torch.nested¶
Introduction¶
Warning
The PyTorch API of nested tensors is in prototype stage and will change in the near future.
NestedTensor allows the user to pack a list of Tensors into a single, efficient datastructure.
The only constraint on the input Tensors is that their dimension must match.
This enables more efficient metadata representations and access to purpose built kernels.
One application of NestedTensors is to express sequential data in various domains.
While the conventional approach is to pad variable length sequences, NestedTensor
enables users to bypass padding. The API for calling operations on a nested tensor is no different
from that of a regular torch.Tensor
, which should allow seamless integration with existing models,
with the main difference being construction of the inputs.
As this is a prototype feature, the operations supported are still limited. However, we welcome issues, feature requests and contributions. More information on contributing can be found in this Readme.
Construction¶
Construction is straightforward and involves passing a list of Tensors to the torch.nested.nested_tensor
constructor.
>>> a, b = torch.arange(3), torch.arange(5) + 3
>>> a
tensor([0, 1, 2])
>>> b
tensor([3, 4, 5, 6, 7])
>>> nt = torch.nested.nested_tensor([a, b])
>>> nt
nested_tensor([
tensor([0, 1, 2]),
tensor([3, 4, 5, 6, 7])
])
Data type, device and whether gradients are required can be chosen via the usual keyword arguments.
>>> nt = torch.nested.nested_tensor([a, b], dtype=torch.float32, device="cuda", requires_grad=True)
>>> nt
nested_tensor([
tensor([0., 1., 2.], device='cuda:0', requires_grad=True),
tensor([3., 4., 5., 6., 7.], device='cuda:0', requires_grad=True)
], device='cuda:0', requires_grad=True)
In the vein of torch.as_tensor
, torch.nested.as_nested_tensor
can be used to preserve autograd
history from the tensors passed to the constructor. For more information, refer to the section on
Nested tensor constructor and conversion functions.
In order to form a valid NestedTensor all the passed Tensors need to match in dimension, but none of the other attributes need to.
>>> a = torch.randn(3, 50, 70) # image 1
>>> b = torch.randn(3, 128, 64) # image 2
>>> nt = torch.nested.nested_tensor([a, b], dtype=torch.float32)
>>> nt.dim()
4
If one of the dimensions doesn’t match, the constructor throws an error.
>>> a = torch.randn(50, 128) # text 1
>>> b = torch.randn(3, 128, 64) # image 2
>>> nt = torch.nested.nested_tensor([a, b], dtype=torch.float32)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
RuntimeError: All Tensors given to nested_tensor must have the same dimension. Found dimension 3 for Tensor at index 1 and dimension 2 for Tensor at index 0.
Note that the passed Tensors are being copied into a contiguous piece of memory. The resulting NestedTensor allocates new memory to store them and does not keep a reference.
At this moment we only support one level of nesting, i.e. a simple, flat list of Tensors. In the future we can add support for multiple levels of nesting, such as a list that consists entirely of lists of Tensors. Note that for this extension it is important to maintain an even level of nesting across entries so that the resulting NestedTensor has a well defined dimension. If you have a need for this feature, please feel encouraged to open a feature request so that we can track it and plan accordingly.
size¶
Even though a NestedTensor does not support .size()
(or .shape
), it supports .size(i)
if dimension i is regular.
>>> a = torch.randn(50, 128) # text 1
>>> b = torch.randn(32, 128) # text 2
>>> nt = torch.nested.nested_tensor([a, b], dtype=torch.float32)
>>> nt.size(0)
2
>>> nt.size(1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
RuntimeError: Given dimension 1 is irregular and does not have a size.
>>> nt.size(2)
128
If all dimensions are regular, the NestedTensor is intended to be semantically indistinguishable from a regular torch.Tensor
.
>>> a = torch.randn(20, 128) # text 1
>>> nt = torch.nested.nested_tensor([a, a], dtype=torch.float32)
>>> nt.size(0)
2
>>> nt.size(1)
20
>>> nt.size(2)
128
>>> torch.stack(nt.unbind()).size()
torch.Size([2, 20, 128])
>>> torch.stack([a, a]).size()
torch.Size([2, 20, 128])
>>> torch.equal(torch.stack(nt.unbind()), torch.stack([a, a]))
True
In the future we might make it easier to detect this condition and convert seamlessly.
Please open a feature request if you have a need for this (or any other related feature for that matter).
unbind¶
unbind
allows you to retrieve a view of the constituents.
>>> import torch
>>> a = torch.randn(2, 3)
>>> b = torch.randn(3, 4)
>>> nt = torch.nested.nested_tensor([a, b], dtype=torch.float32)
>>> nt
nested_tensor([
tensor([[ 1.2286, 1.2343, 1.4842],
[0.7827, 0.6745, 0.0658]]),
tensor([[1.1247, 0.4078, 1.0633, 0.8083],
[0.2871, 0.2980, 0.5559, 1.9885],
[ 0.4074, 2.4855, 0.0733, 0.8285]])
])
>>> nt.unbind()
(tensor([[ 1.2286, 1.2343, 1.4842],
[0.7827, 0.6745, 0.0658]]), tensor([[1.1247, 0.4078, 1.0633, 0.8083],
[0.2871, 0.2980, 0.5559, 1.9885],
[ 0.4074, 2.4855, 0.0733, 0.8285]]))
>>> nt.unbind()[0] is not a
True
>>> nt.unbind()[0].mul_(3)
tensor([[ 3.6858, 3.7030, 4.4525],
[2.3481, 2.0236, 0.1975]])
>>> nt
nested_tensor([
tensor([[ 3.6858, 3.7030, 4.4525],
[2.3481, 2.0236, 0.1975]]),
tensor([[1.1247, 0.4078, 1.0633, 0.8083],
[0.2871, 0.2980, 0.5559, 1.9885],
[ 0.4074, 2.4855, 0.0733, 0.8285]])
])
Note that nt.unbind()[0]
is not a copy, but rather a slice of the underlying memory, which represents the first entry or constituent of the NestedTensor.
Nested tensor constructor and conversion functions¶
The following functions are related to nested tensors:
 torch.nested.nested_tensor(tensor_list, *, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False)[source]¶
Constructs a nested tensor with no autograd history (also known as a “leaf tensor”, see Autograd mechanics) from
tensor_list
a list of tensors. Parameters
tensor_list (List[array_like]) – a list of tensors, or anything that can be passed to torch.tensor,
dimensionality. (where each element of the list has the same) –
 Keyword Arguments
dtype (
torch.dtype
, optional) – the desired type of returned nested tensor. Default: if None, sametorch.dtype
as leftmost tensor in the list.layout (
torch.layout
, optional) – the desired layout of returned nested tensor. Only strided and jagged layouts are supported. Default: if None, the strided layout.device (
torch.device
, optional) – the desired device of returned nested tensor. Default: if None, sametorch.device
as leftmost tensor in the listrequires_grad (bool, optional) – If autograd should record operations on the returned nested tensor. Default:
False
.pin_memory (bool, optional) – If set, returned nested tensor would be allocated in the pinned memory. Works only for CPU tensors. Default:
False
.
 Return type
Example:
>>> a = torch.arange(3, dtype=torch.float, requires_grad=True) >>> b = torch.arange(5, dtype=torch.float, requires_grad=True) >>> nt = torch.nested.nested_tensor([a, b], requires_grad=True) >>> nt.is_leaf True
 torch.nested.as_nested_tensor(tensor_list, dtype=None, device=None, layout=None)[source]¶
Constructs a nested tensor preserving autograd history from
tensor_list
a list of tensors.Note
Tensors within the list are always copied by this function due to current nested tensor semantics.
 Parameters
tensor_list (List[Tensor]) – a list of tensors with the same ndim
 Keyword Arguments
dtype (
torch.dtype
, optional) – the desired type of returned nested tensor. Default: if None, sametorch.dtype
as leftmost tensor in the list.device (
torch.device
, optional) – the desired device of returned nested tensor. Default: if None, sametorch.device
as leftmost tensor in the listlayout (
torch.layout
, optional) – the desired layout of returned nested tensor. Only strided and jagged layouts are supported. Default: if None, the strided layout.
 Return type
Example:
>>> a = torch.arange(3, dtype=torch.float, requires_grad=True) >>> b = torch.arange(5, dtype=torch.float, requires_grad=True) >>> nt = torch.nested.as_nested_tensor([a, b]) >>> nt.is_leaf False >>> fake_grad = torch.nested.nested_tensor([torch.ones_like(a), torch.zeros_like(b)]) >>> nt.backward(fake_grad) >>> a.grad tensor([1., 1., 1.]) >>> b.grad tensor([0., 0., 0., 0., 0.])
 torch.nested.to_padded_tensor(input, padding, output_size=None, out=None) Tensor ¶
Returns a new (nonnested) Tensor by padding the
input
nested tensor. The leading entries will be filled with the nested data, while the trailing entries will be padded.Warning
to_padded_tensor()
always copies the underlying data, since the nested and the nonnested tensors differ in memory layout. Parameters
padding (float) – The padding value for the trailing entries.
 Keyword Arguments
Example:
>>> nt = torch.nested.nested_tensor([torch.randn((2, 5)), torch.randn((3, 4))]) nested_tensor([ tensor([[ 1.6862, 1.1282, 1.1031, 0.0464, 1.3276], [1.9967, 1.0054, 1.8972, 0.9174, 1.4995]]), tensor([[1.8546, 0.7194, 0.2918, 0.1846], [ 0.2773, 0.8793, 0.5183, 0.6447], [ 1.8009, 1.8468, 0.9832, 1.5272]]) ]) >>> pt_infer = torch.nested.to_padded_tensor(nt, 0.0) tensor([[[ 1.6862, 1.1282, 1.1031, 0.0464, 1.3276], [1.9967, 1.0054, 1.8972, 0.9174, 1.4995], [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]], [[1.8546, 0.7194, 0.2918, 0.1846, 0.0000], [ 0.2773, 0.8793, 0.5183, 0.6447, 0.0000], [ 1.8009, 1.8468, 0.9832, 1.5272, 0.0000]]]) >>> pt_large = torch.nested.to_padded_tensor(nt, 1.0, (2, 4, 6)) tensor([[[ 1.6862, 1.1282, 1.1031, 0.0464, 1.3276, 1.0000], [1.9967, 1.0054, 1.8972, 0.9174, 1.4995, 1.0000], [ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000], [ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]], [[1.8546, 0.7194, 0.2918, 0.1846, 1.0000, 1.0000], [ 0.2773, 0.8793, 0.5183, 0.6447, 1.0000, 1.0000], [ 1.8009, 1.8468, 0.9832, 1.5272, 1.0000, 1.0000], [ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]]]) >>> pt_small = torch.nested.to_padded_tensor(nt, 2.0, (2, 2, 2)) RuntimeError: Value in output_size is less than NestedTensor padded size. Truncation is not supported.
Supported operations¶
In this section, we summarize the operations that are currently supported on NestedTensor and any constraints they have.
PyTorch operation 
Constraints 

Supports matrix multiplication between two (>= 3d) nested tensors where the last two dimensions are matrix dimensions and the leading (batch) dimensions have the same size (i.e. no broadcasting support for batch dimensions yet). 

Supports batch matrix multiplication of two 3d nested tensors. 

Supports 3d nested input and a dense 2d weight matrix. 

Supports softmax along all dims except dim=0. 

Behavior is the same as on regular tensors. 

Behavior is the same as on regular tensors. 


Behavior is the same as on regular tensors. 

Behavior is the same as on regular tensors. 

Behavior is the same as on regular tensors. 
Behavior is the same as on regular tensors. 

Behavior is the same as on regular tensors. 

Behavior is the same as on regular tensors. 

Behavior is the same as on regular tensors. 

Supports elementwise subtraction of two nested tensors. 

Supports elementwise addition of two nested tensors. Supports addition of a scalar to a nested tensor. 

Supports elementwise multiplication of two nested tensors. Supports multiplication of a nested tensor by a scalar. 

Supports selecting along all dimensions. 

Behavior is the same as on regular tensors. 


Behavior is the same as on regular tensors. 
Supports unbinding along 

Supports reshaping with size of 

Similar constraint as for 

Supports transposing of all dims except 

Rules for the new shape are similar to that of 

Behavior is analogous to that of regular tensors; returns a new empty nested tensor (i.e. with uninitialized values) matching the nested structure of the input. 

Behavior is analogous to that of regular tensors; returns a new nested tensor with values randomly initialized according to a standard normal distribution matching the nested structure of the input. 

Behavior is analogous to that of regular tensors; returns a new nested tensor with all zero values matching the nested structure of the input. 

The 