from typing import Tuple, Dict, Union, Optional, Any
from torch_geometric.typing import NodeType, EdgeType, Metadata
import copy
import warnings
from collections import defaultdict, deque
import torch
from torch.nn import Module
from torch_geometric.nn.fx import Transformer
try:
from torch.fx import GraphModule, Graph, Node
except ImportError:
GraphModule, Graph, Node = 'GraphModule', 'Graph', 'Node'
[docs]def to_hetero(module: Module, metadata: Metadata, aggr: str = "sum",
input_map: Optional[Dict[str, str]] = None,
debug: bool = False) -> GraphModule:
r"""Converts a homogeneous GNN model into its heterogeneous equivalent in
which node representations are learned for each node type in
:obj:`metadata[0]`, and messages are exchanged between each edge type in
:obj:`metadata[1]`, as denoted in the `"Modeling Relational Data with Graph
Convolutional Networks" <https://arxiv.org/abs/1703.06103>`_ paper:
.. code-block:: python
import torch
from torch_geometric.nn import SAGEConv, to_hetero
class GNN(torch.nn.Module):
def __init__(self):
self.conv1 = SAGEConv((-1, -1), 32)
self.conv2 = SAGEConv((32, 32), 32)
def forward(self, x, edge_index):
x = self.conv1(x, edge_index).relu()
x = self.conv2(x, edge_index).relu()
return x
model = GNN()
node_types = ['paper', 'author']
edge_types = [
('paper', 'cites', 'paper'),
('paper' 'written_by', 'author'),
('author', 'writes', 'paper'),
]
metadata = (node_types, edge_types)
model = to_hetero(model, metadata)
model(x_dict, edge_index_dict)
where :obj:`x_dict` and :obj:`edge_index_dict` denote dictionaries that
hold node features and edge connectivity information for each node type and
edge type, respectively.
The below illustration shows the original computation graph of the
homogeneous model on the left, and the newly obtained computation graph of
the heterogeneous model on the right:
.. figure:: ../_figures/to_hetero.svg
:align: center
:width: 90%
Transforming a model via :func:`to_hetero`.
Here, each :class:`~torch_geometric.nn.conv.MessagePassing` instance
:math:`f_{\theta}^{(\ell)}` is duplicated and stored in a set
:math:`\{ f_{\theta}^{(\ell, r)} : r \in \mathcal{R} \}` (one instance for
each relation in :math:`\mathcal{R}`), and message passing in layer
:math:`\ell` is performed via
.. math::
\mathbf{h}^{(\ell)}_v = \bigoplus_{r \in \mathcal{R}}
f_{\theta}^{(\ell, r)} ( \mathbf{h}^{(\ell - 1)}_v, \{
\mathbf{h}^{(\ell - 1)}_w : w \in \mathcal{N}^{(r)}(v) \}),
where :math:`\mathcal{N}^{(r)}(v)` denotes the neighborhood of :math:`v \in
\mathcal{V}` under relation :math:`r \in \mathcal{R}`, and
:math:`\bigoplus` denotes the aggregation scheme :attr:`aggr` to use for
grouping node embeddings generated by different relations
(:obj:`"sum"`, :obj:`"mean"`, :obj:`"min"`, :obj:`"max"` or :obj:`"mul"`).
Args:
module (torch.nn.Module): The homogeneous model to transform.
metadata (Tuple[List[str], List[Tuple[str, str, str]]]): The metadata
of the heterogeneous graph, *i.e.* its node and edge types given
by a list of strings and a list of string triplets, respectively.
See :meth:`torch_geometric.data.HeteroData.metadata` for more
information.
aggr (string, optional): The aggregation scheme to use for grouping
node embeddings generated by different relations.
(:obj:`"sum"`, :obj:`"mean"`, :obj:`"min"`, :obj:`"max"`,
:obj:`"mul"`). (default: :obj:`"sum"`)
input_map (Dict[str, str], optional): A dictionary holding information
about the type of input arguments of :obj:`module.forward`.
For example, in case :obj:`arg` is a node-level argument, then
:obj:`input_map['arg'] = 'node'`, and
:obj:`input_map['arg'] = 'edge'` otherwise.
In case :obj:`input_map` is not further specified, will try to
automatically determine the correct type of input arguments.
(default: :obj:`None`)
debug: (bool, optional): If set to :obj:`True`, will perform
transformation in debug mode. (default: :obj:`False`)
"""
transformer = ToHeteroTransformer(module, metadata, aggr, input_map, debug)
return transformer.transform()
class ToHeteroTransformer(Transformer):
aggrs = {
'sum': torch.add,
'mean': torch.add,
'max': torch.max,
'min': torch.min,
'mul': torch.mul,
}
def __init__(
self,
module: Module,
metadata: Metadata,
aggr: str = 'sum',
input_map: Optional[Dict[str, str]] = None,
debug: bool = False,
):
super().__init__(module, input_map, debug)
self.metadata = metadata
self.aggr = aggr
assert len(metadata) == 2
assert len(metadata[0]) > 0 and len(metadata[1]) > 0
assert aggr in self.aggrs.keys()
def placeholder(self, node: Node, target: Any, name: str):
# Adds a `get` call to the input dictionary for every node-type or
# edge-type.
if node.type is not None:
Type = EdgeType if self.is_edge_level(node) else NodeType
node.type = Dict[Type, node.type]
self.graph.inserting_after(node)
for key in self.metadata[int(self.is_edge_level(node))]:
out = self.graph.create_node('call_method', target='get',
args=(node, key),
name=f'{name}__{key2str(key)}')
self.graph.inserting_after(out)
def get_attr(self, node: Node, target: Any, name: str):
raise NotImplementedError
def call_message_passing_module(self, node: Node, target: Any, name: str):
# Add calls to edge type-wise `MessagePassing` modules and aggregate
# the outputs to node type-wise embeddings afterwards.
# Group edge-wise keys per destination:
key_name, keys_per_dst = {}, defaultdict(list)
for key in self.metadata[1]:
keys_per_dst[key[-1]].append(key)
key_name[key] = f'{name}__{key[-1]}{len(keys_per_dst[key[-1]])}'
for dst, keys in dict(keys_per_dst).items():
# In case there is only a single edge-wise connection, there is no
# need for any destination-wise aggregation, and we can already set
# the intermediate variable name to the final output name.
if len(keys) == 1:
key_name[keys[0]] = f'{name}__{dst}'
del keys_per_dst[dst]
self.graph.inserting_after(node)
for key in self.metadata[1]:
args, kwargs = self.map_args_kwargs(node, key)
out = self.graph.create_node('call_module',
target=f'{target}.{key2str(key)}',
args=args, kwargs=kwargs,
name=key_name[key])
self.graph.inserting_after(out)
# Perform destination-wise aggregation.
# Here, we aggregate in pairs, popping the first two elements of
# `keys_per_dst` and append the result to the list.
for dst, keys in keys_per_dst.items():
queue = deque([key_name[key] for key in keys])
i = len(queue) + 1
while len(queue) >= 2:
key1, key2 = queue.popleft(), queue.popleft()
args = (self.find_by_name(key1), self.find_by_name(key2))
new_name = f'{name}__{dst}'
if self.aggr == 'mean' or len(queue) > 0:
new_name += f'{i}'
out = self.graph.create_node('call_function',
target=self.aggrs[self.aggr],
args=args, name=new_name)
self.graph.inserting_after(out)
queue.append(new_name)
i += 1
if self.aggr == 'mean':
key = queue.popleft()
out = self.graph.create_node(
'call_function', target=torch.div,
args=(self.find_by_name(key), len(keys_per_dst[dst])),
name=f'{name}__{dst}')
self.graph.inserting_after(out)
def call_module(self, node: Node, target: Any, name: str):
# Add calls to node type-wise or edge type-wise modules.
self.graph.inserting_after(node)
for key in self.metadata[int(self.is_edge_level(node))]:
args, kwargs = self.map_args_kwargs(node, key)
out = self.graph.create_node('call_module',
target=f'{target}.{key2str(key)}',
args=args, kwargs=kwargs,
name=f'{name}__{key2str(key)}')
self.graph.inserting_after(out)
def call_method(self, node: Node, target: Any, name: str):
# Add calls to node type-wise or edge type-wise methods.
self.graph.inserting_after(node)
for key in self.metadata[int(self.is_edge_level(node))]:
args, kwargs = self.map_args_kwargs(node, key)
out = self.graph.create_node('call_method', target=target,
args=args, kwargs=kwargs,
name=f'{name}__{key2str(key)}')
self.graph.inserting_after(out)
def call_function(self, node: Node, target: Any, name: str):
# Add calls to node type-wise or edge type-wise functions.
self.graph.inserting_after(node)
for key in self.metadata[int(self.is_edge_level(node))]:
args, kwargs = self.map_args_kwargs(node, key)
out = self.graph.create_node('call_function', target=target,
args=args, kwargs=kwargs,
name=f'{name}__{key2str(key)}')
self.graph.inserting_after(out)
def output(self, node: Node, target: Any, name: str):
# Replace the output by dictionaries, holding either node type-wise or
# edge type-wise data.
def _recurse(value: Any) -> Any:
if isinstance(value, Node):
return {
key: self.find_by_name(f'{value.name}__{key2str(key)}')
for key in self.metadata[int(self.is_edge_level(value))]
}
elif isinstance(value, dict):
return {k: _recurse(v) for k, v in value.items()}
elif isinstance(value, list):
return [_recurse(v) for v in value]
elif isinstance(value, tuple):
return tuple(_recurse(v) for v in value)
else:
return value
if node.type is not None and isinstance(node.args[0], Node):
output = node.args[0]
Type = EdgeType if self.is_edge_level(output) else NodeType
node.type = Dict[Type, node.type]
else:
node.type = None
node.args = (_recurse(node.args[0]), )
def init_submodule(self, module: Module, target: str) -> Module:
# Replicate each module for each node type or edge type.
has_edge_level_target = bool(
self.find_by_target(f'{target}.{key2str(self.metadata[1][0])}'))
module_dict = torch.nn.ModuleDict()
for key in self.metadata[int(has_edge_level_target)]:
module_dict[key2str(key)] = copy.deepcopy(module)
if len(self.metadata[int(has_edge_level_target)]) <= 1:
continue
if hasattr(module, 'reset_parameters'):
module_dict[key2str(key)].reset_parameters()
elif sum([p.numel() for p in module.parameters()]) > 0:
warnings.warn(
f"'{target}' will be duplicated, but its parameters "
f"cannot be reset. To suppress this warning, add a "
f"'reset_parameters()' method to '{target}'")
return module_dict
# Helper methods ##########################################################
def map_args_kwargs(self, node: Node,
key: Union[NodeType, EdgeType]) -> Tuple[Tuple, Dict]:
def _recurse(value: Any) -> Any:
if isinstance(value, Node):
out = self.find_by_name(f'{value.name}__{key2str(key)}')
if out is None and isinstance(key, tuple):
out = (self.find_by_name(f'{value.name}__{key[0]}'),
self.find_by_name(f'{value.name}__{key[-1]}'))
return out
elif isinstance(value, dict):
return {k: _recurse(v) for k, v in value.items()}
elif isinstance(value, list):
return [_recurse(v) for v in value]
elif isinstance(value, tuple):
return tuple(_recurse(v) for v in value)
else:
return value
args = tuple(_recurse(v) for v in node.args)
kwargs = {k: _recurse(v) for k, v in node.kwargs.items()}
return args, kwargs
def key2str(key: Union[NodeType, EdgeType]) -> str:
return '__'.join(key) if isinstance(key, tuple) else key