Source code for torch.nested
from typing import List, Optional, Union
import torch
from torch import SymInt, Tensor
from torch._C import _add_docstr, _nested # type: ignore[attr-defined]
from torch.types import _device as Device, _dtype as DType
__all__ = [
"to_padded_tensor",
"as_nested_tensor",
"nested_tensor",
"narrow",
]
# Nested Tensor constructor functions
[docs]def as_nested_tensor(
tensor_list: List[Tensor],
dtype: Optional[DType] = None,
device: Optional[Device] = None,
layout=None
) -> Tensor:
r"""
Constructs a nested tensor preserving autograd history from :attr:`tensor_list` a list of tensors.
.. note::
Tensors within the list are always copied by this function due to current nested tensor semantics.
Args:
tensor_list (List[Tensor]): a list of tensors with the same ndim
Keyword arguments:
dtype (:class:`torch.dtype`, optional): the desired type of returned nested tensor.
Default: if None, same :class:`torch.dtype` as leftmost tensor in the list.
device (:class:`torch.device`, optional): the desired device of returned nested tensor.
Default: if None, same :class:`torch.device` as leftmost tensor in the list
layout (:class:`torch.layout`, optional): the desired layout of returned nested tensor.
Only strided and jagged layouts are supported. Default: if None, the strided layout.
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.])
"""
if not isinstance(tensor_list, list) or any(
not isinstance(t, Tensor) for t in tensor_list
):
raise TypeError(
"as_nested_tensor(): Expected first argument to be a list of tensors "
)
if layout is None:
layout = torch.strided
if layout == torch.strided:
return torch._nested_tensor_from_tensor_list(tensor_list, dtype, None, device, None)
elif layout == torch.jagged:
from torch.nested._internal.nested_tensor import jagged_from_list
nt, _ = jagged_from_list(tensor_list, offsets=None, device=device, dtype=dtype)
return nt
else:
raise RuntimeError(f"Specified layout is unsupported for nested tensors: {layout}")
# 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 :attr:`input` 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.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.
""",
)
[docs]def nested_tensor(tensor_list, *, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) -> Tensor:
r"""
Constructs a nested tensor with no autograd history (also known as a “leaf tensor”, see
:ref:`Autograd mechanics <autograd-mechanics>`) from :attr:`tensor_list` a list of tensors.
Args:
tensor_list (List[array_like]): a list of tensors, or anything that can be passed to torch.tensor,
where each element of the list has the same dimensionality.
Keyword arguments:
dtype (:class:`torch.dtype`, optional): the desired type of returned nested tensor.
Default: if None, same :class:`torch.dtype` as leftmost tensor in the list.
layout (:class:`torch.layout`, optional): the desired layout of returned nested tensor.
Only strided and jagged layouts are supported. Default: if None, the strided layout.
device (:class:`torch.device`, optional): the desired device of returned nested tensor.
Default: if None, same :class:`torch.device` as leftmost tensor in the list
requires_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``.
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
"""
if layout is None:
layout = torch.strided
if layout == torch.strided:
return _nested.nested_tensor(
tensor_list,
dtype=dtype,
device=device,
requires_grad=requires_grad,
pin_memory=pin_memory)
elif layout == torch.jagged:
# Need to:
# * Detach tensors to discard autograd history
# * Wrap lists of scalars as tensors
list_of_tensors = [t.detach() if isinstance(t, Tensor) else torch.as_tensor(t)
for t in tensor_list]
from torch.nested._internal.nested_tensor import jagged_from_list
with torch.no_grad():
nt, _ = jagged_from_list(list_of_tensors, offsets=None, device=device, dtype=dtype)
nt.requires_grad_(requires_grad)
if pin_memory:
nt = nt.pin_memory() # type: ignore[assignment]
return nt
else:
raise RuntimeError(f"Specified layout is unsupported for nested tensors: {layout}")
def narrow(tensor: Tensor, dim: int, start: Union[int, Tensor], length: Union[int, Tensor], layout=torch.strided) -> Tensor:
r"""
Constructs a nested tensor (which might be a view) from :attr:`tensor`, a strided tensor. This follows
similar semantics to torch.Tensor.narrow, where in the :attr:`dim`-th dimension the new nested tensor
shows only the elements in the interval `[start, start+length)`. As nested representations
allow for a different `start` and `length` at each 'row' of that dimension, :attr:`start` and :attr:`length`
can also be tensors of shape `tensor.shape[0]`.
There's some differences depending on the layout you use for the nested tensor. If using strided layout,
torch.narrow will do a copy of the narrowed data into a contiguous NT with strided layout, while
jagged layout narrow() will create a non-contiguous view of your original strided tensor. This particular
representation is really useful for representing kv-caches in Transformer models, as specialized
SDPA kernels can deal with format easily, resulting in performance improvements.
Args:
tensor (:class:`torch.Tensor`): a strided tensor, which will be used as the underlying data
for the nested tensor if using the jagged layout or will be copied for the strided layout.
dim (int): the dimension where narrow will be applied. Only `dim=1` is supported for the
jagged layout, while strided supports all dim
start (Union[int, :class:`torch.Tensor`]): starting element for the narrow operation
length (Union[int, :class:`torch.Tensor`]): number of elements taken during the narrow op
Keyword arguments:
layout (:class:`torch.layout`, optional): the desired layout of returned nested tensor.
Only strided and jagged layouts are supported. Default: if None, the strided layout.
Example::
>>> starts = torch.tensor([0, 1, 2, 3, 4], dtype=torch.int64)
>>> lengths = torch.tensor([3, 2, 2, 1, 5], dtype=torch.int64)
>>> narrow_base = torch.randn(5, 10, 20)
>>> nt_narrowed = torch.nested.narrow(narrow_base, 1, starts, lengths, layout=torch.jagged)
>>> nt_narrowed.is_contiguous()
False
"""
if not isinstance(start, (int, SymInt, Tensor)):
raise RuntimeError("start must be an integer or a tensor")
if not isinstance(length, (int, SymInt, Tensor)):
raise RuntimeError("length must be an integer or a tensor")
if layout == torch.strided:
if isinstance(start, Tensor) or isinstance(length, Tensor):
raise RuntimeError("start and length must be integers for the strided layout NT impl")
# TODO: switch to as_nested_tensor(tensor) when it is available
nt = as_nested_tensor(torch.unbind(tensor), layout=torch.strided).narrow(dim, start, length)
elif layout == torch.jagged:
if dim != 1:
raise RuntimeError("jagged layout only supports dim=1")
from torch.nested._internal.nested_tensor import jagged_from_tensor_and_lengths
if isinstance(start, (int, SymInt)):
start = torch.tensor([start], device=tensor.device, dtype=torch.int64)
if isinstance(length, (int, SymInt)):
length = torch.tensor([length], device=tensor.device, dtype=torch.int64)
nt, _, _ = jagged_from_tensor_and_lengths(tensor, start, length)
else:
raise RuntimeError(f"Specified layout is unsupported for nested narrow: {layout}")
return nt
