubleTensor`, :attr:`value`
should be a real number, otherwise it should be an integer
Args:
input (Tensor): the input tensor
value (Number): the number to be multiplied to each element of :attr:`input`
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(3)
>>> a
-0.9374
-0.5254
-0.6069
[torch.FloatTensor of size 3]
>>> torch.mul(a, 100)
-93.7411
-52.5374
-60.6908
[torch.FloatTensor of size 3]
.. function:: mul(input, other, out=None)
Each element of the tensor :attr:`input` is multiplied by each element of the
Tensor :attr:`other`. The resulting tensor is returned.
The shapes of :attr:`input` and :attr:`other` must be
:ref:`broadcastable <broadcasting-semantics>`.
.. math::
out_i = input_i \times other_i
Args:
input (Tensor): the first multiplicand tensor
other (Tensor): the second multiplicand tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4,4)
>>> a
-0.7280 0.0598 -1.4327 -0.5825
-0.1427 -0.0690 0.0821 -0.3270
-0.9241 0.5110 0.4070 -1.1188
-0.8308 0.7426 -0.6240 -1.1582
[torch.FloatTensor of size 4x4]
>>> b = torch.randn(2, 8)
>>> b
0.0430 -1.0775 0.6015 1.1647 -0.6549 0.0308 -0.1670 1.0742
-1.2593 0.0292 -0.0849 0.4530 1.2404 -0.4659 -0.1840 0.5974
[torch.FloatTensor of size 2x8]
>>> torch.mul(a, b)
-0.0313 -0.0645 -0.8618 -0.6784
0.0934 -0.0021 -0.0137 -0.3513
1.1638 0.0149 -0.0346 -0.5068
-1.0304 -0.3460 0.1148 -0.6919
[torch.FloatTensor of size 4x4]
""")
add_docstr(torch._C.multinomial,
u"""
multinomial(input, num_samples, replacement=False, out=None) -> LongTensor
Returns a tensor where each row
contains :attr:`num_samples` indices sampled from the multinomial probability
distribution located in the corresponding row of tensor :attr:`input`.
.. note::
The rows of :attr:`input` do not need to sum to one (in which case we use
the values as weights), but must be non-negative and have a non-zero sum.
Indices are ordered from left to right according to when each was sampled
(first samples are placed in first column).
If :attr:`input` is a vector, :attr:`out` is a vector of size `num_samples`.
If :attr:`input` is a matrix with `m` rows, :attr:`out` is an matrix of shape
`m \u00D7 num_samples`.
If replacement is ``True``, samples are drawn with replacement.
If not, they are drawn without replacement, which means that when a
sample index is drawn for a row, it cannot be drawn again for that row.
This implies the constraint that :attr:`num_samples` must be lower than
:attr:`input` length (or number of columns of :attr:`input` if it is a matrix).
Args:
input (Tensor): the input tensor containing probabilities
num_samples (int): number of samples to draw
replacement (bool, optional): whether to draw with replacement or not
out (Tensor, optional): the output tensor
Example::
>>> weights = torch.Tensor([0, 10, 3, 0]) # create a tensor of weights
>>> torch.multinomial(weights, 4)
1
2
0
0
[torch.LongTensor of size 4]
>>> torch.multinomial(weights, 4, replacement=True)
1
2
1
2
[torch.LongTensor of size 4]
""")
add_docstr(torch._C.mv,
r"""
mv(mat, vec, out=None) -> Tensor
Performs a matrix-vector product of the matrix :attr:`mat` and the vector
:attr:`vec`.
If :attr:`mat` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of
size `m`, :attr:`out` will be 1-D of size `n`.
.. note:: This function does not :ref:`broadcast <broadcasting-semantics>`.
Args:
mat (Tensor): matrix to be multiplied
vec (Tensor): vector to be multiplied
out (Tensor, optional): the output tensor
Example::
>>> mat = torch.randn(2, 3)
>>> vec = torch.randn(3)
>>> torch.mv(mat, vec)
-2.0939
-2.2950
[torch.FloatTensor of size 2]
""")
add_docstr(torch._C.ne,
r"""
ne(input, other, out=None) -> Tensor
Computes `input != other` element-wise.
The second argument can be a number or a tensor whose shape is
:ref:`broadcastable <broadcasting-semantics>` with the first argument.
Args:
input (Tensor): the tensor to compare
other (Tensor or float): the tensor or value to compare
out (Tensor, optional): the output tensor that must be a `ByteTensor` or the same type as `input`
Returns:
Tensor: A ``torch.ByteTensor`` containing a 1 at each location where comparison is true.
Example::
>>> torch.ne(torch.Tensor([[1, 2], [3, 4]]), torch.Tensor([[1, 1], [4, 4]]))
0 1
1 0
[torch.ByteTensor of size 2x2]
""")
add_docstr(torch._C.neg,
r"""
neg(input, out=None) -> Tensor
Returns a new tensor with the negative of the elements of :attr:`input`.
.. math::
out = -1 \times input
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(5)
>>> a
-0.4430
1.1690
-0.8836
-0.4565
0.2968
[torch.FloatTensor of size 5]
>>> torch.neg(a)
0.4430
-1.1690
0.8836
0.4565
-0.2968
[torch.FloatTensor of size 5]
""")
add_docstr(torch._C.nonzero,
r"""
nonzero(input, out=None) -> LongTensor
Returns a tensor containing the indices of all non-zero elements of
:attr:`input`. Each row in the result contains the indices of a non-zero
element in :attr:`input`.
If :attr:`input` has `n` dimensions, then the resulting indices tensor
:attr:`out` is of size :math:`(z \times n)`, where :math:`z` is the total number of
non-zero elements in the :attr:`input` tensor.
Args:
input (Tensor): the input tensor
out (LongTensor, optional): the output tensor containing indices
Example::
>>> torch.nonzero(torch.Tensor([1, 1, 1, 0, 1]))
0
1
2
4
[torch.LongTensor of size 4x1]
>>> torch.nonzero(torch.Tensor([[0.6, 0.0, 0.0, 0.0],
... [0.0, 0.4, 0.0, 0.0],
... [0.0, 0.0, 1.2, 0.0],
... [0.0, 0.0, 0.0,-0.4]]))
0 0
1 1
2 2
3 3
[torch.LongTensor of size 4x2]
""")
add_docstr(torch._C.norm,
r"""
.. function:: norm(input, p=2) -> float
Returns the p-norm of the :attr:`input` tensor.
Args:
input (Tensor): the input tensor
p (float, optional): the exponent value in the norm formulation
Example::
>>> a = torch.randn(1, 3)
>>> a
-0.4376 -0.5328 0.9547
[torch.FloatTensor of size 1x3]
>>> torch.norm(a, 3)
1.0338925067372466
.. function:: norm(input, p, dim, keepdim=False, out=None) -> Tensor
Returns the p-norm of each row of the :attr:`input` tensor in the given
dimension :attr:`dim`.
If :attr:`keepdim` is ``True``, the output tensor is of the same size as
:attr:`input` except in the dimension :attr:`dim` where it is of size 1.
Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting
in the output tensor having 1 fewer dimension than :attr:`input`.
Args:
input (Tensor): the input tensor
p (float): the exponent value in the norm formulation
dim (int): the dimension to reduce
keepdim (bool): whether the output tensor has :attr:`dim` retained or not
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4, 2)
>>> a
-0.6891 -0.6662
0.2697 0.7412
0.5254 -0.7402
0.5528 -0.2399
[torch.FloatTensor of size 4x2]
>>> torch.norm(a, 2, 1)
0.9585
0.7888
0.9077
0.6026
[torch.FloatTensor of size 4]
>>> torch.norm(a, 0, 1, True)
2
2
2
2
[torch.FloatTensor of size 4x1]
""")
add_docstr(torch._C.normal,
r"""
.. function:: normal(means, std, out=None)
Returns a tensor of random numbers drawn from separate normal distributions
who's mean and standard deviation are given.
The :attr:`means` is a tensor with the mean of
each output element's normal distribution
The :attr:`std` is a tensor with the standard deviation of
each output element's normal distribution
The shapes of :attr:`means` and :attr:`std` don't need to match.
The total number of elements in each tensor need to be the same.
.. note:: When the shapes do not match, the shape of :attr:`means`
is used as the shape for the returned output tensor
Args:
means (Tensor): the tensor of per-element means
std (Tensor): the tensor of per-element standard deviations
out (Tensor, optional): the output tensor
Example::
torch.normal(means=torch.arange(1, 11), std=torch.arange(1, 0, -0.1))
1.5104
1.6955
2.4895
4.9185
4.9895
6.9155
7.3683
8.1836
8.7164
9.8916
[torch.FloatTensor of size 10]
.. function:: normal(mean=0.0, std, out=None)
Similar to the function above, but the means are shared among all drawn
elements.
Args:
means (float, optional): the mean for all distributions
std (Tensor): the tensor of per-element standard deviations
out (Tensor, optional): the output tensor
Example::
>>> torch.normal(mean=0.5, std=torch.arange(1, 6))
0.5723
0.0871
-0.3783
-2.5689
10.7893
[torch.FloatTensor of size 5]
.. function:: normal(means, std=1.0, out=None)
Similar to the function above, but the standard-deviations are shared among
all drawn elements.
Args:
means (Tensor): the tensor of per-element means
std (float, optional): the standard deviation for all distributions
out (Tensor, optional): the output tensor
Example::
>>> torch.normal(means=torch.arange(1, 6))
1.1681
2.8884
3.7718
2.5616
4.2500
[torch.FloatTensor of size 5]
""")
add_docstr(torch._C.numel,
r"""
numel(input) -> int
Returns the total number of elements in the :attr:`input` tensor.
Args:
input (Tensor): the input tensor
Example::
>>> a = torch.randn(1,2,3,4,5)
>>> torch.numel(a)
120
>>> a = torch.zeros(4,4)
>>> torch.numel(a)
16
""")
add_docstr(torch._C.ones,
r"""
ones(*sizes, out=None) -> Tensor
Returns a tensor filled with the scalar value `1`, with the shape defined
by the varargs :attr:`sizes`.
Args:
sizes (int...): a set of integers defining the shape of the output tensor
out (Tensor, optional): the output tensor
Example::
>>> torch.ones(2, 3)
1 1 1
1 1 1
[torch.FloatTensor of size 2x3]
>>> torch.ones(5)
1
1
1
1
1
[torch.FloatTensor of size 5]
""")
add_docstr(torch._C.ones_like,
r"""
ones_like(input, out=None) -> Tensor
Returns a tensor filled with the scalar value `1`, with the same size as
:attr:`input`.
Args:
input (Tensor): the size of :attr:`input` will determine size of the output tensor
out (Tensor, optional): the output tensor
Example::
>>> input = torch.FloatTensor(2, 3)
>>> torch.ones_like(input)
1 1 1
1 1 1
[torch.FloatTensor of size 2x3]
""")
add_docstr(torch._C.orgqr,
r"""
orgqr(a, tau) -> Tensor
Computes the orthogal matrix `Q` of a QR factorization, from the `(a, tau)`
tuple returned by :func:`torch.geqrf`.
This directly calls the underlying LAPACK function `?orgqr`.
See `?orgqr LAPACK documentation`_ for further details.
Args:
a (Tensor): the `a` from :func:`torch.geqrf`.
tau (Tensor): the `tau` from `torch.geqrf`.
.. _?orgqr LAPACK documentation:
https://software.intel.com/en-us/mkl-developer-reference-c-orgqr
""")
add_docstr(torch._C.ormqr,
r"""
ormqr(a, tau, mat, left=True, transpose=False) -> (Tensor, Tensor)
Multiplies `mat` by the orthogonal `Q` matrix of the QR factorization
formed by :func:`torch.geqrf` that is represented by `(a, tau)`.
This directly calls the underlying LAPACK function `?ormqr`.
See `?ormqr LAPACK documentation`_ for further details.
.. _?ormqr LAPACK documentation:
https://software.intel.com/en-us/mkl-developer-reference-c-ormqr
""")
add_docstr(torch._C.potrf, r"""
potrf(a, upper=True, out=None) -> Tensor
Computes the Cholesky decomposition of a symmetric positive-definite
matrix :attr:`A`.
If :attr:`upper` is ``True``, the returned matrix `U` is upper-triangular, and
the decomposition has the form:
.. math::
A = U^TU
If :attr:`upper` is ``False``, the returned matrix `L` is lower-triangular, and
the decomposition has the form:
.. math::
A = LL^T
Args:
a (Tensor): the input 2-D tensor, a symmetric positive-definite matrix
upper (bool, optional): flag that indicates whether to return the
upper or lower triangular matrix
out (Tensor, optional): the output matrix
Example::
>>> a = torch.randn(3,3)
>>> a = torch.mm(a, a.t()) # make symmetric positive definite
>>> u = torch.potrf(a)
>>> a
2.3563 3.2318 -0.9406
3.2318 4.9557 -2.1618
-0.9406 -2.1618 2.2443
[torch.FloatTensor of size 3x3]
>>> u
1.5350 2.1054 -0.6127
0.0000 0.7233 -1.2053
0.0000 0.0000 0.6451
[torch.FloatTensor of size 3x3]
>>> torch.mm(u.t(), u)
2.3563 3.2318 -0.9406
3.2318 4.9557 -2.1618
-0.9406 -2.1618 2.2443
[torch.FloatTensor of size 3x3]
""")
add_docstr(torch._C.potri, r"""
potri(u, upper=True, out=None) -> Tensor
Computes the inverse of a positive semidefinite matrix given its
Cholesky factor :attr:`u`: returns matrix `inv`
If `upper` is ``True`` or not provided, `u` is upper triangular
such that :math:`inv = (u^T u)^{-1}`.
If `upper` is ``False``, `u` is lower triangular
such that :math:`inv = (u u^T)^{-1}`.
Args:
u (Tensor): the input 2-D tensor, a upper or lower triangular
Cholesky factor
upper (bool, optional): whether to return a upper (default) or lower triangular matrix
out (Tensor, optional): the output tensor for `inv`
Example::
>>> a = torch.randn(3,3)
>>> a = torch.mm(a, a.t()) # make symmetric positive definite
>>> u = torch.potrf(a)
>>> a
2.3563 3.2318 -0.9406
3.2318 4.9557 -2.1618
-0.9406 -2.1618 2.2443
[torch.FloatTensor of size 3x3]
>>> torch.potri(u)
12.5724 -10.1765 -4.5333
-10.1765 8.5852 4.0047
-4.5333 4.0047 2.4031
[torch.FloatTensor of size 3x3]
>>> a.inverse()
12.5723 -10.1765 -4.5333
-10.1765 8.5852 4.0047
-4.5333 4.0047 2.4031
[torch.FloatTensor of size 3x3]
""")
add_docstr(torch._C.potrs, r"""
potrs(b, u, upper=True, out=None) -> Tensor
Solves a linear system of equations with a positive semidefinite
matrix to be inverted given its Cholesky factor
matrix :attr:`u`: returns matrix `c`
If `upper` is ``True`` or not provided, `u` is and upper triangular
such that :math:`c = (u^T u)^{-1} b`.
If `upper` is ``False``, `u` is and lower triangular
such that :math:`c = (u u^T)^{-1} b`.
.. note:: `b` is always a 2-D tensor, use `b.unsqueeze(1)` to convert a vector.
Args:
b (Tensor): the right hand side 2-D tensor
u (Tensor): the input 2-D tensor, a upper or lower triangular Cholesky factor
upper (bool, optional): whether to return a upper (default) or lower triangular matrix
out (Tensor, optional): the output tensor for `c`
Example::
>>> a = torch.randn(3,3)
>>> a = torch.mm(a, a.t()) # make symmetric positive definite
>>> u = torch.potrf(a)
>>> a
2.3563 3.2318 -0.9406
3.2318 4.9557 -2.1618
-0.9406 -2.1618 2.2443
[torch.FloatTensor of size 3x3]
>>> b = torch.randn(3,2)
>>> b
-0.3119 -1.8224
-0.2798 0.1789
-0.3735 1.7451
[torch.FloatTensor of size 3x2]
>>> torch.potrs(b,u)
0.6187 -32.6438
-0.7234 27.0703
-0.6039 13.1717
[torch.FloatTensor of size 3x2]
>>> torch.mm(a.inverse(),b)
0.6187 -32.6436
-0.7234 27.0702
-0.6039 13.1717
[torch.FloatTensor of size 3x2]
""")
add_docstr(torch._C.pow,
r"""
.. function:: pow(input, exponent, out=None)
Takes the power of each element in :attr:`input` with :attr:`exponent` and
returns a tensor with the result.
:attr:`exponent` can be either a single ``float`` number or a `Tensor`
with the same number of elements as :attr:`input`.
When :attr:`exponent` is a scalar value, the operation applied is:
.. math::
out_i = x_i ^ {exponent}
When :attr:`exponent` is a tensor, the operation applied is:
.. math::
out_i = x_i ^ {exponent_i}
When :attr:`exponent` is a tensor, the shapes of :attr:`input`
and :attr:`exponent` must be :ref:`broadcastable <broadcasting-semantics>`.
Args:
input (Tensor): the input tensor
exponent (float or tensor): the exponent value
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
-0.5274
-0.8232
-2.1128
1.7558
[torch.FloatTensor of size 4]
>>> torch.pow(a, 2)
0.2781
0.6776
4.4640
3.0829
[torch.FloatTensor of size 4]
>>> exp = torch.arange(1, 5)
>>> a = torch.arange(1, 5)
>>> a
1
2
3
4
[torch.FloatTensor of size 4]
>>> exp
1
2
3
4
[torch.FloatTensor of size 4]
>>> torch.pow(a, exp)
1
4
27
256
[torch.FloatTensor of size 4]
.. function:: pow(base, input, out=None)
:attr:`base` is a scalar ``float`` value, and :attr:`input` is a tensor.
The returned tensor :attr:`out` is of the same shape as :attr:`input`
The operation applied is:
.. math::
out_i = base ^ {input_i}
Args:
base (float): the scalar base value for the power operation
input (Tensor): the exponent tensor
out (Tensor, optional): the output tensor
Example::
>>> exp = torch.arange(1, 5)
>>> base = 2
>>> torch.pow(base, exp)
2
4
8
16
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.prod,
r"""
.. function:: prod(input) -> float
Returns the product of all elements in the :attr:`input` tensor.
Args:
input (Tensor): the input tensor
Example::
>>> a = torch.randn(1, 3)
>>> a
0.6170 0.3546 0.0253
[torch.FloatTensor of size 1x3]
>>> torch.prod(a)
0.005537458061418483
.. function:: prod(input, dim, keepdim=False, out=None) -> Tensor
Returns the product of each row of the :attr:`input` tensor in the given
dimension :attr:`dim`.
If :attr:`keepdim` is ``True``, the output tensor is of the same size as
:attr:`input` except in the dimension :attr:`dim` where it is of size 1.
Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting
in the output tensor having 1 fewer dimension than :attr:`input`.
Args:
input (Tensor): the input tensor
dim (int): the dimension to reduce
keepdim (bool): whether the output tensor has :attr:`dim` retained or not
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4, 2)
>>> a
0.1598 -0.6884
-0.1831 -0.4412
-0.9925 -0.6244
-0.2416 -0.8080
[torch.FloatTensor of size 4x2]
>>> torch.prod(a, 1)
-0.1100
0.0808
0.6197
0.1952
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.pstrf, r"""
pstrf(a, upper=True, out=None) -> (Tensor, Tensor)
Computes the pivoted Cholesky decomposition of a positive semidefinite
matrix :attr:`a`: returns matrices `u` and `piv`.
If `upper` is ``True`` or not provided, `u` is and upper triangular
such that :math:`a = p^T u^T u p`, with `p` the permutation given by `piv`.
If `upper` is ``False``, `u` is and lower triangular
such that :math:`a = p^T u u^T p`.
Args:
a (Tensor): the input 2-D tensor
upper (bool, optional): whether to return a upper (default) or lower triangular matrix
out (tuple, optional): tuple of `u` and `piv` tensors
Example::
>>> a = torch.randn(3,3)
>>> a = torch.mm(a, a.t()) # make symmetric positive definite
>>> a
5.4417 -2.5280 1.3643
-2.5280 2.9689 -2.1368
1.3643 -2.1368 4.6116
[torch.FloatTensor of size 3x3]
>>> u,piv = torch.pstrf(a)
>>> u
2.3328 0.5848 -1.0837
0.0000 2.0663 -0.7274
0.0000 0.0000 1.1249
[torch.FloatTensor of size 3x3]
>>> piv
0
2
1
[torch.IntTensor of size 3]
>>> p = torch.eye(3).index_select(0,piv.long()).index_select(0,piv.long()).t() # make pivot permutation
>>> torch.mm(torch.mm(p.t(),torch.mm(u.t(),u)),p) # reconstruct
5.4417 1.3643 -2.5280
1.3643 4.6116 -2.1368
-2.5280 -2.1368 2.9689
[torch.FloatTensor of size 3x3]
""")
add_docstr(torch._C.qr,
r"""
qr(input, out=None) -> (Tensor, Tensor)
Computes the QR decomposition of a matrix :attr:`input`: returns matrices
`q` and `r` such that :math:`x = q r`, with `q` being an orthogonal matrix
and `r` being an upper triangular matrix.
This returns the thin (reduced) QR factorization.
.. note:: precision may be lost if the magnitudes of the elements of `input`
are large
.. note:: while it should always give you a valid decomposition, it may not
give you the same one across platforms - it will depend on your
LAPACK implementation.
.. note:: Irrespective of the original strides, the returned matrix `q` will be
transposed, i.e. with strides `(1, m)` instead of `(m, 1)`.
Args:
input (Tensor): the input 2-D tensor
out (tuple, optional): tuple of `Q` and `R` tensors
Example::
>>> a = torch.Tensor([[12, -51, 4], [6, 167, -68], [-4, 24, -41]])
>>> q, r = torch.qr(a)
>>> q
-0.8571 0.3943 0.3314
-0.4286 -0.9029 -0.0343
0.2857 -0.1714 0.9429
[torch.FloatTensor of size 3x3]
>>> r
-14.0000 -21.0000 14.0000
0.0000 -175.0000 70.0000
0.0000 0.0000 -35.0000
[torch.FloatTensor of size 3x3]
>>> torch.mm(q, r).round()
12 -51 4
6 167 -68
-4 24 -41
[torch.FloatTensor of size 3x3]
>>> torch.mm(q.t(), q).round()
1 -0 0
-0 1 0
0 0 1
[torch.FloatTensor of size 3x3]
""")
add_docstr(torch._C.rand,
r"""
rand(*sizes, out=None) -> Tensor
Returns a tensor filled with random numbers from a uniform distribution
on the interval :math:`[0, 1)`
The shape of the tensor is defined by the varargs :attr:`sizes`.
Args:
sizes (int...): a set of ints defining the shape of the output tensor.
out (Tensor, optional): the output tensor
Example::
>>> torch.rand(4)
0.9193
0.3347
0.3232
0.7715
[torch.FloatTensor of size 4]
>>> torch.rand(2, 3)
0.5010 0.5140 0.0719
0.1435 0.5636 0.0538
[torch.FloatTensor of size 2x3]
""")
add_docstr(torch._C.randn,
r"""
randn(*sizes, out=None) -> Tensor
Returns a tensor filled with random numbers from a normal distribution
with zero mean and variance of one.
The shape of the tensor is defined by the varargs :attr:`sizes`.
Args:
sizes (int...): a set of ints defining the shape of the output tensor.
out (Tensor, optional): the output tensor
Example::
>>> torch.randn(4)
-0.1145
0.0094
-1.1717
0.9846
[torch.FloatTensor of size 4]
>>> torch.randn(2, 3)
1.4339 0.3351 -1.0999
1.5458 -0.9643 -0.3558
[torch.FloatTensor of size 2x3]
""")
add_docstr(torch._C.randperm,
r"""
randperm(n, out=None) -> LongTensor
Returns a random permutation of integers from ``0`` to ``n - 1``.
Args:
n (int): the upper bound (exclusive)
Example::
>>> torch.randperm(4)
2
1
3
0
[torch.LongTensor of size 4]
""")
add_docstr(torch._C.range,
r"""
range(start, end, step=1, out=None) -> Tensor
Returns a 1-D tensor of size :math:`\lfloor \frac{end - start}{step} \rfloor + 1`
with values from :attr:`start` to :attr:`end` with step :attr:`step`. Step is
the gap between two values in the tensor. :math:`x_{i+1} = x_i + step`.
.. warning::
This function is deprecated in favor of :func:`torch.arange`.
Args:
start (float): the starting value for the set of points
end (float): the ending value for the set of points
step (float): the gap between each pair of adjacent points
out (Tensor, optional): the output tensor
Example::
>>> torch.range(1, 4)
1
2
3
4
[torch.FloatTensor of size 4]
>>> torch.range(1, 4, 0.5)
1.0000
1.5000
2.0000
2.5000
3.0000
3.5000
4.0000
[torch.FloatTensor of size 7]
""")
add_docstr(torch._C.arange,
r"""
arange(start=0, end, step=1, out=None) -> Tensor
Returns a 1-D tensor of size :math:`\lfloor \frac{end - start}{step} \rfloor`
with values from the interval ``[start, end)`` taken with step :attr:`step`
starting from `start`.
Args:
start (float): the starting value for the set of points
end (float): the ending value for the set of points
step (float): the gap between each pair of adjacent points
out (Tensor, optional): the output tensor
Example::
>>> torch.arange(5)
0
1
2
3
4
[torch.FloatTensor of size 5]
>>> torch.arange(1, 4)
1
2
3
[torch.FloatTensor of size 3]
>>> torch.arange(1, 2.5, 0.5)
1.0000
1.5000
2.0000
[torch.FloatTensor of size 3]
""")
add_docstr(torch._C.remainder,
r"""
remainder(input, divisor, out=None) -> Tensor
Computes the element-wise remainder of division.
The divisor and dividend may contain both for integer and floating point
numbers. The remainder has the same sign as the divisor.
When :attr:`divisor` is a tensor, the shapes of :attr:`input` and
:attr:`divisor` must be :ref:`broadcastable <broadcasting-semantics>`.
Args:
input (Tensor): the dividend
divisor (Tensor or float): the divisor that may be either a number or a
Tensor of the same shape as the dividend
out (Tensor, optional): the output tensor
Example::
>>> torch.remainder(torch.Tensor([-3, -2, -1, 1, 2, 3]), 2)
torch.FloatTensor([1, 0, 1, 1, 0, 1])
>>> torch.remainder(torch.Tensor([1, 2, 3, 4, 5]), 1.5)
torch.FloatTensor([1.0, 0.5, 0.0, 1.0, 0.5])
.. seealso::
:func:`torch.fmod`, which computes the element-wise remainder of
division equivalently to the C library function ``fmod()``
""")
add_docstr(torch._C.renorm,
r"""
renorm(input, p, dim, maxnorm, out=None) -> Tensor
Returns a tensor where each sub-tensor of :attr:`input` along dimension
:attr:`dim` is normalized such that the `p`-norm of the sub-tensor is lower
than the value :attr:`maxnorm`
.. note:: If the norm of a row is lower than `maxnorm`, the row is unchanged
Args:
input (Tensor): the input tensor
p (float): the power for the norm computation
dim (int): the dimension to slice over to get the sub-tensors
maxnorm (float): the maximum norm to keep each sub-tensor under
out (Tensor, optional): the output tensor
Example::
>>> x = torch.ones(3, 3)
>>> x[1].fill_(2)
>>> x[2].fill_(3)
>>> x
1 1 1
2 2 2
3 3 3
[torch.FloatTensor of size 3x3]
>>> torch.renorm(x, 1, 0, 5)
1.0000 1.0000 1.0000
1.6667 1.6667 1.6667
1.6667 1.6667 1.6667
[torch.FloatTensor of size 3x3]
""")
add_docstr(torch._C.round,
r"""
round(input, out=None) -> Tensor
Returns a new tensor with each of the elements of :attr:`input` rounded
to the closest integer.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
1.2290
1.3409
-0.5662
-0.0899
[torch.FloatTensor of size 4]
>>> torch.round(a)
1
1
-1
-0
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.rsqrt,
r"""
rsqrt(input, out=None) -> Tensor
Returns a new tensor with the reciprocal of the square-root of each of
the elements of :attr:`input`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
1.2290
1.3409
-0.5662
-0.0899
[torch.FloatTensor of size 4]
>>> torch.rsqrt(a)
0.9020
0.8636
nan
nan
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.set_flush_denormal,
r"""
set_flush_denormal(mode) -> bool
Disables denormal floating numbers on CPU.
Returns ``True`` if your system supports flushing denormal numbers and it
successfully configures flush denormal mode. :meth:`~torch.set_flush_denormal`
is only supported on x86 architectures supporting SSE3.
Args:
mode (bool): Controls whether to enable flush denormal mode or not
Example::
>>> torch.set_flush_denormal(True)
True
>>> torch.DoubleTensor([1e-323])
0
[torch.DoubleTensor of size 1]
>>> torch.set_flush_denormal(False)
True
>>> torch.DoubleTensor([1e-323])
9.88131e-324 *
1.0000
[torch.DoubleTensor of size 1]
""")
add_docstr(torch._C.set_num_threads,
r"""
set_num_threads(int)
Sets the number of OpenMP threads used for parallelizing CPU operations
""")
add_docstr(torch._C.sigmoid,
r"""
sigmoid(input, out=None) -> Tensor
Returns a new tensor with the sigmoid of the elements of :attr:`input`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
-0.4972
1.3512
0.1056
-0.2650
[torch.FloatTensor of size 4]
>>> torch.sigmoid(a)
0.3782
0.7943
0.5264
0.4341
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.sign,
r"""
sign(input, out=None) -> Tensor
Returns a new tensor with the sign of the elements of :attr:`input`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
-0.6366
0.2718
0.4469
1.3122
[torch.FloatTensor of size 4]
>>> torch.sign(a)
-1
1
1
1
""")
add_docstr(torch._C.sin,
r"""
sin(input, out=None) -> Tensor
Returns a new tensor with the sine of the elements of :attr:`input`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
-0.6366
0.2718
0.4469
1.3122
[torch.FloatTensor of size 4]
>>> torch.sin(a)
-0.5944
0.2684
0.4322
0.9667
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.sinh,
r"""
sinh(input, out=None) -> Tensor
Returns a new tensor with the hyperbolic sine of the elements of
:attr:`input`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
-0.6366
0.2718
0.4469
1.3122
[torch.FloatTensor of size 4]
>>> torch.sinh(a)
-0.6804
0.2751
0.4619
1.7225
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.sort,
r"""
sort(input, dim=None, descending=False, out=None) -> (Tensor, LongTensor)
Sorts the elements of the :attr:`input` tensor along a given dimension
in ascending order by value.
If :attr:`dim` is not given, the last dimension of the `input` is chosen.
If :attr:`descending` is ``True`` then the elements are sorted in descending
order by value.
A tuple of (sorted_tensor, sorted_indices) is returned, where the
sorted_indices are the indices of the elements in the original `input` tensor.
Args:
input (Tensor): the input tensor
dim (int, optional): the dimension to sort along
descending (bool, optional): controls the sorting order (ascending or descending)
out (tuple, optional): the output tuple of (`Tensor`, `LongTensor`) that can
be optionally given to be used as output buffers
Example::
>>> x = torch.randn(3, 4)
>>> sorted, indices = torch.sort(x)
>>> sorted
-1.6747 0.0610 0.1190 1.4137
-1.4782 0.7159 1.0341 1.3678
-0.3324 -0.0782 0.3518 0.4763
[torch.FloatTensor of size 3x4]
>>> indices
0 1 3 2
2 1 0 3
3 1 0 2
[torch.LongTensor of size 3x4]
>>> sorted, indices = torch.sort(x, 0)
>>> sorted
-1.6747 -0.0782 -1.4782 -0.3324
0.3518 0.0610 0.4763 0.1190
1.0341 0.7159 1.4137 1.3678
[torch.FloatTensor of size 3x4]
>>> indices
0 2 1 2
2 0 2 0
1 1 0 1
[torch.LongTensor of size 3x4]
""")
add_docstr(torch._C.sqrt,
r"""
sqrt(input, out=None) -> Tensor
Returns a new tensor with the square-root of the elements of :attr:`input`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
1.2290
1.3409
-0.5662
-0.0899
[torch.FloatTensor of size 4]
>>> torch.sqrt(a)
1.1086
1.1580
nan
nan
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.squeeze,
r"""
squeeze(input, dim=None, out=None)
Returns a tensor with all the dimensions of :attr:`input` of size `1` removed.
For example, if `input` is of shape:
:math:`(A \times 1 \times B \times C \times 1 \times D)` then the `out` tensor
will be of shape: :math:`(A \times B \times C \times D)`.
When :attr:`dim` is given, a squeeze operation is done only in the given
dimension. If `input` is of shape: :math:`(A \times 1 \times B)`,
`squeeze(input, 0)` leaves the tensor unchanged, but `squeeze(input, 1)` will
squeeze the tensor to the shape :math:`(A \times B)`.
.. note:: As an exception to the above, a 1-dimensional tensor of size 1 will
not have its dimensions changed.
.. note:: The returned tensor shares the storage with the input tensor,
so changing the contents of one will change the contents of the other.
Args:
input (Tensor): the input tensor
dim (int, optional): if given, the input will be squeezed only in
this dimension
out (Tensor, optional): the output tensor
Example::
>>> x = torch.zeros(2,1,2,1,2)
>>> x.size()
(2L, 1L, 2L, 1L, 2L)
>>> y = torch.squeeze(x)
>>> y.size()
(2L, 2L, 2L)
>>> y = torch.squeeze(x, 0)
>>> y.size()
(2L, 1L, 2L, 1L, 2L)
>>> y = torch.squeeze(x, 1)
>>> y.size()
(2L, 2L, 1L, 2L)
""")
add_docstr(torch._C.std,
r"""
.. function:: std(input, unbiased=True) -> float
Returns the standard-deviation of all elements in the :attr:`input` tensor.
If :attr:`unbiased` is ``False``, then the standard-deviation will be calculated
via the biased estimator. Otherwise, Bessel's correction will be used.
Args:
input (Tensor): the input tensor
unbiased (bool): whether to use the unbiased estimation or not
Example::
>>> a = torch.randn(1, 3)
>>> a
-1.3063 1.4182 -0.3061
[torch.FloatTensor of size 1x3]
>>> torch.std(a)
1.3782334731508061
.. function:: std(input, dim, keepdim=False, unbiased=True, out=None) -> Tensor
Returns the standard-deviation of each row of the :attr:`input` tensor in the
given dimension :attr:`dim`.
If :attr:`keepdim` is ``True``, the output tensor is of the same size as
:attr:`input` except in the dimension :attr:`dim` where it is of size 1.
Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting
in the output tensor having 1 fewer dimension than :attr:`input`.
If :attr:`unbiased` is ``False``, then the standard-deviation will be calculated
via the biased estimator. Otherwise, Bessel's correction will be used.
Args:
input (Tensor): the input tensor
dim (int): the dimension to reduce
keepdim (bool): whether the output tensor has :attr:`dim` retained or not
unbiased (bool): whether to use the unbiased estimation or not
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4, 4)
>>> a
0.1889 -2.4856 0.0043 1.8169
-0.7701 -0.4682 -2.2410 0.4098
0.1919 -1.1856 -1.0361 0.9085
0.0173 1.0662 0.2143 -0.5576
[torch.FloatTensor of size 4x4]
>>> torch.std(a, dim=1)
1.7756
1.1025
1.0045
0.6725
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.sum,
r"""
.. function:: sum(input) -> float
Returns the sum of all elements in the :attr:`input` tensor.
Args:
input (Tensor): the input tensor
Example::
>>> a = torch.randn(1, 3)
>>> a
0.6170 0.3546 0.0253
[torch.FloatTensor of size 1x3]
>>> torch.sum(a)
0.9969287421554327
.. function:: sum(input, dim, keepdim=False, out=None) -> Tensor
Returns the sum of each row of the :attr:`input` tensor in the given
dimension :attr:`dim`.
If :attr:`keepdim` is ``True``, the output tensor is of the same size
as :attr:`input` except in the dimension :attr:`dim` where it is of size 1.
Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in
the output tensor having 1 fewer dimension than :attr:`input`.
Args:
input (Tensor): the input tensor
dim (int): the dimension to reduce
keepdim (bool): whether the output tensor has :attr:`dim` retained or not
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4, 4)
>>> a
-0.4640 0.0609 0.1122 0.4784
-1.3063 1.6443 0.4714 -0.7396
-1.3561 -0.1959 1.0609 -1.9855
2.6833 0.5746 -0.5709 -0.4430
[torch.FloatTensor of size 4x4]
>>> torch.sum(a, 1)
0.1874
0.0698
-2.4767
2.2440
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.svd,
r"""
svd(input, some=True, out=None) -> (Tensor, Tensor, Tensor)
`U, S, V = torch.svd(A)` returns the singular value decomposition of a
real matrix `A` of size `(n x m)` such that :math:`A = USV^T`.
`U` is of shape :math:`(n \times n)`.
`S` is a diagonal matrix of shape :math:`(n \times m)`, represented as a vector
of size :math:`\min(n, m)` containing the diagonal entries.
`V` is of shape :math:`(m \times m)`.
If :attr:`some` is ``True`` (default), the returned `U` and `V` matrices will
contain only :math:`min(n, m)` orthonormal columns.
.. note:: Irrespective of the original strides, the returned matrix `U`
will be transposed, i.e. with strides `(1, n)` instead of `(n, 1)`.
.. note:: Extra care needs to be taken when backward through `U` and `V`
outputs. Such operation is really only stable when :attr:`input` is
full rank with all distinct singular values. Otherwise, `NaN` can
appear as the gradients are not properly defined. Also, when
:attr:`some` = `False`, the gradients on `U[:, min(n, m):]` and
`V[:, min(n, m):]` will be ignored as those vectors can be arbitrary
bases of the subspaces.
.. note:: Double backward through :meth:`~torch.svd` is not supported currently.
Args:
input (Tensor): the input 2-D tensor
some (bool, optional): controls the shape of returned `U` and `V`
out (tuple, optional): the output tuple of tensors
Example::
>>> a = torch.Tensor([[8.79, 6.11, -9.15, 9.57, -3.49, 9.84],
... [9.93, 6.91, -7.93, 1.64, 4.02, 0.15],
... [9.83, 5.04, 4.86, 8.83, 9.80, -8.99],
... [5.45, -0.27, 4.85, 0.74, 10.00, -6.02],
... [3.16, 7.98, 3.01, 5.80, 4.27, -5.31]]).t()
>>> a
8.7900 9.9300 9.8300 5.4500 3.1600
6.1100 6.9100 5.0400 -0.2700 7.9800
-9.1500 -7.9300 4.8600 4.8500 3.0100
9.5700 1.6400 8.8300 0.7400 5.8000
-3.4900 4.0200 9.8000 10.0000 4.2700
9.8400 0.1500 -8.9900 -6.0200 -5.3100
[torch.FloatTensor of size 6x5]
>>> u, s, v = torch.svd(a)
>>> u
-0.5911 0.2632 0.3554 0.3143 0.2299
-0.3976 0.2438 -0.2224 -0.7535 -0.3636
-0.0335 -0.6003 -0.4508 0.2334 -0.3055
-0.4297 0.2362 -0.6859 0.3319 0.1649
-0.4697 -0.3509 0.3874 0.1587 -0.5183
0.2934 0.5763 -0.0209 0.3791 -0.6526
[torch.FloatTensor of size 6x5]
>>> s
27.4687
22.6432
8.5584
5.9857
2.0149
[torch.FloatTensor of size 5]
>>> v
-0.2514 0.8148 -0.2606 0.3967 -0.2180
-0.3968 0.3587 0.7008 -0.4507 0.1402
-0.6922 -0.2489 -0.2208 0.2513 0.5891
-0.3662 -0.3686 0.3859 0.4342 -0.6265
-0.4076 -0.0980 -0.4932 -0.6227 -0.4396
[torch.FloatTensor of size 5x5]
>>> torch.dist(a, torch.mm(torch.mm(u, torch.diag(s)), v.t()))
8.934150226306685e-06
""")
add_docstr(torch._C.symeig,
r"""
symeig(input, eigenvectors=False, upper=True, out=None) -> (Tensor, Tensor)
`e, V = torch.symeig(input)` returns eigenvalues and eigenvectors
of a real symmetric matrix :attr:`input`.
`input` and `V` are :math:`(m \times m)` matrices and `e` is a `m` dimensional
vector.
This function calculates all eigenvalues (and vectors) of `input`
such that :math:`input = V diag(e) V^T`.
The boolean argument :attr:`eigenvectors` defines computation of
eigenvectors or eigenvalues only.
If it is ``False``, only eigenvalues are computed. If it is ``True``,
both eigenvalues and eigenvectors are computed.
Since the input matrix `input` is supposed to be symmetric,
only the upper triangular portion is used by default.
If :attr:`upper` is ``False``, then lower triangular portion is used.
Note: Irrespective of the original strides, the returned matrix `V` will
be transposed, i.e. with strides `(1, m)` instead of `(m, 1)`.
Args:
input (Tensor): the input symmetric matrix
eigenvectors(boolean, optional): controls whether eigenvectors have to be computed
upper(boolean, optional): controls whether to consider upper-triangular or lower-triangular region
out (tuple, optional): the output tuple of (Tensor, Tensor)
Examples::
>>> a = torch.Tensor([[ 1.96, 0.00, 0.00, 0.00, 0.00],
... [-6.49, 3.80, 0.00, 0.00, 0.00],
... [-0.47, -6.39, 4.17, 0.00, 0.00],
... [-7.20, 1.50, -1.51, 5.70, 0.00],
... [-0.65, -6.34, 2.67, 1.80, -7.10]]).t()
>>> e, v = torch.symeig(a, eigenvectors=True)
>>> e
-11.0656
-6.2287
0.8640
8.8655
16.0948
[torch.FloatTensor of size 5]
>>> v
-0.2981 -0.6075 0.4026 -0.3745 0.4896
-0.5078 -0.2880 -0.4066 -0.3572 -0.6053
-0.0816 -0.3843 -0.6600 0.5008 0.3991
-0.0036 -0.4467 0.4553 0.6204 -0.4564
-0.8041 0.4480 0.1725 0.3108 0.1622
[torch.FloatTensor of size 5x5]
""")
add_docstr(torch._C.t,
r"""
t(input, out=None) -> Tensor
Expects :attr:`input` to be a matrix (2-D tensor) and transposes dimensions 0 and
1.
Can be seen as a short-hand function for `transpose(input, 0, 1)`
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> x = torch.randn(2, 3)
>>> x
0.4834 0.6907 1.3417
-0.1300 0.5295 0.2321
[torch.FloatTensor of size 2x3]
>>> torch.t(x)
0.4834 -0.1300
0.6907 0.5295
1.3417 0.2321
[torch.FloatTensor of size 3x2]
""")
add_docstr(torch._C.take, r"""\
take(input, indices) -> Tensor
Returns a new tensor with the elements of :attr:`input` at the given indices.
The input tensor is treated as if it were viewed as a 1-D tensor. The result
takes the same shape as the indices.
Args:
input (Tensor): the input tensor
indices (LongTensor): the indices into tensor
Example::
>>> src = torch.Tensor([[4, 3, 5],
... [6, 7, 8]])
>>> torch.take(src, torch.LongTensor([0, 2, 5]))
4
5
8
[torch.FloatTensor of size 3]
""")
add_docstr(torch._C.tan,
r"""
tan(input, out=None) -> Tensor
Returns a new tensor with the tangent of the elements of :attr:`input`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
-0.6366
0.2718
0.4469
1.3122
[torch.FloatTensor of size 4]
>>> torch.tan(a)
-0.7392
0.2786
0.4792
3.7801
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.tanh,
r"""
tanh(input, out=None) -> Tensor
Returns a new tensor with the hyperbolic tangent of the elements
of :attr:`input`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
-0.6366
0.2718
0.4469
1.3122
[torch.FloatTensor of size 4]
>>> torch.tanh(a)
-0.5625
0.2653
0.4193
0.8648
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.topk,
r"""
topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor)
Returns the :attr:`k` largest elements of the given :attr:`input` tensor along
a given dimension.
If :attr:`dim` is not given, the last dimension of the `input` is chosen.
If :attr:`largest` is ``False`` then the `k` smallest elements are returned.
A tuple of `(values, indices)` is returned, where the `indices` are the indices
of the elements in the original `input` tensor.
The boolean option :attr:`sorted` if ``True``, will make sure that the returned
`k` elements are themselves sorted
Args:
input (Tensor): the input tensor
k (int): the k in "top-k"
dim (int, optional): the dimension to sort along
largest (bool, optional): controls whether to return largest or
smallest elements
sorted (bool, optional): controls whether to return the elements
in sorted order
out (tuple, optional): the output tuple of (Tensor, LongTensor) that can be
optionally given to be used as output buffers
Example::
>>> x = torch.arange(1, 6)
>>> x
1
2
3
4
5
[torch.FloatTensor of size 5]
>>> torch.topk(x, 3)
(
5
4
3
[torch.FloatTensor of size 3]
,
4
3
2
[torch.LongTensor of size 3]
)
>>> torch.topk(x, 3, 0, largest=False)
(
1
2
3
[torch.FloatTensor of size 3]
,
0
1
2
[torch.LongTensor of size 3]
)
""")
add_docstr(torch._C.trace,
r"""
trace(input) -> float
Returns the sum of the elements of the diagonal of the input 2-D matrix.
Example::
>>> x = torch.arange(1, 10).view(3, 3)
>>> x
1 2 3
4 5 6
7 8 9
[torch.FloatTensor of size 3x3]
>>> torch.trace(x)
15.0
""")
add_docstr(torch._C.transpose,
r"""
transpose(input, dim0, dim1, out=None) -> Tensor
Returns a tensor that is a transposed version of :attr:`input`.
The given dimensions :attr:`dim0` and :attr:`dim1` are swapped.
The resulting :attr:`out` tensor shares it's underlying storage with the
:attr:`input` tensor, so changing the content of one would change the content
of the other.
Args:
input (Tensor): the input tensor
dim0 (int): the first dimension to be transposed
dim1 (int): the second dimension to be transposed
Example::
>>> x = torch.randn(2, 3)
>>> x
0.5983 -0.0341 2.4918
1.5981 -0.5265 -0.8735
[torch.FloatTensor of size 2x3]
>>> torch.transpose(x, 0, 1)
0.5983 1.5981
-0.0341 -0.5265
2.4918 -0.8735
[torch.FloatTensor of size 3x2]
""")
add_docstr(torch._C.tril,
r"""
tril(input, diagonal=0, out=None) -> Tensor
Returns the lower triangular part of the matrix (2-D tensor) :attr:`input`,
the other elements of the result tensor :attr:`out` are set to 0.
The lower triangular part of the matrix is defined as the elements on and
below the diagonal.
The argument :attr:`diagonal` controls which diagonal to consider:
- If :attr:`diagonal` = 0, it is the main diagonal.
- If :attr:`diagonal` > 0, it is above the main diagonal.
- If :attr:`diagonal` < 0, it is below the main diagonal.
Args:
input (Tensor): the input tensor
diagonal (int, optional): the diagonal to consider
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(3,3)
>>> a
1.3225 1.7304 1.4573
-0.3052 -0.3111 -0.1809
1.2469 0.0064 -1.6250
[torch.FloatTensor of size 3x3]
>>> torch.tril(a)
1.3225 0.0000 0.0000
-0.3052 -0.3111 0.0000
1.2469 0.0064 -1.6250
[torch.FloatTensor of size 3x3]
>>> torch.tril(a, diagonal=1)
1.3225 1.7304 0.0000
-0.3052 -0.3111 -0.1809
1.2469 0.0064 -1.6250
[torch.FloatTensor of size 3x3]
>>> torch.tril(a, diagonal=-1)
0.0000 0.0000 0.0000
-0.3052 0.0000 0.0000
1.2469 0.0064 0.0000
[torch.FloatTensor of size 3x3]
""")
add_docstr(torch._C.triu,
r"""
triu(input, diagonal=0, out=None) -> Tensor
Returns the upper triangular part of the matrix (2-D tensor) :attr:`input`,
the other elements of the result tensor :attr:`out` are set to 0.
The upper triangular part of the matrix is defined as the elements on and
above the diagonal.
The argument :attr:`diagonal` controls which diagonal to consider:
- If :attr:`diagonal` = 0, it is the main diagonal.
- If :attr:`diagonal` > 0, it is above the main diagonal.
- If :attr:`diagonal` < 0, it is below the main diagonal.
Args:
input (Tensor): the input tensor
diagonal (int, optional): the diagonal to consider
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(3,3)
>>> a
1.3225 1.7304 1.4573
-0.3052 -0.3111 -0.1809
1.2469 0.0064 -1.6250
[torch.FloatTensor of size 3x3]
>>> torch.triu(a)
1.3225 1.7304 1.4573
0.0000 -0.3111 -0.1809
0.0000 0.0000 -1.6250
[torch.FloatTensor of size 3x3]
>>> torch.triu(a, diagonal=1)
0.0000 1.7304 1.4573
0.0000 0.0000 -0.1809
0.0000 0.0000 0.0000
[torch.FloatTensor of size 3x3]
>>> torch.triu(a, diagonal=-1)
1.3225 1.7304 1.4573
-0.3052 -0.3111 -0.1809
0.0000 0.0064 -1.6250
[torch.FloatTensor of size 3x3]
""")
add_docstr(torch._C.trtrs,
r"""
trtrs(b, A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor)
Solves a system of equations with a triangular coefficient matrix `A`
and multiple right-hand sides `b`.
In particular, solves :math:`AX = b` and assumes `A` is upper-triangular
with the default keyword arguments.
This method is NOT implemented for CUDA tensors.
Args:
A (Tensor): the input triangular coefficient matrix
b (Tensor): multiple right-hand sides. Each column of `b` is a
right-hand side for the system of equations.
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 `A` should be transposed before
being sent into the solver. Default: False.
unitriangular (bool, optional): whether `A` is unit triangular.
If True, the diagonal elements of `A` are assumed to be
1 and not referenced from `A`. Default: False.
Returns:
A tuple (X, M) where `M` is a clone of `A` and `X` is the solution to
`AX = b` (or whatever variant of the system of equations, depending on
the keyword arguments.)
Shape:
- A: :math:`(N, N)`
- b: :math:`(N, C)`
- output[0]: :math:`(N, C)`
- output[1]: :math:`(N, N)`
Examples::
>>> A = torch.randn(2,2).triu()
>>> A
-1.8793 0.1567
0.0000 -2.1972
[torch.FloatTensor of size 2x2]
>>> b = torch.randn(2,3)
>>> b
1.8776 -0.0759 1.6590
-0.5676 0.4771 0.7477
[torch.FloatTensor of size 2x3]
>>> torch.trtrs(b, A)
(
-0.9775 0.0223 -0.9112
0.2583 -0.2172 -0.3403
[torch.FloatTensor of size 2x3],
-1.8793 0.1567
0.0000 -2.1972
[torch.FloatTensor of size 2x2])
""")
add_docstr(torch._C.trunc,
r"""
trunc(input, out=None) -> Tensor
Returns a new tensor with the truncated integer values of
the elements of :attr:`input`.
Args:
input (Tensor): the input tensor
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4)
>>> a
-0.4972
1.3512
0.1056
-0.2650
[torch.FloatTensor of size 4]
>>> torch.trunc(a)
-0
1
0
-0
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.unsqueeze,
r"""
unsqueeze(input, dim, out=None)
Returns a new tensor with a dimension of size one inserted at the
specified position.
The returned tensor shares the same underlying data with this tensor.
A negative dim value can be used and will correspond to
:math:`dim + input.dim() + 1`
Args:
input (Tensor): the input tensor
dim (int): the index at which to insert the singleton dimension
out (Tensor, optional): the output tensor
Example:
>>> x = torch.Tensor([1, 2, 3, 4])
>>> torch.unsqueeze(x, 0)
1 2 3 4
[torch.FloatTensor of size 1x4]
>>> torch.unsqueeze(x, 1)
1
2
3
4
[torch.FloatTensor of size 4x1]
""")
add_docstr(torch._C.var,
r"""
.. function:: var(input, unbiased=True) -> float
Returns the variance of all elements in the :attr:`input` tensor.
If :attr:`unbiased` is ``False``, then the variance will be calculated via the
biased estimator. Otherwise, Bessel's correction will be used.
Args:
input (Tensor): the input tensor
unbiased (bool): whether to use the unbiased estimation or not
Example::
>>> a = torch.randn(1, 3)
>>> a
-1.3063 1.4182 -0.3061
[torch.FloatTensor of size 1x3]
>>> torch.var(a)
1.899527506513334
.. function:: var(input, dim, keepdim=False, unbiased=True, out=None) -> Tensor
Returns the variance of each row of the :attr:`input` tensor in the given
dimension :attr:`dim`.
If :attr:`keepdim` is ``True``, the output tensors are of the same size
as :attr:`input` except in the dimension :attr:`dim` where they are of size 1.
Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in
the outputs tensor having 1 fewer dimension than :attr:`input`.
If :attr:`unbiased` is ``False``, then the variance will be calculated via the
biased estimator. Otherwise, Bessel's correction will be used.
Args:
input (Tensor): the input tensor
dim (int): the dimension to reduce
keepdim (bool): whether the output tensor has :attr:`dim` retained or not
unbiased (bool): whether to use the unbiased estimation or not
out (Tensor, optional): the output tensor
Example::
>>> a = torch.randn(4, 4)
>>> a
-1.2738 -0.3058 0.1230 -1.9615
0.8771 -0.5430 -0.9233 0.9879
1.4107 0.0317 -0.6823 0.2255
-1.3854 0.4953 -0.2160 0.2435
[torch.FloatTensor of size 4x4]
>>> torch.var(a, 1)
0.8859
0.9509
0.7548
0.6949
[torch.FloatTensor of size 4]
""")
add_docstr(torch._C.zeros,
r"""
zeros(*sizes, out=None) -> Tensor
Returns a tensor filled with the scalar value `0`, with the shape defined
by the varargs :attr:`sizes`.
Args:
sizes (int...): a set of integers defining the shape of the output tensor
out (Tensor, optional): the output tensor
Example::
>>> torch.zeros(2, 3)
0 0 0
0 0 0
[torch.FloatTensor of size 2x3]
>>> torch.zeros(5)
0
0
0
0
0
[torch.FloatTensor of size 5]
""")
add_docstr(torch._C.zeros_like,
r"""
zeros_like(input, out=None) -> Tensor
Returns a tensor filled with the scalar value `0`, with the same size as
:attr:`input`.
Args:
input (Tensor): the size of the input will determine the size of the output.
out (Tensor, optional): the output tensor
Example::
>>> input = torch.FloatTensor(2, 3)
>>> torch.zeros_like(input)
0 0 0
0 0 0
[torch.FloatTensor of size 2x3]
""")
add_docstr(torch._C.btrifact,
r"""
btrifact(A, info=None, pivot=True) -> (Tensor, IntTensor)
Batch LU factorization.
Returns a tuple containing the LU factorization and pivots. Pivoting is done if
:attr:`pivot` is set.
The optional argument :attr:`info` stores information if the factorization
succeeded for each minibatch example. The :attr:`info` is provided as an
`IntTensor`, its values will be filled from dgetrf and a non-zero value
indicates an error occurred. Specifically, the values are from cublas if cuda is
being used, otherwise LAPACK.
.. warning::
The :attr:`info` argument is deprecated in favor of :meth:`torch.btrifact_with_info`.
Arguments:
A (Tensor): the tensor to factor
info (IntTensor, optional): an `IntTensor` to store values indicating whether
factorization succeeds
pivot (bool, optional): controls whether pivoting is done
Returns:
A tuple containing factorization and pivots.
Example::
>>> A = torch.randn(2, 3, 3)
>>> A_LU, pivots = A.btrifact()
""")
add_docstr(torch._C.btrifact_with_info,
r"""
btrifact_with_info(A, pivot=True) -> (Tensor, IntTensor, IntTensor)
Batch LU factorization with additional error information.
This is a version of :meth:`torch.btrifact` that always creates an info
`IntTensor`, and returns it as the third return value
Arguments:
A (Tensor): the tensor to factor
pivot (bool, optional): controls whether pivoting is done
Returns:
A tuple containing factorization, pivots, and an `IntTensor` where nonzero
values indicate whether factorization for each minibatch sample succeeds.
Example::
>>> A = torch.randn(2, 3, 3)
>>> A_LU, pivots, info = A.btrifact_with_info()
>>> if info.nonzero.size(0) == 0:
>>> print('LU factorization succeeded for all samples!')
""")
add_docstr(torch._C.btrisolve,
r"""
btrisolve(b, LU_data, LU_pivots) -> Tensor
Batch LU solve.
Returns the LU solve of the linear system :math:`Ax = b`.
Arguments:
b (Tensor): the RHS tensor
LU_data (Tensor): the pivoted LU factorization of A from :meth:`btrifact`.
LU_pivots (IntTensor): the pivots of the LU factorization
Example::
>>> A = torch.randn(2, 3, 3)
>>> b = torch.randn(2, 3)
>>> A_LU = torch.btrifact(A)
>>> x = b.btrisolve(*A_LU)
>>> torch.norm(A.bmm(x.unsqueeze(2)) - b)
6.664001874625056e-08
""")
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