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 twelve different data types:

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 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.

To find out if a torch.dtype is a complex data type, the property is_complex can be used, which returns True if the data type is a complex 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 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 *= float_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

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', 'cuda' or 'mps') 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('mps')
device(type='mps')

>>> 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('mps', 0)
device(type='mps', index=0)

>>> torch.device('cpu', 0)
device(type='cpu', index=0)

The device object can also be used as a context manager to change the default device tensors are allocated on:

>>> with torch.device('cuda:1'):
...     r = torch.randn(2, 3)
>>> r.device
device(type='cuda', index=1)

This context manager has no effect if a factory function is passed an explicit, non-None device argument. To globally change the default device, see also torch.set_default_device().

>>> # 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')

>>> torch.device(1)
device(type='cuda', index=1)

>>> 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 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 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.

torch.memory_format

class torch.memory_format

A torch.memory_format is an object representing the memory format on which a 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.channels_last_3d: Tensor is or will be allocated in dense non-overlapping memory. Strides represented by values in strides[0] > strides[2] > strides[3] > strides[4] > strides[1] == 1 aka NDHWC 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