mirror of
https://github.com/zebrajr/pytorch.git
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5d45140d68
Summary: BC-breaking note: This PR changes the behavior of the any and all functions to always return a bool tensor. Previously these functions were only defined on bool and uint8 tensors, and when called on uint8 tensors they would also return a uint8 tensor. (When called on a bool tensor they would return a bool tensor.) PR summary: https://github.com/pytorch/pytorch/pull/44790#issuecomment-725596687 Fixes 2 and 3 Also Fixes https://github.com/pytorch/pytorch/issues/48352 Changes * Output dtype is always `bool` (consistent with numpy) **BC Breaking (Previously used to match the input dtype**) * Uses vectorized version for all dtypes on CPU * Enables test for complex * Update doc for `torch.all` and `torch.any` TODO * [x] Update docs * [x] Benchmark * [x] Raise issue on XLA Pull Request resolved: https://github.com/pytorch/pytorch/pull/47878 Reviewed By: albanD Differential Revision: D25714324 Pulled By: mruberry fbshipit-source-id: a87345f725297524242d69402dfe53060521ea5d
653 lines
19 KiB
ReStructuredText
653 lines
19 KiB
ReStructuredText
.. currentmodule:: torch
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.. _tensor-doc:
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torch.Tensor
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===================================
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A :class:`torch.Tensor` is a multi-dimensional matrix containing elements of
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a single data type.
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Torch defines 10 tensor types with CPU and GPU variants which are as follows:
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========================== =========================================== ============================= ================================
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Data type dtype CPU tensor GPU tensor
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========================== =========================================== ============================= ================================
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32-bit floating point ``torch.float32`` or ``torch.float`` :class:`torch.FloatTensor` :class:`torch.cuda.FloatTensor`
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64-bit floating point ``torch.float64`` or ``torch.double`` :class:`torch.DoubleTensor` :class:`torch.cuda.DoubleTensor`
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16-bit floating point [1]_ ``torch.float16`` or ``torch.half`` :class:`torch.HalfTensor` :class:`torch.cuda.HalfTensor`
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16-bit floating point [2]_ ``torch.bfloat16`` :class:`torch.BFloat16Tensor` :class:`torch.cuda.BFloat16Tensor`
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32-bit complex ``torch.complex32``
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64-bit complex ``torch.complex64``
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128-bit complex ``torch.complex128`` or ``torch.cdouble``
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8-bit integer (unsigned) ``torch.uint8`` :class:`torch.ByteTensor` :class:`torch.cuda.ByteTensor`
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8-bit integer (signed) ``torch.int8`` :class:`torch.CharTensor` :class:`torch.cuda.CharTensor`
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16-bit integer (signed) ``torch.int16`` or ``torch.short`` :class:`torch.ShortTensor` :class:`torch.cuda.ShortTensor`
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32-bit integer (signed) ``torch.int32`` or ``torch.int`` :class:`torch.IntTensor` :class:`torch.cuda.IntTensor`
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64-bit integer (signed) ``torch.int64`` or ``torch.long`` :class:`torch.LongTensor` :class:`torch.cuda.LongTensor`
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Boolean ``torch.bool`` :class:`torch.BoolTensor` :class:`torch.cuda.BoolTensor`
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========================== =========================================== ============================= ================================
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.. [1]
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Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10
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significand bits. Useful when precision is important at the expense of range.
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.. [2]
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Sometimes referred to as Brain Floating Point: uses 1 sign, 8 exponent, and 7
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significand bits. Useful when range is important, since it has the same
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number of exponent bits as ``float32``
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:class:`torch.Tensor` is an alias for the default tensor type (:class:`torch.FloatTensor`).
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A tensor can be constructed from a Python :class:`list` or sequence using the
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:func:`torch.tensor` constructor:
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::
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>>> torch.tensor([[1., -1.], [1., -1.]])
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tensor([[ 1.0000, -1.0000],
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[ 1.0000, -1.0000]])
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>>> torch.tensor(np.array([[1, 2, 3], [4, 5, 6]]))
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tensor([[ 1, 2, 3],
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[ 4, 5, 6]])
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.. warning::
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:func:`torch.tensor` always copies :attr:`data`. If you have a Tensor
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:attr:`data` and just want to change its ``requires_grad`` flag, use
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:meth:`~torch.Tensor.requires_grad_` or
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:meth:`~torch.Tensor.detach` to avoid a copy.
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If you have a numpy array and want to avoid a copy, use
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:func:`torch.as_tensor`.
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A tensor of specific data type can be constructed by passing a
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:class:`torch.dtype` and/or a :class:`torch.device` to a
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constructor or tensor creation op:
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::
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>>> torch.zeros([2, 4], dtype=torch.int32)
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tensor([[ 0, 0, 0, 0],
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[ 0, 0, 0, 0]], dtype=torch.int32)
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>>> cuda0 = torch.device('cuda:0')
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>>> torch.ones([2, 4], dtype=torch.float64, device=cuda0)
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tensor([[ 1.0000, 1.0000, 1.0000, 1.0000],
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[ 1.0000, 1.0000, 1.0000, 1.0000]], dtype=torch.float64, device='cuda:0')
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The contents of a tensor can be accessed and modified using Python's indexing
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and slicing notation:
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::
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>>> x = torch.tensor([[1, 2, 3], [4, 5, 6]])
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>>> print(x[1][2])
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tensor(6)
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>>> x[0][1] = 8
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>>> print(x)
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tensor([[ 1, 8, 3],
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[ 4, 5, 6]])
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Use :meth:`torch.Tensor.item` to get a Python number from a tensor containing a
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single value:
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::
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>>> x = torch.tensor([[1]])
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>>> x
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tensor([[ 1]])
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>>> x.item()
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1
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>>> x = torch.tensor(2.5)
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>>> x
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tensor(2.5000)
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>>> x.item()
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2.5
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A tensor can be created with :attr:`requires_grad=True` so that
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:mod:`torch.autograd` records operations on them for automatic differentiation.
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::
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>>> x = torch.tensor([[1., -1.], [1., 1.]], requires_grad=True)
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>>> out = x.pow(2).sum()
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>>> out.backward()
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>>> x.grad
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tensor([[ 2.0000, -2.0000],
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[ 2.0000, 2.0000]])
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Each tensor has an associated :class:`torch.Storage`, which holds its data.
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The tensor class also provides multi-dimensional, `strided <https://en.wikipedia.org/wiki/Stride_of_an_array>`_
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view of a storage and defines numeric operations on it.
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.. note::
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For more information on tensor views, see :ref:`tensor-view-doc`.
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.. note::
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For more information on the :class:`torch.dtype`, :class:`torch.device`, and
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:class:`torch.layout` attributes of a :class:`torch.Tensor`, see
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:ref:`tensor-attributes-doc`.
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.. note::
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Methods which mutate a tensor are marked with an underscore suffix.
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For example, :func:`torch.FloatTensor.abs_` computes the absolute value
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in-place and returns the modified tensor, while :func:`torch.FloatTensor.abs`
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computes the result in a new tensor.
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.. note::
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To change an existing tensor's :class:`torch.device` and/or :class:`torch.dtype`, consider using
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:meth:`~torch.Tensor.to` method on the tensor.
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.. warning::
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Current implementation of :class:`torch.Tensor` introduces memory overhead,
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thus it might lead to unexpectedly high memory usage in the applications with many tiny tensors.
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If this is your case, consider using one large structure.
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.. class:: Tensor()
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There are a few main ways to create a tensor, depending on your use case.
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- To create a tensor with pre-existing data, use :func:`torch.tensor`.
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- To create a tensor with specific size, use ``torch.*`` tensor creation
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ops (see :ref:`tensor-creation-ops`).
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- To create a tensor with the same size (and similar types) as another tensor,
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use ``torch.*_like`` tensor creation ops
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(see :ref:`tensor-creation-ops`).
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- To create a tensor with similar type but different size as another tensor,
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use ``tensor.new_*`` creation ops.
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.. automethod:: new_tensor
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.. automethod:: new_full
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.. automethod:: new_empty
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.. automethod:: new_ones
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.. automethod:: new_zeros
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.. autoattribute:: is_cuda
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.. autoattribute:: is_quantized
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.. autoattribute:: is_meta
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.. autoattribute:: device
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.. autoattribute:: grad
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:noindex:
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.. autoattribute:: ndim
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.. autoattribute:: T
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.. autoattribute:: real
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.. autoattribute:: imag
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.. automethod:: abs
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.. automethod:: abs_
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.. automethod:: absolute
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.. automethod:: absolute_
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.. automethod:: acos
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.. automethod:: acos_
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.. automethod:: arccos
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.. automethod:: arccos_
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.. automethod:: add
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.. automethod:: add_
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.. automethod:: addbmm
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.. automethod:: addbmm_
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.. automethod:: addcdiv
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.. automethod:: addcdiv_
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.. automethod:: addcmul
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.. automethod:: addcmul_
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.. automethod:: addmm
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.. automethod:: addmm_
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.. automethod:: sspaddmm
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:noindex:
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.. automethod:: addmv
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.. automethod:: addmv_
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.. automethod:: addr
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.. automethod:: addr_
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.. automethod:: allclose
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.. automethod:: amax
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.. automethod:: amin
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.. automethod:: angle
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.. automethod:: apply_
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.. automethod:: argmax
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.. automethod:: argmin
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.. automethod:: argsort
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.. automethod:: asin
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.. automethod:: asin_
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.. automethod:: arcsin
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.. automethod:: arcsin_
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.. automethod:: as_strided
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.. automethod:: atan
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.. automethod:: atan_
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.. automethod:: arctan
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.. automethod:: arctan_
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.. automethod:: atan2
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.. automethod:: atan2_
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.. automethod:: all
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.. automethod:: any
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.. automethod:: backward
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:noindex:
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.. automethod:: baddbmm
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.. automethod:: baddbmm_
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.. automethod:: bernoulli
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.. automethod:: bernoulli_
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.. automethod:: bfloat16
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.. automethod:: bincount
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.. automethod:: bitwise_not
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.. automethod:: bitwise_not_
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.. automethod:: bitwise_and
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.. automethod:: bitwise_and_
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.. automethod:: bitwise_or
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.. automethod:: bitwise_or_
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.. automethod:: bitwise_xor
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.. automethod:: bitwise_xor_
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.. automethod:: bmm
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.. automethod:: bool
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.. automethod:: byte
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.. automethod:: broadcast_to
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.. automethod:: cauchy_
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.. automethod:: ceil
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.. automethod:: ceil_
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.. automethod:: char
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.. automethod:: cholesky
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.. automethod:: cholesky_inverse
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.. automethod:: cholesky_solve
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.. automethod:: chunk
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.. automethod:: clamp
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.. automethod:: clamp_
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.. automethod:: clip
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.. automethod:: clip_
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.. automethod:: clone
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.. automethod:: contiguous
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.. automethod:: copy_
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.. automethod:: conj
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.. automethod:: copysign
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.. automethod:: copysign_
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.. automethod:: cos
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.. automethod:: cos_
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.. automethod:: cosh
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.. automethod:: cosh_
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.. automethod:: count_nonzero
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.. automethod:: acosh
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.. automethod:: acosh_
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.. automethod:: arccosh
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.. automethod:: arccosh_
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.. automethod:: cpu
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.. automethod:: cross
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.. automethod:: cuda
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.. automethod:: logcumsumexp
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.. automethod:: cummax
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.. automethod:: cummin
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.. automethod:: cumprod
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.. automethod:: cumprod_
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.. automethod:: cumsum
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.. automethod:: cumsum_
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.. automethod:: data_ptr
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.. automethod:: deg2rad
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.. automethod:: dequantize
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.. automethod:: det
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.. automethod:: dense_dim
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:noindex:
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.. automethod:: detach
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:noindex:
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.. automethod:: detach_
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:noindex:
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.. automethod:: diag
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.. automethod:: diag_embed
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.. automethod:: diagflat
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.. automethod:: diagonal
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.. automethod:: fill_diagonal_
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.. automethod:: digamma
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.. automethod:: digamma_
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.. automethod:: dim
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.. automethod:: dist
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.. automethod:: div
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.. automethod:: div_
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.. automethod:: divide
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.. automethod:: divide_
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.. automethod:: dot
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.. automethod:: double
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.. automethod:: eig
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.. automethod:: element_size
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.. automethod:: eq
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.. automethod:: eq_
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.. automethod:: equal
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.. automethod:: erf
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.. automethod:: erf_
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.. automethod:: erfc
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.. automethod:: erfc_
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.. automethod:: erfinv
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.. automethod:: erfinv_
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.. automethod:: exp
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.. automethod:: exp_
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.. automethod:: expm1
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.. automethod:: expm1_
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.. automethod:: expand
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.. automethod:: expand_as
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.. automethod:: exponential_
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.. automethod:: fix
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.. automethod:: fix_
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.. automethod:: fill_
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.. automethod:: flatten
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.. automethod:: flip
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.. automethod:: fliplr
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.. automethod:: flipud
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.. automethod:: float
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.. automethod:: float_power
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.. automethod:: float_power_
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.. automethod:: floor
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.. automethod:: floor_
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.. automethod:: floor_divide
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.. automethod:: floor_divide_
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.. automethod:: fmod
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.. automethod:: fmod_
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.. automethod:: frac
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.. automethod:: frac_
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.. automethod:: gather
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.. automethod:: gcd
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.. automethod:: gcd_
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.. automethod:: ge
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.. automethod:: ge_
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.. automethod:: greater_equal
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.. automethod:: greater_equal_
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.. automethod:: geometric_
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.. automethod:: geqrf
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.. automethod:: ger
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.. automethod:: get_device
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.. automethod:: gt
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.. automethod:: gt_
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.. automethod:: greater
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.. automethod:: greater_
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.. automethod:: half
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.. automethod:: hardshrink
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.. automethod:: heaviside
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.. automethod:: histc
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.. automethod:: hypot
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.. automethod:: hypot_
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.. automethod:: i0
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.. automethod:: i0_
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.. automethod:: igamma
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.. automethod:: igamma_
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.. automethod:: igammac
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.. automethod:: igammac_
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.. automethod:: index_add_
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.. automethod:: index_add
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.. automethod:: index_copy_
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.. automethod:: index_copy
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.. automethod:: index_fill_
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.. automethod:: index_fill
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.. automethod:: index_put_
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.. automethod:: index_put
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.. automethod:: index_select
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.. automethod:: indices
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:noindex:
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.. automethod:: inner
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.. automethod:: int
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.. automethod:: int_repr
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.. automethod:: inverse
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.. automethod:: isclose
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.. automethod:: isfinite
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.. automethod:: isinf
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.. automethod:: isposinf
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.. automethod:: isneginf
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.. automethod:: isnan
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.. automethod:: is_contiguous
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.. automethod:: is_complex
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.. automethod:: is_floating_point
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.. autoattribute:: is_leaf
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:noindex:
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.. automethod:: is_pinned
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.. automethod:: is_set_to
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.. automethod:: is_shared
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.. automethod:: is_signed
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.. autoattribute:: is_sparse
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:noindex:
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.. automethod:: istft
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.. automethod:: isreal
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.. automethod:: item
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.. automethod:: kthvalue
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.. automethod:: lcm
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.. automethod:: lcm_
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.. automethod:: ldexp
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.. automethod:: ldexp_
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.. automethod:: le
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.. automethod:: le_
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.. automethod:: less_equal
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.. automethod:: less_equal_
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.. automethod:: lerp
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.. automethod:: lerp_
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.. automethod:: lgamma
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.. automethod:: lgamma_
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.. automethod:: log
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.. automethod:: log_
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.. automethod:: logdet
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.. automethod:: log10
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.. automethod:: log10_
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.. automethod:: log1p
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.. automethod:: log1p_
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.. automethod:: log2
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.. automethod:: log2_
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.. automethod:: log_normal_
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.. automethod:: logaddexp
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.. automethod:: logaddexp2
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.. automethod:: logsumexp
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.. automethod:: logical_and
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.. automethod:: logical_and_
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.. automethod:: logical_not
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.. automethod:: logical_not_
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.. automethod:: logical_or
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.. automethod:: logical_or_
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.. automethod:: logical_xor
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.. automethod:: logical_xor_
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.. automethod:: logit
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.. automethod:: logit_
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.. automethod:: long
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.. automethod:: lstsq
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.. automethod:: lt
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.. automethod:: lt_
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.. automethod:: less
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.. automethod:: less_
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.. automethod:: lu
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.. automethod:: lu_solve
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.. automethod:: as_subclass
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.. automethod:: map_
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.. automethod:: masked_scatter_
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.. automethod:: masked_scatter
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.. automethod:: masked_fill_
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.. automethod:: masked_fill
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.. automethod:: masked_select
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.. automethod:: matmul
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.. automethod:: matrix_power
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.. automethod:: matrix_exp
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.. automethod:: max
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.. automethod:: maximum
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.. automethod:: mean
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.. automethod:: median
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.. automethod:: nanmedian
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.. automethod:: min
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.. automethod:: minimum
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.. automethod:: mm
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.. automethod:: smm
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:noindex:
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.. automethod:: mode
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.. automethod:: movedim
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.. automethod:: moveaxis
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.. automethod:: msort
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.. automethod:: mul
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.. automethod:: mul_
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.. automethod:: multiply
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.. automethod:: multiply_
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.. automethod:: multinomial
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.. automethod:: mv
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.. automethod:: mvlgamma
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.. automethod:: mvlgamma_
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.. automethod:: nansum
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.. automethod:: narrow
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.. automethod:: narrow_copy
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.. automethod:: ndimension
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.. automethod:: nan_to_num
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.. automethod:: nan_to_num_
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.. automethod:: ne
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.. automethod:: ne_
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.. automethod:: not_equal
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.. automethod:: not_equal_
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.. automethod:: neg
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.. automethod:: neg_
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.. automethod:: negative
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.. automethod:: negative_
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.. automethod:: nelement
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.. automethod:: nextafter
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.. automethod:: nextafter_
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.. automethod:: nonzero
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.. automethod:: norm
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.. automethod:: normal_
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.. automethod:: numel
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.. automethod:: numpy
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.. automethod:: orgqr
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.. automethod:: ormqr
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.. automethod:: outer
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.. automethod:: permute
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.. automethod:: pin_memory
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.. automethod:: pinverse
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.. automethod:: polygamma
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.. automethod:: polygamma_
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.. automethod:: pow
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.. automethod:: pow_
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.. automethod:: prod
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.. automethod:: put_
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.. automethod:: qr
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.. automethod:: qscheme
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.. automethod:: quantile
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.. automethod:: nanquantile
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.. automethod:: q_scale
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.. automethod:: q_zero_point
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.. automethod:: q_per_channel_scales
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.. automethod:: q_per_channel_zero_points
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.. automethod:: q_per_channel_axis
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.. automethod:: rad2deg
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.. automethod:: random_
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.. automethod:: ravel
|
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.. automethod:: reciprocal
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.. automethod:: reciprocal_
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.. automethod:: record_stream
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.. automethod:: register_hook
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:noindex:
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.. automethod:: remainder
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.. automethod:: remainder_
|
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.. automethod:: renorm
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.. automethod:: renorm_
|
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.. automethod:: repeat
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.. automethod:: repeat_interleave
|
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.. autoattribute:: requires_grad
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:noindex:
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.. automethod:: requires_grad_
|
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.. automethod:: reshape
|
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.. automethod:: reshape_as
|
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.. automethod:: resize_
|
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.. automethod:: resize_as_
|
|
.. automethod:: retain_grad
|
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:noindex:
|
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.. automethod:: roll
|
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.. automethod:: rot90
|
|
.. automethod:: round
|
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.. automethod:: round_
|
|
.. automethod:: rsqrt
|
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.. automethod:: rsqrt_
|
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.. automethod:: scatter
|
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.. automethod:: scatter_
|
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.. automethod:: scatter_add_
|
|
.. automethod:: scatter_add
|
|
.. automethod:: select
|
|
.. automethod:: set_
|
|
.. automethod:: share_memory_
|
|
.. automethod:: short
|
|
.. automethod:: sigmoid
|
|
.. automethod:: sigmoid_
|
|
.. automethod:: sign
|
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.. automethod:: sign_
|
|
.. automethod:: signbit
|
|
.. automethod:: sgn
|
|
.. automethod:: sgn_
|
|
.. automethod:: sin
|
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.. automethod:: sin_
|
|
.. automethod:: sinc
|
|
.. automethod:: sinc_
|
|
.. automethod:: sinh
|
|
.. automethod:: sinh_
|
|
.. automethod:: asinh
|
|
.. automethod:: asinh_
|
|
.. automethod:: arcsinh
|
|
.. automethod:: arcsinh_
|
|
.. automethod:: size
|
|
.. automethod:: slogdet
|
|
.. automethod:: solve
|
|
.. automethod:: sort
|
|
.. automethod:: split
|
|
.. automethod:: sparse_mask
|
|
:noindex:
|
|
.. automethod:: sparse_dim
|
|
:noindex:
|
|
.. automethod:: sqrt
|
|
.. automethod:: sqrt_
|
|
.. automethod:: square
|
|
.. automethod:: square_
|
|
.. automethod:: squeeze
|
|
.. automethod:: squeeze_
|
|
.. automethod:: std
|
|
.. automethod:: stft
|
|
.. automethod:: storage
|
|
.. automethod:: storage_offset
|
|
.. automethod:: storage_type
|
|
.. automethod:: stride
|
|
.. automethod:: sub
|
|
.. automethod:: sub_
|
|
.. automethod:: subtract
|
|
.. automethod:: subtract_
|
|
.. automethod:: sum
|
|
.. automethod:: sum_to_size
|
|
.. automethod:: svd
|
|
.. automethod:: swapaxes
|
|
.. automethod:: swapdims
|
|
.. automethod:: symeig
|
|
.. automethod:: t
|
|
.. automethod:: t_
|
|
.. automethod:: tensor_split
|
|
.. automethod:: tile
|
|
.. automethod:: to
|
|
.. automethod:: to_mkldnn
|
|
.. automethod:: take
|
|
.. automethod:: tan
|
|
.. automethod:: tan_
|
|
.. automethod:: tanh
|
|
.. automethod:: tanh_
|
|
.. automethod:: atanh
|
|
.. automethod:: atanh_
|
|
.. automethod:: arctanh
|
|
.. automethod:: arctanh_
|
|
.. automethod:: tolist
|
|
.. automethod:: topk
|
|
.. automethod:: to_sparse
|
|
:noindex:
|
|
.. automethod:: trace
|
|
.. automethod:: transpose
|
|
.. automethod:: transpose_
|
|
.. automethod:: triangular_solve
|
|
.. automethod:: tril
|
|
.. automethod:: tril_
|
|
.. automethod:: triu
|
|
.. automethod:: triu_
|
|
.. automethod:: true_divide
|
|
.. automethod:: true_divide_
|
|
.. automethod:: trunc
|
|
.. automethod:: trunc_
|
|
.. automethod:: type
|
|
.. automethod:: type_as
|
|
.. automethod:: unbind
|
|
.. automethod:: unfold
|
|
.. automethod:: uniform_
|
|
.. automethod:: unique
|
|
.. automethod:: unique_consecutive
|
|
.. automethod:: unsqueeze
|
|
.. automethod:: unsqueeze_
|
|
.. automethod:: values
|
|
:noindex:
|
|
.. automethod:: var
|
|
.. automethod:: vdot
|
|
.. automethod:: view
|
|
.. automethod:: view_as
|
|
.. automethod:: where
|
|
.. automethod:: xlogy
|
|
.. automethod:: xlogy_
|
|
.. automethod:: zero_
|