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e268fc97c3
pytorch/torch/_tensor_docs.py
Brennan Vincent e268fc97c3 Re-add Tensor.T (#21175) 
Summary:
Something flaky is going on with `test_inplace_view_saved_output` on Windows.

With my PR #20598 applied, the test fails, even though there is no obvious reason it should be related, so the PR was reverted.

Based on commenting out various parts of my change and re-building, I think the problem is with the name -- renaming everything from `T` to `asdf` seems to make the test stop failing. I can't be sure that this is actually the case though, since I could just be seeing patterns in non-deterministic build output...

I spoke with colesbury offline and we agreed that it is okay to just disable this test on Windows for now and not block landing the main change. He will look into why it is failing.

**Test Plan:** I will wait to make sure the Windows CI suite passes before landing this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21175

Differential Revision: D15566970

Pulled By: umanwizard

fbshipit-source-id: edf223375d41faaab0a3a14dca50841f08030da3
2019-06-04 17:38:25 -07:00

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"""Adds docstrings to Tensor functions"""
import torch._C
from torch._C import _add_docstr as add_docstr
from ._torch_docs import parse_kwargs
def add_docstr_all(method, docstr):
add_docstr(getattr(torch._C._TensorBase, method), docstr)
new_common_args = parse_kwargs("""
size (int...): a list, tuple, or :class:`torch.Size` of integers defining the
shape of the output tensor.
dtype (:class:`torch.dtype`, optional): the desired type of returned tensor.
Default: if None, same :class:`torch.dtype` as this tensor.
device (:class:`torch.device`, optional): the desired device of returned tensor.
Default: if None, same :class:`torch.device` as this tensor.
requires_grad (bool, optional): If autograd should record operations on the
returned tensor. Default: ``False``.
pin_memory (bool, optional): If set, returned tensor would be allocated in
the pinned memory. Works only for CPU tensors. Default: ``False``.
""")
add_docstr_all('new_tensor',
r"""
new_tensor(data, dtype=None, device=None, requires_grad=False) -> Tensor
Returns a new Tensor with :attr:`data` as the tensor data.
By default, the returned Tensor has the same :class:`torch.dtype` and
:class:`torch.device` as this tensor.
.. warning::
:func:`new_tensor` always copies :attr:`data`. If you have a Tensor
``data`` and want to avoid a copy, use :func:`torch.Tensor.requires_grad_`
or :func:`torch.Tensor.detach`.
If you have a numpy array and want to avoid a copy, use
:func:`torch.from_numpy`.
.. warning::
When data is a tensor `x`, :func:`new_tensor()` reads out 'the data' from whatever it is passed,
and constructs a leaf variable. Therefore ``tensor.new_tensor(x)`` is equivalent to ``x.clone().detach()``
and ``tensor.new_tensor(x, requires_grad=True)`` is equivalent to ``x.clone().detach().requires_grad_(True)``.
The equivalents using ``clone()`` and ``detach()`` are recommended.
Args:
data (array_like): The returned Tensor copies :attr:`data`.
{dtype}
{device}
{requires_grad}
Example::
>>> tensor = torch.ones((2,), dtype=torch.int8)
>>> data = [[0, 1], [2, 3]]
>>> tensor.new_tensor(data)
tensor([[ 0, 1],
[ 2, 3]], dtype=torch.int8)
""".format(**new_common_args))
add_docstr_all('new_full',
r"""
new_full(size, fill_value, dtype=None, device=None, requires_grad=False) -> Tensor
Returns a Tensor of size :attr:`size` filled with :attr:`fill_value`.
By default, the returned Tensor has the same :class:`torch.dtype` and
:class:`torch.device` as this tensor.
Args:
fill_value (scalar): the number to fill the output tensor with.
{dtype}
{device}
{requires_grad}
Example::
>>> tensor = torch.ones((2,), dtype=torch.float64)
>>> tensor.new_full((3, 4), 3.141592)
tensor([[ 3.1416, 3.1416, 3.1416, 3.1416],
[ 3.1416, 3.1416, 3.1416, 3.1416],
[ 3.1416, 3.1416, 3.1416, 3.1416]], dtype=torch.float64)
""".format(**new_common_args))
add_docstr_all('new_empty',
r"""
new_empty(size, dtype=None, device=None, requires_grad=False) -> Tensor
Returns a Tensor of size :attr:`size` filled with uninitialized data.
By default, the returned Tensor has the same :class:`torch.dtype` and
:class:`torch.device` as this tensor.
Args:
{dtype}
{device}
{requires_grad}
Example::
>>> tensor = torch.ones(())
>>> tensor.new_empty((2, 3))
tensor([[ 5.8182e-18, 4.5765e-41, -1.0545e+30],
[ 3.0949e-41, 4.4842e-44, 0.0000e+00]])
""".format(**new_common_args))
add_docstr_all('new_ones',
r"""
new_ones(size, dtype=None, device=None, requires_grad=False) -> Tensor
Returns a Tensor of size :attr:`size` filled with ``1``.
By default, the returned Tensor has the same :class:`torch.dtype` and
:class:`torch.device` as this tensor.
Args:
size (int...): a list, tuple, or :class:`torch.Size` of integers defining the
shape of the output tensor.
{dtype}
{device}
{requires_grad}
Example::
>>> tensor = torch.tensor((), dtype=torch.int32)
>>> tensor.new_ones((2, 3))
tensor([[ 1, 1, 1],
[ 1, 1, 1]], dtype=torch.int32)
""".format(**new_common_args))
add_docstr_all('new_zeros',
r"""
new_zeros(size, dtype=None, device=None, requires_grad=False) -> Tensor
Returns a Tensor of size :attr:`size` filled with ``0``.
By default, the returned Tensor has the same :class:`torch.dtype` and
:class:`torch.device` as this tensor.
Args:
size (int...): a list, tuple, or :class:`torch.Size` of integers defining the
shape of the output tensor.
{dtype}
{device}
{requires_grad}
Example::
>>> tensor = torch.tensor((), dtype=torch.float64)
>>> tensor.new_zeros((2, 3))
tensor([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=torch.float64)
""".format(**new_common_args))
add_docstr_all('abs',
r"""
abs() -> Tensor
See :func:`torch.abs`
""")
add_docstr_all('abs_',
r"""
abs_() -> Tensor
In-place version of :meth:`~Tensor.abs`
""")
add_docstr_all('acos',
r"""
acos() -> Tensor
See :func:`torch.acos`
""")
add_docstr_all('acos_',
r"""
acos_() -> Tensor
In-place version of :meth:`~Tensor.acos`
""")
add_docstr_all('add',
r"""
add(value) -> Tensor
add(value=1, other) -> Tensor
See :func:`torch.add`
""")
add_docstr_all('add_',
r"""
add_(value) -> Tensor
add_(value=1, other) -> Tensor
In-place version of :meth:`~Tensor.add`
""")
add_docstr_all('addbmm',
r"""
addbmm(beta=1, alpha=1, batch1, batch2) -> Tensor
See :func:`torch.addbmm`
""")
add_docstr_all('addbmm_',
r"""
addbmm_(beta=1, alpha=1, batch1, batch2) -> Tensor
In-place version of :meth:`~Tensor.addbmm`
""")
add_docstr_all('addcdiv',
r"""
addcdiv(value=1, tensor1, tensor2) -> Tensor
See :func:`torch.addcdiv`
""")
add_docstr_all('addcdiv_',
r"""
addcdiv_(value=1, tensor1, tensor2) -> Tensor
In-place version of :meth:`~Tensor.addcdiv`
""")
add_docstr_all('addcmul',
r"""
addcmul(value=1, tensor1, tensor2) -> Tensor
See :func:`torch.addcmul`
""")
add_docstr_all('addcmul_',
r"""
addcmul_(value=1, tensor1, tensor2) -> Tensor
In-place version of :meth:`~Tensor.addcmul`
""")
add_docstr_all('addmm',
r"""
addmm(beta=1, alpha=1, mat1, mat2) -> Tensor
See :func:`torch.addmm`
""")
add_docstr_all('addmm_',
r"""
addmm_(beta=1, alpha=1, mat1, mat2) -> Tensor
In-place version of :meth:`~Tensor.addmm`
""")
add_docstr_all('addmv',
r"""
addmv(beta=1, alpha=1, mat, vec) -> Tensor
See :func:`torch.addmv`
""")
add_docstr_all('addmv_',
r"""
addmv_(beta=1, alpha=1, mat, vec) -> Tensor
In-place version of :meth:`~Tensor.addmv`
""")
add_docstr_all('addr',
r"""
addr(beta=1, alpha=1, vec1, vec2) -> Tensor
See :func:`torch.addr`
""")
add_docstr_all('addr_',
r"""
addr_(beta=1, alpha=1, vec1, vec2) -> Tensor
In-place version of :meth:`~Tensor.addr`
""")
add_docstr_all('all',
r"""
.. function:: all() -> bool
Returns True if all elements in the tensor are non-zero, False otherwise.
Example::
>>> a = torch.randn(1, 3).byte() % 2
>>> a
tensor([[1, 0, 0]], dtype=torch.uint8)
>>> a.all()
tensor(0, dtype=torch.uint8)
.. function:: all(dim, keepdim=False, out=None) -> Tensor
Returns True if all elements in each row of the tensor in the given
dimension :attr:`dim` are non-zero, False otherwise.
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:
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).byte() % 2
>>> a
tensor([[0, 0],
[0, 0],
[0, 1],
[1, 1]], dtype=torch.uint8)
>>> a.all(dim=1)
tensor([0, 0, 0, 1], dtype=torch.uint8)
""")
add_docstr_all('allclose',
r"""
allclose(other, rtol=1e-05, atol=1e-08, equal_nan=False) -> Tensor
See :func:`torch.allclose`
""")
add_docstr_all('any',
r"""
.. function:: any() -> bool
Returns True if any elements in the tensor are non-zero, False otherwise.
Example::
>>> a = torch.randn(1, 3).byte() % 2
>>> a
tensor([[0, 0, 1]], dtype=torch.uint8)
>>> a.any()
tensor(1, dtype=torch.uint8)
.. function:: any(dim, keepdim=False, out=None) -> Tensor
Returns True if any elements in each row of the tensor in the given
dimension :attr:`dim` are non-zero, False otherwise.
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:
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).byte() % 2
>>> a
tensor([[1, 0],
[0, 0],
[0, 1],
[0, 0]], dtype=torch.uint8)
>>> a.any(dim=1)
tensor([1, 0, 1, 0], dtype=torch.uint8)
""")
add_docstr_all('apply_',
r"""
apply_(callable) -> Tensor
Applies the function :attr:`callable` to each element in the tensor, replacing
each element with the value returned by :attr:`callable`.
.. note::
This function only works with CPU tensors and should not be used in code
sections that require high performance.
""")
add_docstr_all('asin', r"""
asin() -> Tensor
See :func:`torch.asin`
""")
add_docstr_all('asin_',
r"""
asin_() -> Tensor
In-place version of :meth:`~Tensor.asin`
""")
add_docstr_all('atan',
r"""
atan() -> Tensor
See :func:`torch.atan`
""")
add_docstr_all('atan2',
r"""
atan2(other) -> Tensor
See :func:`torch.atan2`
""")
add_docstr_all('atan2_',
r"""
atan2_(other) -> Tensor
In-place version of :meth:`~Tensor.atan2`
""")
add_docstr_all('atan_',
r"""
atan_() -> Tensor
In-place version of :meth:`~Tensor.atan`
""")
add_docstr_all('baddbmm',
r"""
baddbmm(beta=1, alpha=1, batch1, batch2) -> Tensor
See :func:`torch.baddbmm`
""")
add_docstr_all('baddbmm_',
r"""
baddbmm_(beta=1, alpha=1, batch1, batch2) -> Tensor
In-place version of :meth:`~Tensor.baddbmm`
""")
add_docstr_all('bernoulli',
r"""
bernoulli(*, generator=None) -> Tensor
Returns a result tensor where each :math:`\texttt{result[i]}` is independently
sampled from :math:`\text{Bernoulli}(\texttt{self[i]})`. :attr:`self` must have
floating point ``dtype``, and the result will have the same ``dtype``.
See :func:`torch.bernoulli`
""")
add_docstr_all('bernoulli_',
r"""
.. function:: bernoulli_(p=0.5, *, generator=None) -> Tensor
Fills each location of :attr:`self` with an independent sample from
:math:`\text{Bernoulli}(\texttt{p})`. :attr:`self` can have integral
``dtype``.
.. function:: bernoulli_(p_tensor, *, generator=None) -> Tensor
:attr:`p_tensor` should be a tensor containing probabilities to be used for
drawing the binary random number.
The :math:`\text{i}^{th}` element of :attr:`self` tensor will be set to a
value sampled from :math:`\text{Bernoulli}(\texttt{p\_tensor[i]})`.
:attr:`self` can have integral ``dtype``, but :attr:`p_tensor` must have
floating point ``dtype``.
See also :meth:`~Tensor.bernoulli` and :func:`torch.bernoulli`
""")
add_docstr_all('bincount',
r"""
bincount(weights=None, minlength=0) -> Tensor
See :func:`torch.bincount`
""")
add_docstr_all('bmm',
r"""
bmm(batch2) -> Tensor
See :func:`torch.bmm`
""")
add_docstr_all('cauchy_',
r"""
cauchy_(median=0, sigma=1, *, generator=None) -> Tensor
Fills the tensor with numbers drawn from the Cauchy distribution:
.. math::
f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2}
""")
add_docstr_all('ceil',
r"""
ceil() -> Tensor
See :func:`torch.ceil`
""")
add_docstr_all('ceil_',
r"""
ceil_() -> Tensor
In-place version of :meth:`~Tensor.ceil`
""")
add_docstr_all('cholesky',
r"""
cholesky(upper=False) -> Tensor
See :func:`torch.cholesky`
""")
add_docstr_all('cholesky_solve',
r"""
cholesky_solve(input2, upper=False) -> Tensor
See :func:`torch.cholesky_solve`
""")
add_docstr_all('cholesky_inverse',
r"""
cholesky_inverse(upper=False) -> Tensor
See :func:`torch.cholesky_inverse`
""")
add_docstr_all('clamp',
r"""
clamp(min, max) -> Tensor
See :func:`torch.clamp`
""")
add_docstr_all('clamp_',
r"""
clamp_(min, max) -> Tensor
In-place version of :meth:`~Tensor.clamp`
""")
add_docstr_all('clone',
r"""
clone() -> Tensor
Returns a copy of the :attr:`self` tensor. The copy has the same size and data
type as :attr:`self`.
.. note::
Unlike `copy_()`, this function is recorded in the computation graph. Gradients
propagating to the cloned tensor will propagate to the original tensor.
""")
add_docstr_all('contiguous',
r"""
contiguous() -> Tensor
Returns a contiguous tensor containing the same data as :attr:`self` tensor. If
:attr:`self` tensor is contiguous, this function returns the :attr:`self`
tensor.
""")
add_docstr_all('copy_',
r"""
copy_(src, non_blocking=False) -> Tensor
Copies the elements from :attr:`src` into :attr:`self` tensor and returns
:attr:`self`.
The :attr:`src` tensor must be :ref:`broadcastable <broadcasting-semantics>`
with the :attr:`self` tensor. It may be of a different data type or reside on a
different device.
Args:
src (Tensor): the source tensor to copy from
non_blocking (bool): if ``True`` and this copy is between CPU and GPU,
the copy may occur asynchronously with respect to the host. For other
cases, this argument has no effect.
""")
add_docstr_all('cos',
r"""
cos() -> Tensor
See :func:`torch.cos`
""")
add_docstr_all('cos_',
r"""
cos_() -> Tensor
In-place version of :meth:`~Tensor.cos`
""")
add_docstr_all('cosh',
r"""
cosh() -> Tensor
See :func:`torch.cosh`
""")
add_docstr_all('cosh_',
r"""
cosh_() -> Tensor
In-place version of :meth:`~Tensor.cosh`
""")
add_docstr_all('cpu',
r"""
cpu() -> Tensor
Returns a copy of this object in CPU memory.
If this object is already in CPU memory and on the correct device,
then no copy is performed and the original object is returned.
""")
add_docstr_all('cross',
r"""
cross(other, dim=-1) -> Tensor
See :func:`torch.cross`
""")
add_docstr_all('cuda',
r"""
cuda(device=None, non_blocking=False) -> Tensor
Returns a copy of this object in CUDA memory.
If this object is already in CUDA memory and on the correct device,
then no copy is performed and the original object is returned.
Args:
device (:class:`torch.device`): The destination GPU device.
Defaults to the current CUDA device.
non_blocking (bool): If ``True`` and the source is in pinned memory,
the copy will be asynchronous with respect to the host.
Otherwise, the argument has no effect. Default: ``False``.
""")
add_docstr_all('cumprod',
r"""
cumprod(dim, dtype=None) -> Tensor
See :func:`torch.cumprod`
""")
add_docstr_all('cumsum',
r"""
cumsum(dim, dtype=None) -> Tensor
See :func:`torch.cumsum`
""")
add_docstr_all('data_ptr',
r"""
data_ptr() -> int
Returns the address of the first element of :attr:`self` tensor.
""")
add_docstr_all('dequantize',
r"""
dequantize() -> Tensor
Given a quantized Tensor, dequantize it and return the dequantized float Tensor.
""")
add_docstr_all('dense_dim',
r"""
dense_dim() -> int
If :attr:`self` is a sparse COO tensor (i.e., with ``torch.sparse_coo`` layout),
this returns a the number of dense dimensions. Otherwise, this throws an
error.
See also :meth:`Tensor.sparse_dim`.
""")
add_docstr_all('diag',
r"""
diag(diagonal=0) -> Tensor
See :func:`torch.diag`
""")
add_docstr_all('diag_embed',
r"""
diag_embed(offset=0, dim1=-2, dim2=-1) -> Tensor
See :func:`torch.diag_embed`
""")
add_docstr_all('diagflat',
r"""
diagflat(diagonal=0) -> Tensor
See :func:`torch.diagflat`
""")
add_docstr_all('diagonal',
r"""
diagonal(offset=0, dim1=0, dim2=1) -> Tensor
See :func:`torch.diagonal`
""")
add_docstr_all('digamma',
r"""
digamma() -> Tensor
See :func:`torch.digamma`
""")
add_docstr_all('digamma_',
r"""
digamma_() -> Tensor
In-place version of :meth:`~Tensor.digamma`
""")
add_docstr_all('dim',
r"""
dim() -> int
Returns the number of dimensions of :attr:`self` tensor.
""")
add_docstr_all('dist',
r"""
dist(other, p=2) -> Tensor
See :func:`torch.dist`
""")
add_docstr_all('div',
r"""
div(value) -> Tensor
See :func:`torch.div`
""")
add_docstr_all('div_',
r"""
div_(value) -> Tensor
In-place version of :meth:`~Tensor.div`
""")
add_docstr_all('dot',
r"""
dot(tensor2) -> Tensor
See :func:`torch.dot`
""")
add_docstr_all('eig',
r"""
eig(eigenvectors=False) -> (Tensor, Tensor)
See :func:`torch.eig`
""")
add_docstr_all('element_size',
r"""
element_size() -> int
Returns the size in bytes of an individual element.
Example::
>>> torch.tensor([]).element_size()
4
>>> torch.tensor([], dtype=torch.uint8).element_size()
1
""")
add_docstr_all('eq',
r"""
eq(other) -> Tensor
See :func:`torch.eq`
""")
add_docstr_all('eq_',
r"""
eq_(other) -> Tensor
In-place version of :meth:`~Tensor.eq`
""")
add_docstr_all('equal',
r"""
equal(other) -> bool
See :func:`torch.equal`
""")
add_docstr_all('erf',
r"""
erf() -> Tensor
See :func:`torch.erf`
""")
add_docstr_all('erf_',
r"""
erf_() -> Tensor
In-place version of :meth:`~Tensor.erf`
""")
add_docstr_all('erfc',
r"""
erfc() -> Tensor
See :func:`torch.erfc`
""")
add_docstr_all('erfc_',
r"""
erfc_() -> Tensor
In-place version of :meth:`~Tensor.erfc`
""")
add_docstr_all('erfinv',
r"""
erfinv() -> Tensor
See :func:`torch.erfinv`
""")
add_docstr_all('erfinv_',
r"""
erfinv_() -> Tensor
In-place version of :meth:`~Tensor.erfinv`
""")
add_docstr_all('exp',
r"""
exp() -> Tensor
See :func:`torch.exp`
""")
add_docstr_all('exp_',
r"""
exp_() -> Tensor
In-place version of :meth:`~Tensor.exp`
""")
add_docstr_all('expm1',
r"""
expm1() -> Tensor
See :func:`torch.expm1`
""")
add_docstr_all('expm1_',
r"""
expm1_() -> Tensor
In-place version of :meth:`~Tensor.expm1`
""")
add_docstr_all('exponential_',
r"""
exponential_(lambd=1, *, generator=None) -> Tensor
Fills :attr:`self` tensor with elements drawn from the exponential distribution:
.. math::
f(x) = \lambda e^{-\lambda x}
""")
add_docstr_all('fill_',
r"""
fill_(value) -> Tensor
Fills :attr:`self` tensor with the specified value.
""")
add_docstr_all('floor',
r"""
floor() -> Tensor
See :func:`torch.floor`
""")
add_docstr_all('flip',
r"""
flip(dims) -> Tensor
See :func:`torch.flip`
""")
add_docstr_all('roll',
r"""
roll(shifts, dims) -> Tensor
See :func:`torch.roll`
""")
add_docstr_all('floor_',
r"""
floor_() -> Tensor
In-place version of :meth:`~Tensor.floor`
""")
add_docstr_all('fmod',
r"""
fmod(divisor) -> Tensor
See :func:`torch.fmod`
""")
add_docstr_all('fmod_',
r"""
fmod_(divisor) -> Tensor
In-place version of :meth:`~Tensor.fmod`
""")
add_docstr_all('frac',
r"""
frac() -> Tensor
See :func:`torch.frac`
""")
add_docstr_all('frac_',
r"""
frac_() -> Tensor
In-place version of :meth:`~Tensor.frac`
""")
add_docstr_all('flatten',
r"""
flatten(input, start_dim=0, end_dim=-1) -> Tensor
see :func:`torch.flatten`
""")
add_docstr_all('gather',
r"""
gather(dim, index) -> Tensor
See :func:`torch.gather`
""")
add_docstr_all('ge',
r"""
ge(other) -> Tensor
See :func:`torch.ge`
""")
add_docstr_all('ge_',
r"""
ge_(other) -> Tensor
In-place version of :meth:`~Tensor.ge`
""")
add_docstr_all('gels',
r"""
gels(A) -> Tensor
See :func:`torch.gels`
""")
add_docstr_all('geometric_',
r"""
geometric_(p, *, generator=None) -> Tensor
Fills :attr:`self` tensor with elements drawn from the geometric distribution:
.. math::
f(X=k) = p^{k - 1} (1 - p)
""")
add_docstr_all('geqrf',
r"""
geqrf() -> (Tensor, Tensor)
See :func:`torch.geqrf`
""")
add_docstr_all('ger',
r"""
ger(vec2) -> Tensor
See :func:`torch.ger`
""")
add_docstr_all('indices',
r"""
indices() -> Tensor
If :attr:`self` is a sparse COO tensor (i.e., with ``torch.sparse_coo`` layout),
this returns a view of the contained indices tensor. Otherwise, this throws an
error.
See also :meth:`Tensor.values`.
.. note::
This method can only be called on a coalesced sparse tensor. See
:meth:`Tensor.coalesce` for details.
""")
add_docstr_all('get_device',
r"""
get_device() -> Device ordinal (Integer)
For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides.
For CPU tensors, an error is thrown.
Example::
>>> x = torch.randn(3, 4, 5, device='cuda:0')
>>> x.get_device()
0
>>> x.cpu().get_device() # RuntimeError: get_device is not implemented for type torch.FloatTensor
""")
add_docstr_all('values',
r"""
values() -> Tensor
If :attr:`self` is a sparse COO tensor (i.e., with ``torch.sparse_coo`` layout),
this returns a view of the contained values tensor. Otherwise, this throws an
error.
See also :meth:`Tensor.indices`.
.. note::
This method can only be called on a coalesced sparse tensor. See
:meth:`Tensor.coalesce` for details.
""")
add_docstr_all('gt',
r"""
gt(other) -> Tensor
See :func:`torch.gt`
""")
add_docstr_all('gt_',
r"""
gt_(other) -> Tensor
In-place version of :meth:`~Tensor.gt`
""")
add_docstr_all('hardshrink',
r"""
hardshrink(lambd=0.5) -> Tensor
See :func:`torch.nn.functional.hardshrink`
""")
add_docstr_all('histc',
r"""
histc(bins=100, min=0, max=0) -> Tensor
See :func:`torch.histc`
""")
add_docstr_all('index_add_',
r"""
index_add_(dim, index, tensor) -> Tensor
Accumulate the elements of :attr:`tensor` into the :attr:`self` tensor by adding
to the indices in the order given in :attr:`index`. For example, if ``dim == 0``
and ``index[i] == j``, then the ``i``\ th row of :attr:`tensor` is added to the
``j``\ th row of :attr:`self`.
The :attr:`dim`\ th dimension of :attr:`tensor` must have the same size as the
length of :attr:`index` (which must be a vector), and all other dimensions must
match :attr:`self`, or an error will be raised.
.. include:: cuda_deterministic.rst
Args:
dim (int): dimension along which to index
index (LongTensor): indices of :attr:`tensor` to select from
tensor (Tensor): the tensor containing values to add
Example::
>>> x = torch.ones(5, 3)
>>> t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float)
>>> index = torch.tensor([0, 4, 2])
>>> x.index_add_(0, index, t)
tensor([[ 2., 3., 4.],
[ 1., 1., 1.],
[ 8., 9., 10.],
[ 1., 1., 1.],
[ 5., 6., 7.]])
""")
add_docstr_all('index_copy_',
r"""
index_copy_(dim, index, tensor) -> Tensor
Copies the elements of :attr:`tensor` into the :attr:`self` tensor by selecting
the indices in the order given in :attr:`index`. For example, if ``dim == 0``
and ``index[i] == j``, then the ``i``\ th row of :attr:`tensor` is copied to the
``j``\ th row of :attr:`self`.
The :attr:`dim`\ th dimension of :attr:`tensor` must have the same size as the
length of :attr:`index` (which must be a vector), and all other dimensions must
match :attr:`self`, or an error will be raised.
Args:
dim (int): dimension along which to index
index (LongTensor): indices of :attr:`tensor` to select from
tensor (Tensor): the tensor containing values to copy
Example::
>>> x = torch.zeros(5, 3)
>>> t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float)
>>> index = torch.tensor([0, 4, 2])
>>> x.index_copy_(0, index, t)
tensor([[ 1., 2., 3.],
[ 0., 0., 0.],
[ 7., 8., 9.],
[ 0., 0., 0.],
[ 4., 5., 6.]])
""")
add_docstr_all('index_fill_',
r"""
index_fill_(dim, index, val) -> Tensor
Fills the elements of the :attr:`self` tensor with value :attr:`val` by
selecting the indices in the order given in :attr:`index`.
Args:
dim (int): dimension along which to index
index (LongTensor): indices of :attr:`self` tensor to fill in
val (float): the value to fill with
Example::
>>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float)
>>> index = torch.tensor([0, 2])
>>> x.index_fill_(1, index, -1)
tensor([[-1., 2., -1.],
[-1., 5., -1.],
[-1., 8., -1.]])
""")
add_docstr_all('index_put_',
r"""
index_put_(indices, value, accumulate=False) -> Tensor
Puts values from the tensor :attr:`value` into the tensor :attr:`self` using
the indices specified in :attr:`indices` (which is a tuple of Tensors). The
expression ``tensor.index_put_(indices, value)`` is equivalent to
``tensor[indices] = value``. Returns :attr:`self`.
If :attr:`accumulate` is ``True``, the elements in :attr:`tensor` are added to
:attr:`self`. If accumulate is ``False``, the behavior is undefined if indices
contain duplicate elements.
Args:
indices (tuple of LongTensor): tensors used to index into `self`.
value (Tensor): tensor of same dtype as `self`.
accumulate (bool): whether to accumulate into self
""")
add_docstr_all('index_put',
r"""
index_put(indices, value, accumulate=False) -> Tensor
Out-place version of :meth:`~Tensor.index_put_`
""")
add_docstr_all('index_select',
r"""
index_select(dim, index) -> Tensor
See :func:`torch.index_select`
""")
add_docstr_all('sparse_mask',
r"""
sparse_mask(input, mask) -> Tensor
Returns a new SparseTensor with values from Tensor :attr:`input` filtered
by indices of :attr:`mask` and values are ignored. :attr:`input` and :attr:`mask`
must have the same shape.
Args:
input (Tensor): an input Tensor
mask (SparseTensor): a SparseTensor which we filter :attr:`input` based on its indices
Example::
>>> nnz = 5
>>> dims = [5, 5, 2, 2]
>>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)),
torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz)
>>> V = torch.randn(nnz, dims[2], dims[3])
>>> size = torch.Size(dims)
>>> S = torch.sparse_coo_tensor(I, V, size).coalesce()
>>> D = torch.randn(dims)
>>> D.sparse_mask(S)
tensor(indices=tensor([[0, 0, 0, 2],
[0, 1, 4, 3]]),
values=tensor([[[ 1.6550, 0.2397],
[-0.1611, -0.0779]],
[[ 0.2326, -1.0558],
[ 1.4711, 1.9678]],
[[-0.5138, -0.0411],
[ 1.9417, 0.5158]],
[[ 0.0793, 0.0036],
[-0.2569, -0.1055]]]),
size=(5, 5, 2, 2), nnz=4, layout=torch.sparse_coo)
""")
add_docstr_all('inverse',
r"""
inverse() -> Tensor
See :func:`torch.inverse`
""")
add_docstr_all('is_contiguous',
r"""
is_contiguous() -> bool
Returns True if :attr:`self` tensor is contiguous in memory in C order.
""")
add_docstr_all('is_floating_point',
r"""
is_floating_point() -> bool
Returns True if the data type of :attr:`self` is a floating point data type.
""")
add_docstr_all('is_signed',
r"""
is_signed() -> bool
Returns True if the data type of :attr:`self` is a signed data type.
""")
add_docstr_all('is_set_to',
r"""
is_set_to(tensor) -> bool
Returns True if this object refers to the same ``THTensor`` object from the
Torch C API as the given tensor.
""")
add_docstr_all('item', r"""
item() -> number
Returns the value of this tensor as a standard Python number. This only works
for tensors with one element. For other cases, see :meth:`~Tensor.tolist`.
This operation is not differentiable.
Example::
>>> x = torch.tensor([1.0])
>>> x.item()
1.0
""")
add_docstr_all('kthvalue',
r"""
kthvalue(k, dim=None, keepdim=False) -> (Tensor, LongTensor)
See :func:`torch.kthvalue`
""")
add_docstr_all('le',
r"""
le(other) -> Tensor
See :func:`torch.le`
""")
add_docstr_all('le_',
r"""
le_(other) -> Tensor
In-place version of :meth:`~Tensor.le`
""")
add_docstr_all('lerp',
r"""
lerp(end, weight) -> Tensor
See :func:`torch.lerp`
""")
add_docstr_all('lerp_',
r"""
lerp_(end, weight) -> Tensor
In-place version of :meth:`~Tensor.lerp`
""")
add_docstr_all('log',
r"""
log() -> Tensor
See :func:`torch.log`
""")
add_docstr_all('log_', r"""
log_() -> Tensor
In-place version of :meth:`~Tensor.log`
""")
add_docstr_all('log10',
r"""
log10() -> Tensor
See :func:`torch.log10`
""")
add_docstr_all('log10_',
r"""
log10_() -> Tensor
In-place version of :meth:`~Tensor.log10`
""")
add_docstr_all('log1p',
r"""
log1p() -> Tensor
See :func:`torch.log1p`
""")
add_docstr_all('log1p_',
r"""
log1p_() -> Tensor
In-place version of :meth:`~Tensor.log1p`
""")
add_docstr_all('log2',
r"""
log2() -> Tensor
See :func:`torch.log2`
""")
add_docstr_all('log2_',
r"""
log2_() -> Tensor
In-place version of :meth:`~Tensor.log2`
""")
add_docstr_all('log_normal_', r"""
log_normal_(mean=1, std=2, *, generator=None)
Fills :attr:`self` tensor with numbers samples from the log-normal distribution
parameterized by the given mean :math:`\mu` and standard deviation
:math:`\sigma`. Note that :attr:`mean` and :attr:`std` are the mean and
standard deviation of the underlying normal distribution, and not of the
returned distribution:
.. math::
f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}}
""")
add_docstr_all('logsumexp',
r"""
logsumexp(dim, keepdim=False) -> Tensor
See :func:`torch.logsumexp`
""")
add_docstr_all('lt',
r"""
lt(other) -> Tensor
See :func:`torch.lt`
""")
add_docstr_all('lt_',
r"""
lt_(other) -> Tensor
In-place version of :meth:`~Tensor.lt`
""")
add_docstr_all('lu_solve',
r"""
lu_solve(LU_data, LU_pivots) -> Tensor
See :func:`torch.lu_solve`
""")
add_docstr_all('map_',
r"""
map_(tensor, callable)
Applies :attr:`callable` for each element in :attr:`self` tensor and the given
:attr:`tensor` and stores the results in :attr:`self` tensor. :attr:`self` tensor and
the given :attr:`tensor` must be :ref:`broadcastable <broadcasting-semantics>`.
The :attr:`callable` should have the signature::
def callable(a, b) -> number
""")
add_docstr_all('masked_scatter_',
r"""
masked_scatter_(mask, source)
Copies elements from :attr:`source` into :attr:`self` tensor at positions where
the :attr:`mask` is one.
The shape of :attr:`mask` must be :ref:`broadcastable <broadcasting-semantics>`
with the shape of the underlying tensor. The :attr:`source` should have at least
as many elements as the number of ones in :attr:`mask`
Args:
mask (ByteTensor): the binary mask
source (Tensor): the tensor to copy from
.. note::
The :attr:`mask` operates on the :attr:`self` tensor, not on the given
:attr:`source` tensor.
""")
add_docstr_all('masked_fill_',
r"""
masked_fill_(mask, value)
Fills elements of :attr:`self` tensor with :attr:`value` where :attr:`mask` is
one. The shape of :attr:`mask` must be
:ref:`broadcastable <broadcasting-semantics>` with the shape of the underlying
tensor.
Args:
mask (ByteTensor): the binary mask
value (float): the value to fill in with
""")
add_docstr_all('masked_select',
r"""
masked_select(mask) -> Tensor
See :func:`torch.masked_select`
""")
add_docstr_all('matrix_power',
r"""
matrix_power(n) -> Tensor
See :func:`torch.matrix_power`
""")
add_docstr_all('max',
r"""
max(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)
See :func:`torch.max`
""")
add_docstr_all('argmax',
r"""
argmax(dim=None, keepdim=False) -> LongTensor
See :func:`torch.argmax`
""")
add_docstr_all('mean',
r"""
mean(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)
See :func:`torch.mean`
""")
add_docstr_all('median',
r"""
median(dim=None, keepdim=False) -> (Tensor, LongTensor)
See :func:`torch.median`
""")
add_docstr_all('min',
r"""
min(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)
See :func:`torch.min`
""")
add_docstr_all('argmin',
r"""
argmin(dim=None, keepdim=False) -> LongTensor
See :func:`torch.argmin`
""")
add_docstr_all('mm',
r"""
mm(mat2) -> Tensor
See :func:`torch.mm`
""")
add_docstr_all('mode',
r"""
mode(dim=None, keepdim=False) -> (Tensor, LongTensor)
See :func:`torch.mode`
""")
add_docstr_all('mul',
r"""
mul(value) -> Tensor
See :func:`torch.mul`
""")
add_docstr_all('mul_',
r"""
mul_(value)
In-place version of :meth:`~Tensor.mul`
""")
add_docstr_all('multinomial',
r"""
multinomial(num_samples, replacement=False, *, generator=None) -> Tensor
See :func:`torch.multinomial`
""")
add_docstr_all('mv',
r"""
mv(vec) -> Tensor
See :func:`torch.mv`
""")
add_docstr_all('mvlgamma',
r"""
mvlgamma(p) -> Tensor
See :func:`torch.mvlgamma`
""")
add_docstr_all('mvlgamma_',
r"""
mvlgamma_(p) -> Tensor
In-place version of :meth:`~Tensor.mvlgamma`
""")
add_docstr_all('narrow',
r"""
narrow(dimension, start, length) -> Tensor
See :func:`torch.narrow`
Example::
>>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> x.narrow(0, 0, 2)
tensor([[ 1, 2, 3],
[ 4, 5, 6]])
>>> x.narrow(1, 1, 2)
tensor([[ 2, 3],
[ 5, 6],
[ 8, 9]])
""")
add_docstr_all('narrow_copy',
r"""
narrow_copy(dimension, start, length) -> Tensor
Same as :meth:`Tensor.narrow` except returning a copy rather
than shared storage. This is primarily for sparse tensors, which
do not have a shared-storage narrow method. Calling ```narrow_copy``
with ```dimemsion > self.sparse_dim()``` will return a copy with the
relevant dense dimension narrowed, and ```self.shape``` updated accordingly.
""")
add_docstr_all('ndimension',
r"""
ndimension() -> int
Alias for :meth:`~Tensor.dim()`
""")
add_docstr_all('ne',
r"""
ne(other) -> Tensor
See :func:`torch.ne`
""")
add_docstr_all('ne_',
r"""
ne_(other) -> Tensor
In-place version of :meth:`~Tensor.ne`
""")
add_docstr_all('neg',
r"""
neg() -> Tensor
See :func:`torch.neg`
""")
add_docstr_all('neg_',
r"""
neg_() -> Tensor
In-place version of :meth:`~Tensor.neg`
""")
add_docstr_all('nelement',
r"""
nelement() -> int
Alias for :meth:`~Tensor.numel`
""")
add_docstr_all('nonzero',
r"""
nonzero() -> LongTensor
See :func:`torch.nonzero`
""")
add_docstr_all('norm',
r"""
norm(p=2, dim=None, keepdim=False) -> Tensor
See :func:`torch.norm`
""")
add_docstr_all('normal_',
r"""
normal_(mean=0, std=1, *, generator=None) -> Tensor
Fills :attr:`self` tensor with elements samples from the normal distribution
parameterized by :attr:`mean` and :attr:`std`.
""")
add_docstr_all('numel',
r"""
numel() -> int
See :func:`torch.numel`
""")
add_docstr_all('numpy',
r"""
numpy() -> numpy.ndarray
Returns :attr:`self` tensor as a NumPy :class:`ndarray`. This tensor and the
returned :class:`ndarray` share the same underlying storage. Changes to
:attr:`self` tensor will be reflected in the :class:`ndarray` and vice versa.
""")
add_docstr_all('orgqr',
r"""
orgqr(input2) -> Tensor
See :func:`torch.orgqr`
""")
add_docstr_all('ormqr',
r"""
ormqr(input2, input3, left=True, transpose=False) -> Tensor
See :func:`torch.ormqr`
""")
add_docstr_all('permute',
r"""
permute(*dims) -> Tensor
Permute the dimensions of this tensor.
Args:
*dims (int...): The desired ordering of dimensions
Example:
>>> x = torch.randn(2, 3, 5)
>>> x.size()
torch.Size([2, 3, 5])
>>> x.permute(2, 0, 1).size()
torch.Size([5, 2, 3])
""")
add_docstr_all('pow',
r"""
pow(exponent) -> Tensor
See :func:`torch.pow`
""")
add_docstr_all('pow_',
r"""
pow_(exponent) -> Tensor
In-place version of :meth:`~Tensor.pow`
""")
add_docstr_all('prod',
r"""
prod(dim=None, keepdim=False, dtype=None) -> Tensor
See :func:`torch.prod`
""")
add_docstr_all('put_',
r"""
put_(indices, tensor, accumulate=False) -> Tensor
Copies the elements from :attr:`tensor` into the positions specified by
indices. For the purpose of indexing, the :attr:`self` tensor is treated as if
it were a 1-D tensor.
If :attr:`accumulate` is ``True``, the elements in :attr:`tensor` are added to
:attr:`self`. If accumulate is ``False``, the behavior is undefined if indices
contain duplicate elements.
Args:
indices (LongTensor): the indices into self
tensor (Tensor): the tensor containing values to copy from
accumulate (bool): whether to accumulate into self
Example::
>>> src = torch.tensor([[4, 3, 5],
[6, 7, 8]])
>>> src.put_(torch.tensor([1, 3]), torch.tensor([9, 10]))
tensor([[ 4, 9, 5],
[ 10, 7, 8]])
""")
add_docstr_all('qr',
r"""
qr(some=True) -> (Tensor, Tensor)
See :func:`torch.qr`
""")
add_docstr_all('q_scale',
r"""
q_scale() -> float
Given a Tensor quantized by linear(affine) quantization,
returns the scale of the underlying quantizer().
""")
add_docstr_all('q_zero_point',
r"""
q_zero_point() -> int
Given a Tensor quantized by linear(affine) quantization,
returns the zero_point of the underlying quantizer().
""")
add_docstr_all('random_',
r"""
random_(from=0, to=None, *, generator=None) -> Tensor
Fills :attr:`self` tensor with numbers sampled from the discrete uniform
distribution over ``[from, to - 1]``. If not specified, the values are usually
only bounded by :attr:`self` tensor's data type. However, for floating point
types, if unspecified, range will be ``[0, 2^mantissa]`` to ensure that every
value is representable. For example, `torch.tensor(1, dtype=torch.double).random_()`
will be uniform in ``[0, 2^53]``.
""")
add_docstr_all('reciprocal',
r"""
reciprocal() -> Tensor
See :func:`torch.reciprocal`
""")
add_docstr_all('reciprocal_',
r"""
reciprocal_() -> Tensor
In-place version of :meth:`~Tensor.reciprocal`
""")
add_docstr_all('remainder',
r"""
remainder(divisor) -> Tensor
See :func:`torch.remainder`
""")
add_docstr_all('remainder_',
r"""
remainder_(divisor) -> Tensor
In-place version of :meth:`~Tensor.remainder`
""")
add_docstr_all('renorm',
r"""
renorm(p, dim, maxnorm) -> Tensor
See :func:`torch.renorm`
""")
add_docstr_all('renorm_',
r"""
renorm_(p, dim, maxnorm) -> Tensor
In-place version of :meth:`~Tensor.renorm`
""")
add_docstr_all('repeat',
r"""
repeat(*sizes) -> Tensor
Repeats this tensor along the specified dimensions.
Unlike :meth:`~Tensor.expand`, this function copies the tensor's data.
.. warning::
:func:`torch.repeat` behaves differently from
`numpy.repeat <https://docs.scipy.org/doc/numpy/reference/generated/numpy.repeat.html>`_,
but is more similar to
`numpy.tile <https://docs.scipy.org/doc/numpy/reference/generated/numpy.tile.html>`_.
For the operator similar to `numpy.repeat`, see :func:`torch.repeat_interleave`.
Args:
sizes (torch.Size or int...): The number of times to repeat this tensor along each
dimension
Example::
>>> x = torch.tensor([1, 2, 3])
>>> x.repeat(4, 2)
tensor([[ 1, 2, 3, 1, 2, 3],
[ 1, 2, 3, 1, 2, 3],
[ 1, 2, 3, 1, 2, 3],
[ 1, 2, 3, 1, 2, 3]])
>>> x.repeat(4, 2, 1).size()
torch.Size([4, 2, 3])
""")
add_docstr_all('repeat_interleave',
r"""
repeat_interleave(repeats, dim=None) -> Tensor
See :func:`torch.repeat_interleave`.
""")
add_docstr_all('requires_grad_',
r"""
requires_grad_(requires_grad=True) -> Tensor
Change if autograd should record operations on this tensor: sets this tensor's
:attr:`requires_grad` attribute in-place. Returns this tensor.
:func:`require_grad_`'s main use case is to tell autograd to begin recording
operations on a Tensor ``tensor``. If ``tensor`` has ``requires_grad=False``
(because it was obtained through a DataLoader, or required preprocessing or
initialization), ``tensor.requires_grad_()`` makes it so that autograd will
begin to record operations on ``tensor``.
Args:
requires_grad (bool): If autograd should record operations on this tensor.
Default: ``True``.
Example::
>>> # Let's say we want to preprocess some saved weights and use
>>> # the result as new weights.
>>> saved_weights = [0.1, 0.2, 0.3, 0.25]
>>> loaded_weights = torch.tensor(saved_weights)
>>> weights = preprocess(loaded_weights) # some function
>>> weights
tensor([-0.5503, 0.4926, -2.1158, -0.8303])
>>> # Now, start to record operations done to weights
>>> weights.requires_grad_()
>>> out = weights.pow(2).sum()
>>> out.backward()
>>> weights.grad
tensor([-1.1007, 0.9853, -4.2316, -1.6606])
""")
add_docstr_all('reshape',
r"""
reshape(*shape) -> Tensor
Returns a tensor with the same data and number of elements as :attr:`self`
but with the specified shape. This method returns a view if :attr:`shape` is
compatible with the current shape. See :meth:`torch.Tensor.view` on when it is
possible to return a view.
See :func:`torch.reshape`
Args:
shape (tuple of ints or int...): the desired shape
""")
add_docstr_all('reshape_as',
r"""
reshape_as(other) -> Tensor
Returns this tensor as the same shape as :attr:`other`.
``self.reshape_as(other)`` is equivalent to ``self.reshape(other.sizes())``.
This method returns a view if ``other.sizes()`` is compatible with the current
shape. See :meth:`torch.Tensor.view` on when it is possible to return a view.
Please see :meth:`reshape` for more information about ``reshape``.
Args:
other (:class:`torch.Tensor`): The result tensor has the same shape
as :attr:`other`.
""")
add_docstr_all('resize_',
r"""
resize_(*sizes) -> Tensor
Resizes :attr:`self` tensor to the specified size. If the number of elements is
larger than the current storage size, then the underlying storage is resized
to fit the new number of elements. If the number of elements is smaller, the
underlying storage is not changed. Existing elements are preserved but any new
memory is uninitialized.
.. warning::
This is a low-level method. The storage is reinterpreted as C-contiguous,
ignoring the current strides (unless the target size equals the current
size, in which case the tensor is left unchanged). For most purposes, you
will instead want to use :meth:`~Tensor.view()`, which checks for
contiguity, or :meth:`~Tensor.reshape()`, which copies data if needed. To
change the size in-place with custom strides, see :meth:`~Tensor.set_()`.
Args:
sizes (torch.Size or int...): the desired size
Example::
>>> x = torch.tensor([[1, 2], [3, 4], [5, 6]])
>>> x.resize_(2, 2)
tensor([[ 1, 2],
[ 3, 4]])
""")
add_docstr_all('resize_as_',
r"""
resize_as_(tensor) -> Tensor
Resizes the :attr:`self` tensor to be the same size as the specified
:attr:`tensor`. This is equivalent to ``self.resize_(tensor.size())``.
""")
add_docstr_all('rot90',
r"""
rot90(k, dims) -> Tensor
See :func:`torch.rot90`
""")
add_docstr_all('round',
r"""
round() -> Tensor
See :func:`torch.round`
""")
add_docstr_all('round_',
r"""
round_() -> Tensor
In-place version of :meth:`~Tensor.round`
""")
add_docstr_all('rsqrt',
r"""
rsqrt() -> Tensor
See :func:`torch.rsqrt`
""")
add_docstr_all('rsqrt_',
r"""
rsqrt_() -> Tensor
In-place version of :meth:`~Tensor.rsqrt`
""")
add_docstr_all('scatter_',
r"""
scatter_(dim, index, src) -> Tensor
Writes all values from the tensor :attr:`src` into :attr:`self` at the indices
specified in the :attr:`index` tensor. For each value in :attr:`src`, its output
index is specified by its index in :attr:`src` for ``dimension != dim`` and by
the corresponding value in :attr:`index` for ``dimension = dim``.
For a 3-D tensor, :attr:`self` is updated as::
self[index[i][j][k]][j][k] = src[i][j][k] # if dim == 0
self[i][index[i][j][k]][k] = src[i][j][k] # if dim == 1
self[i][j][index[i][j][k]] = src[i][j][k] # if dim == 2
This is the reverse operation of the manner described in :meth:`~Tensor.gather`.
:attr:`self`, :attr:`index` and :attr:`src` (if it is a Tensor) should have same
number of dimensions. It is also required that ``index.size(d) <= src.size(d)``
for all dimensions ``d``, and that ``index.size(d) <= self.size(d)`` for all
dimensions ``d != dim``.
Moreover, as for :meth:`~Tensor.gather`, the values of :attr:`index` must be
between ``0`` and ``self.size(dim) - 1`` inclusive, and all values in a row
along the specified dimension :attr:`dim` must be unique.
Args:
dim (int): the axis along which to index
index (LongTensor): the indices of elements to scatter,
can be either empty or the same size of src.
When empty, the operation returns identity
src (Tensor): the source element(s) to scatter,
incase `value` is not specified
value (float): the source element(s) to scatter,
incase `src` is not specified
Example::
>>> x = torch.rand(2, 5)
>>> x
tensor([[ 0.3992, 0.2908, 0.9044, 0.4850, 0.6004],
[ 0.5735, 0.9006, 0.6797, 0.4152, 0.1732]])
>>> torch.zeros(3, 5).scatter_(0, torch.tensor([[0, 1, 2, 0, 0], [2, 0, 0, 1, 2]]), x)
tensor([[ 0.3992, 0.9006, 0.6797, 0.4850, 0.6004],
[ 0.0000, 0.2908, 0.0000, 0.4152, 0.0000],
[ 0.5735, 0.0000, 0.9044, 0.0000, 0.1732]])
>>> z = torch.zeros(2, 4).scatter_(1, torch.tensor([[2], [3]]), 1.23)
>>> z
tensor([[ 0.0000, 0.0000, 1.2300, 0.0000],
[ 0.0000, 0.0000, 0.0000, 1.2300]])
""")
add_docstr_all('scatter_add_',
r"""
scatter_add_(dim, index, other) -> Tensor
Adds all values from the tensor :attr:`other` into :attr:`self` at the indices
specified in the :attr:`index` tensor in a similar fashion as
:meth:`~torch.Tensor.scatter_`. For each value in :attr:`other`, it is added to
an index in :attr:`self` which is specified by its index in :attr:`other`
for ``dimension != dim`` and by the corresponding value in :attr:`index` for
``dimension = dim``.
For a 3-D tensor, :attr:`self` is updated as::
self[index[i][j][k]][j][k] += other[i][j][k] # if dim == 0
self[i][index[i][j][k]][k] += other[i][j][k] # if dim == 1
self[i][j][index[i][j][k]] += other[i][j][k] # if dim == 2
:attr:`self`, :attr:`index` and :attr:`other` should have same number of
dimensions. It is also required that ``index.size(d) <= other.size(d)`` for all
dimensions ``d``, and that ``index.size(d) <= self.size(d)`` for all dimensions
``d != dim``.
Moreover, as for :meth:`~Tensor.gather`, the values of :attr:`index` must be
between ``0`` and ``self.size(dim) - 1`` inclusive, and all values in a row along
the specified dimension :attr:`dim` must be unique.
.. include:: cuda_deterministic.rst
Args:
dim (int): the axis along which to index
index (LongTensor): the indices of elements to scatter and add,
can be either empty or the same size of src.
When empty, the operation returns identity.
other (Tensor): the source elements to scatter and add
Example::
>>> x = torch.rand(2, 5)
>>> x
tensor([[0.7404, 0.0427, 0.6480, 0.3806, 0.8328],
[0.7953, 0.2009, 0.9154, 0.6782, 0.9620]])
>>> torch.ones(3, 5).scatter_add_(0, torch.tensor([[0, 1, 2, 0, 0], [2, 0, 0, 1, 2]]), x)
tensor([[1.7404, 1.2009, 1.9154, 1.3806, 1.8328],
[1.0000, 1.0427, 1.0000, 1.6782, 1.0000],
[1.7953, 1.0000, 1.6480, 1.0000, 1.9620]])
""")
add_docstr_all('select',
r"""
select(dim, index) -> Tensor
Slices the :attr:`self` tensor along the selected dimension at the given index.
This function returns a tensor with the given dimension removed.
Args:
dim (int): the dimension to slice
index (int): the index to select with
.. note::
:meth:`select` is equivalent to slicing. For example,
``tensor.select(0, index)`` is equivalent to ``tensor[index]`` and
``tensor.select(2, index)`` is equivalent to ``tensor[:,:,index]``.
""")
add_docstr_all('set_',
r"""
set_(source=None, storage_offset=0, size=None, stride=None) -> Tensor
Sets the underlying storage, size, and strides. If :attr:`source` is a tensor,
:attr:`self` tensor will share the same storage and have the same size and
strides as :attr:`source`. Changes to elements in one tensor will be reflected
in the other.
If :attr:`source` is a :class:`~torch.Storage`, the method sets the underlying
storage, offset, size, and stride.
Args:
source (Tensor or Storage): the tensor or storage to use
storage_offset (int, optional): the offset in the storage
size (torch.Size, optional): the desired size. Defaults to the size of the source.
stride (tuple, optional): the desired stride. Defaults to C-contiguous strides.
""")
add_docstr_all('sigmoid',
r"""
sigmoid() -> Tensor
See :func:`torch.sigmoid`
""")
add_docstr_all('sigmoid_',
r"""
sigmoid_() -> Tensor
In-place version of :meth:`~Tensor.sigmoid`
""")
add_docstr_all('sign',
r"""
sign() -> Tensor
See :func:`torch.sign`
""")
add_docstr_all('sign_',
r"""
sign_() -> Tensor
In-place version of :meth:`~Tensor.sign`
""")
add_docstr_all('sin',
r"""
sin() -> Tensor
See :func:`torch.sin`
""")
add_docstr_all('sin_',
r"""
sin_() -> Tensor
In-place version of :meth:`~Tensor.sin`
""")
add_docstr_all('sinh',
r"""
sinh() -> Tensor
See :func:`torch.sinh`
""")
add_docstr_all('sinh_',
r"""
sinh_() -> Tensor
In-place version of :meth:`~Tensor.sinh`
""")
add_docstr_all('size',
r"""
size() -> torch.Size
Returns the size of the :attr:`self` tensor. The returned value is a subclass of
:class:`tuple`.
Example::
>>> torch.empty(3, 4, 5).size()
torch.Size([3, 4, 5])
""")
add_docstr_all('solve',
r"""
solve(A) -> Tensor, Tensor
See :func:`torch.solve`
""")
add_docstr_all('sort',
r"""
sort(dim=-1, descending=False) -> (Tensor, LongTensor)
See :func:`torch.sort`
""")
add_docstr_all('argsort',
r"""
argsort(dim=-1, descending=False) -> LongTensor
See :func: `torch.argsort`
""")
add_docstr_all('sparse_dim',
r"""
sparse_dim() -> int
If :attr:`self` is a sparse COO tensor (i.e., with ``torch.sparse_coo`` layout),
this returns a the number of sparse dimensions. Otherwise, this throws an
error.
See also :meth:`Tensor.dense_dim`.
""")
add_docstr_all('sqrt',
r"""
sqrt() -> Tensor
See :func:`torch.sqrt`
""")
add_docstr_all('sqrt_',
r"""
sqrt_() -> Tensor
In-place version of :meth:`~Tensor.sqrt`
""")
add_docstr_all('squeeze',
r"""
squeeze(dim=None) -> Tensor
See :func:`torch.squeeze`
""")
add_docstr_all('squeeze_',
r"""
squeeze_(dim=None) -> Tensor
In-place version of :meth:`~Tensor.squeeze`
""")
add_docstr_all('std',
r"""
std(dim=None, unbiased=True, keepdim=False) -> Tensor
See :func:`torch.std`
""")
add_docstr_all('storage',
r"""
storage() -> torch.Storage
Returns the underlying storage.
""")
add_docstr_all('storage_offset',
r"""
storage_offset() -> int
Returns :attr:`self` tensor's offset in the underlying storage in terms of
number of storage elements (not bytes).
Example::
>>> x = torch.tensor([1, 2, 3, 4, 5])
>>> x.storage_offset()
0
>>> x[3:].storage_offset()
3
""")
add_docstr_all('storage_type',
r"""
storage_type() -> type
Returns the type of the underlying storage.
""")
add_docstr_all('stride',
r"""
stride(dim) -> tuple or int
Returns the stride of :attr:`self` tensor.
Stride is the jump necessary to go from one element to the next one in the
specified dimension :attr:`dim`. A tuple of all strides is returned when no
argument is passed in. Otherwise, an integer value is returned as the stride in
the particular dimension :attr:`dim`.
Args:
dim (int, optional): the desired dimension in which stride is required
Example::
>>> x = torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
>>> x.stride()
(5, 1)
>>>x.stride(0)
5
>>> x.stride(-1)
1
""")
add_docstr_all('sub',
r"""
sub(value, other) -> Tensor
Subtracts a scalar or tensor from :attr:`self` tensor. If both :attr:`value` and
:attr:`other` are specified, each element of :attr:`other` is scaled by
:attr:`value` before being used.
When :attr:`other` is a tensor, the shape of :attr:`other` must be
:ref:`broadcastable <broadcasting-semantics>` with the shape of the underlying
tensor.
""")
add_docstr_all('sub_',
r"""
sub_(x) -> Tensor
In-place version of :meth:`~Tensor.sub`
""")
add_docstr_all('sum',
r"""
sum(dim=None, keepdim=False, dtype=None) -> Tensor
See :func:`torch.sum`
""")
add_docstr_all('svd',
r"""
svd(some=True, compute_uv=True) -> (Tensor, Tensor, Tensor)
See :func:`torch.svd`
""")
add_docstr_all('symeig',
r"""
symeig(eigenvectors=False, upper=True) -> (Tensor, Tensor)
See :func:`torch.symeig`
""")
add_docstr_all('t',
r"""
t() -> Tensor
See :func:`torch.t`
""")
add_docstr_all('t_',
r"""
t_() -> Tensor
In-place version of :meth:`~Tensor.t`
""")
add_docstr_all('to',
r"""
to(*args, **kwargs) -> Tensor
Performs Tensor dtype and/or device conversion. A :class:`torch.dtype` and :class:`torch.device` are
inferred from the arguments of ``self.to(*args, **kwargs)``.
.. note::
If the ``self`` Tensor already
has the correct :class:`torch.dtype` and :class:`torch.device`, then ``self`` is returned.
Otherwise, the returned tensor is a copy of ``self`` with the desired
:class:`torch.dtype` and :class:`torch.device`.
Here are the ways to call ``to``:
.. function:: to(dtype, non_blocking=False, copy=False) -> Tensor
Returns a Tensor with the specified :attr:`dtype`
.. function:: to(device=None, dtype=None, non_blocking=False, copy=False) -> Tensor
Returns a Tensor with the specified :attr:`device` and (optional)
:attr:`dtype`. If :attr:`dtype` is ``None`` it is inferred to be ``self.dtype``.
When :attr:`non_blocking`, tries to convert asynchronously with respect to
the host if possible, e.g., converting a CPU Tensor with pinned memory to a
CUDA Tensor.
When :attr:`copy` is set, a new Tensor is created even when the Tensor
already matches the desired conversion.
.. function:: to(other, non_blocking=False, copy=False) -> Tensor
Returns a Tensor with same :class:`torch.dtype` and :class:`torch.device` as
the Tensor :attr:`other`. When :attr:`non_blocking`, tries to convert
asynchronously with respect to the host if possible, e.g., converting a CPU
Tensor with pinned memory to a CUDA Tensor.
When :attr:`copy` is set, a new Tensor is created even when the Tensor
already matches the desired conversion.
Example::
>>> tensor = torch.randn(2, 2) # Initially dtype=float32, device=cpu
>>> tensor.to(torch.float64)
tensor([[-0.5044, 0.0005],
[ 0.3310, -0.0584]], dtype=torch.float64)
>>> cuda0 = torch.device('cuda:0')
>>> tensor.to(cuda0)
tensor([[-0.5044, 0.0005],
[ 0.3310, -0.0584]], device='cuda:0')
>>> tensor.to(cuda0, dtype=torch.float64)
tensor([[-0.5044, 0.0005],
[ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0')
>>> other = torch.randn((), dtype=torch.float64, device=cuda0)
>>> tensor.to(other, non_blocking=True)
tensor([[-0.5044, 0.0005],
[ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0')
""")
add_docstr_all('byte',
r"""
byte() -> Tensor
``self.byte()`` is equivalent to ``self.to(torch.uint8)``. See :func:`to`.
""")
add_docstr_all('bool',
r"""
bool() -> Tensor
``self.bool()`` is equivalent to ``self.to(torch.bool)``. See :func:`to`.
""")
add_docstr_all('char',
r"""
char() -> Tensor
``self.char()`` is equivalent to ``self.to(torch.int8)``. See :func:`to`.
""")
add_docstr_all('double',
r"""
double() -> Tensor
``self.double()`` is equivalent to ``self.to(torch.float64)``. See :func:`to`.
""")
add_docstr_all('float',
r"""
float() -> Tensor
``self.float()`` is equivalent to ``self.to(torch.float32)``. See :func:`to`.
""")
add_docstr_all('half',
r"""
half() -> Tensor
``self.half()`` is equivalent to ``self.to(torch.float16)``. See :func:`to`.
""")
add_docstr_all('int',
r"""
int() -> Tensor
``self.int()`` is equivalent to ``self.to(torch.int32)``. See :func:`to`.
""")
add_docstr_all('int_repr',
r"""
int_repr() -> Tensor
Given a quantized Tensor,
``self.int_repr()`` returns a CPU Tensor with uint8_t as data type that stores the
underlying uint8_t values of the given Tensor.
""")
add_docstr_all('long',
r"""
long() -> Tensor
``self.long()`` is equivalent to ``self.to(torch.int64)``. See :func:`to`.
""")
add_docstr_all('short',
r"""
short() -> Tensor
``self.short()`` is equivalent to ``self.to(torch.int16)``. See :func:`to`.
""")
add_docstr_all('take',
r"""
take(indices) -> Tensor
See :func:`torch.take`
""")
add_docstr_all('tan',
r"""
tan() -> Tensor
See :func:`torch.tan`
""")
add_docstr_all('tan_',
r"""
tan_() -> Tensor
In-place version of :meth:`~Tensor.tan`
""")
add_docstr_all('tanh',
r"""
tanh() -> Tensor
See :func:`torch.tanh`
""")
add_docstr_all('tanh_',
r"""
tanh_() -> Tensor
In-place version of :meth:`~Tensor.tanh`
""")
add_docstr_all('tolist',
r""""
tolist() -> list or number
Returns the tensor as a (nested) list. For scalars, a standard
Python number is returned, just like with :meth:`~Tensor.item`.
Tensors are automatically moved to the CPU first if necessary.
This operation is not differentiable.
Examples::
>>> a = torch.randn(2, 2)
>>> a.tolist()
[[0.012766935862600803, 0.5415473580360413],
[-0.08909505605697632, 0.7729271650314331]]
>>> a[0,0].tolist()
0.012766935862600803
""")
add_docstr_all('topk',
r"""
topk(k, dim=None, largest=True, sorted=True) -> (Tensor, LongTensor)
See :func:`torch.topk`
""")
add_docstr_all('to_sparse',
r"""
to_sparse(sparseDims) -> Tensor
Returns a sparse copy of the tensor. PyTorch supports sparse tensors in
:ref:`coordinate format <sparse-docs>`.
Args:
sparseDims (int, optional): the number of sparse dimensions to include in the new sparse tensor
Example::
>>> d = torch.tensor([[0, 0, 0], [9, 0, 10], [0, 0, 0]])
>>> d
tensor([[ 0, 0, 0],
[ 9, 0, 10],
[ 0, 0, 0]])
>>> d.to_sparse()
tensor(indices=tensor([[1, 1],
[0, 2]]),
values=tensor([ 9, 10]),
size=(3, 3), nnz=2, layout=torch.sparse_coo)
>>> d.to_sparse(1)
tensor(indices=tensor([[1]]),
values=tensor([[ 9, 0, 10]]),
size=(3, 3), nnz=1, layout=torch.sparse_coo)
""")
add_docstr_all('to_mkldnn',
r"""
to_mkldnn() -> Tensor
Returns a copy of the tensor in ``torch.mkldnn`` layout.
""")
add_docstr_all('trace',
r"""
trace() -> Tensor
See :func:`torch.trace`
""")
add_docstr_all('transpose',
r"""
transpose(dim0, dim1) -> Tensor
See :func:`torch.transpose`
""")
add_docstr_all('transpose_',
r"""
transpose_(dim0, dim1) -> Tensor
In-place version of :meth:`~Tensor.transpose`
""")
add_docstr_all('triangular_solve',
r"""
triangular_solve(A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor)
See :func:`torch.triangular_solve`
""")
add_docstr_all('tril',
r"""
tril(k=0) -> Tensor
See :func:`torch.tril`
""")
add_docstr_all('tril_',
r"""
tril_(k=0) -> Tensor
In-place version of :meth:`~Tensor.tril`
""")
add_docstr_all('triu',
r"""
triu(k=0) -> Tensor
See :func:`torch.triu`
""")
add_docstr_all('triu_',
r"""
triu_(k=0) -> Tensor
In-place version of :meth:`~Tensor.triu`
""")
add_docstr_all('trunc',
r"""
trunc() -> Tensor
See :func:`torch.trunc`
""")
add_docstr_all('trunc_',
r"""
trunc_() -> Tensor
In-place version of :meth:`~Tensor.trunc`
""")
add_docstr_all('type',
r"""
type(dtype=None, non_blocking=False, **kwargs) -> str or Tensor
Returns the type if `dtype` is not provided, else casts this object to
the specified type.
If this is already of the correct type, no copy is performed and the
original object is returned.
Args:
dtype (type or string): The desired type
non_blocking (bool): If ``True``, and the source is in pinned memory
and destination is on the GPU or vice versa, the copy is performed
asynchronously with respect to the host. Otherwise, the argument
has no effect.
**kwargs: For compatibility, may contain the key ``async`` in place of
the ``non_blocking`` argument. The ``async`` arg is deprecated.
""")
add_docstr_all('type_as',
r"""
type_as(tensor) -> Tensor
Returns this tensor cast to the type of the given tensor.
This is a no-op if the tensor is already of the correct type. This is
equivalent to ``self.type(tensor.type())``
Args:
tensor (Tensor): the tensor which has the desired type
""")
add_docstr_all('unfold',
r"""
unfold(dimension, size, step) -> Tensor
Returns a tensor which contains all slices of size :attr:`size` from
:attr:`self` tensor in the dimension :attr:`dimension`.
Step between two slices is given by :attr:`step`.
If `sizedim` is the size of dimension :attr:`dimension` for :attr:`self`, the size of
dimension :attr:`dimension` in the returned tensor will be
`(sizedim - size) / step + 1`.
An additional dimension of size :attr:`size` is appended in the returned tensor.
Args:
dimension (int): dimension in which unfolding happens
size (int): the size of each slice that is unfolded
step (int): the step between each slice
Example::
>>> x = torch.arange(1., 8)
>>> x
tensor([ 1., 2., 3., 4., 5., 6., 7.])
>>> x.unfold(0, 2, 1)
tensor([[ 1., 2.],
[ 2., 3.],
[ 3., 4.],
[ 4., 5.],
[ 5., 6.],
[ 6., 7.]])
>>> x.unfold(0, 2, 2)
tensor([[ 1., 2.],
[ 3., 4.],
[ 5., 6.]])
""")
add_docstr_all('uniform_',
r"""
uniform_(from=0, to=1) -> Tensor
Fills :attr:`self` tensor with numbers sampled from the continuous uniform
distribution:
.. math::
P(x) = \dfrac{1}{\text{to} - \text{from}}
""")
add_docstr_all('unsqueeze',
r"""
unsqueeze(dim) -> Tensor
See :func:`torch.unsqueeze`
""")
add_docstr_all('unsqueeze_',
r"""
unsqueeze_(dim) -> Tensor
In-place version of :meth:`~Tensor.unsqueeze`
""")
add_docstr_all('var',
r"""
var(dim=None, unbiased=True, keepdim=False) -> Tensor
See :func:`torch.var`
""")
add_docstr_all('view',
r"""
view(*shape) -> Tensor
Returns a new tensor with the same data as the :attr:`self` tensor but of a
different :attr:`shape`.
The returned tensor shares the same data and must have the same number
of elements, but may have a different size. For a tensor to be viewed, the new
view size must be compatible with its original size and stride, i.e., each new
view dimension must either be a subspace of an original dimension, or only span
across original dimensions :math:`d, d+1, \dots, d+k` that satisfy the following
contiguity-like condition that :math:`\forall i = 0, \dots, k-1`,
.. math::
\text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]
Otherwise, :meth:`contiguous` needs to be called before the tensor can be
viewed. See also: :meth:`reshape`, which returns a view if the shapes are
compatible, and copies (equivalent to calling :meth:`contiguous`) otherwise.
Args:
shape (torch.Size or int...): the desired size
Example::
>>> x = torch.randn(4, 4)
>>> x.size()
torch.Size([4, 4])
>>> y = x.view(16)
>>> y.size()
torch.Size([16])
>>> z = x.view(-1, 8) # the size -1 is inferred from other dimensions
>>> z.size()
torch.Size([2, 8])
>>> a = torch.randn(1, 2, 3, 4)
>>> a.size()
torch.Size([1, 2, 3, 4])
>>> b = a.transpose(1, 2) # Swaps 2nd and 3rd dimension
>>> b.size()
torch.Size([1, 3, 2, 4])
>>> c = a.view(1, 3, 2, 4) # Does not change tensor layout in memory
>>> c.size()
torch.Size([1, 3, 2, 4])
>>> torch.equal(b, c)
False
""")
add_docstr_all('view_as',
r"""
view_as(other) -> Tensor
View this tensor as the same size as :attr:`other`.
``self.view_as(other)`` is equivalent to ``self.view(other.size())``.
Please see :meth:`~Tensor.view` for more information about ``view``.
Args:
other (:class:`torch.Tensor`): The result tensor has the same size
as :attr:`other`.
""")
add_docstr_all('expand',
r"""
expand(*sizes) -> Tensor
Returns a new view of the :attr:`self` tensor with singleton dimensions expanded
to a larger size.
Passing -1 as the size for a dimension means not changing the size of
that dimension.
Tensor can be also expanded to a larger number of dimensions, and the
new ones will be appended at the front. For the new dimensions, the
size cannot be set to -1.
Expanding a tensor does not allocate new memory, but only creates a
new view on the existing tensor where a dimension of size one is
expanded to a larger size by setting the ``stride`` to 0. Any dimension
of size 1 can be expanded to an arbitrary value without allocating new
memory.
Args:
*sizes (torch.Size or int...): the desired expanded size
.. warning::
More than one element of an expanded tensor may refer to a single
memory location. As a result, in-place operations (especially ones that
are vectorized) may result in incorrect behavior. If you need to write
to the tensors, please clone them first.
Example::
>>> x = torch.tensor([[1], [2], [3]])
>>> x.size()
torch.Size([3, 1])
>>> x.expand(3, 4)
tensor([[ 1, 1, 1, 1],
[ 2, 2, 2, 2],
[ 3, 3, 3, 3]])
>>> x.expand(-1, 4) # -1 means not changing the size of that dimension
tensor([[ 1, 1, 1, 1],
[ 2, 2, 2, 2],
[ 3, 3, 3, 3]])
""")
add_docstr_all('expand_as',
r"""
expand_as(other) -> Tensor
Expand this tensor to the same size as :attr:`other`.
``self.expand_as(other)`` is equivalent to ``self.expand(other.size())``.
Please see :meth:`~Tensor.expand` for more information about ``expand``.
Args:
other (:class:`torch.Tensor`): The result tensor has the same size
as :attr:`other`.
""")
add_docstr_all('sum_to_size',
r"""
sum_to_size(*size) -> Tensor
Sum ``this`` tensor to :attr:`size`.
:attr:`size` must be broadcastable to ``this`` tensor size.
Args:
other (:class:`torch.Tensor`): The result tensor has the same size
as :attr:`other`.
""")
add_docstr_all('zero_',
r"""
zero_() -> Tensor
Fills :attr:`self` tensor with zeros.
""")
add_docstr_all('matmul',
r"""
matmul(tensor2) -> Tensor
See :func:`torch.matmul`
""")
add_docstr_all('chunk',
r"""
chunk(chunks, dim=0) -> List of Tensors
See :func:`torch.chunk`
""")
add_docstr_all('stft',
r"""
stft(frame_length, hop, fft_size=None, return_onesided=True, window=None, pad_end=0) -> Tensor
See :func:`torch.stft`
""")
add_docstr_all('fft',
r"""
fft(signal_ndim, normalized=False) -> Tensor
See :func:`torch.fft`
""")
add_docstr_all('ifft',
r"""
ifft(signal_ndim, normalized=False) -> Tensor
See :func:`torch.ifft`
""")
add_docstr_all('rfft',
r"""
rfft(signal_ndim, normalized=False, onesided=True) -> Tensor
See :func:`torch.rfft`
""")
add_docstr_all('irfft',
r"""
irfft(signal_ndim, normalized=False, onesided=True, signal_sizes=None) -> Tensor
See :func:`torch.irfft`
""")
add_docstr_all('det',
r"""
det() -> Tensor
See :func:`torch.det`
""")
add_docstr_all('where',
r"""
where(condition, y) -> Tensor
``self.where(condition, y)`` is equivalent to ``torch.where(condition, self, y)``.
See :func:`torch.where`
""")
add_docstr_all('logdet',
r"""
logdet() -> Tensor
See :func:`torch.logdet`
""")
add_docstr_all('slogdet',
r"""
slogdet() -> (Tensor, Tensor)
See :func:`torch.slogdet`
""")
add_docstr_all('unbind',
r"""
unbind(dim=0) -> seq
See :func:`torch.unbind`
""")
add_docstr_all('pin_memory',
r"""
pin_memory() -> Tensor
Copies the tensor to pinned memory, if it's not already pinned.
""")
add_docstr_all('pinverse',
r"""
pinverse() -> Tensor
See :func:`torch.pinverse`
""")
add_docstr_all('index_add',
r"""
index_add(dim, index, tensor) -> Tensor
Out-of-place version of :meth:`torch.Tensor.index_add_`
""")
add_docstr_all('index_copy',
r"""
index_copy(dim, index, tensor) -> Tensor
Out-of-place version of :meth:`torch.Tensor.index_copy_`
""")
add_docstr_all('index_fill',
r"""
index_fill(dim, index, value) -> Tensor
Out-of-place version of :meth:`torch.Tensor.index_fill_`
""")
add_docstr_all('scatter',
r"""
scatter(dim, index, source) -> Tensor
Out-of-place version of :meth:`torch.Tensor.scatter_`
""")
add_docstr_all('scatter_add',
r"""
scatter_add(dim, index, source) -> Tensor
Out-of-place version of :meth:`torch.Tensor.scatter_add_`
""")
add_docstr_all('masked_scatter',
r"""
masked_scatter(mask, tensor) -> Tensor
Out-of-place version of :meth:`torch.Tensor.masked_scatter_`
""")
add_docstr_all('masked_fill',
r"""
masked_fill(mask, value) -> Tensor
Out-of-place version of :meth:`torch.Tensor.masked_fill_`
""")
add_docstr_all('grad',
r"""
This attribute is ``None`` by default and becomes a Tensor the first time a call to
:func:`backward` computes gradients for ``self``.
The attribute will then contain the gradients computed and future calls to
:func:`backward` will accumulate (add) gradients into it.
""")
add_docstr_all('requires_grad',
r"""
Is ``True`` if gradients need to be computed for this Tensor, ``False`` otherwise.
.. note::
The fact that gradients need to be computed for a Tensor do not mean that the :attr:`grad`
attribute will be populated, see :attr:`is_leaf` for more details.
""")
add_docstr_all('is_leaf',
r"""
All Tensors that have :attr:`requires_grad` which is ``False`` will be leaf Tensors by convention.
For Tensors that have :attr:`requires_grad` which is ``True``, they will be leaf Tensors if they were
created by the user. This means that they are not the result of an operation and so
:attr:`grad_fn` is None.
Only leaf Tensors will have their :attr:`grad` populated during a call to :func:`backward`.
To get :attr:`grad` populated for non-leaf Tensors, you can use :func:`retain_grad`.
Example::
>>> a = torch.rand(10, requires_grad=True)
>>> a.is_leaf
True
>>> b = torch.rand(10, requires_grad=True).cuda()
>>> b.is_leaf
False
# b was created by the operation that cast a cpu Tensor into a cuda Tensor
>>> c = torch.rand(10, requires_grad=True) + 2
>>> c.is_leaf
False
# c was created by the addition operation
>>> d = torch.rand(10).cuda()
>>> d.is_leaf
True
# d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)
>>> e = torch.rand(10).cuda().requires_grad_()
>>> e.is_leaf
True
# e requires gradients and has no operations creating it
>>> f = torch.rand(10, requires_grad=True, device="cuda")
>>> f.is_leaf
True
# f requires grad, has no operation creating it
""")
add_docstr_all('is_cuda',
r"""
Is ``True`` if the Tensor is stored on the GPU, ``False`` otherwise.
""")
add_docstr_all('device',
r"""
Is the :class:`torch.device` where this Tensor is.
""")
add_docstr_all('ndim',
r"""
Alias for :meth:`~Tensor.dim()`
""")
add_docstr_all('T',
r"""
Is this Tensor with its dimensions reversed.
If ``n`` is the number of dimensions in ``x``,
``x.T`` is equivalent to ``x.permute(n-1, n-2, ..., 0)``.
""")
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