# Source code for dgl.dataloading.neighbor_sampler

```
"""Data loading components for neighbor sampling"""
from .. import backend as F
from ..base import EID, NID
from ..heterograph import DGLGraph
from ..transforms import to_block
from .base import BlockSampler
[docs]class NeighborSampler(BlockSampler):
"""Sampler that builds computational dependency of node representations via
neighbor sampling for multilayer GNN.
This sampler will make every node gather messages from a fixed number of neighbors
per edge type. The neighbors are picked uniformly.
Parameters
----------
fanouts : list[int] or list[dict[etype, int]]
List of neighbors to sample per edge type for each GNN layer, with the i-th
element being the fanout for the i-th GNN layer.
If only a single integer is provided, DGL assumes that every edge type
will have the same fanout.
If -1 is provided for one edge type on one layer, then all inbound edges
of that edge type will be included.
edge_dir : str, default ``'in'``
Can be either ``'in' `` where the neighbors will be sampled according to
incoming edges, or ``'out'`` otherwise, same as :func:`dgl.sampling.sample_neighbors`.
prob : str, optional
If given, the probability of each neighbor being sampled is proportional
to the edge feature value with the given name in ``g.edata``. The feature must be
a scalar on each edge.
This argument is mutually exclusive with :attr:`mask`. If you want to
specify both a mask and a probability, consider multiplying the probability
with the mask instead.
mask : str, optional
If given, a neighbor could be picked only if the edge mask with the given
name in ``g.edata`` is True. The data must be boolean on each edge.
This argument is mutually exclusive with :attr:`prob`. If you want to
specify both a mask and a probability, consider multiplying the probability
with the mask instead.
replace : bool, default False
Whether to sample with replacement
prefetch_node_feats : list[str] or dict[ntype, list[str]], optional
The source node data to prefetch for the first MFG, corresponding to the
input node features necessary for the first GNN layer.
prefetch_labels : list[str] or dict[ntype, list[str]], optional
The destination node data to prefetch for the last MFG, corresponding to
the node labels of the minibatch.
prefetch_edge_feats : list[str] or dict[etype, list[str]], optional
The edge data names to prefetch for all the MFGs, corresponding to the
edge features necessary for all GNN layers.
output_device : device, optional
The device of the output subgraphs or MFGs. Default is the same as the
minibatch of seed nodes.
fused : bool, default True
If True and device is CPU fused sample neighbors is invoked. This version
requires seed_nodes to be unique
Examples
--------
**Node classification**
To train a 3-layer GNN for node classification on a set of nodes ``train_nid`` on
a homogeneous graph where each node takes messages from 5, 10, 15 neighbors for
the first, second, and third layer respectively (assuming the backend is PyTorch):
>>> sampler = dgl.dataloading.NeighborSampler([5, 10, 15])
>>> dataloader = dgl.dataloading.DataLoader(
... g, train_nid, sampler,
... batch_size=1024, shuffle=True, drop_last=False, num_workers=4)
>>> for input_nodes, output_nodes, blocks in dataloader:
... train_on(blocks)
If training on a heterogeneous graph and you want different number of neighbors for each
edge type, one should instead provide a list of dicts. Each dict would specify the
number of neighbors to pick per edge type.
>>> sampler = dgl.dataloading.NeighborSampler([
... {('user', 'follows', 'user'): 5,
... ('user', 'plays', 'game'): 4,
... ('game', 'played-by', 'user'): 3}] * 3)
If you would like non-uniform neighbor sampling:
>>> g.edata['p'] = torch.rand(g.num_edges()) # any non-negative 1D vector works
>>> sampler = dgl.dataloading.NeighborSampler([5, 10, 15], prob='p')
Or sampling on edge masks:
>>> g.edata['mask'] = torch.rand(g.num_edges()) < 0.2 # any 1D boolean mask works
>>> sampler = dgl.dataloading.NeighborSampler([5, 10, 15], prob='mask')
**Edge classification and link prediction**
This class can also work for edge classification and link prediction together
with :func:`as_edge_prediction_sampler`.
>>> sampler = dgl.dataloading.NeighborSampler([5, 10, 15])
>>> sampler = dgl.dataloading.as_edge_prediction_sampler(sampler)
>>> dataloader = dgl.dataloading.DataLoader(
... g, train_eid, sampler,
... batch_size=1024, shuffle=True, drop_last=False, num_workers=4)
See the documentation :func:`as_edge_prediction_sampler` for more details.
Notes
-----
For the concept of MFGs, please refer to
:ref:`User Guide Section 6 <guide-minibatch>` and
:doc:`Minibatch Training Tutorials <tutorials/large/L0_neighbor_sampling_overview>`.
"""
def __init__(
self,
fanouts,
edge_dir="in",
prob=None,
mask=None,
replace=False,
prefetch_node_feats=None,
prefetch_labels=None,
prefetch_edge_feats=None,
output_device=None,
fused=True,
):
super().__init__(
prefetch_node_feats=prefetch_node_feats,
prefetch_labels=prefetch_labels,
prefetch_edge_feats=prefetch_edge_feats,
output_device=output_device,
)
self.fanouts = fanouts
self.edge_dir = edge_dir
if mask is not None and prob is not None:
raise ValueError(
"Mask and probability arguments are mutually exclusive. "
"Consider multiplying the probability with the mask "
"to achieve the same goal."
)
self.prob = prob or mask
self.replace = replace
self.fused = fused
self.mapping = {}
self.g = None
def sample_blocks(self, g, seed_nodes, exclude_eids=None):
output_nodes = seed_nodes
blocks = []
if self.fused:
cpu = F.device_type(g.device) == "cpu"
if isinstance(seed_nodes, dict):
for ntype in list(seed_nodes.keys()):
if not cpu:
break
cpu = (
cpu and F.device_type(seed_nodes[ntype].device) == "cpu"
)
else:
cpu = cpu and F.device_type(seed_nodes.device) == "cpu"
if cpu and isinstance(g, DGLGraph) and F.backend_name == "pytorch":
if self.g != g:
self.mapping = {}
self.g = g
for fanout in reversed(self.fanouts):
block = g.sample_neighbors_fused(
seed_nodes,
fanout,
edge_dir=self.edge_dir,
prob=self.prob,
replace=self.replace,
exclude_edges=exclude_eids,
mapping=self.mapping,
)
seed_nodes = block.srcdata[NID]
blocks.insert(0, block)
return seed_nodes, output_nodes, blocks
for fanout in reversed(self.fanouts):
frontier = g.sample_neighbors(
seed_nodes,
fanout,
edge_dir=self.edge_dir,
prob=self.prob,
replace=self.replace,
output_device=self.output_device,
exclude_edges=exclude_eids,
)
eid = frontier.edata[EID]
block = to_block(frontier, seed_nodes)
block.edata[EID] = eid
seed_nodes = block.srcdata[NID]
blocks.insert(0, block)
return seed_nodes, output_nodes, blocks
MultiLayerNeighborSampler = NeighborSampler
[docs]class MultiLayerFullNeighborSampler(NeighborSampler):
"""Sampler that builds computational dependency of node representations by taking messages
from all neighbors for multilayer GNN.
This sampler will make every node gather messages from every single neighbor per edge type.
Parameters
----------
num_layers : int
The number of GNN layers to sample.
kwargs :
Passed to :class:`dgl.dataloading.NeighborSampler`.
Examples
--------
To train a 3-layer GNN for node classification on a set of nodes ``train_nid`` on
a homogeneous graph where each node takes messages from all neighbors for the first,
second, and third layer respectively (assuming the backend is PyTorch):
>>> sampler = dgl.dataloading.MultiLayerFullNeighborSampler(3)
>>> dataloader = dgl.dataloading.DataLoader(
... g, train_nid, sampler,
... batch_size=1024, shuffle=True, drop_last=False, num_workers=4)
>>> for input_nodes, output_nodes, blocks in dataloader:
... train_on(blocks)
Notes
-----
For the concept of MFGs, please refer to
:ref:`User Guide Section 6 <guide-minibatch>` and
:doc:`Minibatch Training Tutorials <tutorials/large/L0_neighbor_sampling_overview>`.
"""
def __init__(self, num_layers, **kwargs):
super().__init__([-1] * num_layers, **kwargs)
```