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pytorch/torch/functional.py
lezcano a35b4b49d2 Add linalg.lu_factor (#66933) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66933

This PR exposes `torch.lu` as `torch.linalg.lu_factor` and
`torch.linalg.lu_factor_ex`.

This PR also adds support for matrices with zero elements both in
the size of the matrix and the batch. Note that this function simply
returns empty tensors of the correct size in this case.

We add a test and an OpInfo for the new function.

This PR also adds documentation for this new function in line of
the documentation in the rest of `torch.linalg`.

Fixes https://github.com/pytorch/pytorch/issues/56590
Fixes https://github.com/pytorch/pytorch/issues/64014

cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano

Test Plan: Imported from OSS

Reviewed By: gchanan

Differential Revision: D32834069

Pulled By: mruberry

fbshipit-source-id: 51ef12535fa91d292f419acf83b800b86ee9c7eb
2022-01-05 20:32:12 -08:00

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from typing import (
Tuple, Optional, Union, Any, Sequence, TYPE_CHECKING
)
from collections import namedtuple
import itertools
import torch
import torch.nn.functional as F
from ._lowrank import svd_lowrank, pca_lowrank
from .overrides import (
has_torch_function, has_torch_function_unary, has_torch_function_variadic,
handle_torch_function)
from ._jit_internal import boolean_dispatch, List
from ._jit_internal import _overload as overload
Tensor = torch.Tensor
from torch import _VF
__all__ = [
'atleast_1d',
'atleast_2d',
'atleast_3d',
'align_tensors',
'broadcast_shapes',
'broadcast_tensors',
'cartesian_prod',
'block_diag',
'cdist',
'chain_matmul',
'einsum',
'histogramdd',
'istft',
'lu',
'norm',
'meshgrid',
'pca_lowrank',
'split',
'stft',
'svd_lowrank',
'tensordot',
'unique',
'unique_consecutive',
]
def broadcast_tensors(*tensors):
r"""broadcast_tensors(*tensors) -> List of Tensors
Broadcasts the given tensors according to :ref:`broadcasting-semantics`.
Args:
*tensors: any number of tensors of the same type
.. warning::
More than one element of a broadcasted tensor may refer to a single
memory location. As a result, in-place operations (especially ones that
are vectorized) may result in incorrect behavior. If you need to write
to the tensors, please clone them first.
Example::
>>> x = torch.arange(3).view(1, 3)
>>> y = torch.arange(2).view(2, 1)
>>> a, b = torch.broadcast_tensors(x, y)
>>> a.size()
torch.Size([2, 3])
>>> a
tensor([[0, 1, 2],
[0, 1, 2]])
"""
# This wrapper exists to support variadic args.
if has_torch_function(tensors):
return handle_torch_function(broadcast_tensors, tensors, *tensors)
return _VF.broadcast_tensors(tensors) # type: ignore[attr-defined]
def broadcast_shapes(*shapes):
r"""broadcast_shapes(*shapes) -> Size
Similar to :func:`broadcast_tensors` but for shapes.
This is equivalent to
``torch.broadcast_tensors(*map(torch.empty, shapes))[0].shape``
but avoids the need create to intermediate tensors. This is useful for
broadcasting tensors of common batch shape but different rightmost shape,
e.g. to broadcast mean vectors with covariance matrices.
Example::
>>> torch.broadcast_shapes((2,), (3, 1), (1, 1, 1))
torch.Size([1, 3, 2])
Args:
\*shapes (torch.Size): Shapes of tensors.
Returns:
shape (torch.Size): A shape compatible with all input shapes.
Raises:
RuntimeError: If shapes are incompatible.
"""
# This wrapper exists to support variadic args.
# TODO Movie this to C++ once the jit has better support for torch.Size.
with torch.no_grad():
scalar = torch.zeros((), device="cpu")
tensors = [scalar.expand(shape) for shape in shapes]
tensors = broadcast_tensors(*tensors)
return tensors[0].shape
def split(tensor, split_size_or_sections, dim=0):
r"""Splits the tensor into chunks. Each chunk is a view of the original tensor.
If :attr:`split_size_or_sections` is an integer type, then :attr:`tensor` will
be split into equally sized chunks (if possible). Last chunk will be smaller if
the tensor size along the given dimension :attr:`dim` is not divisible by
:attr:`split_size`.
If :attr:`split_size_or_sections` is a list, then :attr:`tensor` will be split
into ``len(split_size_or_sections)`` chunks with sizes in :attr:`dim` according
to :attr:`split_size_or_sections`.
Args:
tensor (Tensor): tensor to split.
split_size_or_sections (int) or (list(int)): size of a single chunk or
list of sizes for each chunk
dim (int): dimension along which to split the tensor.
Example::
>>> a = torch.arange(10).reshape(5,2)
>>> a
tensor([[0, 1],
[2, 3],
[4, 5],
[6, 7],
[8, 9]])
>>> torch.split(a, 2)
(tensor([[0, 1],
[2, 3]]),
tensor([[4, 5],
[6, 7]]),
tensor([[8, 9]]))
>>> torch.split(a, [1,4])
(tensor([[0, 1]]),
tensor([[2, 3],
[4, 5],
[6, 7],
[8, 9]]))
"""
if has_torch_function_unary(tensor):
return handle_torch_function(
split, (tensor,), tensor, split_size_or_sections, dim=dim)
# Overwriting reason:
# This dispatches to two ATen functions depending on the type of
# split_size_or_sections. The branching code is in _tensor.py, which we
# call here.
return tensor.split(split_size_or_sections, dim)
def einsum(*args):
r"""einsum(equation, *operands) -> Tensor
Sums the product of the elements of the input :attr:`operands` along dimensions specified using a notation
based on the Einstein summation convention.
Einsum allows computing many common multi-dimensional linear algebraic array operations by representing them
in a short-hand format based on the Einstein summation convention, given by :attr:`equation`. The details of
this format are described below, but the general idea is to label every dimension of the input :attr:`operands`
with some subscript and define which subscripts are part of the output. The output is then computed by summing
the product of the elements of the :attr:`operands` along the dimensions whose subscripts are not part of the
output. For example, matrix multiplication can be computed using einsum as `torch.einsum("ij,jk->ik", A, B)`.
Here, j is the summation subscript and i and k the output subscripts (see section below for more details on why).
Equation:
The :attr:`equation` string specifies the subscripts (letters in `[a-zA-Z]`) for each dimension of
the input :attr:`operands` in the same order as the dimensions, separating subcripts for each operand by a
comma (','), e.g. `'ij,jk'` specify subscripts for two 2D operands. The dimensions labeled with the same subscript
must be broadcastable, that is, their size must either match or be `1`. The exception is if a subscript is
repeated for the same input operand, in which case the dimensions labeled with this subscript for this operand
must match in size and the operand will be replaced by its diagonal along these dimensions. The subscripts that
appear exactly once in the :attr:`equation` will be part of the output, sorted in increasing alphabetical order.
The output is computed by multiplying the input :attr:`operands` element-wise, with their dimensions aligned based
on the subscripts, and then summing out the dimensions whose subscripts are not part of the output.
Optionally, the output subscripts can be explicitly defined by adding an arrow ('->') at the end of the equation
followed by the subscripts for the output. For instance, the following equation computes the transpose of a
matrix multiplication: 'ij,jk->ki'. The output subscripts must appear at least once for some input operand and
at most once for the output.
Ellipsis ('...') can be used in place of subscripts to broadcast the dimensions covered by the ellipsis.
Each input operand may contain at most one ellipsis which will cover the dimensions not covered by subscripts,
e.g. for an input operand with 5 dimensions, the ellipsis in the equation `'ab...c'` cover the third and fourth
dimensions. The ellipsis does not need to cover the same number of dimensions across the :attr:`operands` but the
'shape' of the ellipsis (the size of the dimensions covered by them) must broadcast together. If the output is not
explicitly defined with the arrow ('->') notation, the ellipsis will come first in the output (left-most dimensions),
before the subscript labels that appear exactly once for the input operands. e.g. the following equation implements
batch matrix multiplication `'...ij,...jk'`.
A few final notes: the equation may contain whitespaces between the different elements (subscripts, ellipsis,
arrow and comma) but something like `'. . .'` is not valid. An empty string `''` is valid for scalar operands.
.. note::
``torch.einsum`` handles ellipsis ('...') differently from NumPy in that it allows dimensions
covered by the ellipsis to be summed over, that is, ellipsis are not required to be part of the output.
.. note::
This function does not optimize the given expression, so a different formula for the same computation may
run faster or consume less memory. Projects like opt_einsum (https://optimized-einsum.readthedocs.io/en/stable/)
can optimize the formula for you.
.. note::
As of PyTorch 1.10 :func:`torch.einsum` also supports the sublist format (see examples below). In this format,
subscripts for each operand are specified by sublists, list of integers in the range [0, 52). These sublists
follow their operands, and an extra sublist can appear at the end of the input to specify the output's
subscripts., e.g. `torch.einsum(op1, sublist1, op2, sublist2, ..., [subslist_out])`. Python's `Ellipsis` object
may be provided in a sublist to enable broadcasting as described in the Equation section above.
Args:
equation (string): The subscripts for the Einstein summation.
operands (List[Tensor]): The tensors to compute the Einstein summation of.
Examples::
# trace
>>> torch.einsum('ii', torch.randn(4, 4))
tensor(-1.2104)
# diagonal
>>> torch.einsum('ii->i', torch.randn(4, 4))
tensor([-0.1034, 0.7952, -0.2433, 0.4545])
# outer product
>>> x = torch.randn(5)
>>> y = torch.randn(4)
>>> torch.einsum('i,j->ij', x, y)
tensor([[ 0.1156, -0.2897, -0.3918, 0.4963],
[-0.3744, 0.9381, 1.2685, -1.6070],
[ 0.7208, -1.8058, -2.4419, 3.0936],
[ 0.1713, -0.4291, -0.5802, 0.7350],
[ 0.5704, -1.4290, -1.9323, 2.4480]])
# batch matrix multiplication
>>> As = torch.randn(3,2,5)
>>> Bs = torch.randn(3,5,4)
>>> torch.einsum('bij,bjk->bik', As, Bs)
tensor([[[-1.0564, -1.5904, 3.2023, 3.1271],
[-1.6706, -0.8097, -0.8025, -2.1183]],
[[ 4.2239, 0.3107, -0.5756, -0.2354],
[-1.4558, -0.3460, 1.5087, -0.8530]],
[[ 2.8153, 1.8787, -4.3839, -1.2112],
[ 0.3728, -2.1131, 0.0921, 0.8305]]])
# with sublist format and ellipsis
>>> torch.einsum(As, [..., 0, 1], Bs, [..., 1, 2], [..., 0, 2])
tensor([[[-1.0564, -1.5904, 3.2023, 3.1271],
[-1.6706, -0.8097, -0.8025, -2.1183]],
[[ 4.2239, 0.3107, -0.5756, -0.2354],
[-1.4558, -0.3460, 1.5087, -0.8530]],
[[ 2.8153, 1.8787, -4.3839, -1.2112],
[ 0.3728, -2.1131, 0.0921, 0.8305]]])
# batch permute
>>> A = torch.randn(2, 3, 4, 5)
>>> torch.einsum('...ij->...ji', A).shape
torch.Size([2, 3, 5, 4])
# equivalent to torch.nn.functional.bilinear
>>> A = torch.randn(3,5,4)
>>> l = torch.randn(2,5)
>>> r = torch.randn(2,4)
>>> torch.einsum('bn,anm,bm->ba', l, A, r)
tensor([[-0.3430, -5.2405, 0.4494],
[ 0.3311, 5.5201, -3.0356]])
"""
# This wrapper exists to support variadic args.
if len(args) < 2:
raise ValueError('einsum(): must specify the equation string and at least one operand, '
'or at least one operand and its subscripts list')
equation = None
operands = None
if isinstance(args[0], torch.Tensor):
# Convert the subscript list format which is an interleaving of operand and its subscripts
# list with an optional output subscripts list at the end (see documentation for more details on this)
# to the equation string format by creating the equation string from the subscripts list and grouping the
# input operands into a tensorlist (List[Tensor]).
def parse_subscript(n: int) -> str:
if n == Ellipsis:
return '...'
if n >= 0 and n < 26:
return chr(ord('A') + n)
if n >= 26 and n < 52:
return chr(ord('a') + n - 26)
raise ValueError('einsum(): subscript in subscript list is not within the valid range [0, 52)')
# Parse subscripts for input operands
equation = ','.join(''.join(parse_subscript(s) for s in l) for l in args[1::2])
# Parse optional output subscripts (provided when the number of arguments is odd)
if len(args) % 2 == 1:
equation += '->' + ''.join(parse_subscript(s) for s in args[-1])
operands = args[:-1:2]
else:
operands = args[::2]
else:
equation = args[0]
operands = args[1:]
if has_torch_function(operands):
return handle_torch_function(einsum, operands, equation, *operands)
if len(operands) == 1 and isinstance(operands[0], (list, tuple)):
# the old interface of passing the operands as one list argument
_operands = operands[0]
# recurse incase operands contains value that has torch function
# in the original implementation this line is omitted
return einsum(equation, *_operands)
return _VF.einsum(equation, operands) # type: ignore[attr-defined]
# Wrapper around _histogramdd and _histogramdd_bin_edges needed due to (Tensor, Tensor[]) return type.
if TYPE_CHECKING:
# The JIT doesn't understand Union, so only add type annotation for mypy
def histogramdd(input: Tensor,
bins: Union[List[Tensor], List[int], int],
range: Optional[List[float]] = None,
weight: Optional[Tensor] = None,
density: bool = False):
pass
else:
def histogramdd(input, bins, range=None, weight=None, density=False):
r"""
histogramdd(input, bins, *, range=None, weight=None, density=False, out=None) -> (Tensor, Tensor[])
Computes a multi-dimensional histogram of the values in a tensor.
Interprets the elements of an input tensor whose innermost dimension has size N
as a collection of N-dimensional points. Maps each of the points into a set of
N-dimensional bins and returns the number of points (or total weight) in each bin.
:attr:`input` must be a tensor with at least 2 dimensions.
If input has shape (M, N), each of its M rows defines a point in N-dimensional space.
If input has three or more dimensions, all but the last dimension are flattened.
Each dimension is independently associated with its own strictly increasing sequence
of bin edges. Bin edges may be specified explicitly by passing a sequence of 1D
tensors. Alternatively, bin edges may be constructed automatically by passing a
sequence of integers specifying the number of equal-width bins in each dimension.
For each N-dimensional point in input:
- Each of its coordinates is binned independently among the bin edges
corresponding to its dimension
- Binning results are combined to identify the N-dimensional bin (if any)
into which the point falls
- If the point falls into a bin, the bin's count (or total weight) is incremented
- Points which do not fall into any bin do not contribute to the output
:attr:`bins` can be a sequence of N 1D tensors, a sequence of N ints, or a single int.
If :attr:`bins` is a sequence of N 1D tensors, it explicitly specifies the N sequences
of bin edges. Each 1D tensor should contain a strictly increasing sequence with at
least one element. A sequence of K bin edges defines K-1 bins, explicitly specifying
the left and right edges of all bins. Every bin is exclusive of its left edge. Only
the rightmost bin is inclusive of its right edge.
If :attr:`bins` is a sequence of N ints, it specifies the number of equal-width bins
in each dimension. By default, the leftmost and rightmost bin edges in each dimension
are determined by the minimum and maximum elements of the input tensor in the
corresponding dimension. The :attr:`range` argument can be provided to manually
specify the leftmost and rightmost bin edges in each dimension.
If :attr:`bins` is an int, it specifies the number of equal-width bins for all dimensions.
.. note::
See also :func:`torch.histogram`, which specifically computes 1D histograms.
While :func:`torch.histogramdd` infers the dimensionality of its bins and
binned values from the shape of :attr:`input`, :func:`torch.histogram`
accepts and flattens :attr:`input` of any shape.
Args:
{input}
bins: Tensor[], int[], or int.
If Tensor[], defines the sequences of bin edges.
If int[], defines the number of equal-width bins in each dimension.
If int, defines the number of equal-width bins for all dimensions.
Keyword args:
range (sequence of float): Defines the leftmost and rightmost bin edges
in each dimension.
weight (Tensor): By default, each value in the input has weight 1. If a weight
tensor is passed, each N-dimensional coordinate in input
contributes its associated weight towards its bin's result.
The weight tensor should have the same shape as the :attr:`input`
tensor excluding its innermost dimension N.
density (bool): If False (default), the result will contain the count (or total weight)
in each bin. If True, each count (weight) is divided by the total count
(total weight), then divided by the volume of its associated bin.
Returns:
hist (Tensor): N-dimensional Tensor containing the values of the histogram.
bin_edges(Tensor[]): sequence of N 1D Tensors containing the bin edges.
Example::
>>> torch.histogramdd(torch.tensor([[0., 1.], [1., 0.], [2., 0.], [2., 2.]]), bins=[3, 3],
... weight=torch.tensor([1., 2., 4., 8.]))
histogramdd_return_type(hist=tensor([[0., 1., 0.],
[2., 0., 0.],
[4., 0., 8.]]),
bin_edges=(tensor([0.0000, 0.6667, 1.3333, 2.0000]),
tensor([0.0000, 0.6667, 1.3333, 2.0000])))
>>> torch.histogramdd(torch.tensor([[0., 0.], [1., 1.], [2., 2.]]), bins=[2, 2],
... range=[0., 1., 0., 1.], density=True)
histogramdd_return_type(hist=tensor([[2., 0.],
[0., 2.]]),
bin_edges=(tensor([0.0000, 0.5000, 1.0000]),
tensor([0.0000, 0.5000, 1.0000])))
"""
if isinstance(bins, int):
# If a single int is passed, repeat it for all dimensions
bins = list(itertools.repeat(bins, input.size()[-1]))
if bins and isinstance(bins[0], int):
"""
If bins is int[], the histogram kernel runs faster knowing that the bin edges form
a linear progression (see comments in aten/src/ATen/native/cpu/HistogramKernel.cpp).
However, we end up constructing the bin edge tensors twice because
_histogramdd_from_bin_cts cannot pass back (Tensor, Tensor[]).
"""
bin_edges = _VF._histogramdd_bin_edges(input, bins, range=range, weight=weight, density=density)
hist = _VF._histogramdd_from_bin_cts(input, bins, range=range, weight=weight, density=density)
else:
"""
If bins is Tensor[] we simply return it back.
"""
bin_edges = bins
hist = _VF._histogramdd_from_bin_tensors(input, bin_edges, weight=weight, density=density)
# TODO: figure out how to return torch.return_types.histogramdd
histogramdd_return_type = namedtuple('histogramdd_return_type', 'hist bin_edges')
return histogramdd_return_type(hist, bin_edges)
# This wrapper exists to support variadic args.
if TYPE_CHECKING:
# The JIT doesn't understand Union, so only add type annotation for mypy
def meshgrid(*tensors: Union[Tensor, List[Tensor]],
indexing: Optional[str] = None) -> Tuple[Tensor, ...]:
return _meshgrid(*tensors, indexing=indexing)
else:
def meshgrid(*tensors, indexing: Optional[str] = None) -> Tuple[Tensor, ...]:
r"""Creates grids of coordinates specified by the 1D inputs in `attr`:tensors.
This is helpful when you want to visualize data over some
range of inputs. See below for a plotting example.
Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as
inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`,
this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots
G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where
the output :math:`G_i` is constructed by expanding :math:`T_i`
to the result shape.
.. note::
0D inputs are treated equivalently to 1D inputs of a
single element.
.. warning::
`torch.meshgrid(*tensors)` currently has the same behavior
as calling `numpy.meshgrid(*arrays, indexing='ij')`.
In the future `torch.meshgrid` will transition to
`indexing='xy'` as the default.
https://github.com/pytorch/pytorch/issues/50276 tracks
this issue with the goal of migrating to NumPy's behavior.
.. seealso::
:func:`torch.cartesian_prod` has the same effect but it
collects the data in a tensor of vectors.
Args:
tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be
treated as tensors of size :math:`(1,)` automatically
indexing: (str, optional): the indexing mode, either "xy"
or "ij", defaults to "ij". See warning for future changes.
If "xy" is selected, the first dimension corresponds
to the cardinality of the second input and the second
dimension corresponds to the cardinality of the first
input.
If "ij" is selected, the dimensions are in the same
order as the cardinality of the inputs.
Returns:
seq (sequence of Tensors): If the input has :math:`N`
tensors of size :math:`S_0 \ldots S_{N-1}``, then the
output will also have :math:`N` tensors, where each tensor
is of shape :math:`(S_0, ..., S_{N-1})`.
Example::
>>> x = torch.tensor([1, 2, 3])
>>> y = torch.tensor([4, 5, 6])
Observe the element-wise pairings across the grid, (1, 4),
(1, 5), ..., (3, 6). This is the same thing as the
cartesian product.
>>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij')
>>> grid_x
tensor([[1, 1, 1],
[2, 2, 2],
[3, 3, 3]])
>>> grid_y
tensor([[4, 5, 6],
[4, 5, 6],
[4, 5, 6]])
This correspondence can be seen when these grids are
stacked properly.
>>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))),
... torch.cartesian_prod(x, y))
True
`torch.meshgrid` is commonly used to produce a grid for
plotting.
>>> import matplotlib.pyplot as plt
>>> xs = torch.linspace(-5, 5, steps=100)
>>> ys = torch.linspace(-5, 5, steps=100)
>>> x, y = torch.meshgrid(xs, ys, indexing='xy')
>>> z = torch.sin(torch.sqrt(x * x + y * y))
>>> ax = plt.axes(projection='3d')
>>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy())
<mpl_toolkits.mplot3d.art3d.Poly3DCollection object at 0x7f8f30d40100>
>>> plt.show()
.. image:: ../_static/img/meshgrid.png
:width: 512
"""
return _meshgrid(*tensors, indexing=indexing)
def _meshgrid(*tensors, indexing: Optional[str]):
if has_torch_function(tensors):
return handle_torch_function(meshgrid, tensors, *tensors, indexing=indexing)
if len(tensors) == 1 and isinstance(tensors[0], (list, tuple)):
# the old interface of passing the operands as one list argument
tensors = tensors[0] # type: ignore[assignment]
# Continue allowing call of old method that takes no indexing
# kwarg for forward compatibility reasons.
#
# Remove this two weeks after landing.
kwargs = {} if indexing is None else {'indexing': indexing}
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
def stft(input: Tensor, n_fft: int, hop_length: Optional[int] = None,
win_length: Optional[int] = None, window: Optional[Tensor] = None,
center: bool = True, pad_mode: str = 'reflect', normalized: bool = False,
onesided: Optional[bool] = None,
return_complex: Optional[bool] = None) -> Tensor:
r"""Short-time Fourier transform (STFT).
.. warning::
From version 1.8.0, :attr:`return_complex` must always be given
explicitly for real inputs and `return_complex=False` has been
deprecated. Strongly prefer `return_complex=True` as in a future
pytorch release, this function will only return complex tensors.
Note that :func:`torch.view_as_real` can be used to recover a real
tensor with an extra last dimension for real and imaginary components.
The STFT computes the Fourier transform of short overlapping windows of the
input. This giving frequency components of the signal as they change over
time. The interface of this function is modeled after the librosa_ stft function.
.. _librosa: https://librosa.org/doc/latest/generated/librosa.stft.html
Ignoring the optional batch dimension, this method computes the following
expression:
.. math::
X[\omega, m] = \sum_{k = 0}^{\text{win\_length-1}}%
\text{window}[k]\ \text{input}[m \times \text{hop\_length} + k]\ %
\exp\left(- j \frac{2 \pi \cdot \omega k}{\text{win\_length}}\right),
where :math:`m` is the index of the sliding window, and :math:`\omega` is
the frequency :math:`0 \leq \omega < \text{n\_fft}` for ``onesided=False``,
or :math:`0 \leq \omega < \lfloor \text{n\_fft} / 2 \rfloor + 1` for ``onesided=True``.
* :attr:`input` must be either a 1-D time sequence or a 2-D batch of time
sequences.
* If :attr:`hop_length` is ``None`` (default), it is treated as equal to
``floor(n_fft / 4)``.
* If :attr:`win_length` is ``None`` (default), it is treated as equal to
:attr:`n_fft`.
* :attr:`window` can be a 1-D tensor of size :attr:`win_length`, e.g., from
:meth:`torch.hann_window`. If :attr:`window` is ``None`` (default), it is
treated as if having :math:`1` everywhere in the window. If
:math:`\text{win\_length} < \text{n\_fft}`, :attr:`window` will be padded on
both sides to length :attr:`n_fft` before being applied.
* If :attr:`center` is ``True`` (default), :attr:`input` will be padded on
both sides so that the :math:`t`-th frame is centered at time
:math:`t \times \text{hop\_length}`. Otherwise, the :math:`t`-th frame
begins at time :math:`t \times \text{hop\_length}`.
* :attr:`pad_mode` determines the padding method used on :attr:`input` when
:attr:`center` is ``True``. See :meth:`torch.nn.functional.pad` for
all available options. Default is ``"reflect"``.
* If :attr:`onesided` is ``True`` (default for real input), only values for
:math:`\omega` in :math:`\left[0, 1, 2, \dots, \left\lfloor
\frac{\text{n\_fft}}{2} \right\rfloor + 1\right]` are returned because
the real-to-complex Fourier transform satisfies the conjugate symmetry,
i.e., :math:`X[m, \omega] = X[m, \text{n\_fft} - \omega]^*`.
Note if the input or window tensors are complex, then :attr:`onesided`
output is not possible.
* If :attr:`normalized` is ``True`` (default is ``False``), the function
returns the normalized STFT results, i.e., multiplied by :math:`(\text{frame\_length})^{-0.5}`.
* If :attr:`return_complex` is ``True`` (default if input is complex), the
return is a ``input.dim() + 1`` dimensional complex tensor. If ``False``,
the output is a ``input.dim() + 2`` dimensional real tensor where the last
dimension represents the real and imaginary components.
Returns either a complex tensor of size :math:`(* \times N \times T)` if
:attr:`return_complex` is true, or a real tensor of size :math:`(* \times N
\times T \times 2)`. Where :math:`*` is the optional batch size of
:attr:`input`, :math:`N` is the number of frequencies where STFT is applied
and :math:`T` is the total number of frames used.
.. warning::
This function changed signature at version 0.4.1. Calling with the
previous signature may cause error or return incorrect result.
Args:
input (Tensor): the input tensor
n_fft (int): size of Fourier transform
hop_length (int, optional): the distance between neighboring sliding window
frames. Default: ``None`` (treated as equal to ``floor(n_fft / 4)``)
win_length (int, optional): the size of window frame and STFT filter.
Default: ``None`` (treated as equal to :attr:`n_fft`)
window (Tensor, optional): the optional window function.
Default: ``None`` (treated as window of all :math:`1` s)
center (bool, optional): whether to pad :attr:`input` on both sides so
that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`.
Default: ``True``
pad_mode (string, optional): controls the padding method used when
:attr:`center` is ``True``. Default: ``"reflect"``
normalized (bool, optional): controls whether to return the normalized STFT results
Default: ``False``
onesided (bool, optional): controls whether to return half of results to
avoid redundancy for real inputs.
Default: ``True`` for real :attr:`input` and :attr:`window`, ``False`` otherwise.
return_complex (bool, optional): whether to return a complex tensor, or
a real tensor with an extra last dimension for the real and
imaginary components.
Returns:
Tensor: A tensor containing the STFT result with shape described above
"""
if has_torch_function_unary(input):
return handle_torch_function(
stft, (input,), input, n_fft, hop_length=hop_length, win_length=win_length,
window=window, center=center, pad_mode=pad_mode, normalized=normalized,
onesided=onesided, return_complex=return_complex)
# TODO: after having proper ways to map Python strings to ATen Enum, move
# this and F.pad to ATen.
if center:
signal_dim = input.dim()
extended_shape = [1] * (3 - signal_dim) + list(input.size())
pad = int(n_fft // 2)
input = F.pad(input.view(extended_shape), [pad, pad], pad_mode)
input = input.view(input.shape[-signal_dim:])
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
normalized, onesided, return_complex)
def istft(input: Tensor, n_fft: int, hop_length: Optional[int] = None,
win_length: Optional[int] = None, window: Optional[Tensor] = None,
center: bool = True, normalized: bool = False,
onesided: Optional[bool] = None, length: Optional[int] = None,
return_complex: bool = False) -> Tensor:
r"""Inverse short time Fourier Transform. This is expected to be the inverse of :func:`~torch.stft`.
It has the same parameters (+ additional optional parameter of :attr:`length`) and it should return the
least squares estimation of the original signal. The algorithm will check using the NOLA condition (
nonzero overlap).
Important consideration in the parameters :attr:`window` and :attr:`center` so that the envelop
created by the summation of all the windows is never zero at certain point in time. Specifically,
:math:`\sum_{t=-\infty}^{\infty} |w|^2[n-t\times hop\_length] \cancel{=} 0`.
Since :func:`~torch.stft` discards elements at the end of the signal if they do not fit in a frame,
``istft`` may return a shorter signal than the original signal (can occur if :attr:`center` is False
since the signal isn't padded). If `length` is given in the arguments and is longer than expected,
``istft`` will pad zeros to the end of the returned signal.
If :attr:`center` is ``True``, then there will be padding e.g. ``'constant'``, ``'reflect'``, etc.
Left padding can be trimmed off exactly because they can be calculated but right padding cannot be
calculated without additional information.
Example: Suppose the last window is:
``[17, 18, 0, 0, 0]`` vs ``[18, 0, 0, 0, 0]``
The :attr:`n_fft`, :attr:`hop_length`, :attr:`win_length` are all the same which prevents the calculation
of right padding. These additional values could be zeros or a reflection of the signal so providing
:attr:`length` could be useful. If :attr:`length` is ``None`` then padding will be aggressively removed
(some loss of signal).
[1] D. W. Griffin and J. S. Lim, "Signal estimation from modified short-time Fourier transform,"
IEEE Trans. ASSP, vol.32, no.2, pp.236-243, Apr. 1984.
Args:
input (Tensor): The input tensor. Expected to be output of :func:`~torch.stft`,
can either be complex (``channel``, ``fft_size``, ``n_frame``), or real
(``channel``, ``fft_size``, ``n_frame``, 2) where the ``channel``
dimension is optional.
.. deprecated:: 1.8.0
Real input is deprecated, use complex inputs as returned by
``stft(..., return_complex=True)`` instead.
n_fft (int): Size of Fourier transform
hop_length (Optional[int]): The distance between neighboring sliding window frames.
(Default: ``n_fft // 4``)
win_length (Optional[int]): The size of window frame and STFT filter. (Default: ``n_fft``)
window (Optional[torch.Tensor]): The optional window function.
(Default: ``torch.ones(win_length)``)
center (bool): Whether :attr:`input` was padded on both sides so that the :math:`t`-th frame is
centered at time :math:`t \times \text{hop\_length}`.
(Default: ``True``)
normalized (bool): Whether the STFT was normalized. (Default: ``False``)
onesided (Optional[bool]): Whether the STFT was onesided.
(Default: ``True`` if ``n_fft != fft_size`` in the input size)
length (Optional[int]): The amount to trim the signal by (i.e. the
original signal length). (Default: whole signal)
return_complex (Optional[bool]):
Whether the output should be complex, or if the input should be
assumed to derive from a real signal and window.
Note that this is incompatible with ``onesided=True``.
(Default: ``False``)
Returns:
Tensor: Least squares estimation of the original signal of size (..., signal_length)
"""
if has_torch_function_unary(input):
return handle_torch_function(
istft, (input,), input, n_fft, hop_length=hop_length, win_length=win_length,
window=window, center=center, normalized=normalized, onesided=onesided,
length=length, return_complex=return_complex)
return _VF.istft(input, n_fft, hop_length, win_length, window, center, # type: ignore[attr-defined]
normalized, onesided, length, return_complex)
if TYPE_CHECKING:
# These _impl functions return a variable number of tensors as output with
# __torch_function__; tuple unpacking is done already rather than being
# done by the caller of the _impl function
_unique_impl_out = Any
else:
_unique_impl_out = Tuple[Tensor, Tensor, Tensor]
def _unique_impl(input: Tensor, sorted: bool = True,
return_inverse: bool = False, return_counts: bool = False,
dim: Optional[int] = None) -> _unique_impl_out:
r"""unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor]
Returns the unique elements of the input tensor.
.. note:: This function is different from :func:`torch.unique_consecutive` in the sense that
this function also eliminates non-consecutive duplicate values.
.. note:: Currently in the CUDA implementation and the CPU implementation when dim is specified,
`torch.unique` always sort the tensor at the beginning regardless of the `sort` argument.
Sorting could be slow, so if your input tensor is already sorted, it is recommended to use
:func:`torch.unique_consecutive` which avoids the sorting.
Args:
input (Tensor): the input tensor
sorted (bool): Whether to sort the unique elements in ascending order
before returning as output.
return_inverse (bool): Whether to also return the indices for where
elements in the original input ended up in the returned unique list.
return_counts (bool): Whether to also return the counts for each unique
element.
dim (int): the dimension to apply unique. If ``None``, the unique of the
flattened input is returned. default: ``None``
Returns:
(Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing
- **output** (*Tensor*): the output list of unique scalar elements.
- **inverse_indices** (*Tensor*): (optional) if
:attr:`return_inverse` is True, there will be an additional
returned tensor (same shape as input) representing the indices
for where elements in the original input map to in the output;
otherwise, this function will only return a single tensor.
- **counts** (*Tensor*): (optional) if
:attr:`return_counts` is True, there will be an additional
returned tensor (same shape as output or output.size(dim),
if dim was specified) representing the number of occurrences
for each unique value or tensor.
Example::
>>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long))
>>> output
tensor([ 2, 3, 1])
>>> output, inverse_indices = torch.unique(
... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True)
>>> output
tensor([ 1, 2, 3])
>>> inverse_indices
tensor([ 0, 2, 1, 2])
>>> output, inverse_indices = torch.unique(
... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True)
>>> output
tensor([ 1, 2, 3])
>>> inverse_indices
tensor([[ 0, 2],
[ 1, 2]])
"""
if has_torch_function_unary(input):
return handle_torch_function(
unique, (input,), input, sorted=sorted, return_inverse=return_inverse,
return_counts=return_counts, dim=dim)
if dim is not None:
output, inverse_indices, counts = _VF.unique_dim(
input,
dim,
sorted=sorted,
return_inverse=return_inverse,
return_counts=return_counts,
)
else:
output, inverse_indices, counts = torch._unique2(
input,
sorted=sorted,
return_inverse=return_inverse,
return_counts=return_counts,
)
return output, inverse_indices, counts
def _unique_consecutive_impl(input: Tensor, return_inverse: bool = False,
return_counts: bool = False,
dim: Optional[int] = None) -> _unique_impl_out:
r"""Eliminates all but the first element from every consecutive group of equivalent elements.
.. note:: This function is different from :func:`torch.unique` in the sense that this function
only eliminates consecutive duplicate values. This semantics is similar to `std::unique`
in C++.
Args:
input (Tensor): the input tensor
return_inverse (bool): Whether to also return the indices for where
elements in the original input ended up in the returned unique list.
return_counts (bool): Whether to also return the counts for each unique
element.
dim (int): the dimension to apply unique. If ``None``, the unique of the
flattened input is returned. default: ``None``
Returns:
(Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing
- **output** (*Tensor*): the output list of unique scalar elements.
- **inverse_indices** (*Tensor*): (optional) if
:attr:`return_inverse` is True, there will be an additional
returned tensor (same shape as input) representing the indices
for where elements in the original input map to in the output;
otherwise, this function will only return a single tensor.
- **counts** (*Tensor*): (optional) if
:attr:`return_counts` is True, there will be an additional
returned tensor (same shape as output or output.size(dim),
if dim was specified) representing the number of occurrences
for each unique value or tensor.
Example::
>>> x = torch.tensor([1, 1, 2, 2, 3, 1, 1, 2])
>>> output = torch.unique_consecutive(x)
>>> output
tensor([1, 2, 3, 1, 2])
>>> output, inverse_indices = torch.unique_consecutive(x, return_inverse=True)
>>> output
tensor([1, 2, 3, 1, 2])
>>> inverse_indices
tensor([0, 0, 1, 1, 2, 3, 3, 4])
>>> output, counts = torch.unique_consecutive(x, return_counts=True)
>>> output
tensor([1, 2, 3, 1, 2])
>>> counts
tensor([2, 2, 1, 2, 1])
"""
if has_torch_function_unary(input):
return handle_torch_function(
unique_consecutive, (input,), input, return_inverse=return_inverse,
return_counts=return_counts, dim=dim)
output, inverse_indices, counts = _VF.unique_consecutive( # type: ignore[attr-defined]
input, return_inverse=return_inverse, return_counts=return_counts, dim=dim)
return output, inverse_indices, counts
def _return_counts(input, sorted=True, return_inverse=False, return_counts=False, dim=None):
# type: (Tensor, bool, bool, bool, Optional[int]) -> Tuple[Tensor, Tensor]
if has_torch_function_unary(input):
return _unique_impl(input, sorted, return_inverse, return_counts, dim)
output, _, counts = _unique_impl(input, sorted, return_inverse, return_counts, dim)
return output, counts
def _return_output(input, sorted=True, return_inverse=False, return_counts=False, dim=None):
# type: (Tensor, bool, bool, bool, Optional[int]) -> Tensor
if has_torch_function_unary(input):
return _unique_impl(input, sorted, return_inverse, return_counts, dim)
output, _, _ = _unique_impl(input, sorted, return_inverse, return_counts, dim)
return output
def _return_inverse(input, sorted=True, return_inverse=False, return_counts=False, dim=None):
# type: (Tensor, bool, bool, bool, Optional[int]) -> Tuple[Tensor, Tensor]
if has_torch_function_unary(input):
return _unique_impl(input, sorted, return_inverse, return_counts, dim)
output, inverse_indices, _ = _unique_impl(input, sorted, return_inverse, return_counts, dim)
return output, inverse_indices
_return_inverse_false = boolean_dispatch(
arg_name='return_counts',
arg_index=3,
default=False,
if_true=_return_counts,
if_false=_return_output,
module_name=__name__,
func_name='unique')
_return_inverse_true = boolean_dispatch(
arg_name='return_counts',
arg_index=3,
default=False,
if_true=_unique_impl,
if_false=_return_inverse,
module_name=__name__,
func_name='unique')
# The return type of unique depends on `return_inverse`, and `return_counts` so in order to
# resolve the output type in TorchScript we need to statically know the value of both parameters
unique = boolean_dispatch(
arg_name='return_inverse',
arg_index=2,
default=False,
if_true=_return_inverse_true,
if_false=_return_inverse_false,
module_name=__name__,
func_name='unique')
unique.__doc__ = _unique_impl.__doc__
def _consecutive_return_counts(input, return_inverse=False, return_counts=False, dim=None):
# type: (Tensor, bool, bool, Optional[int]) -> Tuple[Tensor, Tensor]
if has_torch_function_unary(input):
return _unique_consecutive_impl(input, return_inverse, return_counts, dim)
output, _, counts = _unique_consecutive_impl(input, return_inverse, return_counts, dim)
return output, counts
def _consecutive_return_output(input, return_inverse=False, return_counts=False, dim=None):
# type: (Tensor, bool, bool, Optional[int]) -> Tensor
if has_torch_function_unary(input):
return _unique_consecutive_impl(input, return_inverse, return_counts, dim)
output, _, _ = _unique_consecutive_impl(input, return_inverse, return_counts, dim)
return output
def _consecutive_return_inverse(input, return_inverse=False, return_counts=False, dim=None):
# type: (Tensor, bool, bool, Optional[int]) -> Tuple[Tensor, Tensor]
if has_torch_function_unary(input):
return _unique_consecutive_impl(input, return_inverse, return_counts, dim)
output, inverse_indices, _ = _unique_consecutive_impl(input, return_inverse, return_counts, dim)
return output, inverse_indices
_consecutive_return_inverse_false = boolean_dispatch(
arg_name='return_counts',
arg_index=1,
default=False,
if_true=_consecutive_return_counts,
if_false=_consecutive_return_output,
module_name=__name__,
func_name='unique_consecutive')
_consecutive_return_inverse_true = boolean_dispatch(
arg_name='return_counts',
arg_index=1,
default=False,
if_true=_unique_consecutive_impl,
if_false=_consecutive_return_inverse,
module_name=__name__,
func_name='unique_consecutive')
# The return type of unique depends on `return_inverse`, and `return_counts` so in order to
# resolve the output type in TorchScript we need to statically know the value of both parameters
unique_consecutive = boolean_dispatch(
arg_name='return_inverse',
arg_index=2,
default=False,
if_true=_consecutive_return_inverse_true,
if_false=_consecutive_return_inverse_false,
module_name=__name__,
func_name='unique_consecutive')
unique_consecutive.__doc__ = _unique_consecutive_impl.__doc__
if TYPE_CHECKING:
pass
# There's no good way to use this type annotation without breaking JIT
# overloads. So leave untyped for mypy for now.
else:
@overload
def tensordot(a, b, dims: int = 2, out: Optional[torch.Tensor] = None):
pass
@overload # noqa: F811
def tensordot(a, b, dims: Tuple[List[int], List[int]], out: Optional[torch.Tensor] = None): # noqa: F811
pass
@overload # noqa: F811
def tensordot(a, b, dims: List[List[int]], out: Optional[torch.Tensor] = None): # noqa: F811
pass
@overload # noqa: F811
def tensordot(a, b, dims: torch.Tensor, out: Optional[torch.Tensor] = None): # noqa: F811
pass
def tensordot(a, b, dims=2, out: Optional[torch.Tensor] = None): # noqa: F811
r"""Returns a contraction of a and b over multiple dimensions.
:attr:`tensordot` implements a generalized matrix product.
Args:
a (Tensor): Left tensor to contract
b (Tensor): Right tensor to contract
dims (int or Tuple[List[int], List[int]] or List[List[int]] containing two lists or Tensor): number of dimensions to
contract or explicit lists of dimensions for :attr:`a` and
:attr:`b` respectively
When called with a non-negative integer argument :attr:`dims` = :math:`d`, and
the number of dimensions of :attr:`a` and :attr:`b` is :math:`m` and :math:`n`,
respectively, :func:`~torch.tensordot` computes
.. math::
r_{i_0,...,i_{m-d}, i_d,...,i_n}
= \sum_{k_0,...,k_{d-1}} a_{i_0,...,i_{m-d},k_0,...,k_{d-1}} \times b_{k_0,...,k_{d-1}, i_d,...,i_n}.
When called with :attr:`dims` of the list form, the given dimensions will be contracted
in place of the last :math:`d` of :attr:`a` and the first :math:`d` of :math:`b`. The sizes
in these dimensions must match, but :func:`~torch.tensordot` will deal with broadcasted
dimensions.
Examples::
>>> a = torch.arange(60.).reshape(3, 4, 5)
>>> b = torch.arange(24.).reshape(4, 3, 2)
>>> torch.tensordot(a, b, dims=([1, 0], [0, 1]))
tensor([[4400., 4730.],
[4532., 4874.],
[4664., 5018.],
[4796., 5162.],
[4928., 5306.]])
>>> a = torch.randn(3, 4, 5, device='cuda')
>>> b = torch.randn(4, 5, 6, device='cuda')
>>> c = torch.tensordot(a, b, dims=2).cpu()
tensor([[ 8.3504, -2.5436, 6.2922, 2.7556, -1.0732, 3.2741],
[ 3.3161, 0.0704, 5.0187, -0.4079, -4.3126, 4.8744],
[ 0.8223, 3.9445, 3.2168, -0.2400, 3.4117, 1.7780]])
>>> a = torch.randn(3, 5, 4, 6)
>>> b = torch.randn(6, 4, 5, 3)
>>> torch.tensordot(a, b, dims=([2, 1, 3], [1, 2, 0]))
tensor([[ 7.7193, -2.4867, -10.3204],
[ 1.5513, -14.4737, -6.5113],
[ -0.2850, 4.2573, -3.5997]])
"""
if has_torch_function_variadic(a, b):
return handle_torch_function(tensordot, (a, b), a, b, dims=dims)
if not isinstance(dims, (tuple, list, torch.Tensor, int)):
raise RuntimeError("tensordot expects dims to be int or "
+ "Tuple[List[int], List[int]] or "
+ "List[List[int]] containing two lists, but got "
+ f"dims={dims}")
dims_a: List[int] = []
dims_b: List[int] = []
if isinstance(dims, (tuple, list)):
dims_a, dims_b = dims
if isinstance(dims, torch.Tensor):
num_elements = dims.numel()
if num_elements > 1:
assert dims.size()[0] == 2
dims_a = torch.jit.annotate(List[int], dims[0].tolist())
dims_b = torch.jit.annotate(List[int], dims[1].tolist())
else:
dims_val = int(dims.item())
if dims_val < 0:
raise RuntimeError(f"tensordot expects dims >= 0, but got dims={dims}")
dims_a = list(range(-dims_val, 0))
dims_b = list(range(dims_val))
if isinstance(dims, int):
if dims < 0:
raise RuntimeError(f"tensordot expects dims >= 0, but got dims={dims}")
dims_a = list(range(-dims, 0))
dims_b = list(range(dims))
if out is None:
return _VF.tensordot(a, b, dims_a, dims_b) # type: ignore[attr-defined]
else:
return _VF.tensordot(a, b, dims_a, dims_b, out=out) # type: ignore[attr-defined]
def cartesian_prod(*tensors):
"""Do cartesian product of the given sequence of tensors. The behavior is similar to
python's `itertools.product`.
Args:
*tensors: any number of 1 dimensional tensors.
Returns:
Tensor: A tensor equivalent to converting all the input tensors into lists,
do `itertools.product` on these lists, and finally convert the resulting list
into tensor.
Example::
>>> a = [1, 2, 3]
>>> b = [4, 5]
>>> list(itertools.product(a, b))
[(1, 4), (1, 5), (2, 4), (2, 5), (3, 4), (3, 5)]
>>> tensor_a = torch.tensor(a)
>>> tensor_b = torch.tensor(b)
>>> torch.cartesian_prod(tensor_a, tensor_b)
tensor([[1, 4],
[1, 5],
[2, 4],
[2, 5],
[3, 4],
[3, 5]])
"""
# This wrapper exists to support variadic args.
if has_torch_function(tensors):
return handle_torch_function(cartesian_prod, tensors, *tensors)
return _VF.cartesian_prod(tensors) # type: ignore[attr-defined]
def block_diag(*tensors):
"""Create a block diagonal matrix from provided tensors.
Args:
*tensors: One or more tensors with 0, 1, or 2 dimensions.
Returns:
Tensor: A 2 dimensional tensor with all the input tensors arranged in
order such that their upper left and lower right corners are
diagonally adjacent. All other elements are set to 0.
Example::
>>> import torch
>>> A = torch.tensor([[0, 1], [1, 0]])
>>> B = torch.tensor([[3, 4, 5], [6, 7, 8]])
>>> C = torch.tensor(7)
>>> D = torch.tensor([1, 2, 3])
>>> E = torch.tensor([[4], [5], [6]])
>>> torch.block_diag(A, B, C, D, E)
tensor([[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 3, 4, 5, 0, 0, 0, 0, 0],
[0, 0, 6, 7, 8, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 7, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 2, 3, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 4],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 5],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 6]])
"""
# This wrapper exists to support variadic args.
if has_torch_function(tensors):
return handle_torch_function(block_diag, tensors, *tensors)
return torch._C._VariableFunctions.block_diag(tensors) # type: ignore[attr-defined]
def cdist(x1, x2, p=2., compute_mode='use_mm_for_euclid_dist_if_necessary'):
# type: (Tensor, Tensor, float, str) -> (Tensor)
r"""Computes batched the p-norm distance between each pair of the two collections of row vectors.
Args:
x1 (Tensor): input tensor of shape :math:`B \times P \times M`.
x2 (Tensor): input tensor of shape :math:`B \times R \times M`.
p: p value for the p-norm distance to calculate between each vector pair
:math:`\in [0, \infty]`.
compute_mode:
'use_mm_for_euclid_dist_if_necessary' - will use matrix multiplication approach to calculate
euclidean distance (p = 2) if P > 25 or R > 25
'use_mm_for_euclid_dist' - will always use matrix multiplication approach to calculate
euclidean distance (p = 2)
'donot_use_mm_for_euclid_dist' - will never use matrix multiplication approach to calculate
euclidean distance (p = 2)
Default: use_mm_for_euclid_dist_if_necessary.
If x1 has shape :math:`B \times P \times M` and x2 has shape :math:`B \times R \times M` then the
output will have shape :math:`B \times P \times R`.
This function is equivalent to `scipy.spatial.distance.cdist(input,'minkowski', p=p)`
if :math:`p \in (0, \infty)`. When :math:`p = 0` it is equivalent to
`scipy.spatial.distance.cdist(input, 'hamming') * M`. When :math:`p = \infty`, the closest
scipy function is `scipy.spatial.distance.cdist(xn, lambda x, y: np.abs(x - y).max())`.
Example:
>>> a = torch.tensor([[0.9041, 0.0196], [-0.3108, -2.4423], [-0.4821, 1.059]])
>>> a
tensor([[ 0.9041, 0.0196],
[-0.3108, -2.4423],
[-0.4821, 1.0590]])
>>> b = torch.tensor([[-2.1763, -0.4713], [-0.6986, 1.3702]])
>>> b
tensor([[-2.1763, -0.4713],
[-0.6986, 1.3702]])
>>> torch.cdist(a, b, p=2)
tensor([[3.1193, 2.0959],
[2.7138, 3.8322],
[2.2830, 0.3791]])
"""
if has_torch_function_variadic(x1, x2):
return handle_torch_function(
cdist, (x1, x2), x1, x2, p=p, compute_mode=compute_mode)
if compute_mode == 'use_mm_for_euclid_dist_if_necessary':
return _VF.cdist(x1, x2, p, None) # type: ignore[attr-defined]
elif compute_mode == 'use_mm_for_euclid_dist':
return _VF.cdist(x1, x2, p, 1) # type: ignore[attr-defined]
elif compute_mode == 'donot_use_mm_for_euclid_dist':
return _VF.cdist(x1, x2, p, 2) # type: ignore[attr-defined]
else:
raise ValueError(f"{compute_mode} is not a valid value for compute_mode")
def atleast_1d(*tensors):
r"""
Returns a 1-dimensional view of each input tensor with zero dimensions.
Input tensors with one or more dimensions are returned as-is.
Args:
input (Tensor or list of Tensors)
Returns:
output (Tensor or tuple of Tensors)
Example::
>>> x = torch.randn(2)
>>> x
tensor([1.4584, 0.7583])
>>> torch.atleast_1d(x)
tensor([1.4584, 0.7583])
>>> x = torch.tensor(1.)
>>> x
tensor(1.)
>>> torch.atleast_1d(x)
tensor([1.])
>>> x = torch.tensor(0.5)
>>> y = torch.tensor(1.)
>>> torch.atleast_1d((x,y))
(tensor([0.5000]), tensor([1.]))
"""
# This wrapper exists to support variadic args.
if has_torch_function(tensors):
return handle_torch_function(atleast_1d, tensors, *tensors)
if len(tensors) == 1:
tensors = tensors[0]
return _VF.atleast_1d(tensors) # type: ignore[attr-defined]
def atleast_2d(*tensors):
r"""
Returns a 2-dimensional view of each input tensor with zero dimensions.
Input tensors with two or more dimensions are returned as-is.
Args:
input (Tensor or list of Tensors)
Returns:
output (Tensor or tuple of Tensors)
Example::
>>> x = torch.tensor(1.)
>>> x
tensor(1.)
>>> torch.atleast_2d(x)
tensor([[1.]])
>>> x = torch.randn(2,2)
>>> x
tensor([[2.2086, 2.5165],
[0.1757, 0.5194]])
>>> torch.atleast_2d(x)
tensor([[2.2086, 2.5165],
[0.1757, 0.5194]])
>>> x = torch.tensor(0.5)
>>> y = torch.tensor(1.)
>>> torch.atleast_2d((x,y))
(tensor([[0.5000]]), tensor([[1.]]))
"""
# This wrapper exists to support variadic args.
if has_torch_function(tensors):
return handle_torch_function(atleast_2d, tensors, *tensors)
if len(tensors) == 1:
tensors = tensors[0]
return _VF.atleast_2d(tensors) # type: ignore[attr-defined]
def atleast_3d(*tensors):
r"""
Returns a 3-dimensional view of each input tensor with zero dimensions.
Input tensors with three or more dimensions are returned as-is.
Args:
input (Tensor or list of Tensors)
Returns:
output (Tensor or tuple of Tensors)
Example:
>>> x = torch.tensor(0.5)
>>> x
tensor(0.5000)
>>> torch.atleast_3d(x)
tensor([[[0.5000]]])
>>> y = torch.randn(2,2)
>>> y
tensor([[-0.8079, 0.7460],
[-1.1647, 1.4734]])
>>> torch.atleast_3d(y)
tensor([[[-0.8079],
[ 0.7460]],
<BLANKLINE>
[[-1.1647],
[ 1.4734]]])
>>> x = torch.randn(1,1,1)
>>> x
tensor([[[-1.5689]]])
>>> torch.atleast_3d(x)
tensor([[[-1.5689]]])
>>> x = torch.tensor(0.5)
>>> y = torch.tensor(1.)
>>> torch.atleast_3d((x,y))
(tensor([[[0.5000]]]), tensor([[[1.]]]))
"""
# This wrapper exists to support variadic args.
if has_torch_function(tensors):
return handle_torch_function(atleast_3d, tensors, *tensors)
if len(tensors) == 1:
tensors = tensors[0]
return _VF.atleast_3d(tensors) # type: ignore[attr-defined]
if TYPE_CHECKING:
pass
# There's no good way to use this type annotation; cannot rename norm() to
# _norm_impl() in a way that doesn't break JIT overloads. So leave untyped
# for mypy for now.
# def norm(input: Tensor,
# p: Optional[Union[str, Number]] = "fro",
# dim: Optional[Union[int, List[int]]] = None,
# keepdim: bool = False,
# out: Optional[Tensor] = None,
# dtype: _dtype = None) -> Tensor:
# return _norm_impl(input, p, dim, keepdim, out, dtype)
else:
# TODO: type dim as BroadcastingList when
# https://github.com/pytorch/pytorch/issues/33782 is fixed
@overload
def norm(input, p="fro", dim=None, keepdim=False, out=None, dtype=None):
# type: (Tensor, str, Optional[List[int]], bool, Optional[Tensor], Optional[int]) -> Tensor
pass
@overload # noqa: F811
def norm(input, p="fro", dim=None, keepdim=False, out=None, dtype=None): # noqa: F811
# type: (Tensor, Optional[number], Optional[List[int]], bool, Optional[Tensor], Optional[int]) -> Tensor
pass
@overload # noqa: F811
def norm(input, p="fro", dim=None, keepdim=False, out=None, dtype=None): # noqa: F811
# type: (Tensor, Optional[number], Optional[int], bool, Optional[Tensor], Optional[int]) -> Tensor
pass
@overload # noqa: F811
def norm(input, p="fro", dim=None, keepdim=False, out=None, dtype=None): # noqa: F811
# type: (Tensor, str, Optional[int], bool, Optional[Tensor], Optional[int]) -> Tensor
pass
def norm(input, p="fro", dim=None, keepdim=False, out=None, dtype=None): # noqa: F811
r"""Returns the matrix norm or vector norm of a given tensor.
.. warning::
torch.norm is deprecated and may be removed in a future PyTorch release.
Its documentation and behavior may be incorrect, and it is no longer
actively maintained.
Use :func:`torch.linalg.norm`, instead, or :func:`torch.linalg.vector_norm`
when computing vector norms and :func:`torch.linalg.matrix_norm` when
computing matrix norms. Note, however, the signature for these functions
is slightly different than the signature for torch.norm.
Args:
input (Tensor): The input tensor. Its data type must be either a floating
point or complex type. For complex inputs, the norm is calculated using the
absolute value of each element. If the input is complex and neither
:attr:`dtype` nor :attr:`out` is specified, the result's data type will
be the corresponding floating point type (e.g. float if :attr:`input` is
complexfloat).
p (int, float, inf, -inf, 'fro', 'nuc', optional): the order of norm. Default: ``'fro'``
The following norms can be calculated:
====== ============== ==========================
ord matrix norm vector norm
====== ============== ==========================
'fro' Frobenius norm --
'nuc' nuclear norm --
Number -- sum(abs(x)**ord)**(1./ord)
====== ============== ==========================
The vector norm can be calculated across any number of dimensions.
The corresponding dimensions of :attr:`input` are flattened into
one dimension, and the norm is calculated on the flattened
dimension.
Frobenius norm produces the same result as ``p=2`` in all cases
except when :attr:`dim` is a list of three or more dims, in which
case Frobenius norm throws an error.
Nuclear norm can only be calculated across exactly two dimensions.
dim (int, tuple of ints, list of ints, optional):
Specifies which dimension or dimensions of :attr:`input` to
calculate the norm across. If :attr:`dim` is ``None``, the norm will
be calculated across all dimensions of :attr:`input`. If the norm
type indicated by :attr:`p` does not support the specified number of
dimensions, an error will occur.
keepdim (bool, optional): whether the output tensors have :attr:`dim`
retained or not. Ignored if :attr:`dim` = ``None`` and
:attr:`out` = ``None``. Default: ``False``
out (Tensor, optional): the output tensor. Ignored if
:attr:`dim` = ``None`` and :attr:`out` = ``None``.
dtype (:class:`torch.dtype`, optional): the desired data type of
returned tensor. If specified, the input tensor is casted to
:attr:`dtype` while performing the operation. Default: None.
.. note::
Even though ``p='fro'`` supports any number of dimensions, the true
mathematical definition of Frobenius norm only applies to tensors with
exactly two dimensions. :func:`torch.linalg.norm` with ``ord='fro'`` aligns
with the mathematical definition, since it can only be applied across
exactly two dimensions.
Example::
>>> import torch
>>> a = torch.arange(9, dtype= torch.float) - 4
>>> b = a.reshape((3, 3))
>>> torch.norm(a)
tensor(7.7460)
>>> torch.norm(b)
tensor(7.7460)
>>> torch.norm(a, float('inf'))
tensor(4.)
>>> torch.norm(b, float('inf'))
tensor(4.)
>>> c = torch.tensor([[ 1, 2, 3],[-1, 1, 4]] , dtype= torch.float)
>>> torch.norm(c, dim=0)
tensor([1.4142, 2.2361, 5.0000])
>>> torch.norm(c, dim=1)
tensor([3.7417, 4.2426])
>>> torch.norm(c, p=1, dim=1)
tensor([6., 6.])
>>> d = torch.arange(8, dtype= torch.float).reshape(2,2,2)
>>> torch.norm(d, dim=(1,2))
tensor([ 3.7417, 11.2250])
>>> torch.norm(d[0, :, :]), torch.norm(d[1, :, :])
(tensor(3.7417), tensor(11.2250))
"""
if has_torch_function_unary(input):
return handle_torch_function(
norm, (input,), input, p=p, dim=dim, keepdim=keepdim, out=out, dtype=dtype)
ndim = input.dim()
# catch default case
if dim is None and out is None and dtype is None and p is not None:
if isinstance(p, str):
if p == "fro":
return _VF.frobenius_norm(input, dim=(), keepdim=keepdim)
if not isinstance(p, str):
_dim = [i for i in range(ndim)] # noqa: C416 TODO: rewrite as list(range(m))
return _VF.norm(input, p, dim=_dim, keepdim=keepdim) # type: ignore[attr-defined]
# TODO: when https://github.com/pytorch/pytorch/issues/33782 is fixed
# remove the overloads where dim is an int and replace with BraodcastingList1
# and remove next four lines, replace _dim with dim
if dim is not None:
if isinstance(dim, int):
_dim = [dim]
else:
_dim = dim
else:
_dim = None # type: ignore[assignment]
if isinstance(p, str):
if p == "fro":
if dtype is not None:
raise ValueError("dtype argument is not supported in frobenius norm")
if _dim is None:
_dim = list(range(ndim))
if out is None:
return _VF.frobenius_norm(input, _dim, keepdim=keepdim)
else:
return _VF.frobenius_norm(input, _dim, keepdim=keepdim, out=out)
elif p == "nuc":
if dtype is not None:
raise ValueError("dtype argument is not supported in nuclear norm")
if _dim is None:
if out is None:
return _VF.nuclear_norm(input, keepdim=keepdim)
else:
return _VF.nuclear_norm(input, keepdim=keepdim, out=out)
else:
if out is None:
return _VF.nuclear_norm(input, _dim, keepdim=keepdim)
else:
return _VF.nuclear_norm(input, _dim, keepdim=keepdim, out=out)
raise RuntimeError(f"only valid string values are 'fro' and 'nuc', found {p}")
else:
if _dim is None:
_dim = list(range(ndim))
if out is None:
if dtype is None:
return _VF.norm(input, p, _dim, keepdim=keepdim) # type: ignore[attr-defined]
else:
return _VF.norm(input, p, _dim, keepdim=keepdim, dtype=dtype) # type: ignore[attr-defined]
else:
if dtype is None:
return _VF.norm(input, p, _dim, keepdim=keepdim, out=out) # type: ignore[attr-defined]
else:
return _VF.norm(input, p, _dim, keepdim=keepdim, dtype=dtype, out=out) # type: ignore[attr-defined]
def chain_matmul(*matrices, out=None):
r"""Returns the matrix product of the :math:`N` 2-D tensors. This product is efficiently computed
using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms
of arithmetic operations (`[CLRS]`_). Note that since this is a function to compute the product, :math:`N`
needs to be greater than or equal to 2; if equal to 2 then a trivial matrix-matrix product is returned.
If :math:`N` is 1, then this is a no-op - the original matrix is returned as is.
.. warning::
:func:`torch.chain_matmul` is deprecated and will be removed in a future PyTorch release.
Use :func:`torch.linalg.multi_dot` instead, which accepts a list of two or more tensors
rather than multiple arguments.
Args:
matrices (Tensors...): a sequence of 2 or more 2-D tensors whose product is to be determined.
out (Tensor, optional): the output tensor. Ignored if :attr:`out` = ``None``.
Returns:
Tensor: if the :math:`i^{th}` tensor was of dimensions :math:`p_{i} \times p_{i + 1}`, then the product
would be of dimensions :math:`p_{1} \times p_{N + 1}`.
Example::
>>> a = torch.randn(3, 4)
>>> b = torch.randn(4, 5)
>>> c = torch.randn(5, 6)
>>> d = torch.randn(6, 7)
>>> torch.chain_matmul(a, b, c, d)
tensor([[ -2.3375, -3.9790, -4.1119, -6.6577, 9.5609, -11.5095, -3.2614],
[ 21.4038, 3.3378, -8.4982, -5.2457, -10.2561, -2.4684, 2.7163],
[ -0.9647, -5.8917, -2.3213, -5.2284, 12.8615, -12.2816, -2.5095]])
.. _`[CLRS]`: https://mitpress.mit.edu/books/introduction-algorithms-third-edition
"""
# This wrapper exists to support variadic args.
if has_torch_function(matrices):
return handle_torch_function(chain_matmul, matrices, *matrices)
if out is None:
return _VF.chain_matmul(matrices) # type: ignore[attr-defined]
else:
return _VF.chain_matmul(matrices, out=out) # type: ignore[attr-defined]
def _lu_impl(A, pivot=True, get_infos=False, out=None):
# type: (Tensor, bool, bool, Any) -> Tuple[Tensor, Tensor, Tensor]
r"""Computes the LU factorization of a matrix or batches of matrices
:attr:`A`. Returns a tuple containing the LU factorization and
pivots of :attr:`A`. Pivoting is done if :attr:`pivot` is set to
``True``.
.. note::
* The returned permutation matrix for every matrix in the batch is
represented by a 1-indexed vector of size ``min(A.shape[-2], A.shape[-1])``.
``pivots[i] == j`` represents that in the ``i``-th step of the algorithm,
the ``i``-th row was permuted with the ``j-1``-th row.
* LU factorization with :attr:`pivot` = ``False`` is not available
for CPU, and attempting to do so will throw an error. However,
LU factorization with :attr:`pivot` = ``False`` is available for
CUDA.
* This function does not check if the factorization was successful
or not if :attr:`get_infos` is ``True`` since the status of the
factorization is present in the third element of the return tuple.
* In the case of batches of square matrices with size less or equal
to 32 on a CUDA device, the LU factorization is repeated for
singular matrices due to the bug in the MAGMA library
(see magma issue 13).
* ``L``, ``U``, and ``P`` can be derived using :func:`torch.lu_unpack`.
.. warning::
The gradients of this function will only be finite when :attr:`A` is full rank.
This is because the LU decomposition is just differentiable at full rank matrices.
Furthermore, if :attr:`A` is close to not being full rank,
the gradient will be numerically unstable as it depends on the computation of :math:`L^{-1}` and :math:`U^{-1}`.
Args:
A (Tensor): the tensor to factor of size :math:`(*, m, n)`
pivot (bool, optional): controls whether pivoting is done. Default: ``True``
get_infos (bool, optional): if set to ``True``, returns an info IntTensor.
Default: ``False``
out (tuple, optional): optional output tuple. If :attr:`get_infos` is ``True``,
then the elements in the tuple are Tensor, IntTensor,
and IntTensor. If :attr:`get_infos` is ``False``, then the
elements in the tuple are Tensor, IntTensor. Default: ``None``
Returns:
(Tensor, IntTensor, IntTensor (optional)): A tuple of tensors containing
- **factorization** (*Tensor*): the factorization of size :math:`(*, m, n)`
- **pivots** (*IntTensor*): the pivots of size :math:`(*, \text{min}(m, n))`.
``pivots`` stores all the intermediate transpositions of rows.
The final permutation ``perm`` could be reconstructed by
applying ``swap(perm[i], perm[pivots[i] - 1])`` for ``i = 0, ..., pivots.size(-1) - 1``,
where ``perm`` is initially the identity permutation of :math:`m` elements
(essentially this is what :func:`torch.lu_unpack` is doing).
- **infos** (*IntTensor*, *optional*): if :attr:`get_infos` is ``True``, this is a tensor of
size :math:`(*)` where non-zero values indicate whether factorization for the matrix or
each minibatch has succeeded or failed
Example::
>>> A = torch.randn(2, 3, 3)
>>> A_LU, pivots = torch.lu(A)
>>> A_LU
tensor([[[ 1.3506, 2.5558, -0.0816],
[ 0.1684, 1.1551, 0.1940],
[ 0.1193, 0.6189, -0.5497]],
[[ 0.4526, 1.2526, -0.3285],
[-0.7988, 0.7175, -0.9701],
[ 0.2634, -0.9255, -0.3459]]])
>>> pivots
tensor([[ 3, 3, 3],
[ 3, 3, 3]], dtype=torch.int32)
>>> A_LU, pivots, info = torch.lu(A, get_infos=True)
>>> if info.nonzero().size(0) == 0:
... print('LU factorization succeeded for all samples!')
LU factorization succeeded for all samples!
"""
# If get_infos is True, then we don't need to check for errors and vice versa
return torch._lu_with_info(A, pivot=pivot, check_errors=(not get_infos))
if TYPE_CHECKING:
_ListOrSeq = Sequence[Tensor]
else:
_ListOrSeq = List[Tensor]
def _check_list_size(out_len: int, get_infos: bool, out: _ListOrSeq) -> None:
get_infos_int = 1 if get_infos else 0
if out_len - get_infos_int != 2:
raise TypeError(f"expected tuple of {2 + int(get_infos)} elements but got {out_len}")
if not isinstance(out, (tuple, list)):
raise TypeError(f"argument 'out' must be tuple of Tensors, not {type(out).__name__}")
def _lu_with_infos(A, pivot=True, get_infos=False, out=None):
# type: (Tensor, bool, bool, Optional[Tuple[Tensor, Tensor, Tensor]]) -> Tuple[Tensor, Tensor, Tensor]
if has_torch_function_unary(A):
return handle_torch_function(
lu, (A,), A, pivot=pivot, get_infos=get_infos, out=out)
result = _lu_impl(A, pivot, get_infos, out)
if out is not None:
_check_list_size(len(out), get_infos, out)
for i in range(len(out)):
out[i].resize_as_(result[i]).copy_(result[i])
return out
else:
return result # A_LU, pivots, infos
def _lu_no_infos(A, pivot=True, get_infos=False, out=None):
# type: (Tensor, bool, bool, Optional[Tuple[Tensor, Tensor]]) -> Tuple[Tensor, Tensor]
# need to check for torch_function here so that we exit if
if has_torch_function_unary(A):
return handle_torch_function(
lu, (A,), A, pivot=pivot, get_infos=get_infos, out=out)
result = _lu_impl(A, pivot, get_infos, out)
if out is not None:
_check_list_size(len(out), get_infos, out)
for i in range(len(out)):
out[i].resize_as_(result[i]).copy_(result[i])
return out
else:
return result[0], result[1] # A_LU, pivots
# The return type of lu depends on `get_infos`, so in order to resolve the output type
# of lu in TorchScript we need to statically know the value of `get_infos`
lu = boolean_dispatch(
arg_name='get_infos',
arg_index=2,
default=False,
if_true=_lu_with_infos,
if_false=_lu_no_infos,
module_name=__name__,
func_name='lu')
lu.__doc__ = _lu_impl.__doc__
def align_tensors(*tensors):
raise RuntimeError('`align_tensors` not yet implemented.')
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