torch/nested/__init__.py

import torch
from torch._C import _add_docstr, _nested  # type: ignore[attr-defined]

__all__ = [
    'to_padded_tensor'
]

Tensor = torch.Tensor

# Note: This not only adds doc strings for the nested ops, but
# also connects the torch.nested Python namespace to the torch._C._nested builtins.

to_padded_tensor = _add_docstr(_nested.nested_to_padded_tensor,
                               r"""
to_padded_tensor(input, padding, output_size=None, out=None) -> Tensor

Returns a new (non-nested) Tensor by padding the nested tensor.
The leading entries will be filled with the nested data,
while the trailing entries will be padded.

.. warning::

    :func:`to_padded_tensor` always copies the underlying data,
    since the nested and the non-nested tensors differ in memory layout.

Args:
    padding (float): The padding value for the trailing entries.

Keyword args:
    output_size (Tuple[int]): The size of the output tensor.
                              If given, it must be large enough to contain all nested data;
                              else, will infer by taking the max size of each nested sub-tensor along each dimension.
    out (Tensor, optional): the output tensor.

Example::

    >>> nt = torch.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.

""")