.. 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.*_liketensor 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
.. autosummary::
:toctree: generated
:nosignatures:
Tensor.new_tensor
Tensor.new_full
Tensor.new_empty
Tensor.new_ones
Tensor.new_zeros
Tensor.is_cuda
Tensor.is_quantized
Tensor.is_meta
Tensor.device
Tensor.grad
Tensor.ndim
Tensor.real
Tensor.imag
Tensor.abs
Tensor.abs_
Tensor.absolute
Tensor.absolute_
Tensor.acos
Tensor.acos_
Tensor.arccos
Tensor.arccos_
Tensor.add
Tensor.add_
Tensor.addbmm
Tensor.addbmm_
Tensor.addcdiv
Tensor.addcdiv_
Tensor.addcmul
Tensor.addcmul_
Tensor.addmm
Tensor.addmm_
Tensor.sspaddmm
Tensor.addmv
Tensor.addmv_
Tensor.addr
Tensor.addr_
Tensor.adjoint
Tensor.allclose
Tensor.amax
Tensor.amin
Tensor.aminmax
Tensor.angle
Tensor.apply_
Tensor.argmax
Tensor.argmin
Tensor.argsort
Tensor.argwhere
Tensor.asin
Tensor.asin_
Tensor.arcsin
Tensor.arcsin_
Tensor.as_strided
Tensor.atan
Tensor.atan_
Tensor.arctan
Tensor.arctan_
Tensor.atan2
Tensor.atan2_
Tensor.arctan2
Tensor.arctan2_
Tensor.all
Tensor.any
Tensor.backward
Tensor.baddbmm
Tensor.baddbmm_
Tensor.bernoulli
Tensor.bernoulli_
Tensor.bfloat16
Tensor.bincount
Tensor.bitwise_not
Tensor.bitwise_not_
Tensor.bitwise_and
Tensor.bitwise_and_
Tensor.bitwise_or
Tensor.bitwise_or_
Tensor.bitwise_xor
Tensor.bitwise_xor_
Tensor.bitwise_left_shift
Tensor.bitwise_left_shift_
Tensor.bitwise_right_shift
Tensor.bitwise_right_shift_
Tensor.bmm
Tensor.bool
Tensor.byte
Tensor.broadcast_to
Tensor.cauchy_
Tensor.ceil
Tensor.ceil_
Tensor.char
Tensor.cholesky
Tensor.cholesky_inverse
Tensor.cholesky_solve
Tensor.chunk
Tensor.clamp
Tensor.clamp_
Tensor.clip
Tensor.clip_
Tensor.clone
Tensor.contiguous
Tensor.copy_
Tensor.conj
Tensor.conj_physical
Tensor.conj_physical_
Tensor.resolve_conj
Tensor.resolve_neg
Tensor.copysign
Tensor.copysign_
Tensor.cos
Tensor.cos_
Tensor.cosh
Tensor.cosh_
Tensor.corrcoef
Tensor.count_nonzero
Tensor.cov
Tensor.acosh
Tensor.acosh_
Tensor.arccosh
Tensor.arccosh_
Tensor.cpu
Tensor.cross
Tensor.cuda
Tensor.logcumsumexp
Tensor.cummax
Tensor.cummin
Tensor.cumprod
Tensor.cumprod_
Tensor.cumsum
Tensor.cumsum_
Tensor.chalf
Tensor.cfloat
Tensor.cdouble
Tensor.data_ptr
Tensor.deg2rad
Tensor.dequantize
Tensor.det
Tensor.dense_dim
Tensor.detach
Tensor.detach_
Tensor.diag
Tensor.diag_embed
Tensor.diagflat
Tensor.diagonal
Tensor.diagonal_scatter
Tensor.fill_diagonal_
Tensor.fmax
Tensor.fmin
Tensor.diff
Tensor.digamma
Tensor.digamma_
Tensor.dim
Tensor.dist
Tensor.div
Tensor.div_
Tensor.divide
Tensor.divide_
Tensor.dot
Tensor.double
Tensor.dsplit
Tensor.element_size
Tensor.eq
Tensor.eq_
Tensor.equal
Tensor.erf
Tensor.erf_
Tensor.erfc
Tensor.erfc_
Tensor.erfinv
Tensor.erfinv_
Tensor.exp
Tensor.exp_
Tensor.expm1
Tensor.expm1_
Tensor.expand
Tensor.expand_as
Tensor.exponential_
Tensor.fix
Tensor.fix_
Tensor.fill_
Tensor.flatten
Tensor.flip
Tensor.fliplr
Tensor.flipud
Tensor.float
Tensor.float_power
Tensor.float_power_
Tensor.floor
Tensor.floor_
Tensor.floor_divide
Tensor.floor_divide_
Tensor.fmod
Tensor.fmod_
Tensor.frac
Tensor.frac_
Tensor.frexp
Tensor.gather
Tensor.gcd
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
Tensor.kthvalue
Tensor.lcm
Tensor.lcm_
Tensor.ldexp
Tensor.ldexp_
Tensor.le
Tensor.le_
Tensor.less_equal
Tensor.less_equal_
Tensor.lerp
Tensor.lerp_
Tensor.lgamma
Tensor.lgamma_
Tensor.log
Tensor.log_
Tensor.logdet
Tensor.log10
Tensor.log10_
Tensor.log1p
Tensor.log1p_
Tensor.log2
Tensor.log2_
Tensor.log_normal_
Tensor.logaddexp
Tensor.logaddexp2
Tensor.logsumexp
Tensor.logical_and
Tensor.logical_and_
Tensor.logical_not
Tensor.logical_not_
Tensor.logical_or
Tensor.logical_or_
Tensor.logical_xor
Tensor.logical_xor_
Tensor.logit
Tensor.logit_
Tensor.long
Tensor.lt
Tensor.lt_
Tensor.less
Tensor.less_
Tensor.lu
Tensor.lu_solve
Tensor.as_subclass
Tensor.map_
Tensor.masked_scatter_
Tensor.masked_scatter
Tensor.masked_fill_
Tensor.masked_fill
Tensor.masked_select
Tensor.matmul
Tensor.matrix_power
Tensor.matrix_exp
Tensor.max
Tensor.maximum
Tensor.mean
Tensor.nanmean
Tensor.median
Tensor.nanmedian
Tensor.min
Tensor.minimum
Tensor.mm
Tensor.smm
Tensor.mode
Tensor.movedim
Tensor.moveaxis
Tensor.msort
Tensor.mul
Tensor.mul_
Tensor.multiply
Tensor.multiply_
Tensor.multinomial
Tensor.mv
Tensor.mvlgamma
Tensor.mvlgamma_
Tensor.nansum
Tensor.narrow
Tensor.narrow_copy
Tensor.ndimension
Tensor.nan_to_num
Tensor.nan_to_num_
Tensor.ne
Tensor.ne_
Tensor.not_equal
Tensor.not_equal_
Tensor.neg
Tensor.neg_
Tensor.negative
Tensor.negative_
Tensor.nelement
Tensor.nextafter
Tensor.nextafter_
Tensor.nonzero
Tensor.norm
Tensor.normal_
Tensor.numel
Tensor.numpy
Tensor.orgqr
Tensor.ormqr
Tensor.outer
Tensor.permute
Tensor.pin_memory
Tensor.pinverse
Tensor.polygamma
Tensor.polygamma_
Tensor.positive
Tensor.pow
Tensor.pow_
Tensor.prod
Tensor.put_
Tensor.qr
Tensor.qscheme
Tensor.quantile
Tensor.nanquantile
Tensor.q_scale
Tensor.q_zero_point
Tensor.q_per_channel_scales
Tensor.q_per_channel_zero_points
Tensor.q_per_channel_axis
Tensor.rad2deg
Tensor.random_
Tensor.ravel
Tensor.reciprocal
Tensor.reciprocal_
Tensor.record_stream
Tensor.register_hook
Tensor.remainder
Tensor.remainder_
Tensor.renorm
Tensor.renorm_
Tensor.repeat
Tensor.repeat_interleave
Tensor.requires_grad
Tensor.requires_grad_
Tensor.reshape
Tensor.reshape_as
Tensor.resize_
Tensor.resize_as_
Tensor.retain_grad
Tensor.retains_grad
Tensor.roll
Tensor.rot90
Tensor.round
Tensor.round_
Tensor.rsqrt
Tensor.rsqrt_
Tensor.scatter
Tensor.scatter_
Tensor.scatter_add_
Tensor.scatter_add
Tensor.scatter_reduce_
Tensor.scatter_reduce
Tensor.select
Tensor.select_scatter
Tensor.set_
Tensor.share_memory_
Tensor.short
Tensor.sigmoid
Tensor.sigmoid_
Tensor.sign
Tensor.sign_
Tensor.signbit
Tensor.sgn
Tensor.sgn_
Tensor.sin
Tensor.sin_
Tensor.sinc
Tensor.sinc_
Tensor.sinh
Tensor.sinh_
Tensor.asinh
Tensor.asinh_
Tensor.arcsinh
Tensor.arcsinh_
Tensor.size
Tensor.slogdet
Tensor.slice_scatter
Tensor.softmax
Tensor.sort
Tensor.split
Tensor.sparse_mask
Tensor.sparse_dim
Tensor.sqrt
Tensor.sqrt_
Tensor.square
Tensor.square_
Tensor.squeeze
Tensor.squeeze_
Tensor.std
Tensor.stft
Tensor.storage
Tensor.untyped_storage
Tensor.storage_offset
Tensor.storage_type
Tensor.stride
Tensor.sub
Tensor.sub_
Tensor.subtract
Tensor.subtract_
Tensor.sum
Tensor.sum_to_size
Tensor.svd
Tensor.swapaxes
Tensor.swapdims
Tensor.t
Tensor.t_
Tensor.tensor_split
Tensor.tile
Tensor.to
Tensor.to_mkldnn
Tensor.take
Tensor.take_along_dim
Tensor.tan
Tensor.tan_
Tensor.tanh
Tensor.tanh_
Tensor.atanh
Tensor.atanh_
Tensor.arctanh
Tensor.arctanh_
Tensor.tolist
Tensor.topk
Tensor.to_dense
Tensor.to_sparse
Tensor.to_sparse_csr
Tensor.to_sparse_csc
Tensor.to_sparse_bsr
Tensor.to_sparse_bsc
Tensor.trace
Tensor.transpose
Tensor.transpose_
Tensor.triangular_solve
Tensor.tril
Tensor.tril_
Tensor.triu
Tensor.triu_
Tensor.true_divide
Tensor.true_divide_
Tensor.trunc
Tensor.trunc_
Tensor.type
Tensor.type_as
Tensor.unbind
Tensor.unflatten
Tensor.unfold
Tensor.uniform_
Tensor.unique
Tensor.unique_consecutive
Tensor.unsqueeze
Tensor.unsqueeze_
Tensor.values
Tensor.var
Tensor.vdot
Tensor.view
Tensor.view_as
Tensor.vsplit
Tensor.where
Tensor.xlogy
Tensor.xlogy_
Tensor.zero_