Named Tensors
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:
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_imgs = imgs.view(32, 3, 128, 128)
>>> unflattened_named_imgs = named_flat_imgs.unflatten(
'features', [('C', 3), ('H', 128), ('W', 128)])
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:
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 dimensionidx
. Names are either a string if the dimension is named orNone
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)[source]¶ 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 mappingrename_map
.self.rename(*names)
returns a view on tensor, renaming all dimensions positionally usingnames
. Useself.rename(None)
to drop names on a tensor.One cannot specify both positional args
names
and keyword argsrename_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,) >>> 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.
-
refine_names
(*names)[source]¶ Refines the dimension names of
self
according tonames
.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 fillnames
to the same length asself.dim()
using names from the corresponding indices ofself.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 theother
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 inother.names
.other
may contain named dimensions that are not inself.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)[source]¶ Permutes the dimensions of the
self
tensor to match the order specified innames
, 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 innames
.names
may contain additional names that are not inself.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 ofself
that are not mentioned innames
, in the order that they appear inself
.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.
-
unflatten
(dim, sizes)[source]¶ Expands the dimension
dim
of theself
tensor over multiple dimensions of sizes given bysizes
.sizes
is the new shape of the unflattened dimension and it can be a Tuple[int] as well as torch.Size ifself
is a Tensor, or namedshape (Tuple[(name: str, size: int)]) ifself
is a NamedTensor. The total number of elements in sizes must match the number of elements in the original dim being unflattened.
- Parameters
Examples
>>> torch.randn(3, 4, 1).unflatten(1, (2, 2)).shape torch.Size([3, 2, 2, 1]) >>> torch.randn(2, 4, names=('A', 'B')).unflatten('B', (('B1', 2), ('B2', 2))) tensor([[[-1.1772, 0.0180], [ 0.2412, 0.1431]],
[[-1.1819, -0.8899], [ 1.5813, 0.2274]]], names=(‘A’, ‘B1’, ‘B2’))
Warning
The named tensor API is experimental and subject to change.
-
flatten
(dims, out_dim) → Tensor Flattens
dims
into a single dimension with nameout_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.
-
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