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Summary: Fixes https://github.com/pytorch/pytorch/issues/41637 This is regularized lower incomplete gamma function, equivalent to scipy's `gammainc` and tensorflow `igamma`. cc fritzo mruberry Pull Request resolved: https://github.com/pytorch/pytorch/pull/46183 Reviewed By: gchanan Differential Revision: D24479126 Pulled By: mruberry fbshipit-source-id: fdf8ea289fe4ca1b408810732192411e948fcdfe
4486 lines
101 KiB
Python
4486 lines
101 KiB
Python
"""Adds docstrings to Tensor functions"""
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import torch._C
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from torch._C import _add_docstr as add_docstr
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from ._torch_docs import parse_kwargs
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from ._torch_docs import reproducibility_notes
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def add_docstr_all(method, docstr):
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add_docstr(getattr(torch._C._TensorBase, method), docstr)
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common_args = parse_kwargs("""
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memory_format (:class:`torch.memory_format`, optional): the desired memory format of
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returned Tensor. Default: ``torch.preserve_format``.
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""")
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new_common_args = parse_kwargs("""
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size (int...): a list, tuple, or :class:`torch.Size` of integers defining the
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shape of the output tensor.
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dtype (:class:`torch.dtype`, optional): the desired type of returned tensor.
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Default: if None, same :class:`torch.dtype` as this tensor.
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device (:class:`torch.device`, optional): the desired device of returned tensor.
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Default: if None, same :class:`torch.device` as this tensor.
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requires_grad (bool, optional): If autograd should record operations on the
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returned tensor. Default: ``False``.
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pin_memory (bool, optional): If set, returned tensor would be allocated in
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the pinned memory. Works only for CPU tensors. Default: ``False``.
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""")
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add_docstr_all('new_tensor',
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r"""
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new_tensor(data, dtype=None, device=None, requires_grad=False) -> Tensor
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Returns a new Tensor with :attr:`data` as the tensor data.
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By default, the returned Tensor has the same :class:`torch.dtype` and
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:class:`torch.device` as this tensor.
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.. warning::
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:func:`new_tensor` always copies :attr:`data`. If you have a Tensor
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``data`` and want to avoid a copy, use :func:`torch.Tensor.requires_grad_`
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or :func:`torch.Tensor.detach`.
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If you have a numpy array and want to avoid a copy, use
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:func:`torch.from_numpy`.
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.. warning::
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When data is a tensor `x`, :func:`new_tensor()` reads out 'the data' from whatever it is passed,
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and constructs a leaf variable. Therefore ``tensor.new_tensor(x)`` is equivalent to ``x.clone().detach()``
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and ``tensor.new_tensor(x, requires_grad=True)`` is equivalent to ``x.clone().detach().requires_grad_(True)``.
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The equivalents using ``clone()`` and ``detach()`` are recommended.
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Args:
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data (array_like): The returned Tensor copies :attr:`data`.
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{dtype}
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{device}
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{requires_grad}
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Example::
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>>> tensor = torch.ones((2,), dtype=torch.int8)
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>>> data = [[0, 1], [2, 3]]
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>>> tensor.new_tensor(data)
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tensor([[ 0, 1],
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[ 2, 3]], dtype=torch.int8)
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""".format(**new_common_args))
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add_docstr_all('new_full',
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r"""
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new_full(size, fill_value, dtype=None, device=None, requires_grad=False) -> Tensor
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Returns a Tensor of size :attr:`size` filled with :attr:`fill_value`.
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By default, the returned Tensor has the same :class:`torch.dtype` and
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:class:`torch.device` as this tensor.
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Args:
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fill_value (scalar): the number to fill the output tensor with.
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{dtype}
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{device}
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{requires_grad}
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Example::
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>>> tensor = torch.ones((2,), dtype=torch.float64)
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>>> tensor.new_full((3, 4), 3.141592)
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tensor([[ 3.1416, 3.1416, 3.1416, 3.1416],
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[ 3.1416, 3.1416, 3.1416, 3.1416],
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[ 3.1416, 3.1416, 3.1416, 3.1416]], dtype=torch.float64)
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""".format(**new_common_args))
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add_docstr_all('new_empty',
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r"""
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new_empty(size, dtype=None, device=None, requires_grad=False) -> Tensor
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Returns a Tensor of size :attr:`size` filled with uninitialized data.
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By default, the returned Tensor has the same :class:`torch.dtype` and
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:class:`torch.device` as this tensor.
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Args:
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{dtype}
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{device}
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{requires_grad}
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Example::
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>>> tensor = torch.ones(())
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>>> tensor.new_empty((2, 3))
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tensor([[ 5.8182e-18, 4.5765e-41, -1.0545e+30],
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[ 3.0949e-41, 4.4842e-44, 0.0000e+00]])
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""".format(**new_common_args))
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add_docstr_all('new_ones',
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r"""
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new_ones(size, dtype=None, device=None, requires_grad=False) -> Tensor
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Returns a Tensor of size :attr:`size` filled with ``1``.
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By default, the returned Tensor has the same :class:`torch.dtype` and
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:class:`torch.device` as this tensor.
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Args:
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size (int...): a list, tuple, or :class:`torch.Size` of integers defining the
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shape of the output tensor.
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{dtype}
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{device}
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{requires_grad}
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Example::
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>>> tensor = torch.tensor((), dtype=torch.int32)
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>>> tensor.new_ones((2, 3))
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tensor([[ 1, 1, 1],
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[ 1, 1, 1]], dtype=torch.int32)
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""".format(**new_common_args))
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add_docstr_all('new_zeros',
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r"""
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new_zeros(size, dtype=None, device=None, requires_grad=False) -> Tensor
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Returns a Tensor of size :attr:`size` filled with ``0``.
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By default, the returned Tensor has the same :class:`torch.dtype` and
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:class:`torch.device` as this tensor.
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Args:
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size (int...): a list, tuple, or :class:`torch.Size` of integers defining the
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shape of the output tensor.
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{dtype}
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{device}
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{requires_grad}
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Example::
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>>> tensor = torch.tensor((), dtype=torch.float64)
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>>> tensor.new_zeros((2, 3))
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tensor([[ 0., 0., 0.],
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[ 0., 0., 0.]], dtype=torch.float64)
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""".format(**new_common_args))
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add_docstr_all('abs',
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r"""
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abs() -> Tensor
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See :func:`torch.abs`
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""")
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add_docstr_all('abs_',
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r"""
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abs_() -> Tensor
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In-place version of :meth:`~Tensor.abs`
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""")
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add_docstr_all('absolute',
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r"""
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absolute() -> Tensor
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Alias for :func:`abs`
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""")
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add_docstr_all('absolute_',
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r"""
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absolute_() -> Tensor
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In-place version of :meth:`~Tensor.absolute`
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Alias for :func:`abs_`
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""")
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add_docstr_all('acos',
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r"""
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acos() -> Tensor
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See :func:`torch.acos`
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""")
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add_docstr_all('acos_',
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r"""
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acos_() -> Tensor
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In-place version of :meth:`~Tensor.acos`
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""")
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add_docstr_all('arccos', r"""
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arccos() -> Tensor
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See :func:`torch.arccos`
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""")
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add_docstr_all('arccos_', r"""
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arccos_() -> Tensor
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In-place version of :meth:`~Tensor.arccos`
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""")
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add_docstr_all('acosh',
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r"""
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acosh() -> Tensor
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See :func:`torch.acosh`
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""")
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add_docstr_all('acosh_',
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r"""
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acosh_() -> Tensor
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In-place version of :meth:`~Tensor.acosh`
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""")
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add_docstr_all('arccosh', r"""
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acosh() -> Tensor
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See :func:`torch.arccosh`
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""")
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add_docstr_all('arccosh_', r"""
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acosh_() -> Tensor
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In-place version of :meth:`~Tensor.arccosh`
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""")
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add_docstr_all('add',
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r"""
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add(other, *, alpha=1) -> Tensor
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Add a scalar or tensor to :attr:`self` tensor. If both :attr:`alpha`
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and :attr:`other` are specified, each element of :attr:`other` is scaled by
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:attr:`alpha` before being used.
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When :attr:`other` is a tensor, the shape of :attr:`other` must be
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:ref:`broadcastable <broadcasting-semantics>` with the shape of the underlying
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tensor
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See :func:`torch.add`
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""")
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add_docstr_all('add_',
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r"""
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add_(other, *, alpha=1) -> Tensor
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In-place version of :meth:`~Tensor.add`
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""")
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add_docstr_all('addbmm',
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r"""
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addbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor
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See :func:`torch.addbmm`
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""")
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add_docstr_all('addbmm_',
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r"""
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addbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor
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In-place version of :meth:`~Tensor.addbmm`
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""")
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add_docstr_all('addcdiv',
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r"""
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addcdiv(tensor1, tensor2, *, value=1) -> Tensor
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See :func:`torch.addcdiv`
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""")
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add_docstr_all('addcdiv_',
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r"""
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addcdiv_(tensor1, tensor2, *, value=1) -> Tensor
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In-place version of :meth:`~Tensor.addcdiv`
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""")
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add_docstr_all('addcmul',
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r"""
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addcmul(tensor1, tensor2, *, value=1) -> Tensor
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See :func:`torch.addcmul`
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""")
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add_docstr_all('addcmul_',
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r"""
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addcmul_(tensor1, tensor2, *, value=1) -> Tensor
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In-place version of :meth:`~Tensor.addcmul`
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""")
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add_docstr_all('addmm',
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r"""
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addmm(mat1, mat2, *, beta=1, alpha=1) -> Tensor
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See :func:`torch.addmm`
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""")
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add_docstr_all('addmm_',
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r"""
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addmm_(mat1, mat2, *, beta=1, alpha=1) -> Tensor
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In-place version of :meth:`~Tensor.addmm`
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""")
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add_docstr_all('addmv',
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r"""
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addmv(mat, vec, *, beta=1, alpha=1) -> Tensor
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See :func:`torch.addmv`
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""")
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add_docstr_all('addmv_',
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r"""
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addmv_(mat, vec, *, beta=1, alpha=1) -> Tensor
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In-place version of :meth:`~Tensor.addmv`
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""")
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add_docstr_all('sspaddmm',
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r"""
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sspaddmm(mat1, mat2, *, beta=1, alpha=1) -> Tensor
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See :func:`torch.sspaddmm`
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""")
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add_docstr_all('smm',
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r"""
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smm(mat) -> Tensor
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See :func:`torch.smm`
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""")
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add_docstr_all('addr',
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r"""
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addr(vec1, vec2, *, beta=1, alpha=1) -> Tensor
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See :func:`torch.addr`
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""")
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add_docstr_all('addr_',
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r"""
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addr_(vec1, vec2, *, beta=1, alpha=1) -> Tensor
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In-place version of :meth:`~Tensor.addr`
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""")
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add_docstr_all('align_as',
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r"""
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align_as(other) -> Tensor
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Permutes the dimensions of the :attr:`self` tensor to match the dimension order
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in the :attr:`other` tensor, adding size-one dims for any new names.
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This operation is useful for explicit broadcasting by names (see examples).
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All of the dims of :attr:`self` must be named in order to use this method.
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The resulting tensor is a view on the original tensor.
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All dimension names of :attr:`self` must be present in ``other.names``.
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:attr:`other` may contain named dimensions that are not in ``self.names``;
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the output tensor has a size-one dimension for each of those new names.
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To align a tensor to a specific order, use :meth:`~Tensor.align_to`.
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Examples::
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# Example 1: Applying a mask
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>>> mask = torch.randint(2, [127, 128], dtype=torch.bool).refine_names('W', 'H')
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>>> imgs = torch.randn(32, 128, 127, 3, names=('N', 'H', 'W', 'C'))
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>>> imgs.masked_fill_(mask.align_as(imgs), 0)
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# Example 2: Applying a per-channel-scale
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>>> def scale_channels(input, scale):
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>>> scale = scale.refine_names('C')
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>>> return input * scale.align_as(input)
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>>> num_channels = 3
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>>> scale = torch.randn(num_channels, names=('C',))
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>>> imgs = torch.rand(32, 128, 128, num_channels, names=('N', 'H', 'W', 'C'))
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>>> more_imgs = torch.rand(32, num_channels, 128, 128, names=('N', 'C', 'H', 'W'))
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>>> videos = torch.randn(3, num_channels, 128, 128, 128, names=('N', 'C', 'H', 'W', 'D'))
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# scale_channels is agnostic to the dimension order of the input
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>>> scale_channels(imgs, scale)
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>>> scale_channels(more_imgs, scale)
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>>> scale_channels(videos, scale)
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.. warning::
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The named tensor API is experimental and subject to change.
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""")
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add_docstr_all('all',
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r"""
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.. function:: all() -> bool
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Returns True if all elements in the tensor are True, False otherwise.
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Example::
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>>> a = torch.rand(1, 2).bool()
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>>> a
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tensor([[False, True]], dtype=torch.bool)
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>>> a.all()
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tensor(False, dtype=torch.bool)
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.. function:: all(dim, keepdim=False, out=None) -> Tensor
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Returns True if all elements in each row of the tensor in the given
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dimension :attr:`dim` are True, False otherwise.
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If :attr:`keepdim` is ``True``, the output tensor is of the same size as
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:attr:`input` except in the dimension :attr:`dim` where it is of size 1.
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Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting
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in the output tensor having 1 fewer dimension than :attr:`input`.
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Args:
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dim (int): the dimension to reduce
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keepdim (bool): whether the output tensor has :attr:`dim` retained or not
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out (Tensor, optional): the output tensor
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Example::
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>>> a = torch.rand(4, 2).bool()
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>>> a
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tensor([[True, True],
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[True, False],
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[True, True],
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[True, True]], dtype=torch.bool)
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>>> a.all(dim=1)
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tensor([ True, False, True, True], dtype=torch.bool)
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>>> a.all(dim=0)
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tensor([ True, False], dtype=torch.bool)
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""")
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add_docstr_all('allclose',
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r"""
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allclose(other, rtol=1e-05, atol=1e-08, equal_nan=False) -> Tensor
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See :func:`torch.allclose`
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""")
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add_docstr_all('angle',
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r"""
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angle() -> Tensor
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See :func:`torch.angle`
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""")
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add_docstr_all('any',
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r"""
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.. function:: any() -> bool
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Returns True if any elements in the tensor are True, False otherwise.
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Example::
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>>> a = torch.rand(1, 2).bool()
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>>> a
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tensor([[False, True]], dtype=torch.bool)
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>>> a.any()
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tensor(True, dtype=torch.bool)
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.. function:: any(dim, keepdim=False, out=None) -> Tensor
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Returns True if any elements in each row of the tensor in the given
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dimension :attr:`dim` are True, False otherwise.
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If :attr:`keepdim` is ``True``, the output tensor is of the same size as
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:attr:`input` except in the dimension :attr:`dim` where it is of size 1.
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Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting
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in the output tensor having 1 fewer dimension than :attr:`input`.
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Args:
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dim (int): the dimension to reduce
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keepdim (bool): whether the output tensor has :attr:`dim` retained or not
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out (Tensor, optional): the output tensor
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Example::
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>>> a = torch.randn(4, 2) < 0
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>>> a
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tensor([[ True, True],
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[False, True],
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[ True, True],
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[False, False]])
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>>> a.any(1)
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tensor([ True, True, True, False])
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>>> a.any(0)
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tensor([True, True])
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""")
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add_docstr_all('apply_',
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r"""
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apply_(callable) -> Tensor
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Applies the function :attr:`callable` to each element in the tensor, replacing
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each element with the value returned by :attr:`callable`.
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.. note::
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This function only works with CPU tensors and should not be used in code
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sections that require high performance.
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""")
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add_docstr_all('asin', r"""
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asin() -> Tensor
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See :func:`torch.asin`
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""")
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add_docstr_all('asin_',
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r"""
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|
asin_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.asin`
|
|
""")
|
|
|
|
add_docstr_all('arcsin', r"""
|
|
arcsin() -> Tensor
|
|
|
|
See :func:`torch.arcsin`
|
|
""")
|
|
|
|
add_docstr_all('arcsin_', r"""
|
|
arcsin_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.arcsin`
|
|
""")
|
|
|
|
add_docstr_all('asinh', r"""
|
|
asinh() -> Tensor
|
|
|
|
See :func:`torch.asinh`
|
|
""")
|
|
|
|
add_docstr_all('asinh_',
|
|
r"""
|
|
asinh_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.asinh`
|
|
""")
|
|
|
|
add_docstr_all('arcsinh', r"""
|
|
arcsinh() -> Tensor
|
|
|
|
See :func:`torch.arcsinh`
|
|
""")
|
|
|
|
add_docstr_all('arcsinh_', r"""
|
|
arcsinh_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.arcsinh`
|
|
""")
|
|
|
|
add_docstr_all('as_strided', r"""
|
|
as_strided(size, stride, storage_offset=0) -> Tensor
|
|
|
|
See :func:`torch.as_strided`
|
|
""")
|
|
|
|
add_docstr_all('atan', r"""
|
|
atan() -> Tensor
|
|
|
|
See :func:`torch.atan`
|
|
""")
|
|
|
|
add_docstr_all('atan_', r"""
|
|
atan_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.atan`
|
|
""")
|
|
|
|
add_docstr_all('arctan', r"""
|
|
arctan() -> Tensor
|
|
|
|
See :func:`torch.arctan`
|
|
""")
|
|
|
|
add_docstr_all('arctan_', r"""
|
|
arctan_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.arctan`
|
|
""")
|
|
|
|
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('atanh', r"""
|
|
atanh() -> Tensor
|
|
|
|
See :func:`torch.atanh`
|
|
""")
|
|
|
|
add_docstr_all('atanh_', r"""
|
|
atanh_(other) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.atanh`
|
|
""")
|
|
|
|
add_docstr_all('arctanh', r"""
|
|
arctanh() -> Tensor
|
|
|
|
See :func:`torch.arctanh`
|
|
""")
|
|
|
|
add_docstr_all('arctanh_', r"""
|
|
arctanh_(other) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.arctanh`
|
|
""")
|
|
|
|
add_docstr_all('baddbmm',
|
|
r"""
|
|
baddbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor
|
|
|
|
See :func:`torch.baddbmm`
|
|
""")
|
|
|
|
add_docstr_all('baddbmm_',
|
|
r"""
|
|
baddbmm_(batch1, batch2, *, beta=1, alpha=1) -> 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('bitwise_not',
|
|
r"""
|
|
bitwise_not() -> Tensor
|
|
|
|
See :func:`torch.bitwise_not`
|
|
""")
|
|
|
|
add_docstr_all('bitwise_not_',
|
|
r"""
|
|
bitwise_not_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.bitwise_not`
|
|
""")
|
|
|
|
add_docstr_all('bitwise_and',
|
|
r"""
|
|
bitwise_and() -> Tensor
|
|
|
|
See :func:`torch.bitwise_and`
|
|
""")
|
|
|
|
add_docstr_all('bitwise_and_',
|
|
r"""
|
|
bitwise_and_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.bitwise_and`
|
|
""")
|
|
|
|
add_docstr_all('bitwise_or',
|
|
r"""
|
|
bitwise_or() -> Tensor
|
|
|
|
See :func:`torch.bitwise_or`
|
|
""")
|
|
|
|
add_docstr_all('bitwise_or_',
|
|
r"""
|
|
bitwise_or_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.bitwise_or`
|
|
""")
|
|
|
|
add_docstr_all('bitwise_xor',
|
|
r"""
|
|
bitwise_xor() -> Tensor
|
|
|
|
See :func:`torch.bitwise_xor`
|
|
""")
|
|
|
|
add_docstr_all('bitwise_xor_',
|
|
r"""
|
|
bitwise_xor_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.bitwise_xor`
|
|
""")
|
|
|
|
add_docstr_all('logical_and',
|
|
r"""
|
|
logical_and() -> Tensor
|
|
|
|
See :func:`torch.logical_and`
|
|
""")
|
|
|
|
add_docstr_all('logical_and_',
|
|
r"""
|
|
logical_and_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.logical_and`
|
|
""")
|
|
|
|
add_docstr_all('logical_not',
|
|
r"""
|
|
logical_not() -> Tensor
|
|
|
|
See :func:`torch.logical_not`
|
|
""")
|
|
|
|
add_docstr_all('logical_not_',
|
|
r"""
|
|
logical_not_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.logical_not`
|
|
""")
|
|
|
|
add_docstr_all('logical_or',
|
|
r"""
|
|
logical_or() -> Tensor
|
|
|
|
See :func:`torch.logical_or`
|
|
""")
|
|
|
|
add_docstr_all('logical_or_',
|
|
r"""
|
|
logical_or_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.logical_or`
|
|
""")
|
|
|
|
add_docstr_all('logical_xor',
|
|
r"""
|
|
logical_xor() -> Tensor
|
|
|
|
See :func:`torch.logical_xor`
|
|
""")
|
|
|
|
add_docstr_all('logical_xor_',
|
|
r"""
|
|
logical_xor_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.logical_xor`
|
|
""")
|
|
|
|
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('clip', r"""
|
|
clip(min, max) -> Tensor
|
|
|
|
Alias for :meth:`~Tensor.clamp`.
|
|
""")
|
|
|
|
add_docstr_all('clip_', r"""
|
|
clip_(min, max) -> Tensor
|
|
|
|
Alias for :meth:`~Tensor.clamp_`.
|
|
""")
|
|
|
|
add_docstr_all('clone', r"""
|
|
clone(*, memory_format=torch.preserve_format) -> Tensor
|
|
|
|
See :func:`torch.clone`
|
|
""".format(**common_args))
|
|
|
|
add_docstr_all('coalesce',
|
|
r"""
|
|
coalesce() -> Tensor
|
|
|
|
Returns a coalesced copy of :attr:`self` if :attr:`self` is an
|
|
:ref:`uncoalesced tensor <sparse-uncoalesced-coo-docs>`.
|
|
|
|
Returns :attr:`self` if :attr:`self` is a coalesced tensor.
|
|
|
|
.. warning::
|
|
Throws an error if :attr:`self` is not a sparse COO tensor.
|
|
""")
|
|
|
|
add_docstr_all('contiguous',
|
|
r"""
|
|
contiguous(memory_format=torch.contiguous_format) -> Tensor
|
|
|
|
Returns a contiguous in memory tensor containing the same data as :attr:`self` tensor. If
|
|
:attr:`self` tensor is already in the specified memory format, this function returns the
|
|
:attr:`self` tensor.
|
|
|
|
Args:
|
|
memory_format (:class:`torch.memory_format`, optional): the desired memory format of
|
|
returned Tensor. Default: ``torch.contiguous_format``.
|
|
""")
|
|
|
|
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('conj',
|
|
r"""
|
|
conj() -> Tensor
|
|
|
|
See :func:`torch.conj`
|
|
""")
|
|
|
|
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(memory_format=torch.preserve_format) -> 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.
|
|
|
|
Args:
|
|
{memory_format}
|
|
|
|
""".format(**common_args))
|
|
|
|
add_docstr_all('count_nonzero',
|
|
r"""
|
|
count_nonzero(dim=None) -> Tensor
|
|
|
|
See :func:`torch.count_nonzero`
|
|
""")
|
|
|
|
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, memory_format=torch.preserve_format) -> 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``.
|
|
{memory_format}
|
|
""".format(**common_args))
|
|
|
|
add_docstr_all('logcumsumexp',
|
|
r"""
|
|
logcumsumexp(dim) -> Tensor
|
|
|
|
See :func:`torch.logcumsumexp`
|
|
""")
|
|
|
|
add_docstr_all('cummax',
|
|
r"""
|
|
cummax(dim) -> (Tensor, Tensor)
|
|
|
|
See :func:`torch.cummax`
|
|
""")
|
|
|
|
add_docstr_all('cummin',
|
|
r"""
|
|
cummin(dim) -> (Tensor, Tensor)
|
|
|
|
See :func:`torch.cummin`
|
|
""")
|
|
|
|
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
|
|
|
|
Return the number of dense dimensions in a :ref:`sparse tensor <sparse-docs>` :attr:`self`.
|
|
|
|
.. warning::
|
|
Throws an error if :attr:`self` is not a sparse tensor.
|
|
|
|
See also :meth:`Tensor.sparse_dim` and :ref:`hybrid tensors <sparse-hybrid-coo-docs>`.
|
|
""")
|
|
|
|
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(offset=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('fill_diagonal_',
|
|
r"""
|
|
fill_diagonal_(fill_value, wrap=False) -> Tensor
|
|
|
|
Fill the main diagonal of a tensor that has at least 2-dimensions.
|
|
When dims>2, all dimensions of input must be of equal length.
|
|
This function modifies the input tensor in-place, and returns the input tensor.
|
|
|
|
Arguments:
|
|
fill_value (Scalar): the fill value
|
|
wrap (bool): the diagonal 'wrapped' after N columns for tall matrices.
|
|
|
|
Example::
|
|
|
|
>>> a = torch.zeros(3, 3)
|
|
>>> a.fill_diagonal_(5)
|
|
tensor([[5., 0., 0.],
|
|
[0., 5., 0.],
|
|
[0., 0., 5.]])
|
|
>>> b = torch.zeros(7, 3)
|
|
>>> b.fill_diagonal_(5)
|
|
tensor([[5., 0., 0.],
|
|
[0., 5., 0.],
|
|
[0., 0., 5.],
|
|
[0., 0., 0.],
|
|
[0., 0., 0.],
|
|
[0., 0., 0.],
|
|
[0., 0., 0.]])
|
|
>>> c = torch.zeros(7, 3)
|
|
>>> c.fill_diagonal_(5, wrap=True)
|
|
tensor([[5., 0., 0.],
|
|
[0., 5., 0.],
|
|
[0., 0., 5.],
|
|
[0., 0., 0.],
|
|
[5., 0., 0.],
|
|
[0., 5., 0.],
|
|
[0., 0., 5.]])
|
|
|
|
""")
|
|
|
|
add_docstr_all('floor_divide',
|
|
r"""
|
|
floor_divide(value) -> Tensor
|
|
|
|
See :func:`torch.floor_divide`
|
|
""")
|
|
|
|
add_docstr_all('floor_divide_',
|
|
r"""
|
|
floor_divide_(value) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.floor_divide`
|
|
""")
|
|
|
|
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('divide', r"""
|
|
divide(value) -> Tensor
|
|
|
|
See :func:`torch.divide`
|
|
""")
|
|
|
|
add_docstr_all('divide_', r"""
|
|
divide_(value) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.divide`
|
|
""")
|
|
|
|
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('exp2',
|
|
r"""
|
|
exp2() -> Tensor
|
|
|
|
See :func:`torch.exp2`
|
|
""")
|
|
|
|
add_docstr_all('exp2_',
|
|
r"""
|
|
exp2_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.exp2`
|
|
""")
|
|
|
|
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('fliplr',
|
|
r"""
|
|
fliplr() -> Tensor
|
|
|
|
See :func:`torch.fliplr`
|
|
""")
|
|
|
|
add_docstr_all('flipud',
|
|
r"""
|
|
flipud() -> Tensor
|
|
|
|
See :func:`torch.flipud`
|
|
""")
|
|
|
|
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('gcd',
|
|
r"""
|
|
gcd(other) -> Tensor
|
|
|
|
See :func:`torch.gcd`
|
|
""")
|
|
|
|
add_docstr_all('gcd_',
|
|
r"""
|
|
gcd_(other) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.gcd`
|
|
""")
|
|
|
|
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('greater_equal', r"""
|
|
greater_equal(other) -> Tensor
|
|
|
|
See :func:`torch.greater_equal`.
|
|
""")
|
|
|
|
add_docstr_all('greater_equal_', r"""
|
|
greater_equal_(other) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.greater_equal`.
|
|
""")
|
|
|
|
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('outer', r"""
|
|
outer(vec2) -> Tensor
|
|
|
|
See :func:`torch.outer`.
|
|
""")
|
|
|
|
add_docstr_all('hypot',
|
|
r"""
|
|
hypot(other) -> Tensor
|
|
|
|
See :func:`torch.hypot`
|
|
""")
|
|
|
|
add_docstr_all('hypot_',
|
|
r"""
|
|
hypot_(other) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.hypot`
|
|
""")
|
|
|
|
add_docstr_all('i0',
|
|
r"""
|
|
i0() -> Tensor
|
|
|
|
See :func:`torch.i0`
|
|
""")
|
|
|
|
add_docstr_all('i0_',
|
|
r"""
|
|
i0_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.i0`
|
|
""")
|
|
|
|
add_docstr_all('igamma',
|
|
r"""
|
|
igamma(other) -> Tensor
|
|
|
|
See :func:`torch.igamma`
|
|
""")
|
|
|
|
add_docstr_all('igamma_',
|
|
r"""
|
|
igamma_(other) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.igamma`
|
|
""")
|
|
|
|
add_docstr_all('indices',
|
|
r"""
|
|
indices() -> Tensor
|
|
|
|
Return the indices tensor of a :ref:`sparse COO tensor <sparse-coo-docs>`.
|
|
|
|
.. warning::
|
|
Throws an error if :attr:`self` is not a sparse COO tensor.
|
|
|
|
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
|
|
|
|
Return the values tensor of a :ref:`sparse COO tensor <sparse-coo-docs>`.
|
|
|
|
.. warning::
|
|
Throws an error if :attr:`self` is not a sparse COO tensor.
|
|
|
|
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('greater', r"""
|
|
greater(other) -> Tensor
|
|
|
|
See :func:`torch.greater`.
|
|
""")
|
|
|
|
add_docstr_all('greater_', r"""
|
|
greater_(other) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.greater`.
|
|
""")
|
|
|
|
add_docstr_all('has_names',
|
|
r"""
|
|
Is ``True`` if any of this tensor's dimensions are named. Otherwise, is ``False``.
|
|
""")
|
|
|
|
add_docstr_all('hardshrink',
|
|
r"""
|
|
hardshrink(lambd=0.5) -> Tensor
|
|
|
|
See :func:`torch.nn.functional.hardshrink`
|
|
""")
|
|
|
|
add_docstr_all('heaviside',
|
|
r"""
|
|
heaviside(values) -> Tensor
|
|
|
|
See :func:`torch.heaviside`
|
|
""")
|
|
|
|
add_docstr_all('heaviside_',
|
|
r"""
|
|
heaviside_(values) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.heaviside`
|
|
""")
|
|
|
|
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.
|
|
|
|
Note:
|
|
{forward_reproducibility_note}
|
|
|
|
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.]])
|
|
""".format(**reproducibility_notes))
|
|
|
|
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:`value` 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(tensor1, indices, value, accumulate=False) -> Tensor
|
|
|
|
Out-place version of :meth:`~Tensor.index_put_`.
|
|
`tensor1` corresponds to `self` in :meth:`torch.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(mask) -> Tensor
|
|
|
|
Returns a new :ref:`sparse tensor <sparse-docs>` with values from a
|
|
strided tensor :attr:`self` filtered by the indices of the sparse
|
|
tensor :attr:`mask`. The values of :attr:`mask` sparse tensor are
|
|
ignored. :attr:`self` and :attr:`mask` tensors must have the same
|
|
shape.
|
|
|
|
.. note::
|
|
|
|
The returned sparse tensor has the same indices as the sparse tensor
|
|
:attr:`mask`, even when the corresponding values in :attr:`self` are
|
|
zeros.
|
|
|
|
Args:
|
|
mask (Tensor): a sparse tensor whose indices are used as a filter
|
|
|
|
Example::
|
|
|
|
>>> nse = 5
|
|
>>> dims = (5, 5, 2, 2)
|
|
>>> I = torch.cat([torch.randint(0, dims[0], size=(nse,)),
|
|
torch.randint(0, dims[1], size=(nse,))], 0).reshape(2, nse)
|
|
>>> V = torch.randn(nse, dims[2], dims[3])
|
|
>>> S = torch.sparse_coo_tensor(I, V, dims).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('isnan',
|
|
r"""
|
|
isnan() -> Tensor
|
|
|
|
See :func:`torch.isnan`
|
|
""")
|
|
|
|
add_docstr_all('isinf',
|
|
r"""
|
|
isinf() -> Tensor
|
|
|
|
See :func:`torch.isinf`
|
|
""")
|
|
|
|
add_docstr_all('isposinf',
|
|
r"""
|
|
isposinf() -> Tensor
|
|
|
|
See :func:`torch.isposinf`
|
|
""")
|
|
|
|
add_docstr_all('isneginf',
|
|
r"""
|
|
isneginf() -> Tensor
|
|
|
|
See :func:`torch.isneginf`
|
|
""")
|
|
|
|
add_docstr_all('isfinite',
|
|
r"""
|
|
isfinite() -> Tensor
|
|
|
|
See :func:`torch.isfinite`
|
|
""")
|
|
|
|
add_docstr_all('isclose',
|
|
r"""
|
|
isclose(other, rtol=1e-05, atol=1e-08, equal_nan=False) -> Tensor
|
|
|
|
See :func:`torch.isclose`
|
|
""")
|
|
|
|
add_docstr_all('isreal',
|
|
r"""
|
|
isreal() -> Tensor
|
|
|
|
See :func:`torch.isreal`
|
|
""")
|
|
|
|
add_docstr_all('is_coalesced',
|
|
r"""
|
|
is_coalesced() -> bool
|
|
|
|
Returns ``True`` if :attr:`self` is a :ref:`sparse COO tensor
|
|
<sparse-coo-docs>` that is coalesced, ``False`` otherwise.
|
|
|
|
.. warning::
|
|
Throws an error if :attr:`self` is not a sparse COO tensor.
|
|
|
|
See :meth:`coalesce` and :ref:`uncoalesced tensors <sparse-uncoalesced-coo-docs>`.
|
|
""")
|
|
|
|
add_docstr_all('is_contiguous',
|
|
r"""
|
|
is_contiguous(memory_format=torch.contiguous_format) -> bool
|
|
|
|
Returns True if :attr:`self` tensor is contiguous in memory in the order specified
|
|
by memory format.
|
|
|
|
Args:
|
|
memory_format (:class:`torch.memory_format`, optional): Specifies memory allocation
|
|
order. Default: ``torch.contiguous_format``.
|
|
""")
|
|
|
|
add_docstr_all('is_pinned',
|
|
r"""
|
|
Returns true if this tensor resides in pinned memory.
|
|
""")
|
|
|
|
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_complex',
|
|
r"""
|
|
is_complex() -> bool
|
|
|
|
Returns True if the data type of :attr:`self` is a complex 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 both tensors are pointing to the exact same memory (same
|
|
storage, offset, size and stride).
|
|
""")
|
|
|
|
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('lcm',
|
|
r"""
|
|
lcm(other) -> Tensor
|
|
|
|
See :func:`torch.lcm`
|
|
""")
|
|
|
|
add_docstr_all('lcm_',
|
|
r"""
|
|
lcm_(other) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.lcm`
|
|
""")
|
|
|
|
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('less_equal', r"""
|
|
less_equal(other) -> Tensor
|
|
|
|
See :func:`torch.less_equal`.
|
|
""")
|
|
|
|
add_docstr_all('less_equal_', r"""
|
|
less_equal_(other) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.less_equal`.
|
|
""")
|
|
|
|
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('lgamma',
|
|
r"""
|
|
lgamma() -> Tensor
|
|
|
|
See :func:`torch.lgamma`
|
|
""")
|
|
|
|
add_docstr_all('lgamma_', r"""
|
|
lgamma_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.lgamma`
|
|
""")
|
|
|
|
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('logaddexp',
|
|
r"""
|
|
logaddexp(other) -> Tensor
|
|
|
|
See :func:`torch.logaddexp`
|
|
""")
|
|
|
|
add_docstr_all('logaddexp2',
|
|
r"""
|
|
logaddexp2(other) -> Tensor
|
|
|
|
See :func:`torch.logaddexp2`
|
|
""")
|
|
|
|
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('lstsq',
|
|
r"""
|
|
lstsq(A) -> (Tensor, Tensor)
|
|
|
|
See :func:`torch.lstsq`
|
|
""")
|
|
|
|
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('less', r"""
|
|
lt(other) -> Tensor
|
|
|
|
See :func:`torch.less`.
|
|
""")
|
|
|
|
add_docstr_all('less_', r"""
|
|
less_(other) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.less`.
|
|
""")
|
|
|
|
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 True.
|
|
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 (BoolTensor): the boolean 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
|
|
True. The shape of :attr:`mask` must be
|
|
:ref:`broadcastable <broadcasting-semantics>` with the shape of the underlying
|
|
tensor.
|
|
|
|
Args:
|
|
mask (BoolTensor): the boolean 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('matrix_exp',
|
|
r"""
|
|
matrix_exp() -> Tensor
|
|
|
|
See :func:`torch.matrix_exp`
|
|
""")
|
|
|
|
add_docstr_all('max',
|
|
r"""
|
|
max(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)
|
|
|
|
See :func:`torch.max`
|
|
""")
|
|
|
|
add_docstr_all('amax',
|
|
r"""
|
|
amax(dim=None, keepdim=False) -> Tensor
|
|
|
|
See :func:`torch.amax`
|
|
""")
|
|
|
|
add_docstr_all('maximum',
|
|
r"""
|
|
maximum(other) -> Tensor
|
|
|
|
See :func:`torch.maximum`
|
|
""")
|
|
|
|
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('nanmedian',
|
|
r"""
|
|
nanmedian(dim=None, keepdim=False) -> (Tensor, LongTensor)
|
|
|
|
See :func:`torch.nanmedian`
|
|
""")
|
|
|
|
add_docstr_all('min',
|
|
r"""
|
|
min(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)
|
|
|
|
See :func:`torch.min`
|
|
""")
|
|
|
|
add_docstr_all('amin',
|
|
r"""
|
|
amin(dim=None, keepdim=False) -> Tensor
|
|
|
|
See :func:`torch.amin`
|
|
""")
|
|
|
|
add_docstr_all('minimum',
|
|
r"""
|
|
minimum(other) -> Tensor
|
|
|
|
See :func:`torch.minimum`
|
|
""")
|
|
|
|
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('movedim', r"""
|
|
movedim(source, destination) -> Tensor
|
|
|
|
See :func:`torch.movedim`
|
|
""")
|
|
|
|
add_docstr_all('mul', r"""
|
|
mul(value) -> Tensor
|
|
|
|
See :func:`torch.mul`.
|
|
""")
|
|
|
|
add_docstr_all('mul_', r"""
|
|
mul_(value) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.mul`.
|
|
""")
|
|
|
|
add_docstr_all('multiply', r"""
|
|
multiply(value) -> Tensor
|
|
|
|
See :func:`torch.multiply`.
|
|
""")
|
|
|
|
add_docstr_all('multiply_', r"""
|
|
multiply_(value) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.multiply`.
|
|
""")
|
|
|
|
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('nan_to_num', r"""
|
|
nan_to_num(nan=0.0, posinf=None, neginf=None) -> Tensor
|
|
|
|
See :func:`torch.nan_to_num`.
|
|
""")
|
|
|
|
add_docstr_all('nan_to_num_', r"""
|
|
nan_to_num_(nan=0.0, posinf=None, neginf=None) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.nan_to_num`.
|
|
""")
|
|
|
|
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('not_equal', r"""
|
|
not_equal(other) -> Tensor
|
|
|
|
See :func:`torch.not_equal`.
|
|
""")
|
|
|
|
add_docstr_all('not_equal_', r"""
|
|
not_equal_(other) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.not_equal`.
|
|
""")
|
|
|
|
add_docstr_all('neg',
|
|
r"""
|
|
neg() -> Tensor
|
|
|
|
See :func:`torch.neg`
|
|
""")
|
|
|
|
add_docstr_all('negative',
|
|
r"""
|
|
negative() -> Tensor
|
|
|
|
See :func:`torch.negative`
|
|
""")
|
|
|
|
add_docstr_all('neg_',
|
|
r"""
|
|
neg_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.neg`
|
|
""")
|
|
|
|
add_docstr_all('negative_',
|
|
r"""
|
|
negative_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.negative`
|
|
""")
|
|
|
|
add_docstr_all('nelement',
|
|
r"""
|
|
nelement() -> int
|
|
|
|
Alias for :meth:`~Tensor.numel`
|
|
""")
|
|
|
|
add_docstr_all('nextafter',
|
|
r"""
|
|
nextafter(other) -> Tensor
|
|
See :func:`torch.nextafter`
|
|
""")
|
|
|
|
add_docstr_all('nextafter_',
|
|
r"""
|
|
nextafter_(other) -> Tensor
|
|
In-place version of :meth:`~Tensor.nextafter`
|
|
""")
|
|
|
|
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
|
|
|
|
Returns a view of the original tensor with its dimensions permuted.
|
|
|
|
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('polygamma',
|
|
r"""
|
|
polygamma(n) -> Tensor
|
|
|
|
See :func:`torch.polygamma`
|
|
""")
|
|
|
|
add_docstr_all('polygamma_',
|
|
r"""
|
|
polygamma_(n) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.polygamma`
|
|
""")
|
|
|
|
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('qscheme',
|
|
r"""
|
|
qscheme() -> torch.qscheme
|
|
|
|
Returns the quantization scheme of a given QTensor.
|
|
""")
|
|
|
|
add_docstr_all('quantile',
|
|
r"""
|
|
quantile(q, dim=None, keepdim=False) -> Tensor
|
|
|
|
See :func:`torch.quantile`
|
|
""")
|
|
|
|
add_docstr_all('nanquantile',
|
|
r"""
|
|
nanquantile(q, dim=None, keepdim=False) -> Tensor
|
|
|
|
See :func:`torch.nanquantile`
|
|
""")
|
|
|
|
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('q_per_channel_scales',
|
|
r"""
|
|
q_per_channel_scales() -> Tensor
|
|
|
|
Given a Tensor quantized by linear (affine) per-channel quantization,
|
|
returns a Tensor of scales of the underlying quantizer. It has the number of
|
|
elements that matches the corresponding dimensions (from q_per_channel_axis) of
|
|
the tensor.
|
|
""")
|
|
|
|
add_docstr_all('q_per_channel_zero_points',
|
|
r"""
|
|
q_per_channel_zero_points() -> Tensor
|
|
|
|
Given a Tensor quantized by linear (affine) per-channel quantization,
|
|
returns a tensor of zero_points of the underlying quantizer. It has the number of
|
|
elements that matches the corresponding dimensions (from q_per_channel_axis) of
|
|
the tensor.
|
|
""")
|
|
|
|
add_docstr_all('q_per_channel_axis',
|
|
r"""
|
|
q_per_channel_axis() -> int
|
|
|
|
Given a Tensor quantized by linear (affine) per-channel quantization,
|
|
returns the index of dimension on which per-channel quantization is applied.
|
|
""")
|
|
|
|
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('rad2deg',
|
|
r"""
|
|
rad2deg() -> Tensor
|
|
|
|
See :func:`torch.rad2deg`
|
|
""")
|
|
|
|
add_docstr_all('rad2deg_',
|
|
r"""
|
|
rad2deg_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.rad2deg`
|
|
""")
|
|
|
|
add_docstr_all('deg2rad',
|
|
r"""
|
|
deg2rad() -> Tensor
|
|
|
|
See :func:`torch.deg2rad`
|
|
""")
|
|
|
|
add_docstr_all('deg2rad_',
|
|
r"""
|
|
deg2rad_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.deg2rad`
|
|
""")
|
|
|
|
add_docstr_all('ravel',
|
|
r"""
|
|
ravel(input) -> Tensor
|
|
|
|
see :func:`torch.ravel`
|
|
""")
|
|
|
|
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('record_stream',
|
|
r"""
|
|
record_stream(stream)
|
|
|
|
Ensures that the tensor memory is not reused for another tensor until all
|
|
current work queued on :attr:`stream` are complete.
|
|
|
|
.. note::
|
|
|
|
The caching allocator is aware of only the stream where a tensor was
|
|
allocated. Due to the awareness, it already correctly manages the life
|
|
cycle of tensors on only one stream. But if a tensor is used on a stream
|
|
different from the stream of origin, the allocator might reuse the memory
|
|
unexpectedly. Calling this method lets the allocator know which streams
|
|
have used the tensor.
|
|
|
|
""")
|
|
|
|
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::
|
|
|
|
:meth:`~Tensor.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:`requires_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, memory_format=torch.contiguous_format) -> 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
|
|
memory_format (:class:`torch.memory_format`, optional): the desired memory format of
|
|
Tensor. Default: ``torch.contiguous_format``. Note that memory format of
|
|
:attr:`self` is going to be unaffected if ``self.size()`` matches ``sizes``.
|
|
|
|
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, memory_format=torch.contiguous_format) -> Tensor
|
|
|
|
Resizes the :attr:`self` tensor to be the same size as the specified
|
|
:attr:`tensor`. This is equivalent to ``self.resize_(tensor.size())``.
|
|
|
|
Args:
|
|
memory_format (:class:`torch.memory_format`, optional): the desired memory format of
|
|
Tensor. Default: ``torch.contiguous_format``. Note that memory format of
|
|
:attr:`self` is going to be unaffected if ``self.size()`` matches ``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, reduce=None) -> 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.
|
|
|
|
Additionally accepts an optional :attr:`reduce` argument that allows
|
|
specification of an optional reduction operation, which is applied to all
|
|
values in the tensor :attr:`src` into :attr:`self` at the indicies
|
|
specified in the :attr:`index`. For each value in :attr:`src`, the reduction
|
|
operation is applied to an index in :attr:`self` which is specified by
|
|
its index in :attr:`src` for ``dimension != dim`` and by the corresponding
|
|
value in :attr:`index` for ``dimension = dim``.
|
|
|
|
Given a 3-D tensor and reduction using the multiplication operation, :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
|
|
|
|
Reducing with the addition operation is the same as using
|
|
:meth:`~torch.Tensor.scatter_add_`.
|
|
|
|
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
|
|
reduce (string): reduction operation to apply,
|
|
can be either 'add' or 'multiply'.
|
|
|
|
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]])
|
|
|
|
>>> z = torch.ones(2, 4).scatter_(1, torch.tensor([[2], [3]]), 1.23, reduce='multiply')
|
|
>>> z
|
|
tensor([[1.0000, 1.0000, 1.2300, 1.0000],
|
|
[1.0000, 1.0000, 1.0000, 1.2300]])
|
|
""")
|
|
|
|
add_docstr_all('scatter_add_',
|
|
r"""
|
|
scatter_add_(dim, index, src) -> 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:`src`, it is added to
|
|
an index in :attr:`self` which 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
|
|
|
|
:attr:`self`, :attr:`index` and :attr:`src` 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``.
|
|
|
|
Note:
|
|
{forward_reproducibility_note}
|
|
|
|
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.
|
|
src (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]])
|
|
|
|
""".format(**reproducibility_notes))
|
|
|
|
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 view of the original 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('logit',
|
|
r"""
|
|
logit() -> Tensor
|
|
|
|
See :func:`torch.logit`
|
|
""")
|
|
|
|
add_docstr_all('logit_',
|
|
r"""
|
|
logit_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.logit`
|
|
""")
|
|
|
|
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('signbit',
|
|
r"""
|
|
signbit() -> Tensor
|
|
|
|
See :func:`torch.signbit`
|
|
""")
|
|
|
|
add_docstr_all('sgn',
|
|
r"""
|
|
sgn() -> Tensor
|
|
|
|
See :func:`torch.sgn`
|
|
""")
|
|
|
|
add_docstr_all('sgn_',
|
|
r"""
|
|
sgn_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.sgn`
|
|
""")
|
|
|
|
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
|
|
|
|
Return the number of sparse dimensions in a :ref:`sparse tensor <sparse-docs>` :attr:`self`.
|
|
|
|
.. warning::
|
|
Throws an error if :attr:`self` is not a sparse tensor.
|
|
|
|
See also :meth:`Tensor.dense_dim` and :ref:`hybrid tensors <sparse-hybrid-coo-docs>`.
|
|
""")
|
|
|
|
add_docstr_all('sparse_resize_',
|
|
r"""
|
|
sparse_resize_(size, sparse_dim, dense_dim) -> Tensor
|
|
|
|
Resizes :attr:`self` :ref:`sparse tensor <sparse-docs>` to the desired
|
|
size and the number of sparse and dense dimensions.
|
|
|
|
.. note::
|
|
If the number of specified elements in :attr:`self` is zero, then
|
|
:attr:`size`, :attr:`sparse_dim`, and :attr:`dense_dim` can be any
|
|
size and positive integers such that ``len(size) == sparse_dim +
|
|
dense_dim``.
|
|
|
|
If :attr:`self` specifies one or more elements, however, then each
|
|
dimension in :attr:`size` must not be smaller than the corresponding
|
|
dimension of :attr:`self`, :attr:`sparse_dim` must equal the number
|
|
of sparse dimensions in :attr:`self`, and :attr:`dense_dim` must
|
|
equal the number of dense dimensions in :attr:`self`.
|
|
|
|
.. warning::
|
|
Throws an error if :attr:`self` is not a sparse tensor.
|
|
|
|
Args:
|
|
size (torch.Size): the desired size. If :attr:`self` is non-empty
|
|
sparse tensor, the desired size cannot be smaller than the
|
|
original size.
|
|
sparse_dim (int): the number of sparse dimensions
|
|
dense_dim (int): the number of dense dimensions
|
|
""")
|
|
|
|
add_docstr_all('sparse_resize_and_clear_',
|
|
r"""
|
|
sparse_resize_and_clear_(size, sparse_dim, dense_dim) -> Tensor
|
|
|
|
Removes all specified elements from a :ref:`sparse tensor
|
|
<sparse-docs>` :attr:`self` and resizes :attr:`self` to the desired
|
|
size and the number of sparse and dense dimensions.
|
|
|
|
.. warning:
|
|
Throws an error if :attr:`self` is not a sparse tensor.
|
|
|
|
Args:
|
|
size (torch.Size): the desired size.
|
|
sparse_dim (int): the number of sparse dimensions
|
|
dense_dim (int): the number of dense dimensions
|
|
""")
|
|
|
|
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('square',
|
|
r"""
|
|
square() -> Tensor
|
|
|
|
See :func:`torch.square`
|
|
""")
|
|
|
|
add_docstr_all('square_',
|
|
r"""
|
|
square_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.square`
|
|
""")
|
|
|
|
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(other, *, alpha=1) -> Tensor
|
|
|
|
See :func:`torch.sub`.
|
|
""")
|
|
|
|
add_docstr_all('sub_',
|
|
r"""
|
|
sub_(other, *, alpha=1) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.sub`
|
|
""")
|
|
|
|
add_docstr_all('subtract', r"""
|
|
subtract(other, *, alpha=1) -> Tensor
|
|
|
|
See :func:`torch.subtract`.
|
|
""")
|
|
|
|
add_docstr_all('subtract_', r"""
|
|
subtract_(other, *, alpha=1) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.subtract`.
|
|
""")
|
|
|
|
add_docstr_all('sum',
|
|
r"""
|
|
sum(dim=None, keepdim=False, dtype=None) -> Tensor
|
|
|
|
See :func:`torch.sum`
|
|
""")
|
|
|
|
add_docstr_all('nansum',
|
|
r"""
|
|
nansum(dim=None, keepdim=False, dtype=None) -> Tensor
|
|
|
|
See :func:`torch.nansum`
|
|
""")
|
|
|
|
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, memory_format=torch.preserve_format) -> Tensor
|
|
|
|
Returns a Tensor with the specified :attr:`dtype`
|
|
|
|
Args:
|
|
{memory_format}
|
|
|
|
.. function:: to(device=None, dtype=None, non_blocking=False, copy=False, memory_format=torch.preserve_format) -> 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.
|
|
|
|
Args:
|
|
{memory_format}
|
|
|
|
.. 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')
|
|
""".format(**common_args))
|
|
|
|
add_docstr_all('byte',
|
|
r"""
|
|
byte(memory_format=torch.preserve_format) -> Tensor
|
|
|
|
``self.byte()`` is equivalent to ``self.to(torch.uint8)``. See :func:`to`.
|
|
|
|
Args:
|
|
{memory_format}
|
|
""".format(**common_args))
|
|
|
|
add_docstr_all('bool',
|
|
r"""
|
|
bool(memory_format=torch.preserve_format) -> Tensor
|
|
|
|
``self.bool()`` is equivalent to ``self.to(torch.bool)``. See :func:`to`.
|
|
|
|
Args:
|
|
{memory_format}
|
|
""".format(**common_args))
|
|
|
|
add_docstr_all('char',
|
|
r"""
|
|
char(memory_format=torch.preserve_format) -> Tensor
|
|
|
|
``self.char()`` is equivalent to ``self.to(torch.int8)``. See :func:`to`.
|
|
|
|
Args:
|
|
{memory_format}
|
|
""".format(**common_args))
|
|
|
|
add_docstr_all('bfloat16',
|
|
r"""
|
|
bfloat16(memory_format=torch.preserve_format) -> Tensor
|
|
``self.bfloat16()`` is equivalent to ``self.to(torch.bfloat16)``. See :func:`to`.
|
|
|
|
Args:
|
|
{memory_format}
|
|
""".format(**common_args))
|
|
|
|
add_docstr_all('double',
|
|
r"""
|
|
double(memory_format=torch.preserve_format) -> Tensor
|
|
|
|
``self.double()`` is equivalent to ``self.to(torch.float64)``. See :func:`to`.
|
|
|
|
Args:
|
|
{memory_format}
|
|
""".format(**common_args))
|
|
|
|
add_docstr_all('float',
|
|
r"""
|
|
float(memory_format=torch.preserve_format) -> Tensor
|
|
|
|
``self.float()`` is equivalent to ``self.to(torch.float32)``. See :func:`to`.
|
|
|
|
Args:
|
|
{memory_format}
|
|
""".format(**common_args))
|
|
|
|
add_docstr_all('half',
|
|
r"""
|
|
half(memory_format=torch.preserve_format) -> Tensor
|
|
|
|
``self.half()`` is equivalent to ``self.to(torch.float16)``. See :func:`to`.
|
|
|
|
Args:
|
|
{memory_format}
|
|
""".format(**common_args))
|
|
|
|
add_docstr_all('int',
|
|
r"""
|
|
int(memory_format=torch.preserve_format) -> Tensor
|
|
|
|
``self.int()`` is equivalent to ``self.to(torch.int32)``. See :func:`to`.
|
|
|
|
Args:
|
|
{memory_format}
|
|
""".format(**common_args))
|
|
|
|
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(memory_format=torch.preserve_format) -> Tensor
|
|
|
|
``self.long()`` is equivalent to ``self.to(torch.int64)``. See :func:`to`.
|
|
|
|
Args:
|
|
{memory_format}
|
|
""".format(**common_args))
|
|
|
|
add_docstr_all('short',
|
|
r"""
|
|
short(memory_format=torch.preserve_format) -> Tensor
|
|
|
|
``self.short()`` is equivalent to ``self.to(torch.int16)``. See :func:`to`.
|
|
|
|
Args:
|
|
{memory_format}
|
|
""".format(**common_args))
|
|
|
|
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_dense',
|
|
r"""
|
|
to_dense() -> Tensor
|
|
|
|
Creates a strided copy of :attr:`self`.
|
|
|
|
.. warning::
|
|
Throws an error if :attr:`self` is a strided tensor.
|
|
|
|
Example::
|
|
|
|
>>> s = torch.sparse_coo_tensor(
|
|
torch.tensor([[1, 1],
|
|
[0, 2]]),
|
|
torch.tensor([9, 10]),
|
|
size=(3, 3))
|
|
>>> s.to_dense()
|
|
tensor([[ 0, 0, 0],
|
|
[ 9, 0, 10],
|
|
[ 0, 0, 0]])
|
|
""")
|
|
|
|
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-coo-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('true_divide',
|
|
r"""
|
|
true_divide(value) -> Tensor
|
|
|
|
See :func:`torch.true_divide`
|
|
""")
|
|
|
|
add_docstr_all('true_divide_',
|
|
r"""
|
|
true_divide_(value) -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.true_divide_`
|
|
""")
|
|
|
|
add_docstr_all('trunc',
|
|
r"""
|
|
trunc() -> Tensor
|
|
|
|
See :func:`torch.trunc`
|
|
""")
|
|
|
|
add_docstr_all('fix',
|
|
r"""
|
|
fix() -> Tensor
|
|
|
|
See :func:`torch.fix`.
|
|
""")
|
|
|
|
add_docstr_all('trunc_',
|
|
r"""
|
|
trunc_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.trunc`
|
|
""")
|
|
|
|
add_docstr_all('fix_',
|
|
r"""
|
|
fix_() -> Tensor
|
|
|
|
In-place version of :meth:`~Tensor.fix`
|
|
""")
|
|
|
|
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 view of the original 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('vdot',
|
|
r"""
|
|
dot(other) -> Tensor
|
|
|
|
See :func:`torch.vdot`
|
|
""")
|
|
|
|
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 = d, \dots, d+k-1`,
|
|
|
|
.. math::
|
|
|
|
\text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]
|
|
|
|
Otherwise, it will not be possible to view :attr:`self` tensor as :attr:`shape`
|
|
without copying it (e.g., via :meth:`contiguous`). When it is unclear whether a
|
|
:meth:`view` can be performed, it is advisable to use :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:
|
|
size (int...): a sequence of integers defining the shape of the output tensor.
|
|
""")
|
|
|
|
|
|
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('unsafe_chunk',
|
|
r"""
|
|
unsafe_chunk(chunks, dim=0) -> List of Tensors
|
|
|
|
See :func:`torch.unsafe_chunk`
|
|
""")
|
|
|
|
add_docstr_all('unsafe_split',
|
|
r"""
|
|
unsafe_split(split_size, dim=0) -> List of Tensors
|
|
|
|
See :func:`torch.unsafe_split`
|
|
""")
|
|
|
|
add_docstr_all('tensor_split',
|
|
r"""
|
|
tensor_split(indices_or_sections, dim=0) -> List of Tensors
|
|
|
|
See :func:`torch.tensor_split`
|
|
""")
|
|
|
|
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('istft',
|
|
r"""
|
|
istft(n_fft, hop_length=None, win_length=None, window=None,
|
|
center=True, normalized=False, onesided=True, length=None) -> Tensor
|
|
|
|
See :func:`torch.istft`
|
|
""")
|
|
|
|
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(tensor1, dim, index, tensor2) -> Tensor
|
|
|
|
Out-of-place version of :meth:`torch.Tensor.index_add_`.
|
|
`tensor1` corresponds to `self` in :meth:`torch.Tensor.index_add_`.
|
|
""")
|
|
|
|
add_docstr_all('index_copy',
|
|
r"""
|
|
index_copy(tensor1, dim, index, tensor2) -> Tensor
|
|
|
|
Out-of-place version of :meth:`torch.Tensor.index_copy_`.
|
|
`tensor1` corresponds to `self` in :meth:`torch.Tensor.index_copy_`.
|
|
""")
|
|
|
|
add_docstr_all('index_fill',
|
|
r"""
|
|
index_fill(tensor1, dim, index, value) -> Tensor
|
|
|
|
Out-of-place version of :meth:`torch.Tensor.index_fill_`.
|
|
`tensor1` corresponds to `self` in :meth:`torch.Tensor.index_fill_`.
|
|
""")
|
|
|
|
add_docstr_all('scatter',
|
|
r"""
|
|
scatter(dim, index, src) -> Tensor
|
|
|
|
Out-of-place version of :meth:`torch.Tensor.scatter_`
|
|
""")
|
|
|
|
add_docstr_all('scatter_add',
|
|
r"""
|
|
scatter_add(dim, index, src) -> 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('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('names',
|
|
r"""
|
|
Stores names for each of this tensor's dimensions.
|
|
|
|
``names[idx]`` corresponds to the name of tensor dimension ``idx``.
|
|
Names are either a string if the dimension is named or ``None`` if the
|
|
dimension is unnamed.
|
|
|
|
Dimension names may contain characters or underscore. Furthermore, a dimension
|
|
name must be a valid Python variable name (i.e., does not start with underscore).
|
|
|
|
Tensors may not have two named dimensions with the same name.
|
|
|
|
.. warning::
|
|
The named tensor API is experimental and subject to change.
|
|
|
|
""")
|
|
|
|
add_docstr_all('is_cuda',
|
|
r"""
|
|
Is ``True`` if the Tensor is stored on the GPU, ``False`` otherwise.
|
|
""")
|
|
|
|
add_docstr_all('is_quantized',
|
|
r"""
|
|
Is ``True`` if the Tensor is quantized, ``False`` otherwise.
|
|
""")
|
|
|
|
add_docstr_all('is_meta',
|
|
r"""
|
|
Is ``True`` if the Tensor is a meta tensor, ``False`` otherwise. Meta tensors
|
|
are like normal tensors, but they carry no data.
|
|
""")
|
|
|
|
add_docstr_all('is_sparse',
|
|
r"""
|
|
Is ``True`` if the Tensor uses sparse storage layout, ``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)``.
|
|
""")
|
|
|
|
add_docstr_all('real',
|
|
r"""
|
|
Returns a new tensor containing real values of the :attr:`self` tensor.
|
|
The returned tensor and :attr:`self` share the same underlying storage.
|
|
|
|
.. warning::
|
|
:func:`real` is only supported for tensors with complex dtypes.
|
|
|
|
Example::
|
|
>>> x=torch.randn(4, dtype=torch.cfloat)
|
|
>>> x
|
|
tensor([(0.3100+0.3553j), (-0.5445-0.7896j), (-1.6492-0.0633j), (-0.0638-0.8119j)])
|
|
>>> x.real
|
|
tensor([ 0.3100, -0.5445, -1.6492, -0.0638])
|
|
|
|
""")
|
|
|
|
add_docstr_all('imag',
|
|
r"""
|
|
Returns a new tensor containing imaginary values of the :attr:`self` tensor.
|
|
The returned tensor and :attr:`self` share the same underlying storage.
|
|
|
|
.. warning::
|
|
:func:`imag` is only supported for tensors with complex dtypes.
|
|
|
|
Example::
|
|
>>> x=torch.randn(4, dtype=torch.cfloat)
|
|
>>> x
|
|
tensor([(0.3100+0.3553j), (-0.5445-0.7896j), (-1.6492-0.0633j), (-0.0638-0.8119j)])
|
|
>>> x.imag
|
|
tensor([ 0.3553, -0.7896, -0.0633, -0.8119])
|
|
|
|
""")
|
|
|
|
add_docstr_all('as_subclass',
|
|
r"""
|
|
as_subclass(cls) -> Tensor
|
|
|
|
Makes a ``cls`` instance with the same data pointer as ``self``. Changes
|
|
in the output mirror changes in ``self``, and the output stays attached
|
|
to the autograd graph. ``cls`` must be a subclass of ``Tensor``.
|
|
""")
|