# Source code for dgl.dataloading.labor_sampler

```
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# Copyright (c) 2022 by Contributors
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# Licensed under the Apache License, Version 2.0 (the "License");
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#
# Based off of neighbor_sampler.py
#
"""Data loading components for labor sampling"""
from .. import backend as F
from ..base import EID, NID
from ..random import choice
from ..transforms import to_block
from .base import BlockSampler
[docs]class LaborSampler(BlockSampler):
"""Sampler that builds computational dependency of node representations via
labor sampling for multilayer GNN from
`(LA)yer-neigh(BOR) Sampling: Defusing Neighborhood Explosion in GNNs
<https://arxiv.org/abs/2210.13339>`__
This sampler will make every node gather messages from a fixed number of
neighbors per edge type. The neighbors are picked uniformly with default
parameters. For every vertex t that will be considered to be sampled, there
will be a single random variate r_t.
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. In this case, the returned
blocks edata include ``'edge_weights'`` that needs to be used in the
message passing operation.
importance_sampling : int, default ``0``
Whether to use importance sampling or uniform sampling, use of negative
values optimizes importance sampling probabilities until convergence
while use of positive values runs optimization steps that many times.
If the value is i, then LABOR-i variant is used. When used with a
nonzero parameter, the returned blocks edata include ``'edge_weights'``
that needs to be used in the message passing operation.
layer_dependency : bool, default ``False``
Specifies whether different layers should use same random variates.
Results into a reduction in the number of vertices sampled, but may
degrade the quality slightly.
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.
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.LaborSampler([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.LaborSampler([
... {('user', 'follows', 'user'): 5,
... ('user', 'plays', 'game'): 4,
... ('game', 'played-by', 'user'): 3}] * 3)
If you would like non-uniform labor sampling:
>>> # any non-negative 1D vector works
>>> g.edata['p'] = torch.rand(g.num_edges())
>>> sampler = dgl.dataloading.LaborSampler([5, 10, 15], prob='p')
**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.LaborSampler([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,
importance_sampling=0,
layer_dependency=False,
prefetch_node_feats=None,
prefetch_labels=None,
prefetch_edge_feats=None,
output_device=None,
):
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
self.prob = prob
self.importance_sampling = importance_sampling
self.layer_dependency = layer_dependency
self.set_seed()
def set_seed(self, random_seed=None):
"""Updates the underlying seed for the sampler
Calling this function enforces the sampling algorithm to use the same
seed on every edge type. This can reduce the number of nodes being
sampled because the passed random_seed makes it so that for any seed
vertex ``s`` and its neighbor ``t``, the rolled random variate ``r_t``
is the same for any instance of this class with the same random seed.
When sampling as part of the same batch, one would want identical seeds
so that LABOR can globally sample. One example is that for heterogenous
graphs, there is a single random seed passed for each edge type. This
will sample much fewer vertices compared to having unique random seeds
for each edge type. If one called this function individually for each
edge type for a heterogenous graph with different random seeds, then it
would run LABOR locally for each edge type, resulting into a larger
number of vertices being sampled.
If this function is called without any parameters, we get the random
seed by getting a random number from DGL. Call this function if multiple
instances of LaborSampler are used to sample as part of a single batch.
Parameters
----------
random_seed : int, default ``None``
The random seed to be used for next sampling call.
"""
if random_seed is None:
self.random_seed = choice(1e18, 1)
else:
self.random_seed = F.tensor(random_seed, F.int64)
def sample_blocks(self, g, seed_nodes, exclude_eids=None):
output_nodes = seed_nodes
blocks = []
for i, fanout in enumerate(reversed(self.fanouts)):
random_seed_i = F.zerocopy_to_dgl_ndarray(
self.random_seed + (i if not self.layer_dependency else 0)
)
frontier, importances = g.sample_labors(
seed_nodes,
fanout,
edge_dir=self.edge_dir,
prob=self.prob,
importance_sampling=self.importance_sampling,
random_seed=random_seed_i,
output_device=self.output_device,
exclude_edges=exclude_eids,
)
eid = frontier.edata[EID]
block = to_block(
frontier, seed_nodes, include_dst_in_src=True, src_nodes=None
)
block.edata[EID] = eid
if len(g.canonical_etypes) > 1:
for etype, importance in zip(g.canonical_etypes, importances):
if importance.shape[0] == block.num_edges(etype):
block.edata["edge_weights"][etype] = importance
elif importances[0].shape[0] == block.num_edges():
block.edata["edge_weights"] = importances[0]
seed_nodes = block.srcdata[NID]
blocks.insert(0, block)
self.set_seed()
return seed_nodes, output_nodes, blocks
```