.. currentmodule:: torch
.. _tensor-attributes-doc:
Tensor Attributes
=================
Each ``torch.Tensor`` has a :class:`torch.dtype`, :class:`torch.device`, and :class:`torch.layout`.
.. _dtype-doc:
torch.dtype
-----------
.. class:: torch.dtype
A :class:`torch.dtype` is an object that represents the data type of a
:class:`torch.Tensor`. PyTorch has twelve different data types:
========================== =========================================== ===========================
Data type dtype Legacy Constructors
========================== =========================================== ===========================
32-bit floating point ``torch.float32`` or ``torch.float`` ``torch.*.FloatTensor``
64-bit floating point ``torch.float64`` or ``torch.double`` ``torch.*.DoubleTensor``
64-bit complex ``torch.complex64`` or ``torch.cfloat``
128-bit complex ``torch.complex128`` or ``torch.cdouble``
16-bit floating point [1]_ ``torch.float16`` or ``torch.half`` ``torch.*.HalfTensor``
16-bit floating point [2]_ ``torch.bfloat16`` ``torch.*.BFloat16Tensor``
8-bit integer (unsigned) ``torch.uint8`` ``torch.*.ByteTensor``
8-bit integer (signed) ``torch.int8`` ``torch.*.CharTensor``
16-bit integer (signed) ``torch.int16`` or ``torch.short`` ``torch.*.ShortTensor``
32-bit integer (signed) ``torch.int32`` or ``torch.int`` ``torch.*.IntTensor``
64-bit integer (signed) ``torch.int64`` or ``torch.long`` ``torch.*.LongTensor``
Boolean ``torch.bool`` ``torch.*.BoolTensor``
========================== =========================================== ===========================
.. [1] Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10
significand bits. Useful when precision is important.
.. [2] Sometimes referred to as Brain Floating Point: use 1 sign, 8 exponent and 7
significand bits. Useful when range is important, since it has the same
number of exponent bits as ``float32``
To find out if a :class:`torch.dtype` is a floating point data type, the property :attr:`is_floating_point`
can be used, which returns ``True`` if the data type is a floating point data type.
To find out if a :class:`torch.dtype` is a complex data type, the property :attr:`is_complex`
can be used, which returns ``True`` if the data type is a complex data type.
.. _type-promotion-doc:
When the dtypes of inputs to an arithmetic operation (`add`, `sub`, `div`, `mul`) differ, we promote
by finding the minimum dtype that satisfies the following rules:
* If the type of a scalar operand is of a higher category than tensor operands
(where complex > floating > integral > boolean), we promote to a type with sufficient size to hold
all scalar operands of that category.
* If a zero-dimension tensor operand has a higher category than dimensioned operands,
we promote to a type with sufficient size and category to hold all zero-dim tensor operands of
that category.
* If there are no higher-category zero-dim operands, we promote to a type with sufficient size
and category to hold all dimensioned operands.
A floating point scalar operand has dtype `torch.get_default_dtype()` and an integral
non-boolean scalar operand has dtype `torch.int64`. Unlike numpy, we do not inspect
values when determining the minimum `dtypes` of an operand. Quantized and complex types
are not yet supported.
Promotion Examples::
>>> float_tensor = torch.ones(1, dtype=torch.float)
>>> double_tensor = torch.ones(1, dtype=torch.double)
>>> complex_float_tensor = torch.ones(1, dtype=torch.complex64)
>>> complex_double_tensor = torch.ones(1, dtype=torch.complex128)
>>> int_tensor = torch.ones(1, dtype=torch.int)
>>> long_tensor = torch.ones(1, dtype=torch.long)
>>> uint_tensor = torch.ones(1, dtype=torch.uint8)
>>> double_tensor = torch.ones(1, dtype=torch.double)
>>> bool_tensor = torch.ones(1, dtype=torch.bool)
# zero-dim tensors
>>> long_zerodim = torch.tensor(1, dtype=torch.long)
>>> int_zerodim = torch.tensor(1, dtype=torch.int)
>>> torch.add(5, 5).dtype
torch.int64
# 5 is an int64, but does not have higher category than int_tensor so is not considered.
>>> (int_tensor + 5).dtype
torch.int32
>>> (int_tensor + long_zerodim).dtype
torch.int32
>>> (long_tensor + int_tensor).dtype
torch.int64
>>> (bool_tensor + long_tensor).dtype
torch.int64
>>> (bool_tensor + uint_tensor).dtype
torch.uint8
>>> (float_tensor + double_tensor).dtype
torch.float64
>>> (complex_float_tensor + complex_double_tensor).dtype
torch.complex128
>>> (bool_tensor + int_tensor).dtype
torch.int32
# Since long is a different kind than float, result dtype only needs to be large enough
# to hold the float.
>>> torch.add(long_tensor, float_tensor).dtype
torch.float32
When the output tensor of an arithmetic operation is specified, we allow casting to its `dtype` except that:
* An integral output tensor cannot accept a floating point tensor.
* A boolean output tensor cannot accept a non-boolean tensor.
* A non-complex output tensor cannot accept a complex tensor
Casting Examples::
# allowed:
>>> float_tensor *= double_tensor
>>> float_tensor *= int_tensor
>>> float_tensor *= uint_tensor
>>> float_tensor *= bool_tensor
>>> float_tensor *= double_tensor
>>> int_tensor *= long_tensor
>>> int_tensor *= uint_tensor
>>> uint_tensor *= int_tensor
# disallowed (RuntimeError: result type can't be cast to the desired output type):
>>> int_tensor *= float_tensor
>>> bool_tensor *= int_tensor
>>> bool_tensor *= uint_tensor
>>> float_tensor *= complex_float_tensor
.. _device-doc:
torch.device
------------
.. class:: torch.device
A :class:`torch.device` is an object representing the device on which a :class:`torch.Tensor` is
or will be allocated.
The :class:`torch.device` contains a device type (``'cpu'`` or ``'cuda'``) and optional device
ordinal for the device type. If the device ordinal is not present, this object will always represent
the current device for the device type, even after :func:`torch.cuda.set_device()` is called; e.g.,
a :class:`torch.Tensor` constructed with device ``'cuda'`` is equivalent to ``'cuda:X'`` where X is
the result of :func:`torch.cuda.current_device()`.
A :class:`torch.Tensor`'s device can be accessed via the :attr:`Tensor.device` property.
A :class:`torch.device` can be constructed via a string or via a string and device ordinal
Via a string:
::
>>> torch.device('cuda:0')
device(type='cuda', index=0)
>>> torch.device('cpu')
device(type='cpu')
>>> torch.device('cuda') # current cuda device
device(type='cuda')
Via a string and device ordinal:
::
>>> torch.device('cuda', 0)
device(type='cuda', index=0)
>>> torch.device('cpu', 0)
device(type='cpu', index=0)
.. note::
The :class:`torch.device` argument in functions can generally be substituted with a string.
This allows for fast prototyping of code.
>>> # Example of a function that takes in a torch.device
>>> cuda1 = torch.device('cuda:1')
>>> torch.randn((2,3), device=cuda1)
>>> # You can substitute the torch.device with a string
>>> torch.randn((2,3), device='cuda:1')
.. note::
For legacy reasons, a device can be constructed via a single device ordinal, which is treated
as a cuda device. This matches :meth:`Tensor.get_device`, which returns an ordinal for cuda
tensors and is not supported for cpu tensors.
>>> torch.device(1)
device(type='cuda', index=1)
.. note::
Methods which take a device will generally accept a (properly formatted) string
or (legacy) integer device ordinal, i.e. the following are all equivalent:
>>> torch.randn((2,3), device=torch.device('cuda:1'))
>>> torch.randn((2,3), device='cuda:1')
>>> torch.randn((2,3), device=1) # legacy
.. _layout-doc:
torch.layout
------------
.. class:: torch.layout
.. warning::
The ``torch.layout`` class is in beta and subject to change.
A :class:`torch.layout` is an object that represents the memory layout of a
:class:`torch.Tensor`. Currently, we support ``torch.strided`` (dense Tensors)
and have beta support for ``torch.sparse_coo`` (sparse COO Tensors).
``torch.strided`` represents dense Tensors and is the memory layout that
is most commonly used. Each strided tensor has an associated
:class:`torch.Storage`, which holds its data. These tensors provide
multi-dimensional, `strided `_
view of a storage. Strides are a list of integers: the k-th stride
represents the jump in the memory necessary to go from one element to the
next one in the k-th dimension of the Tensor. This concept makes it possible
to perform many tensor operations efficiently.
Example::
>>> x = torch.Tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
>>> x.stride()
(5, 1)
>>> x.t().stride()
(1, 5)
For more information on ``torch.sparse_coo`` tensors, see :ref:`sparse-docs`.
torch.memory_format
-------------------
.. class:: torch.memory_format
A :class:`torch.memory_format` is an object representing the memory format on which a :class:`torch.Tensor` is
or will be allocated.
Possible values are:
- ``torch.contiguous_format``:
Tensor is or will be allocated in dense non-overlapping memory. Strides represented by values in decreasing order.
- ``torch.channels_last``:
Tensor is or will be allocated in dense non-overlapping memory. Strides represented by values in
``strides[0] > strides[2] > strides[3] > strides[1] == 1`` aka NHWC order.
- ``torch.preserve_format``:
Used in functions like `clone` to preserve the memory format of the input tensor. If input tensor is
allocated in dense non-overlapping memory, the output tensor strides will be copied from the input.
Otherwise output strides will follow ``torch.contiguous_format``