"""dgl sddmm operator module."""
import sys
from itertools import product
from .. import backend as F
from ..backend import (
gsddmm as gsddmm_internal,
gsddmm_hetero as gsddmm_internal_hetero,
)
__all__ = ["gsddmm", "copy_u", "copy_v", "copy_e"]
def reshape_lhs_rhs(lhs_data, rhs_data):
r"""Expand dims so that there will be no broadcasting issues with different
number of dimensions. For example, given two shapes (N, 3, 1), (E, 5, 3, 4)
that are valid broadcastable shapes, change them to (N, 1, 3, 1) and
(E, 5, 3, 4)
Parameters
----------
lhs_data : tensor or None
The left operand, could be None if it's not required by op.
rhs_data : tensor or None
The right operand, could be None if it's not required by op.
"""
lhs_shape = F.shape(lhs_data)
rhs_shape = F.shape(rhs_data)
if len(lhs_shape) != len(rhs_shape):
max_ndims = max(len(lhs_shape), len(rhs_shape))
lhs_pad_ndims = max_ndims - len(lhs_shape)
rhs_pad_ndims = max_ndims - len(rhs_shape)
new_lhs_shape = (lhs_shape[0],) + (1,) * lhs_pad_ndims + lhs_shape[1:]
new_rhs_shape = (rhs_shape[0],) + (1,) * rhs_pad_ndims + rhs_shape[1:]
lhs_data = F.reshape(lhs_data, new_lhs_shape)
rhs_data = F.reshape(rhs_data, new_rhs_shape)
return lhs_data, rhs_data
[docs]def gsddmm(g, op, lhs_data, rhs_data, lhs_target="u", rhs_target="v"):
r"""Generalized Sampled-Dense-Dense Matrix Multiplication interface.
It computes edge features by :attr:`op` lhs features and rhs features.
.. math::
x_{e} = \phi(x_{lhs}, x_{rhs}), \forall (u,e,v)\in \mathcal{G}
where :math:`x_{e}` is the returned feature on edges and :math:`x_u`,
:math:`x_v` refers to :attr:`u`, :attr:`v` respectively. :math:`\phi`
is the binary operator :attr:`op`, and :math:`\mathcal{G}` is the graph
we apply gsddmm on: :attr:`g`. :math:`lhs` and :math:`rhs` are one of
:math:`u,v,e`'s.
Parameters
----------
g : DGLGraph
The input graph.
op : str
Binary operator, could be ``add``, ``sub``, ``mul``, ``div``, ``dot``,
``copy_lhs``, ``copy_rhs``.
lhs_data : tensor or None
The left operand, could be None if it's not required by op.
rhs_data : tensor or None
The right operand, could be None if it's not required by op.
lhs_target: str
Choice of ``u``(source), ``e``(edge) or ``v``(destination) for left operand.
rhs_target: str
Choice of ``u``(source), ``e``(edge) or ``v``(destination) for right operand.
Returns
-------
tensor
The result tensor.
"""
if g._graph.number_of_etypes() == 1:
if op not in ["copy_lhs", "copy_rhs"]:
lhs_data, rhs_data = reshape_lhs_rhs(lhs_data, rhs_data)
return gsddmm_internal(
g._graph, op, lhs_data, rhs_data, lhs_target, rhs_target
)
else:
if op == "copy_lhs":
rhs_data = [None] * g._graph.number_of_etypes()
elif op == "copy_rhs":
lhs_data = [None] * g._graph.number_of_ntypes()
# TODO (Israt): Call reshape_lhs_rhs() on lhs and rhs data to match their dimension
# and avoid broadcasting issue. Handle the case where different nodes have
# different dimensions, and different etypes may need different broadcasting
# dims for the same node.
lhs_and_rhs_tuple = tuple(list(lhs_data) + list(rhs_data))
return gsddmm_internal_hetero(
g._graph,
op,
len(lhs_data),
lhs_target,
rhs_target,
*lhs_and_rhs_tuple
)
def _gen_sddmm_func(lhs_target, rhs_target, binary_op):
name = "{}_{}_{}".format(lhs_target, binary_op, rhs_target)
target_dict = {"u": "source node", "e": "edge", "v": "destination node"}
lhs_str = target_dict[lhs_target]
rhs_str = target_dict[rhs_target]
docstring = r"""Generalized SDDMM function.
It computes edge features by {op} {lhs} features and {rhs} features.
Parameters
----------
g : DGLGraph
The input graph
x : tensor
The {lhs} features.
y : tensor
The {rhs} features.
Returns
-------
tensor
The result tensor.
Notes
-----
This function supports autograd (computing input gradients given the output gradient). If the
feature shape of two input operands do not match, we first broadcasts the features to a unified
shape (note that the memory usage will not increase accordingly) and then performs the operation.
Broadcasting follows NumPy semantics. Please see
https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html
for more details about the NumPy broadcasting semantics.
""".format(
op=binary_op, lhs=lhs_str, rhs=rhs_str
)
def func(g, x, y):
return gsddmm(
g, binary_op, x, y, lhs_target=lhs_target, rhs_target=rhs_target
)
func.__name__ = name
func.__doc__ = docstring
return func
def _register_sddmm_func():
"""Register sddmm functions"""
target = ["u", "v", "e"]
for lhs, rhs in product(target, target):
if lhs != rhs:
for binary_op in ["add", "sub", "mul", "div", "dot"]:
func = _gen_sddmm_func(lhs, rhs, binary_op)
setattr(sys.modules[__name__], func.__name__, func)
__all__.append(func.__name__)
[docs]def copy_u(g, x):
r"""Generalized SDDMM function that copies source node features to edges.
Parameters
----------
g : DGLGraph
The input graph.
x : tensor
The source node features.
Returns
-------
tensor
The result tensor.
Notes
-----
This function supports autograd (computing input gradients given the output gradient).
"""
return gsddmm(g, "copy_lhs", x, None)
[docs]def copy_v(g, x):
r"""Generalized SDDMM function that copies destination node features to edges.
Parameters
----------
g : DGLGraph
The input graph.
x : tensor
The destination node features.
Returns
-------
tensor
The result tensor.
Notes
-----
This function supports autograd (computing input gradients given the output gradient).
"""
return gsddmm(g, "copy_rhs", None, x)
# pylint: disable=unused-argument
def copy_e(g, x):
r"""Generalized SDDMM function that copies destination node features to edges."""
return x
_register_sddmm_func()