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Source code for torch.sparse

# The Tensor classes are added to this module by python_tensor.cpp
from typing import Optional, Tuple, List, Union

import torch
from torch import Tensor

# A workaround to support both TorchScript and MyPy:
from typing import TYPE_CHECKING
if TYPE_CHECKING:
    from torch.types import _dtype as DType
    DimOrDims = Optional[Union[int, Tuple[int], List[int]]]
else:
    # The JIT doesn't understand Union, nor torch.dtype here
    DType = int
    DimOrDims = Optional[Tuple[int]]


__all__ = [
    'addmm',
    'mm',
    'sum',
    'softmax',
    'log_softmax',
]


[docs]def addmm(mat: Tensor, mat1: Tensor, mat2: Tensor, beta: float = 1., alpha: float = 1.) -> Tensor: r""" This function does exact same thing as :func:`torch.addmm` in the forward, except that it supports backward for sparse matrix :attr:`mat1`. :attr:`mat1` need to have `sparse_dim = 2`. Note that the gradients of :attr:`mat1` is a coalesced sparse tensor. Args: mat (Tensor): a dense matrix to be added mat1 (Tensor): a sparse matrix to be multiplied mat2 (Tensor): a dense matrix to be multiplied beta (Number, optional): multiplier for :attr:`mat` (:math:`\beta`) alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) """ return torch._sparse_addmm(mat, mat1, mat2, beta=beta, alpha=alpha)
[docs]def mm(mat1: Tensor, mat2: Tensor) -> Tensor: r""" Performs a matrix multiplication of the sparse matrix :attr:`mat1` and dense matrix :attr:`mat2`. Similar to :func:`torch.mm`, If :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a :math:`(m \times p)` tensor, out will be a :math:`(n \times p)` dense tensor. :attr:`mat1` need to have `sparse_dim = 2`. This function also supports backward for both matrices. Note that the gradients of :attr:`mat1` is a coalesced sparse tensor. Args: mat1 (Tensor): the first sparse matrix to be multiplied mat2 (Tensor): the second dense matrix to be multiplied Example:: >>> a = torch.randn(2, 3).to_sparse().requires_grad_(True) >>> a tensor(indices=tensor([[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]), values=tensor([ 1.5901, 0.0183, -0.6146, 1.8061, -0.0112, 0.6302]), size=(2, 3), nnz=6, layout=torch.sparse_coo, requires_grad=True) >>> b = torch.randn(3, 2, requires_grad=True) >>> b tensor([[-0.6479, 0.7874], [-1.2056, 0.5641], [-1.1716, -0.9923]], requires_grad=True) >>> y = torch.sparse.mm(a, b) >>> y tensor([[-0.3323, 1.8723], [-1.8951, 0.7904]], grad_fn=<SparseAddmmBackward>) >>> y.sum().backward() >>> a.grad tensor(indices=tensor([[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]), values=tensor([ 0.1394, -0.6415, -2.1639, 0.1394, -0.6415, -2.1639]), size=(2, 3), nnz=6, layout=torch.sparse_coo) """ return torch._sparse_mm(mat1, mat2)
[docs]def sum(input: Tensor, dim: DimOrDims = None, dtype: Optional[DType] = None) -> Tensor: r""" Returns the sum of each row of the sparse tensor :attr:`input` in the given dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, reduce over all of them. When sum over all ``sparse_dim``, this method returns a dense tensor instead of a sparse tensor. All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output tensor having :attr:`dim` fewer dimensions than :attr:`input`. During backward, only gradients at ``nnz`` locations of :attr:`input` will propagate back. Note that the gradients of :attr:`input` is coalesced. Args: input (Tensor): the input sparse tensor dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce over all dims. dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. Default: dtype of :attr:`input`. Example:: >>> nnz = 3 >>> dims = [5, 5, 2, 3] >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) >>> V = torch.randn(nnz, dims[2], dims[3]) >>> size = torch.Size(dims) >>> S = torch.sparse_coo_tensor(I, V, size) >>> S tensor(indices=tensor([[2, 0, 3], [2, 4, 1]]), values=tensor([[[-0.6438, -1.6467, 1.4004], [ 0.3411, 0.0918, -0.2312]], [[ 0.5348, 0.0634, -2.0494], [-0.7125, -1.0646, 2.1844]], [[ 0.1276, 0.1874, -0.6334], [-1.9682, -0.5340, 0.7483]]]), size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) # when sum over only part of sparse_dims, return a sparse tensor >>> torch.sparse.sum(S, [1, 3]) tensor(indices=tensor([[0, 2, 3]]), values=tensor([[-1.4512, 0.4073], [-0.8901, 0.2017], [-0.3183, -1.7539]]), size=(5, 2), nnz=3, layout=torch.sparse_coo) # when sum over all sparse dim, return a dense tensor # with summed dims squeezed >>> torch.sparse.sum(S, [0, 1, 3]) tensor([-2.6596, -1.1450]) """ if dtype is None: if dim is not None: return torch._sparse_sum(input, dim) else: return torch._sparse_sum(input) else: if dim is not None: return torch._sparse_sum(input, dim, dtype=dtype) else: return torch._sparse_sum(input, dtype=dtype)
[docs]def softmax(input: Tensor, dim: int, dtype: Optional[DType] = None) -> Tensor: r"""Applies a softmax function. Softmax is defined as: :math:`\text{Softmax}(x_{i}) = \frac{exp(x_i)}{\sum_j exp(x_j)}` where :math:`i, j` run over sparse tensor indices and unspecified entries are ignores. This is equivalent to defining unspecified entries as negative infinity so that :math:`exp(x_k) = 0` when the entry with index :math:`k` has not specified. It is applied to all slices along `dim`, and will re-scale them so that the elements lie in the range `[0, 1]` and sum to 1. Arguments: input (Tensor): input dim (int): A dimension along which softmax will be computed. dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. If specified, the input tensor is casted to :attr:`dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None """ return torch._sparse_softmax(input, dim, dtype=dtype)
[docs]def log_softmax(input: Tensor, dim: int, dtype: Optional[DType] = None) -> Tensor: r"""Applies a softmax function followed by logarithm. See :class:`~torch.sparse.softmax` for more details. Arguments: input (Tensor): input dim (int): A dimension along which softmax will be computed. dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. If specified, the input tensor is casted to :attr:`dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None """ return torch._sparse_log_softmax(input, dim, dtype=dtype)

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