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Tensor Attributes — PyTorch main documentation

Created On: Apr 21, 2018 | Last Updated On: Jun 27, 2025

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

class torch.dtype#

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

Floating point dtypes

Integer dtypes

shell(1,2,3,4,5,6,7,8,9)

a shell dtype a specialized dtype with limited op and backend support. Specifically, ops that support tensor creation (torch.empty, torch.fill, torch.zeros) and operations which do not peek inside the data elements (torch.cat, torch.view, torch.reshape) are supported. Ops that peek inside the data elements such as casting, matrix multiplication, nan/inf checks are supported only on a case by case basis, depending on maturity and presence of hardware accelerated kernels and established use cases.

Note: legacy constructors such as torch.*.FloatTensor, torch.*.DoubleTensor, torch.*.HalfTensor, torch.*.BFloat16Tensor, torch.*.ByteTensor, torch.*.CharTensor, torch.*.ShortTensor, torch.*.IntTensor, torch.*.LongTensor, torch.*.BoolTensor only remain for backwards compatibility and should no longer be used.

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. Complex types are not yet supported. Promotion for shell dtypes is not defined.

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)
>>> 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

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 (most commonly “cpu” or “cuda”, but also potentially “mps”, “xpu”, “xla” or “meta”) 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 using:

• A device string, which is a string representation of the device type and optionally the device ordinal.

• A device type and a device ordinal.

• A device ordinal, where the current accelerator type will be used.

Via a device 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')  # implicit index is the "current device index"
device(type='cuda')

Via a device type and a 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)

Via a device ordinal:

Note

This method will raise a RuntimeError if no accelerator is currently detected.

>>> torch.device(0)  # the current accelerator is cuda
device(type='cuda', index=0)

>>> torch.device(1)  # the current accelerator is xpu
device(type='xpu', index=1)

>>> torch.device(0)  # no current accelerator detected
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: Cannot access accelerator device when none is available.

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().

Warning

This function imposes a slight performance cost on every Python call to the torch API (not just factory functions). If this is causing problems for you, please comment on pytorch/pytorch#92701

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

Methods which take a device will generally accept a (properly formatted) string or an 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)  # equivalent to 'cuda:1' if the current accelerator is cuda

Note

Tensors are never moved automatically between devices and require an explicit call from the user. Scalar Tensors (with tensor.dim()==0) are the only exception to this rule and they are automatically transferred from CPU to GPU when needed as this operation can be done “for free”. Example:

>>> # two scalars
>>> torch.ones(()) + torch.ones(()).cuda()  # OK, scalar auto-transferred from CPU to GPU
>>> torch.ones(()).cuda() + torch.ones(())  # OK, scalar auto-transferred from CPU to GPU

>>> # one scalar (CPU), one vector (GPU)
>>> torch.ones(()) + torch.ones(1).cuda()  # OK, scalar auto-transferred from CPU to GPU
>>> torch.ones(1).cuda() + torch.ones(())  # OK, scalar auto-transferred from CPU to GPU

>>> # one scalar (GPU), one vector (CPU)
>>> torch.ones(()).cuda() + torch.ones(1)  # Fail, scalar not auto-transferred from GPU to CPU and non-scalar not auto-transferred from CPU to GPU
>>> torch.ones(1) + torch.ones(()).cuda()  # Fail, scalar not auto-transferred from GPU to CPU and non-scalar not auto-transferred from CPU to GPU

class torch.layout#

Warning

The torch.layout class is in beta and subject to change.

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.

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

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