# Tensor Attributes¶

Each torch.Tensor has a torch.dtype, torch.device, and torch.layout.

## torch.dtype¶

class torch.dtype

A torch.dtype is an object that represents the data type of a 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 torch.dtype is a floating point data type, the property 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.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.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¶

class torch.device

A torch.device is an object representing the device on which a torch.Tensor is or will be allocated.

The 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 torch.cuda.set_device() is called; e.g., a torch.Tensor constructed with device 'cuda' is equivalent to 'cuda:X' where X is the result of torch.cuda.current_device().

A torch.Tensor’s device can be accessed via the Tensor.device property.

A 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 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 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¶

class torch.layout

A torch.layout is an object that represents the memory layout of a 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 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 torch.sparse.