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Source code for torch.nested

from typing import List, Optional, Union, Sequence

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: Sequence[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 wrap lists of scalars as tensors list_of_tensors = [t 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

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