Named Tensors

Named Tensors allow users to give explicit names to tensor dimensions. In most cases, operations that take dimension parameters will accept dimension names, avoiding the need to track dimensions by position. In addition, named tensors use names to automatically check that APIs are being used correctly at runtime, providing extra safety. Names can also be used to rearrange dimensions, for example, to support “broadcasting by name” rather than “broadcasting by position”.

Warning

The named tensor API is a prototype feature and subject to change.

Creating named tensors

Factory functions now take a new names argument that associates a name with each dimension.

>>> torch.zeros(2, 3, names=('N', 'C'))
tensor([[0., 0., 0.],
        [0., 0., 0.]], names=('N', 'C'))

Named dimensions, like regular Tensor dimensions, are ordered. tensor.names[i] is the name of dimension i of tensor.

The following factory functions support named tensors:

• torch.empty()

• torch.rand()

• torch.randn()

• torch.ones()

• torch.tensor()

• torch.zeros()

Named dimensions

See names for restrictions on tensor names.

Use names to access the dimension names of a tensor and rename() to rename named dimensions.

>>> imgs = torch.randn(1, 2, 2, 3 , names=('N', 'C', 'H', 'W'))
>>> imgs.names
('N', 'C', 'H', 'W')

>>> renamed_imgs = imgs.rename(H='height', W='width')
>>> renamed_imgs.names
('N', 'C', 'height', 'width)

Named tensors can coexist with unnamed tensors; named tensors are instances of torch.Tensor. Unnamed tensors have None-named dimensions. Named tensors do not require all dimensions to be named.

>>> imgs = torch.randn(1, 2, 2, 3 , names=(None, 'C', 'H', 'W'))
>>> imgs.names
(None, 'C', 'H', 'W')

Name propagation semantics

Named tensors use names to automatically check that APIs are being called correctly at runtime. This occurs in a process called name inference. More formally, name inference consists of the following two steps:

• Check names: an operator may perform automatic checks at runtime that check that certain dimension names must match.

• Propagate names: name inference propagates names to output tensors.

All operations that support named tensors propagate names.

>>> x = torch.randn(3, 3, names=('N', 'C'))
>>> x.abs().names
('N', 'C')

match semantics

Two names match if they are equal (string equality) or if at least one is None. Nones are essentially a special “wildcard” name.

unify(A, B) determines which of the names A and B to propagate to the outputs. It returns the more specific of the two names, if they match. If the names do not match, then it errors.

Note

In practice, when working with named tensors, one should avoid having unnamed dimensions because their handling can be complicated. It is recommended to lift all unnamed dimensions to be named dimensions by using refine_names().

Basic name inference rules

Let’s see how match and unify are used in name inference in the case of adding two one-dim tensors with no broadcasting.

x = torch.randn(3, names=('X',))
y = torch.randn(3)
z = torch.randn(3, names=('Z',))

Check names: check that the names of the two tensors match.

For the following examples:

>>> # x + y  # match('X', None) is True
>>> # x + z  # match('X', 'Z') is False
>>> # x + x  # match('X', 'X') is True

>>> x + z
Error when attempting to broadcast dims ['X'] and dims ['Z']: dim 'X' and dim 'Z' are at the same position from the right but do not match.

Propagate names: unify the names to select which one to propagate. In the case of x + y, unify('X', None) = 'X' because 'X' is more specific than None.

>>> (x + y).names
('X',)
>>> (x + x).names
('X',)

For a comprehensive list of name inference rules, see Named Tensors operator coverage. Here are two common operations that may be useful to go over:

• Binary arithmetic ops: Unifies names from inputs

• Matrix multiplication ops: Contracts away dims

Explicit alignment by names

Use align_as() or align_to() to align tensor dimensions by name to a specified ordering. This is useful for performing “broadcasting by names”.

# This function is agnostic to the dimension ordering of `input`,
# as long as it has a `C` dimension somewhere.
def scale_channels(input, scale):
    scale = scale.refine_names('C')
    return input * scale.align_as(input)

>>> num_channels = 3
>>> scale = torch.randn(num_channels, names=('C',))
>>> imgs = torch.rand(3, 3, 3, num_channels, names=('N', 'H', 'W', 'C'))
>>> more_imgs = torch.rand(3, num_channels, 3, 3, names=('N', 'C', 'H', 'W'))
>>> videos = torch.randn(3, num_channels, 3, 3, 3, names=('N', 'C', 'H', 'W', 'D')

>>> scale_channels(imgs, scale)
>>> scale_channels(more_imgs, scale)
>>> scale_channels(videos, scale)

Manipulating dimensions

Use align_to() to permute large amounts of dimensions without mentioning all of them as in required by permute().

>>> tensor = torch.randn(2, 2, 2, 2, 2, 2)
>>> named_tensor = tensor.refine_names('A', 'B', 'C', 'D', 'E', 'F')

# Move the F (dim 5) and E dimension (dim 4) to the front while keeping
# the rest in the same order
>>> tensor.permute(5, 4, 0, 1, 2, 3)
>>> named_tensor.align_to('F', 'E', ...)

Use flatten() and unflatten() to flatten and unflatten dimensions, respectively. These methods are more verbose than view() and reshape(), but have more semantic meaning to someone reading the code.

>>> imgs = torch.randn(32, 3, 128, 128)
>>> named_imgs = imgs.refine_names('N', 'C', 'H', 'W')

>>> flat_imgs = imgs.view(32, -1)
>>> named_flat_imgs = named_imgs.flatten(['C', 'H', 'W'], 'features')
>>> named_flat_imgs.names
('N', 'features')

>>> unflattened_named_imgs = named_flat_imgs.unflatten('features', [('C', 3), ('H', 128), ('W', 128)])
>>> unflattened_named_imgs.names
('N', 'C', 'H', 'W')

Autograd support

Autograd currently supports named tensors in a limited manner: autograd ignores names on all tensors. Gradient computation is still correct but we lose the safety that names give us.

>>> x = torch.randn(3, names=('D',))
>>> weight = torch.randn(3, names=('D',), requires_grad=True)
>>> loss = (x - weight).abs()
>>> grad_loss = torch.randn(3)
>>> loss.backward(grad_loss)
>>> weight.grad  # Unnamed for now. Will be named in the future
tensor([-1.8107, -0.6357,  0.0783])

>>> weight.grad.zero_()
>>> grad_loss = grad_loss.refine_names('C')
>>> loss = (x - weight).abs()
# Ideally we'd check that the names of loss and grad_loss match but we don't yet.
>>> loss.backward(grad_loss)
>>> weight.grad
tensor([-1.8107, -0.6357,  0.0783])

Currently supported operations and subsystems

Operators

See Named Tensors operator coverage for a full list of the supported torch and tensor operations. We do not yet support the following that is not covered by the link:

• indexing, advanced indexing.

For torch.nn.functional operators, we support the following:

• torch.nn.functional.relu()

• torch.nn.functional.softmax()

• torch.nn.functional.log_softmax()

• torch.nn.functional.tanh()

• torch.nn.functional.sigmoid()

• torch.nn.functional.dropout()

Subsystems

Autograd is supported, see Autograd support. Because gradients are currently unnamed, optimizers may work but are untested.

NN modules are currently unsupported. This can lead to the following when calling modules with named tensor inputs:

• NN module parameters are unnamed, so outputs may be partially named.

• NN module forward passes have code that don’t support named tensors and will error out appropriately.

We also do not support the following subsystems, though some may work out of the box:

• distributions

• serialization (torch.load(), torch.save())

• multiprocessing

• JIT

• distributed

• ONNX

If any of these would help your use case, please search if an issue has already been filed and if not, file one.

Named tensor API reference

In this section please find the documentation for named tensor specific APIs. For a comprehensive reference for how names are propagated through other PyTorch operators, see Named Tensors operator coverage.

class torch.Tensor

names

Stores names for each of this tensor’s dimensions.

names[idx] corresponds to the name of tensor dimension idx. Names are either a string if the dimension is named or None if the dimension is unnamed.

Dimension names may contain characters or underscore. Furthermore, a dimension name must be a valid Python variable name (i.e., does not start with underscore).

Tensors may not have two named dimensions with the same name.

Warning

The named tensor API is experimental and subject to change.

rename(*names, **rename_map)

Renames dimension names of self.

There are two main usages:

self.rename(**rename_map) returns a view on tensor that has dims renamed as specified in the mapping rename_map.

self.rename(*names) returns a view on tensor, renaming all dimensions positionally using names. Use self.rename(None) to drop names on a tensor.

One cannot specify both positional args names and keyword args rename_map.

Examples:

>>> imgs = torch.rand(2, 3, 5, 7, names=('N', 'C', 'H', 'W'))
>>> renamed_imgs = imgs.rename(N='batch', C='channels')
>>> renamed_imgs.names
('batch', 'channels', 'H', 'W')

>>> renamed_imgs = imgs.rename(None)
>>> renamed_imgs.names
(None, None, None, None)

>>> renamed_imgs = imgs.rename('batch', 'channel', 'height', 'width')
>>> renamed_imgs.names
('batch', 'channel', 'height', 'width')

Warning

The named tensor API is experimental and subject to change.

rename_(*names, **rename_map)

In-place version of rename().

refine_names(*names)

Refines the dimension names of self according to names.

Refining is a special case of renaming that “lifts” unnamed dimensions. A None dim can be refined to have any name; a named dim can only be refined to have the same name.

Because named tensors can coexist with unnamed tensors, refining names gives a nice way to write named-tensor-aware code that works with both named and unnamed tensors.

names may contain up to one Ellipsis (...). The Ellipsis is expanded greedily; it is expanded in-place to fill names to the same length as self.dim() using names from the corresponding indices of self.names.

Python 2 does not support Ellipsis but one may use a string literal instead ('...').

Parameters

names (iterable of str) – The desired names of the output tensor. May contain up to one Ellipsis.

Examples:

>>> imgs = torch.randn(32, 3, 128, 128)
>>> named_imgs = imgs.refine_names('N', 'C', 'H', 'W')
>>> named_imgs.names
('N', 'C', 'H', 'W')

>>> tensor = torch.randn(2, 3, 5, 7, 11)
>>> tensor = tensor.refine_names('A', ..., 'B', 'C')
>>> tensor.names
('A', None, None, 'B', 'C')

Warning

The named tensor API is experimental and subject to change.

align_as(other) → Tensor

Permutes the dimensions of the self tensor to match the dimension order in the other tensor, adding size-one dims for any new names.

This operation is useful for explicit broadcasting by names (see examples).

All of the dims of self must be named in order to use this method. The resulting tensor is a view on the original tensor.

All dimension names of self must be present in other.names. other may contain named dimensions that are not in self.names; the output tensor has a size-one dimension for each of those new names.

To align a tensor to a specific order, use align_to().

Examples:

# Example 1: Applying a mask
>>> mask = torch.randint(2, [127, 128], dtype=torch.bool).refine_names('W', 'H')
>>> imgs = torch.randn(32, 128, 127, 3, names=('N', 'H', 'W', 'C'))
>>> imgs.masked_fill_(mask.align_as(imgs), 0)


# Example 2: Applying a per-channel-scale
>>> def scale_channels(input, scale):
>>>    scale = scale.refine_names('C')
>>>    return input * scale.align_as(input)

>>> num_channels = 3
>>> scale = torch.randn(num_channels, names=('C',))
>>> imgs = torch.rand(32, 128, 128, num_channels, names=('N', 'H', 'W', 'C'))
>>> more_imgs = torch.rand(32, num_channels, 128, 128, names=('N', 'C', 'H', 'W'))
>>> videos = torch.randn(3, num_channels, 128, 128, 128, names=('N', 'C', 'H', 'W', 'D'))

# scale_channels is agnostic to the dimension order of the input
>>> scale_channels(imgs, scale)
>>> scale_channels(more_imgs, scale)
>>> scale_channels(videos, scale)

Warning

The named tensor API is experimental and subject to change.

align_to(*names)

Permutes the dimensions of the self tensor to match the order specified in names, adding size-one dims for any new names.

All of the dims of self must be named in order to use this method. The resulting tensor is a view on the original tensor.

All dimension names of self must be present in names. names may contain additional names that are not in self.names; the output tensor has a size-one dimension for each of those new names.

names may contain up to one Ellipsis (...). The Ellipsis is expanded to be equal to all dimension names of self that are not mentioned in names, in the order that they appear in self.

Python 2 does not support Ellipsis but one may use a string literal instead ('...').

Parameters

names (iterable of str) – The desired dimension ordering of the output tensor. May contain up to one Ellipsis that is expanded to all unmentioned dim names of self.

Examples:

>>> tensor = torch.randn(2, 2, 2, 2, 2, 2)
>>> named_tensor = tensor.refine_names('A', 'B', 'C', 'D', 'E', 'F')

# Move the F and E dims to the front while keeping the rest in order
>>> named_tensor.align_to('F', 'E', ...)

Warning

The named tensor API is experimental and subject to change.

flatten(dims, out_dim) → Tensor

Flattens dims into a single dimension with name out_dim.

All of dims must be consecutive in order in the self tensor, but not necessary contiguous in memory.

Examples:

>>> imgs = torch.randn(32, 3, 128, 128, names=('N', 'C', 'H', 'W'))
>>> flat_imgs = imgs.flatten(['C', 'H', 'W'], 'features')
>>> flat_imgs.names, flat_imgs.shape
(('N', 'features'), torch.Size([32, 49152]))

Warning

The named tensor API is experimental and subject to change.