Source code for torch.sparse
# The Tensor classes are added to this module by python_tensor.cpp
from typing import Optional, Tuple, List, Union, Any
import torch
from torch._C import _add_docstr, _sparse # type: ignore[attr-defined]
from torch import Tensor
# Semi structured sparsity support
from .semi_structured import (
SparseSemiStructuredTensor,
SparseSemiStructuredTensorCUSPARSELT,
SparseSemiStructuredTensorCUTLASS,
to_sparse_semi_structured
)
# 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',
'check_sparse_tensor_invariants',
'mm',
'sum',
'softmax',
'log_softmax',
'SparseSemiStructuredTensor',
'SparseSemiStructuredTensorCUTLASS',
'SparseSemiStructuredTensorCUSPARSELT',
'to_sparse_semi_structured',
'as_sparse_gradcheck',
]
addmm = _add_docstr(_sparse._sparse_addmm, r"""
sparse.addmm(mat, mat1, mat2, *, beta=1., alpha=1.) -> Tensor
This function does exact same thing as :func:`torch.addmm` in the forward,
except that it supports backward for sparse COO matrix :attr:`mat1`.
When :attr:`mat1` is a COO tensor it must have `sparse_dim = 2`.
When inputs are COO tensors, this function also supports backward for both inputs.
Supports both CSR and COO storage formats.
.. note::
This function doesn't support computing derivaties with respect to CSR matrices.
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`)
""")
mm = _add_docstr(_sparse._sparse_mm, r"""
Performs a matrix multiplication of the sparse matrix :attr:`mat1`
and the (sparse or strided) 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)` tensor.
When :attr:`mat1` is a COO tensor it must have `sparse_dim = 2`.
When inputs are COO tensors, this function also supports backward for both inputs.
Supports both CSR and COO storage formats.
.. note::
This function doesn't support computing derivaties with respect to CSR matrices.
This function also additionally accepts an optional :attr:`reduce` argument that allows
specification of an optional reduction operation, mathematically performs the following operation:
.. math::
z_{ij} = \bigoplus_{k = 0}^{K - 1} x_{ik} y_{kj}
where :math:`\bigoplus` defines the reduce operator. :attr:`reduce` is implemented only for
CSR storage format on CPU device.
Args:
mat1 (Tensor): the first sparse matrix to be multiplied
mat2 (Tensor): the second matrix to be multiplied, which could be sparse or dense
reduce (str, optional): the reduction operation to apply for non-unique indices
(:obj:`"sum"`, :obj:`"mean"`, :obj:`"amax"`, :obj:`"amin"`). Default :obj:`"sum"`.
Shape:
The format of the output tensor of this function follows:
- sparse x sparse -> sparse
- sparse x dense -> dense
Example::
>>> a = torch.tensor([[1., 0, 2], [0, 3, 0]]).to_sparse().requires_grad_()
>>> a
tensor(indices=tensor([[0, 0, 1],
[0, 2, 1]]),
values=tensor([1., 2., 3.]),
size=(2, 3), nnz=3, layout=torch.sparse_coo, requires_grad=True)
>>> b = torch.tensor([[0, 1.], [2, 0], [0, 0]], requires_grad=True)
>>> b
tensor([[0., 1.],
[2., 0.],
[0., 0.]], requires_grad=True)
>>> y = torch.sparse.mm(a, b)
>>> y
tensor([[0., 1.],
[6., 0.]], grad_fn=<SparseAddmmBackward0>)
>>> y.sum().backward()
>>> a.grad
tensor(indices=tensor([[0, 0, 1],
[0, 2, 1]]),
values=tensor([1., 0., 2.]),
size=(2, 3), nnz=3, layout=torch.sparse_coo)
>>> c = a.detach().to_sparse_csr()
>>> c
tensor(crow_indices=tensor([0, 2, 3]),
col_indices=tensor([0, 2, 1]),
values=tensor([1., 2., 3.]), size=(2, 3), nnz=3,
layout=torch.sparse_csr)
>>> y1 = torch.sparse.mm(c, b, 'sum')
>>> y1
tensor([[0., 1.],
[6., 0.]], grad_fn=<SparseMmReduceImplBackward0>)
>>> y2 = torch.sparse.mm(c, b, 'max')
>>> y2
tensor([[0., 1.],
[6., 0.]], grad_fn=<SparseMmReduceImplBackward0>)
""")
sampled_addmm = _add_docstr(_sparse.sparse_sampled_addmm, r"""
sparse.sampled_addmm(input, mat1, mat2, *, beta=1., alpha=1., out=None) -> Tensor
Performs a matrix multiplication of the dense matrices :attr:`mat1` and :attr:`mat2` at the locations
specified by the sparsity pattern of :attr:`input`. The matrix :attr:`input` is added to the final result.
Mathematically this performs the following operation:
.. math::
\text{out} = \alpha\ (\text{mat1} \mathbin{@} \text{mat2})*\text{spy}(\text{input}) + \beta\ \text{input}
where :math:`\text{spy}(\text{input})` is the sparsity pattern matrix of :attr:`input`, :attr:`alpha`
and :attr:`beta` are the scaling factors.
:math:`\text{spy}(\text{input})` has value 1 at the positions where :attr:`input` has non-zero values, and 0 elsewhere.
.. note::
:attr:`input` must be a sparse CSR tensor. :attr:`mat1` and :attr:`mat2` must be dense tensors.
Args:
input (Tensor): a sparse CSR matrix of shape `(m, n)` to be added and used to compute
the sampled matrix multiplication
mat1 (Tensor): a dense matrix of shape `(m, k)` to be multiplied
mat2 (Tensor): a dense matrix of shape `(k, n)` to be multiplied
Keyword args:
beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`)
alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`)
out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
Examples::
>>> input = torch.eye(3, device='cuda').to_sparse_csr()
>>> mat1 = torch.randn(3, 5, device='cuda')
>>> mat2 = torch.randn(5, 3, device='cuda')
>>> torch.sparse.sampled_addmm(input, mat1, mat2)
tensor(crow_indices=tensor([0, 1, 2, 3]),
col_indices=tensor([0, 1, 2]),
values=tensor([ 0.2847, -0.7805, -0.1900]), device='cuda:0',
size=(3, 3), nnz=3, layout=torch.sparse_csr)
>>> torch.sparse.sampled_addmm(input, mat1, mat2).to_dense()
tensor([[ 0.2847, 0.0000, 0.0000],
[ 0.0000, -0.7805, 0.0000],
[ 0.0000, 0.0000, -0.1900]], device='cuda:0')
>>> torch.sparse.sampled_addmm(input, mat1, mat2, beta=0.5, alpha=0.5)
tensor(crow_indices=tensor([0, 1, 2, 3]),
col_indices=tensor([0, 1, 2]),
values=tensor([ 0.1423, -0.3903, -0.0950]), device='cuda:0',
size=(3, 3), nnz=3, layout=torch.sparse_csr)
""")
[docs]def sum(input: Tensor, dim: DimOrDims = None,
dtype: Optional[DType] = None) -> Tensor:
r"""Return the sum of each row of the given sparse tensor.
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)
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> 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)
softmax = _add_docstr(_sparse._sparse_softmax, r"""
sparse.softmax(input, dim, *, dtype=None) -> Tensor
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.
Args:
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
""")
log_softmax = _add_docstr(_sparse._sparse_log_softmax, r"""
sparse.log_softmax(input, dim, *, dtype=None) -> Tensor
Applies a softmax function followed by logarithm.
See :class:`~torch.sparse.softmax` for more details.
Args:
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
""")
spdiags = _add_docstr(
_sparse._spdiags,
r"""
sparse.spdiags(diagonals, offsets, shape, layout=None) -> Tensor
Creates a sparse 2D tensor by placing the values from rows of
:attr:`diagonals` along specified diagonals of the output
The :attr:`offsets` tensor controls which diagonals are set.
- If :attr:`offsets[i]` = 0, it is the main diagonal
- If :attr:`offsets[i]` < 0, it is below the main diagonal
- If :attr:`offsets[i]` > 0, it is above the main diagonal
The number of rows in :attr:`diagonals` must match the length of :attr:`offsets`,
and an offset may not be repeated.
Args:
diagonals (Tensor): Matrix storing diagonals row-wise
offsets (Tensor): The diagonals to be set, stored as a vector
shape (2-tuple of ints): The desired shape of the result
Keyword args:
layout (:class:`torch.layout`, optional): The desired layout of the
returned tensor. ``torch.sparse_coo``, ``torch.sparse_csc`` and ``torch.sparse_csr``
are supported. Default: ``torch.sparse_coo``
Examples:
Set the main and first two lower diagonals of a matrix::
>>> diags = torch.arange(9).reshape(3, 3)
>>> diags
tensor([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> s = torch.sparse.spdiags(diags, torch.tensor([0, -1, -2]), (3, 3))
>>> s
tensor(indices=tensor([[0, 1, 2, 1, 2, 2],
[0, 1, 2, 0, 1, 0]]),
values=tensor([0, 1, 2, 3, 4, 6]),
size=(3, 3), nnz=6, layout=torch.sparse_coo)
>>> s.to_dense()
tensor([[0, 0, 0],
[3, 1, 0],
[6, 4, 2]])
Change the output layout::
>>> diags = torch.arange(9).reshape(3, 3)
>>> diags
tensor([[0, 1, 2],[3, 4, 5], [6, 7, 8])
>>> s = torch.sparse.spdiags(diags, torch.tensor([0, -1, -2]), (3, 3), layout=torch.sparse_csr)
>>> s
tensor(crow_indices=tensor([0, 1, 3, 6]),
col_indices=tensor([0, 0, 1, 0, 1, 2]),
values=tensor([0, 3, 1, 6, 4, 2]), size=(3, 3), nnz=6,
layout=torch.sparse_csr)
>>> s.to_dense()
tensor([[0, 0, 0],
[3, 1, 0],
[6, 4, 2]])
Set partial diagonals of a large output::
>>> diags = torch.tensor([[1, 2], [3, 4]])
>>> offsets = torch.tensor([0, -1])
>>> torch.sparse.spdiags(diags, offsets, (5, 5)).to_dense()
tensor([[1, 0, 0, 0, 0],
[3, 2, 0, 0, 0],
[0, 4, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
.. note::
When setting the values along a given diagonal the index into the diagonal
and the index into the row of :attr:`diagonals` is taken as the
column index in the output. This has the effect that when setting a diagonal
with a positive offset `k` the first value along that diagonal will be
the value in position `k` of the row of :attr:`diagonals`
Specifying a positive offset::
>>> diags = torch.tensor([[1, 2, 3], [1, 2, 3], [1, 2, 3]])
>>> torch.sparse.spdiags(diags, torch.tensor([0, 1, 2]), (5, 5)).to_dense()
tensor([[1, 2, 3, 0, 0],
[0, 2, 3, 0, 0],
[0, 0, 3, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
""")
[docs]class check_sparse_tensor_invariants:
"""A tool to control checking sparse tensor invariants.
The following options exists to manage sparsr tensor invariants
checking in sparse tensor construction:
1. Using a context manager:
.. code:: python
with torch.sparse.check_sparse_tensor_invariants():
run_my_model()
2. Using a procedural approach:
.. code:: python
prev_checks_enabled = torch.sparse.check_sparse_tensor_invariants.is_enabled()
torch.sparse.check_sparse_tensor_invariants.enable()
run_my_model()
if not prev_checks_enabled:
torch.sparse.check_sparse_tensor_invariants.disable()
3. Using function decoration:
.. code:: python
@torch.sparse.check_sparse_tensor_invariants()
def run_my_model():
...
run_my_model()
4. Using ``check_invariants`` keyword argument in sparse tensor constructor call.
For example:
>>> torch.sparse_csr_tensor([0, 1, 3], [0, 1], [1, 2], check_invariants=True)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
RuntimeError: `crow_indices[..., -1] == nnz` is not satisfied.
"""
[docs] @staticmethod
def is_enabled():
r"""Return True if the sparse tensor invariants checking is enabled.
.. note::
Use :func:`torch.sparse.check_sparse_tensor_invariants.enable` or
:func:`torch.sparse.check_sparse_tensor_invariants.disable` to
manage the state of the sparse tensor invariants checks.
"""
return torch._C._check_sparse_tensor_invariants()
[docs] @staticmethod
def enable():
r"""Enable sparse tensor invariants checking in sparse tensor constructors.
.. note::
By default, the sparse tensor invariants checks are disabled. Use
:func:`torch.sparse.check_sparse_tensor_invariants.is_enabled` to
retrieve the current state of sparse tensor invariants checking.
.. note::
The sparse tensor invariants check flag is effective to all sparse
tensor constructors, both in Python and ATen.
The flag can be locally overridden by the ``check_invariants``
optional argument of the sparse tensor constructor functions.
"""
torch._C._set_check_sparse_tensor_invariants(True)
[docs] @staticmethod
def disable():
r"""Disable sparse tensor invariants checking in sparse tensor constructors.
See :func:`torch.sparse.check_sparse_tensor_invariants.enable` for more information.
"""
torch._C._set_check_sparse_tensor_invariants(False)
# context manager support
def __init__(self, enable=True):
self.state = enable
self.saved_state : Optional[bool] = None
def __enter__(self):
if self.saved_state is not None:
raise RuntimeError('This context manager instance is already activated.'
' Use a different context manager instance for context nesting.')
self.saved_state = self.is_enabled()
torch._C._set_check_sparse_tensor_invariants(self.state)
def __exit__(self, type, value, traceback):
assert self.saved_state is not None
torch._C._set_check_sparse_tensor_invariants(self.saved_state)
self.saved_state = None
# decorator support
def __call__(self, mth):
def test_mth(*args, **kwargs):
with type(self)(self.state):
return mth(*args, **kwargs)
return test_mth
[docs]def as_sparse_gradcheck(gradcheck):
"""Decorate function, to extend gradcheck for sparse tensors.
Decorator for torch.autograd.gradcheck or its functools.partial
variants that extends the gradcheck function with support to input
functions that operate on or/and return sparse tensors.
The specified gradcheck function itself is guaranteed to operate
on strided tensors only.
For example:
>>> gradcheck = torch.sparse.as_sparse_gradcheck(torch.autograd.gradcheck)
>>> x = torch.tensor([[0, 1], [2, 3]], dtype=torch.float64).to_sparse_coo().requires_grad_(True)
>>> gradcheck(lambda x: x.to_sparse_csr(), x)
True
"""
def gradcheck_with_sparse_support(func, inputs, **kwargs):
"""
Create gradcheck with support for sparse tensors.
Same as :func:`torch.autograd.gradcheck` but with sparse tensors inputs and outputs support.
"""
masked = kwargs.pop('masked', False)
sparse_layouts = {torch.sparse_coo, torch.sparse_csr, torch.sparse_csc, torch.sparse_bsr, torch.sparse_bsc}
sparse_compressed_layouts = {torch.sparse_csr, torch.sparse_csc, torch.sparse_bsr, torch.sparse_bsc}
sparse_block_layouts = {torch.sparse_bsr, torch.sparse_bsc}
STRIDED_REPRESENTATION = '__STRIDED_REPRESENTATION__'
def convert_to_strided_representation(args):
"""Convert differentiable non-strided tensors to a representation containing differentiable strided tensors."""
if not isinstance(args, (list, tuple)):
args = args,
new_args: List[Any] = []
for obj in args:
if isinstance(obj, torch.Tensor) and obj.requires_grad and obj.layout in sparse_layouts:
d = dict(layout=obj.layout, shape=obj.shape)
if not masked:
# Materialize unspecified elements with zero values
batch_dim = obj.ndim - obj.dense_dim() - obj.sparse_dim()
blocksize = obj.values().shape[batch_dim + 1:batch_dim + 3] if obj.layout in sparse_block_layouts else None
full_mask = torch.ones(obj.shape, device=obj.device, dtype=torch.bool).to_sparse(
layout=obj.layout, blocksize=blocksize, dense_dim=obj.dense_dim())
obj = obj.to_dense().sparse_mask(full_mask)
if obj.layout is torch.sparse_coo:
d.update(indices=obj._indices(), is_coalesced=obj.is_coalesced())
values = obj._values()
elif obj.layout in {torch.sparse_csr, torch.sparse_bsr}:
d.update(compressed_indices=obj.crow_indices(), plain_indices=obj.col_indices())
values = obj.values()
else:
d.update(compressed_indices=obj.ccol_indices(), plain_indices=obj.row_indices())
values = obj.values()
new_args.extend((STRIDED_REPRESENTATION, d, values.requires_grad_(True)))
else:
new_args.append(obj)
return tuple(new_args)
def restore_from_strided_representation(args):
"""Restore non-strided differentiable tensosr from their strided representations."""
new_args = []
args = list(args)
while args:
a = args.pop(0)
if a == STRIDED_REPRESENTATION:
d, values = args.pop(0), args.pop(0)
if d['layout'] is torch.sparse_coo:
a = torch.sparse_coo_tensor(d['indices'], values, size=d['shape'], is_coalesced=d['is_coalesced'])
elif d['layout'] in sparse_compressed_layouts:
a = torch.sparse_compressed_tensor(d['compressed_indices'], d['plain_indices'], values,
size=d['shape'], layout=d['layout'])
else:
raise NotImplementedError(f'conversion of {d["layout"]} strided representation to tensor')
new_args.append(a)
return tuple(new_args)
def func_wrapper(*args, **kwargs):
restored_args = restore_from_strided_representation(args)
# convert differentiable output sparse tensors to strided
# tensors:
outputs = func(*restored_args, **kwargs)
strided_outputs = tuple(outputs) if isinstance(outputs, (list, tuple)) else (outputs,)
strided_outputs = tuple((o.to_dense(masked_grad=masked)
if isinstance(o, torch.Tensor) and o.requires_grad and o.layout in sparse_layouts else o)
for o in strided_outputs)
return strided_outputs if isinstance(outputs, (list, tuple)) else strided_outputs[0]
args = (func_wrapper, convert_to_strided_representation(inputs))
return gradcheck(*args, **kwargs)
return gradcheck_with_sparse_support
