from typing import List, Optional, Tuple, NamedTuple
import torch
from torch_sparse import SparseTensor
class Adj(NamedTuple):
edge_index: torch.Tensor
e_id: torch.Tensor
size: Tuple[int, int]
def to(self, *args, **kwargs):
return Adj(self.edge_index.to(*args, **kwargs),
self.e_id.to(*args, **kwargs), self.size)
class NeighborSampler(torch.utils.data.DataLoader):
r"""The neighbor sampler from the `"Inductive Representation Learning on
Large Graphs" <https://arxiv.org/abs/1706.02216>`_ paper, which allows
for mini-batch training of GNNs on large-scale graphs where full-batch
training is not feasible.
Given a GNN with :math:`L` layers and a specific mini-batch of nodes
:obj:`node_idx` for which we want to compute embeddings, this module
iteratively samples neighbors and constructs bipartite graphs that simulate
the actual computation flow of GNNs.
More specifically, :obj:`sizes` denotes how much neighbors we want to
sample for each node in each layer.
This module then takes in these :obj:`sizes` and iteratively samples
:obj:`sizes[l]` for each node involved in layer :obj:`l`.
In the next layer, sampling is repeated for the union of nodes that were
already encountered.
The actual computation graphs are then returned in reverse-mode, meaning
that we pass messages from a larger set of nodes to a smaller one, until we
reach the nodes for which we originally wanted to compute embeddings.
Hence, an item returned by :class:`NeighborSampler` holds the current
:obj:`batch_size`, the IDs :obj:`n_id` of all nodes involved in the
computation, and a list of bipartite graph objects via the tuple
:obj:`(edge_index, e_id, size)`, where :obj:`edge_index` represents the
bipartite edges between source and target nodes, :obj:`e_id` denotes the
IDs of original edges in the full graph, and :obj:`size` holds the shape
of the bipartite graph.
For each bipartite graph, target nodes are also included at the beginning
of the list of source nodes so that one can easily apply skip-connections
or add self-loops.
.. note::
For an example of using :obj:`NeighborSampler`, see
`examples/reddit.py
<https://github.com/rusty1s/pytorch_geometric/blob/master/examples/
reddit.py>`_ or
`examples/ogbn_products_sage.py
<https://github.com/rusty1s/pytorch_geometric/blob/master/examples/
ogbn_products_sage.py>`_.
Args:
edge_index (LongTensor): The edge indices of the full-graph.
size ([int]): The number of neighbors to
sample for each node in each layer. If set to :obj:`sizes[i] = -1`,
all neighbors are included in layer :obj:`l`.
node_idx (LongTensor, optional): The nodes that should be considered
for creating mini-batches. If set to :obj:`None`, all nodes will be
considered.
flow (string, optional): The flow direction of message passing
(:obj:`"source_to_target"` or :obj:`"target_to_source"`).
(default: :obj:`"source_to_target"`)
**kwargs (optional): Additional arguments of
:class:`torch.utils.data.DataLoader`, such as :obj:`batch_size`,
:obj:`shuffle`, :obj:`drop_last` or :obj:`num_workers`.
"""
def __init__(self, edge_index: torch.Tensor, sizes: List[int],
node_idx: Optional[torch.Tensor] = None,
num_nodes: Optional[int] = None,
flow: str = "source_to_target", **kwargs):
N = int(edge_index.max() + 1) if num_nodes is None else num_nodes
edge_attr = torch.arange(edge_index.size(1))
adj = SparseTensor(row=edge_index[0], col=edge_index[1],
value=edge_attr, sparse_sizes=(N, N),
is_sorted=False)
adj = adj.t() if flow == 'source_to_target' else adj
self.adj = adj.to('cpu')
if node_idx is None:
node_idx = torch.arange(N)
elif node_idx.dtype == torch.bool:
node_idx = node_idx.nonzero().view(-1)
self.sizes = sizes
self.flow = flow
assert self.flow in ['source_to_target', 'target_to_source']
super(NeighborSampler, self).__init__(node_idx.tolist(),
collate_fn=self.sample, **kwargs)
def sample(self, batch):
if not isinstance(batch, torch.Tensor):
batch = torch.tensor(batch)
batch_size: int = len(batch)
adjs: List[Adj] = []
n_id = batch
for size in self.sizes:
adj, n_id = self.adj.sample_adj(n_id, size, replace=False)
if self.flow == 'source_to_target':
adj = adj.t()
row, col, e_id = adj.coo()
size = adj.sparse_sizes()
edge_index = torch.stack([row, col], dim=0)
adjs.append(Adj(edge_index, e_id, size))
if len(adjs) > 1:
return batch_size, n_id, adjs[::-1]
else:
return batch_size, n_id, adjs[0]
def __repr__(self):
return '{}(sizes={})'.format(self.__class__.__name__, self.sizes)
