.. currentmodule:: torch
A :class:`torch.Tensor` is a multi-dimensional matrix containing elements of a single data type.
Torch defines 10 tensor types with CPU and GPU variants which are as follows:
Data type | dtype | CPU tensor | GPU tensor |
---|---|---|---|
32-bit floating point | torch.float32 or torch.float |
:class:`torch.FloatTensor` | :class:`torch.cuda.FloatTensor` |
64-bit floating point | torch.float64 or torch.double |
:class:`torch.DoubleTensor` | :class:`torch.cuda.DoubleTensor` |
16-bit floating point [1] | torch.float16 or torch.half |
:class:`torch.HalfTensor` | :class:`torch.cuda.HalfTensor` |
16-bit floating point [2] | torch.bfloat16 |
:class:`torch.BFloat16Tensor` | :class:`torch.cuda.BFloat16Tensor` |
32-bit complex | torch.complex32 or torch.chalf |
||
64-bit complex | torch.complex64 or torch.cfloat |
||
128-bit complex | torch.complex128 or torch.cdouble |
||
8-bit integer (unsigned) | torch.uint8 |
:class:`torch.ByteTensor` | :class:`torch.cuda.ByteTensor` |
8-bit integer (signed) | torch.int8 |
:class:`torch.CharTensor` | :class:`torch.cuda.CharTensor` |
16-bit integer (signed) | torch.int16 or torch.short |
:class:`torch.ShortTensor` | :class:`torch.cuda.ShortTensor` |
32-bit integer (signed) | torch.int32 or torch.int |
:class:`torch.IntTensor` | :class:`torch.cuda.IntTensor` |
64-bit integer (signed) | torch.int64 or torch.long |
:class:`torch.LongTensor` | :class:`torch.cuda.LongTensor` |
Boolean | torch.bool |
:class:`torch.BoolTensor` | :class:`torch.cuda.BoolTensor` |
quantized 8-bit integer (unsigned) | torch.quint8 |
:class:`torch.ByteTensor` | / |
quantized 8-bit integer (signed) | torch.qint8 |
:class:`torch.CharTensor` | / |
quantized 32-bit integer (signed) | torch.qint32 |
:class:`torch.IntTensor` | / |
quantized 4-bit integer (unsigned) [3] | torch.quint4x2 |
:class:`torch.ByteTensor` | / |
[1] | Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10 significand bits. Useful when precision is important at the expense of range. |
[2] | Sometimes referred to as Brain Floating Point: uses 1 sign, 8 exponent, and 7
significand bits. Useful when range is important, since it has the same
number of exponent bits as float32 |
[3] | quantized 4-bit integer is stored as a 8-bit signed integer. Currently it's only supported in EmbeddingBag operator. |
:class:`torch.Tensor` is an alias for the default tensor type (:class:`torch.FloatTensor`).
A tensor can be constructed from a Python :class:`list` or sequence using the :func:`torch.tensor` constructor:
>>> torch.tensor([[1., -1.], [1., -1.]]) tensor([[ 1.0000, -1.0000], [ 1.0000, -1.0000]]) >>> torch.tensor(np.array([[1, 2, 3], [4, 5, 6]])) tensor([[ 1, 2, 3], [ 4, 5, 6]])
Warning
:func:`torch.tensor` always copies :attr:`data`. If you have a Tensor
:attr:`data` and just want to change its requires_grad
flag, use
:meth:`~torch.Tensor.requires_grad_` or
:meth:`~torch.Tensor.detach` to avoid a copy.
If you have a numpy array and want to avoid a copy, use
:func:`torch.as_tensor`.
A tensor of specific data type can be constructed by passing a :class:`torch.dtype` and/or a :class:`torch.device` to a constructor or tensor creation op:
>>> torch.zeros([2, 4], dtype=torch.int32) tensor([[ 0, 0, 0, 0], [ 0, 0, 0, 0]], dtype=torch.int32) >>> cuda0 = torch.device('cuda:0') >>> torch.ones([2, 4], dtype=torch.float64, device=cuda0) tensor([[ 1.0000, 1.0000, 1.0000, 1.0000], [ 1.0000, 1.0000, 1.0000, 1.0000]], dtype=torch.float64, device='cuda:0')
For more information about building Tensors, see :ref:`tensor-creation-ops`
The contents of a tensor can be accessed and modified using Python's indexing and slicing notation:
>>> x = torch.tensor([[1, 2, 3], [4, 5, 6]]) >>> print(x[1][2]) tensor(6) >>> x[0][1] = 8 >>> print(x) tensor([[ 1, 8, 3], [ 4, 5, 6]])
Use :meth:`torch.Tensor.item` to get a Python number from a tensor containing a single value:
>>> x = torch.tensor([[1]]) >>> x tensor([[ 1]]) >>> x.item() 1 >>> x = torch.tensor(2.5) >>> x tensor(2.5000) >>> x.item() 2.5
For more information about indexing, see :ref:`indexing-slicing-joining`
A tensor can be created with :attr:`requires_grad=True` so that :mod:`torch.autograd` records operations on them for automatic differentiation.
>>> x = torch.tensor([[1., -1.], [1., 1.]], requires_grad=True) >>> out = x.pow(2).sum() >>> out.backward() >>> x.grad tensor([[ 2.0000, -2.0000], [ 2.0000, 2.0000]])
Each tensor has an associated :class:`torch.Storage`, which holds its data. The tensor class also provides multi-dimensional, strided view of a storage and defines numeric operations on it.
Note
For more information on tensor views, see :ref:`tensor-view-doc`.
Note
For more information on the :class:`torch.dtype`, :class:`torch.device`, and :class:`torch.layout` attributes of a :class:`torch.Tensor`, see :ref:`tensor-attributes-doc`.
Note
Methods which mutate a tensor are marked with an underscore suffix. For example, :func:`torch.FloatTensor.abs_` computes the absolute value in-place and returns the modified tensor, while :func:`torch.FloatTensor.abs` computes the result in a new tensor.
Note
To change an existing tensor's :class:`torch.device` and/or :class:`torch.dtype`, consider using :meth:`~torch.Tensor.to` method on the tensor.
Warning
Current implementation of :class:`torch.Tensor` introduces memory overhead, thus it might lead to unexpectedly high memory usage in the applications with many tiny tensors. If this is your case, consider using one large structure.
There are a few main ways to create a tensor, depending on your use case.
- To create a tensor with pre-existing data, use :func:`torch.tensor`.
- To create a tensor with specific size, use
torch.*
tensor creation ops (see :ref:`tensor-creation-ops`). - To create a tensor with the same size (and similar types) as another tensor,
use
torch.*_like
tensor creation ops (see :ref:`tensor-creation-ops`). - To create a tensor with similar type but different size as another tensor,
use
tensor.new_*
creation ops.
.. autoattribute:: Tensor.T
.. autoattribute:: Tensor.H
.. autoattribute:: Tensor.mT
.. autoattribute:: Tensor.mH
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Tensor.gcd_ Tensor.ge Tensor.ge_ Tensor.greater_equal Tensor.greater_equal_ Tensor.geometric_ Tensor.geqrf Tensor.ger Tensor.get_device Tensor.gt Tensor.gt_ Tensor.greater Tensor.greater_ Tensor.half Tensor.hardshrink Tensor.heaviside Tensor.histc Tensor.histogram Tensor.hsplit Tensor.hypot Tensor.hypot_ Tensor.i0 Tensor.i0_ Tensor.igamma Tensor.igamma_ Tensor.igammac Tensor.igammac_ Tensor.index_add_ Tensor.index_add Tensor.index_copy_ Tensor.index_copy Tensor.index_fill_ Tensor.index_fill Tensor.index_put_ Tensor.index_put Tensor.index_reduce_ Tensor.index_reduce Tensor.index_select Tensor.indices Tensor.inner Tensor.int Tensor.int_repr Tensor.inverse Tensor.isclose Tensor.isfinite Tensor.isinf Tensor.isposinf Tensor.isneginf Tensor.isnan Tensor.is_contiguous Tensor.is_complex Tensor.is_conj Tensor.is_floating_point Tensor.is_inference Tensor.is_leaf Tensor.is_pinned Tensor.is_set_to Tensor.is_shared Tensor.is_signed Tensor.is_sparse Tensor.istft Tensor.isreal Tensor.item 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