tensor_attributes.rst

.. currentmodule:: torch

Tensor Attributes

Each torch.Tensor has a :class:`torch.dtype`, :class:`torch.device`, and :class:`torch.layout`.

torch.dtype

A :class:`torch.dtype` is an object that represents the data type of a :class:`torch.Tensor`. PyTorch has nine different data types:

Data type	dtype	Tensor types
32-bit floating point	torch.float32 or torch.float	torch.*.FloatTensor
64-bit floating point	torch.float64 or torch.double	torch.*.DoubleTensor
16-bit floating point	torch.float16 or torch.half	torch.*.HalfTensor
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

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.

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 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)
>>> 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
>>> (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.

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

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

torch.layout

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 experimental 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`.