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Source code for torch.functional

from typing import (
    Tuple, Optional, Union, Any, Sequence, TYPE_CHECKING
)

import torch
import torch.nn.functional as F
from torch.types import _size
from ._lowrank import svd_lowrank, pca_lowrank
from .overrides import has_torch_function, handle_torch_function
from ._jit_internal import boolean_dispatch, List
from ._jit_internal import _overload as overload
from torch._autograd_functions import _LU

Tensor = torch.Tensor
from torch import _VF

__all__ = [
    'atleast_1d',
    'atleast_2d',
    'atleast_3d',
    'align_tensors',
    'broadcast_tensors',
    'cartesian_prod',
    'block_diag',
    'cdist',
    'chain_matmul',
    'einsum',
    'istft',
    'lu',
    'lu_unpack',
    'norm',
    'meshgrid',
    'pca_lowrank',
    'split',
    'stft',
    'svd_lowrank',
    'tensordot',
    'unique',
    'unique_consecutive',
]


[docs]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]]) """ if not torch.jit.is_scripting(): if any(type(t) is not Tensor for t in tensors) and has_torch_function(tensors): return handle_torch_function(broadcast_tensors, tensors, *tensors) return _VF.broadcast_tensors(tensors) # type: ignore
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`. Arguments: 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 not torch.jit.is_scripting(): if type(tensor) is not Tensor and has_torch_function((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) if TYPE_CHECKING: _Indices = _size else: _Indices = List[int] # equivalent to itertools.product(indices) def _indices_product(indices: _Indices) -> List[List[int]]: empty_list = torch.jit.annotate(List[int], []) result = [empty_list] for idx in indices: result_temp = torch.jit.annotate(List[List[int]], []) for res in result: for i in range(idx): result_temp.append(res + [i]) result = result_temp return result def _index_tensor_with_indices_list(tensor, indices): # type: (Tensor, List[int]) -> Tensor out = tensor for index in indices: out = out[index] return out def lu_unpack(LU_data, LU_pivots, unpack_data=True, unpack_pivots=True): # type: (Tensor, Tensor, bool, bool) -> (Tuple[Optional[Tensor], Optional[Tensor], Optional[Tensor]]) r"""Unpacks the data and pivots from a LU factorization of a tensor. Returns a tuple of tensors as ``(the pivots, the L tensor, the U tensor)``. Arguments: LU_data (Tensor): the packed LU factorization data LU_pivots (Tensor): the packed LU factorization pivots unpack_data (bool): flag indicating if the data should be unpacked unpack_pivots (bool): flag indicating if the pivots should be unpacked Examples:: >>> A = torch.randn(2, 3, 3) >>> A_LU, pivots = A.lu() >>> P, A_L, A_U = torch.lu_unpack(A_LU, pivots) >>> >>> # can recover A from factorization >>> A_ = torch.bmm(P, torch.bmm(A_L, A_U)) >>> # LU factorization of a rectangular matrix: >>> A = torch.randn(2, 3, 2) >>> A_LU, pivots = A.lu() >>> P, A_L, A_U = torch.lu_unpack(A_LU, pivots) >>> P tensor([[[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]], [[0., 0., 1.], [0., 1., 0.], [1., 0., 0.]]]) >>> A_L tensor([[[ 1.0000, 0.0000], [ 0.4763, 1.0000], [ 0.3683, 0.1135]], [[ 1.0000, 0.0000], [ 0.2957, 1.0000], [-0.9668, -0.3335]]]) >>> A_U tensor([[[ 2.1962, 1.0881], [ 0.0000, -0.8681]], [[-1.0947, 0.3736], [ 0.0000, 0.5718]]]) >>> A_ = torch.bmm(P, torch.bmm(A_L, A_U)) >>> torch.norm(A_ - A) tensor(2.9802e-08) """ if not torch.jit.is_scripting(): tens_ops = (LU_data, LU_pivots) if any([type(t) is not Tensor for t in tens_ops]) and has_torch_function(tens_ops): return handle_torch_function( lu_unpack, tens_ops, LU_data, LU_pivots, unpack_data=unpack_data, unpack_pivots=unpack_pivots) shape = LU_data.shape # In generalized LU factorization, the following shape relations hold: # A.shape[-2:] == (m, n) # P.shape[-2:] == (m, m) # L.shape[-2:] == (m, k) # U.shape[-2:] == (k, n) # where k = min(m, n) m, n = shape[-2:] k = min(m, n) if unpack_data: U: Optional[Tensor] = LU_data.triu() assert U is not None if m != k: U = U.narrow(-2, 0, k) L: Optional[Tensor] = LU_data.tril() assert L is not None if k != n: L = L.narrow(-1, 0, k) L.diagonal(dim1=-2, dim2=-1).fill_(1) else: L = U = None if unpack_pivots: LU_pivots_zero_idx = LU_pivots - 1 if LU_data.dim() > 2: P: Optional[Tensor] = torch.eye(m, device=LU_data.device, dtype=LU_data.dtype) \ .expand(shape[:-1] + (m,)) \ .clone(memory_format=torch.contiguous_format) assert P is not None # TODO: rewrite when TorchScript supports product and map as # product(*map(lambda x: list(range(x)), shape[:-2])) when issue 33781 is fixed indices = _indices_product(shape[:-2]) for idx in indices: final_order = [i for i in range(m)] # noqa: C416 TODO: rewrite as list(range(m)) for k, j in enumerate(_index_tensor_with_indices_list(LU_pivots_zero_idx, idx)): final_order[k], final_order[j] = final_order[j], final_order[k] # TODO: remove _index_tensor_with_indices_list when TorchScript supports indexing Tensor with list p_idx = _index_tensor_with_indices_list(P, idx) p_idx.copy_(p_idx.index_select(1, torch.as_tensor(final_order, device=LU_pivots.device))) else: P = torch.eye(m, device=LU_data.device, dtype=LU_data.dtype) final_order = [i for i in range(m)] # noqa: C416 TODO: rewrite as list(range(m)) for k, j, in enumerate(LU_pivots_zero_idx): final_order[k], final_order[j] = final_order[j], final_order[k] P = P.index_select(1, torch.as_tensor(final_order, device=LU_pivots.device)) else: P = None return P, L, U def einsum(equation, *operands): r"""einsum(equation, *operands) -> Tensor This function provides a way of computing multilinear expressions (i.e. sums of products) using the Einstein summation convention. Args: equation (string): The equation is given in terms of lower case letters (indices) to be associated with each dimension of the operands and result. The left hand side lists the operands dimensions, separated by commas. There should be one index letter per tensor dimension. The right hand side follows after `->` and gives the indices for the output. If the `->` and right hand side are omitted, it implicitly defined as the alphabetically sorted list of all indices appearing exactly once in the left hand side. The indices not apprearing in the output are summed over after multiplying the operands entries. If an index appears several times for the same operand, a diagonal is taken. Ellipses `...` represent a fixed number of dimensions. If the right hand side is inferred, the ellipsis dimensions are at the beginning of the output. operands (Tensor): The operands to compute the Einstein sum of. .. 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. Examples:: >>> x = torch.randn(5) >>> y = torch.randn(4) >>> torch.einsum('i,j->ij', x, y) # outer product tensor([[-0.0570, -0.0286, -0.0231, 0.0197], [ 1.2616, 0.6335, 0.5113, -0.4351], [ 1.4452, 0.7257, 0.5857, -0.4984], [-0.4647, -0.2333, -0.1883, 0.1603], [-1.1130, -0.5588, -0.4510, 0.3838]]) >>> 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) # compare torch.nn.functional.bilinear tensor([[-0.3430, -5.2405, 0.4494], [ 0.3311, 5.5201, -3.0356]]) >>> As = torch.randn(3,2,5) >>> Bs = torch.randn(3,5,4) >>> torch.einsum('bij,bjk->bik', As, Bs) # batch matrix multiplication 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]]]) >>> A = torch.randn(3, 3) >>> torch.einsum('ii->i', A) # diagonal tensor([-0.7825, 0.8291, -0.1936]) >>> A = torch.randn(4, 3, 3) >>> torch.einsum('...ii->...i', A) # batch diagonal tensor([[-1.0864, 0.7292, 0.0569], [-0.9725, -1.0270, 0.6493], [ 0.5832, -1.1716, -1.5084], [ 0.4041, -1.1690, 0.8570]]) >>> A = torch.randn(2, 3, 4, 5) >>> torch.einsum('...ij->...ji', A).shape # batch permute torch.Size([2, 3, 5, 4]) """ if not torch.jit.is_scripting(): if any(type(t) is not Tensor for t in operands) and 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 if TYPE_CHECKING: # The JIT doesn't understand Union, so only add type annotation for mypy def meshgrid(*tensors: Union[Tensor, List[Tensor]]) -> Tuple[Tensor, ...]: return _meshgrid(*tensors) else: def meshgrid(*tensors): return _meshgrid(*tensors) def _meshgrid(*tensors): r"""Take :math:`N` tensors, each of which can be either scalar or 1-dimensional vector, and create :math:`N` N-dimensional grids, where the :math:`i` :sup:`th` grid is defined by expanding the :math:`i` :sup:`th` input over dimensions defined by other inputs. Args: tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be treated as tensors of size :math:`(1,)` automatically Returns: seq (sequence of Tensors): If the input has :math:`k` tensors of size :math:`(N_1,), (N_2,), \ldots , (N_k,)`, then the output would also have :math:`k` tensors, where all tensors are of size :math:`(N_1, N_2, \ldots , N_k)`. Example:: >>> x = torch.tensor([1, 2, 3]) >>> y = torch.tensor([4, 5, 6]) >>> grid_x, grid_y = torch.meshgrid(x, y) >>> grid_x tensor([[1, 1, 1], [2, 2, 2], [3, 3, 3]]) >>> grid_y tensor([[4, 5, 6], [4, 5, 6], [4, 5, 6]]) """ if not torch.jit.is_scripting(): if any(type(t) is not Tensor for t in tensors) and has_torch_function(tensors): return handle_torch_function(meshgrid, tensors, *tensors) 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 return _VF.meshgrid(tensors) # type: ignore 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:: Setting :attr:`return_complex` explicitly will be required in a future PyTorch release. Set it to False to preserve the current behavior or True to return a complex output. 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[m, \omega] = \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 that :math:`0 \leq \omega < \text{n\_fft}`. When :attr:`onesided` is the default value ``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. Arguments: 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 not torch.jit.is_scripting(): if type(input) is not Tensor and has_torch_function((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 normalized, onesided, return_complex)
[docs]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 :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. Arguments: 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. 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 not torch.jit.is_scripting(): if type(input) is not Tensor and has_torch_function((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 normalized, onesided, length, return_complex)
del torch.unique_dim 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"""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. Arguments: 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 not torch.jit.is_scripting(): if type(input) is not Tensor and has_torch_function((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( # type: ignore 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++. Arguments: 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 not torch.jit.is_scripting(): if type(input) is not Tensor and has_torch_function((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 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 not torch.jit.is_scripting(): if type(input) is not Tensor and has_torch_function((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 not torch.jit.is_scripting(): if type(input) is not Tensor and has_torch_function((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 not torch.jit.is_scripting(): if type(input) is not Tensor and has_torch_function((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 not torch.jit.is_scripting(): if type(input) is not Tensor and has_torch_function((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 not torch.jit.is_scripting(): if type(input) is not Tensor and has_torch_function((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 not torch.jit.is_scripting(): if type(input) is not Tensor and has_torch_function((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__ def tensordot(a, b, dims=2): 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]] containing two lists): 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 not torch.jit.is_scripting(): if (type(a) is not Tensor or type(b) is not Tensor) and has_torch_function((a, b)): return handle_torch_function(tensordot, (a, b), a, b, dims=dims) if isinstance(dims, (list, tuple)) or \ (isinstance(dims, torch.Tensor) and dims.numel() > 1): dims_a, dims_b = dims else: if isinstance(dims, torch.Tensor): dims = dims.item() 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)) return _VF.tensordot(a, b, dims_a, dims_b) # type: ignore
[docs]def cartesian_prod(*tensors): """Do cartesian product of the given sequence of tensors. The behavior is similar to python's `itertools.product`. Arguments: *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]]) """ if not torch.jit.is_scripting(): if any(type(t) is not Tensor for t in tensors) and has_torch_function(tensors): return handle_torch_function(cartesian_prod, tensors, *tensors) return _VF.cartesian_prod(tensors) # type: ignore
[docs]def block_diag(*tensors): """Create a block diagonal matrix from provided tensors. Arguments: *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]]) """ if any(type(t) is not Tensor for t in tensors) and has_torch_function(tensors): return handle_torch_function(block_diag, tensors, *tensors) return torch._C._VariableFunctions.block_diag(tensors) # type: ignore
[docs]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 not torch.jit.is_scripting(): if (type(x1) is not Tensor or type(x2) is not Tensor) and has_torch_function((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 elif compute_mode == 'use_mm_for_euclid_dist': return _VF.cdist(x1, x2, p, 1) # type: ignore elif compute_mode == 'donot_use_mm_for_euclid_dist': return _VF.cdist(x1, x2, p, 2) # type: ignore else: raise ValueError(f"{compute_mode} is not a valid value for compute_mode")
[docs]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.])) """ if not torch.jit.is_scripting(): if any(type(t) is not Tensor for t in tensors) and 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
[docs]def atleast_2d(*tensors): r""" Returns a 2-dimensional view of each 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.]])) """ if not torch.jit.is_scripting(): if any(type(t) is not Tensor for t in tensors) and 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
[docs]def atleast_3d(*tensors): r""" Returns a 3-dimensional view of each 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.]]])) """ if not torch.jit.is_scripting(): if any(type(t) is not Tensor for t in tensors) and 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
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 # noqa: 749 def norm(input, p="fro", dim=None, keepdim=False, out=None, dtype=None): # noqa: 749 # type: (Tensor, str, Optional[List[int]], bool, Optional[Tensor], Optional[int]) -> Tensor pass @overload # noqa: 749 def norm(input, p="fro", dim=None, keepdim=False, out=None, dtype=None): # noqa: 749 # type: (Tensor, Optional[number], Optional[List[int]], bool, Optional[Tensor], Optional[int]) -> Tensor pass @overload # noqa: 749 def norm(input, p="fro", dim=None, keepdim=False, out=None, dtype=None): # noqa: 749 # type: (Tensor, Optional[number], Optional[int], bool, Optional[Tensor], Optional[int]) -> Tensor pass @overload # noqa: 749 def norm(input, p="fro", dim=None, keepdim=False, out=None, dtype=None): # noqa: 749 # 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: 749 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. Use :func:`torch.linalg.norm` instead, but note that :func:`torch.linalg.norm` has a different signature and slightly different behavior that is more consistent with NumPy's numpy.linalg.norm. Args: input (Tensor): the input tensor 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 not torch.jit.is_scripting(): if type(input) is not Tensor and has_torch_function((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) # type: ignore 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 # 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 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 = [i for i in range(ndim)] # noqa: C416 TODO: rewrite as list(range(m)) if out is None: return _VF.frobenius_norm(input, _dim, keepdim=keepdim) # type: ignore else: return _VF.frobenius_norm(input, _dim, keepdim=keepdim, out=out) # type: ignore 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) # type: ignore else: return _VF.nuclear_norm(input, keepdim=keepdim, out=out) # type: ignore else: if out is None: return _VF.nuclear_norm(input, _dim, keepdim=keepdim) # type: ignore else: return _VF.nuclear_norm(input, _dim, keepdim=keepdim, out=out) # type: ignore raise RuntimeError(f"only valid string values are 'fro' and 'nuc', found {p}") else: if _dim is None: _dim = [i for i in range(ndim)] # noqa: C416 TODO: rewrite as list(range(m)) if out is None: if dtype is None: return _VF.norm(input, p, _dim, keepdim=keepdim) # type: ignore else: return _VF.norm(input, p, _dim, keepdim=keepdim, dtype=dtype) # type: ignore else: if dtype is None: return _VF.norm(input, p, _dim, keepdim=keepdim, out=out) # type: ignore else: return _VF.norm(input, p, _dim, keepdim=keepdim, dtype=dtype, out=out) # type: ignore def chain_matmul(*matrices): 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. Args: matrices (Tensors...): a sequence of 2 or more 2-D tensors whose product is to be determined. 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 """ if not torch.jit.is_scripting(): if any(type(t) is not Tensor for t in matrices) and has_torch_function(matrices): return handle_torch_function(chain_matmul, matrices, *matrices) return _VF.chain_matmul(matrices) # type: ignore 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 pivots returned by the function are 1-indexed. If :attr:`pivot` is ``False``, then the returned pivots is a tensor filled with zeros of the appropriate size. .. note:: 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. .. note:: 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. .. note:: 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). .. note:: ``L``, ``U``, and ``P`` can be derived using :func:`torch.lu_unpack`. .. warning:: The LU factorization does have backward support, but only for square inputs of full rank. Arguments: 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:`(*, m)` - **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 not torch._jit_internal.is_scripting(): if A.requires_grad: if not (A.size(-2) == A.size(-1) and A.dtype.is_floating_point): raise ValueError( 'lu.backward works only with batches of squared full-rank matrices' ' of floating types.' ) return _LU.apply(A, pivot, get_infos) else: if A.requires_grad: raise RuntimeError( 'Script and require gradients is not supported at the moment.' 'If you just want to do the forward, use .detach()' 'on the input before calling the function.' ) # 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 not torch.jit.is_scripting(): if type(A) is not Tensor and has_torch_function((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 not torch.jit.is_scripting(): if type(A) is not Tensor and has_torch_function((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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