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dmlc--dgl
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distgnn
T
Quan (Andy) Gan 48b9ecd201 Fix discrepancy in dgl.dataloading.EdgeDataLoader (#2819) 
* fix discrepancy

* address comments
2021-04-07 16:08:14 +08:00

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Python
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"""Data loaders"""
from collections.abc import Mapping, Sequence
from abc import ABC, abstractproperty, abstractmethod
import re
import numpy as np
from .. import transform
from ..base import NID, EID
from .. import backend as F
from .. import utils
from ..batch import batch
from ..convert import heterograph
from ..heterograph import DGLHeteroGraph as DGLGraph
from ..distributed.dist_graph import DistGraph
# pylint: disable=unused-argument
def assign_block_eids(block, frontier):
"""Assigns edge IDs from the original graph to the message flow graph (MFG).
See also
--------
BlockSampler
"""
for etype in block.canonical_etypes:
block.edges[etype].data[EID] = frontier.edges[etype].data[EID][
block.edges[etype].data[EID]]
return block
def _tensor_or_dict_to_numpy(ids):
if isinstance(ids, Mapping):
return {k: F.zerocopy_to_numpy(v) for k, v in ids.items()}
else:
return F.zerocopy_to_numpy(ids)
def _locate_eids_to_exclude(frontier_parent_eids, exclude_eids):
"""Find the edges whose IDs in parent graph appeared in exclude_eids.
Note that both arguments are numpy arrays or numpy dicts.
"""
if isinstance(frontier_parent_eids, Mapping):
result = {
k: np.isin(frontier_parent_eids[k], exclude_eids[k]).nonzero()[0]
for k in frontier_parent_eids.keys() if k in exclude_eids.keys()}
return {k: F.zerocopy_from_numpy(v) for k, v in result.items()}
else:
result = np.isin(frontier_parent_eids, exclude_eids).nonzero()[0]
return F.zerocopy_from_numpy(result)
def _find_exclude_eids_with_reverse_id(g, eids, reverse_eid_map):
if isinstance(eids, Mapping):
eids = {g.to_canonical_etype(k): v for k, v in eids.items()}
exclude_eids = {
k: F.cat([v, F.gather_row(reverse_eid_map[k], v)], 0)
for k, v in eids.items()}
else:
exclude_eids = F.cat([eids, F.gather_row(reverse_eid_map, eids)], 0)
return exclude_eids
def _find_exclude_eids_with_reverse_types(g, eids, reverse_etype_map):
exclude_eids = {g.to_canonical_etype(k): v for k, v in eids.items()}
reverse_etype_map = {
g.to_canonical_etype(k): g.to_canonical_etype(v)
for k, v in reverse_etype_map.items()}
exclude_eids.update({reverse_etype_map[k]: v for k, v in exclude_eids.items()})
return exclude_eids
def _find_exclude_eids(g, exclude_mode, eids, **kwargs):
"""Find all edge IDs to exclude according to :attr:`exclude_mode`.
Parameters
----------
g : DGLGraph
The graph.
exclude_mode : str, optional
Can be either of the following,
None (default)
Does not exclude any edge.
'reverse_id'
Exclude all edges specified in ``eids``, as well as their reverse edges
of the same edge type.
The mapping from each edge ID to its reverse edge ID is specified in
the keyword argument ``reverse_eid_map``.
This mode assumes that the reverse of an edge with ID ``e`` and type
``etype`` will have ID ``reverse_eid_map[e]`` and type ``etype``.
'reverse_types'
Exclude all edges specified in ``eids``, as well as their reverse
edges of the corresponding edge types.
The mapping from each edge type to its reverse edge type is specified
in the keyword argument ``reverse_etype_map``.
This mode assumes that the reverse of an edge with ID ``e`` and type ``etype``
will have ID ``e`` and type ``reverse_etype_map[etype]``.
eids : Tensor or dict[etype, Tensor]
The edge IDs.
reverse_eid_map : Tensor or dict[etype, Tensor]
The mapping from edge ID to its reverse edge ID.
reverse_etype_map : dict[etype, etype]
The mapping from edge etype to its reverse edge type.
"""
if exclude_mode is None:
return None
elif exclude_mode == 'reverse_id':
return _find_exclude_eids_with_reverse_id(g, eids, kwargs['reverse_eid_map'])
elif exclude_mode == 'reverse_types':
return _find_exclude_eids_with_reverse_types(g, eids, kwargs['reverse_etype_map'])
else:
raise ValueError('unsupported mode {}'.format(exclude_mode))
class BlockSampler(object):
"""Abstract class specifying the neighborhood sampling strategy for DGL data loaders.
The main method for BlockSampler is :meth:`sample_blocks`,
which generates a list of message flow graphs (MFGs) for a multi-layer GNN given a set of
seed nodes to have their outputs computed.
The default implementation of :meth:`sample_blocks` is
to repeat :attr:`num_layers` times the following procedure from the last layer to the first
layer:
* Obtain a frontier. The frontier is defined as a graph with the same nodes as the
original graph but only the edges involved in message passing on the current layer.
Customizable via :meth:`sample_frontier`.
* Optionally, if the task is link prediction or edge classfication, remove edges
connecting training node pairs. If the graph is undirected, also remove the
reverse edges. This is controlled by the argument :attr:`exclude_eids` in
:meth:`sample_blocks` method.
* Convert the frontier into a MFG.
* Optionally assign the IDs of the edges in the original graph selected in the first step
to the MFG, controlled by the argument ``return_eids`` in
:meth:`sample_blocks` method.
* Prepend the MFG to the MFG list to be returned.
All subclasses should override :meth:`sample_frontier`
method while specifying the number of layers to sample in :attr:`num_layers` argument.
Parameters
----------
num_layers : int
The number of layers to sample.
return_eids : bool, default False
Whether to return the edge IDs involved in message passing in the MFG.
If True, the edge IDs will be stored as an edge feature named ``dgl.EID``.
Notes
-----
For the concept of frontiers and 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, return_eids=False):
self.num_layers = num_layers
self.return_eids = return_eids
def sample_frontier(self, block_id, g, seed_nodes):
"""Generate the frontier given the destination nodes.
The subclasses should override this function.
Parameters
----------
block_id : int
Represents which GNN layer the frontier is generated for.
g : DGLGraph
The original graph.
seed_nodes : Tensor or dict[ntype, Tensor]
The destination nodes by node type.
If the graph only has one node type, one can just specify a single tensor
of node IDs.
Returns
-------
DGLGraph
The frontier generated for the current layer.
Notes
-----
For the concept of frontiers and MFGs, please refer to
:ref:`User Guide Section 6 <guide-minibatch>` and
:doc:`Minibatch Training Tutorials <tutorials/large/L0_neighbor_sampling_overview>`.
"""
raise NotImplementedError
def sample_blocks(self, g, seed_nodes, exclude_eids=None):
"""Generate the a list of MFGs given the destination nodes.
Parameters
----------
g : DGLGraph
The original graph.
seed_nodes : Tensor or dict[ntype, Tensor]
The destination nodes by node type.
If the graph only has one node type, one can just specify a single tensor
of node IDs.
exclude_eids : Tensor or dict[etype, Tensor]
The edges to exclude from computation dependency.
Returns
-------
list[DGLGraph]
The MFGs generated for computing the multi-layer GNN output.
Notes
-----
For the concept of frontiers and MFGs, please refer to
:ref:`User Guide Section 6 <guide-minibatch>` and
:doc:`Minibatch Training Tutorials <tutorials/large/L0_neighbor_sampling_overview>`.
"""
blocks = []
exclude_eids = (
_tensor_or_dict_to_numpy(exclude_eids) if exclude_eids is not None else None)
for block_id in reversed(range(self.num_layers)):
frontier = self.sample_frontier(block_id, g, seed_nodes)
# Removing edges from the frontier for link prediction training falls
# into the category of frontier postprocessing
if exclude_eids is not None:
parent_eids = frontier.edata[EID]
parent_eids_np = _tensor_or_dict_to_numpy(parent_eids)
located_eids = _locate_eids_to_exclude(parent_eids_np, exclude_eids)
if not isinstance(located_eids, Mapping):
# (BarclayII) If frontier already has a EID field and located_eids is empty,
# the returned graph will keep EID intact. Otherwise, EID will change
# to the mapping from the new graph to the old frontier.
# So we need to test if located_eids is empty, and do the remapping ourselves.
if len(located_eids) > 0:
frontier = transform.remove_edges(
frontier, located_eids, store_ids=True)
frontier.edata[EID] = F.gather_row(parent_eids, frontier.edata[EID])
else:
# (BarclayII) remove_edges only accepts removing one type of edges,
# so I need to keep track of the edge IDs left one by one.
new_eids = parent_eids.copy()
for k, v in located_eids.items():
if len(v) > 0:
frontier = transform.remove_edges(
frontier, v, etype=k, store_ids=True)
new_eids[k] = F.gather_row(parent_eids[k], frontier.edges[k].data[EID])
frontier.edata[EID] = new_eids
block = transform.to_block(frontier, seed_nodes)
if self.return_eids:
assign_block_eids(block, frontier)
seed_nodes = {ntype: block.srcnodes[ntype].data[NID] for ntype in block.srctypes}
blocks.insert(0, block)
return blocks
class Collator(ABC):
"""Abstract DGL collator for training GNNs on downstream tasks stochastically.
Provides a :attr:`dataset` object containing the collection of all nodes or edges,
as well as a :attr:`collate` method that combines a set of items from
:attr:`dataset` and obtains the message flow graphs (MFGs).
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>`.
"""
@abstractproperty
def dataset(self):
"""Returns the dataset object of the collator."""
raise NotImplementedError
@abstractmethod
def collate(self, items):
"""Combines the items from the dataset object and obtains the list of MFGs.
Parameters
----------
items : list[str, int]
The list of node or edge IDs or type-ID pairs.
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>`.
"""
raise NotImplementedError
# TODO(BarclayII): DistGraph.idtype and DistGraph.device are in the code, however
# the underlying DGLGraph object could be None. I was unable to figure out how
# to properly implement those two properties so I'm working around that. If the
# graph is a DistGraph, I assume that the dtype and device of the data should
# be the same as the graph already.
#
# After idtype and device get properly implemented, we should remove these two
# _prepare_* functions.
def _prepare_tensor_dict(g, data, name, is_distributed):
if is_distributed:
x = F.tensor(next(iter(data.values())))
return {k: F.copy_to(F.astype(v, F.dtype(x)), F.context(x)) for k, v in data.items()}
else:
return utils.prepare_tensor_dict(g, data, name)
def _prepare_tensor(g, data, name, is_distributed):
return F.tensor(data) if is_distributed else utils.prepare_tensor(g, data, name)
class NodeCollator(Collator):
"""DGL collator to combine nodes and their computation dependencies within a minibatch for
training node classification or regression on a single graph with neighborhood sampling.
Parameters
----------
g : DGLGraph
The graph.
nids : Tensor or dict[ntype, Tensor]
The node set to compute outputs.
block_sampler : dgl.dataloading.BlockSampler
The neighborhood sampler.
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 (assume
the backend is PyTorch):
>>> sampler = dgl.dataloading.MultiLayerNeighborSampler([15, 10, 5])
>>> collator = dgl.dataloading.NodeCollator(g, train_nid, sampler)
>>> dataloader = torch.utils.data.DataLoader(
... collator.dataset, collate_fn=collator.collate,
... batch_size=1024, shuffle=True, drop_last=False, num_workers=4)
>>> for input_nodes, output_nodes, blocks in dataloader:
... train_on(input_nodes, output_nodes, 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, g, nids, block_sampler):
self.g = g
self._is_distributed = isinstance(g, DistGraph)
if not isinstance(nids, Mapping):
assert len(g.ntypes) == 1, \
"nids should be a dict of node type and ids for graph with multiple node types"
self.block_sampler = block_sampler
if isinstance(nids, Mapping):
self.nids = _prepare_tensor_dict(g, nids, 'nids', self._is_distributed)
self._dataset = utils.FlattenedDict(self.nids)
else:
self.nids = _prepare_tensor(g, nids, 'nids', self._is_distributed)
self._dataset = self.nids
@property
def dataset(self):
return self._dataset
def collate(self, items):
"""Find the list of MFGs necessary for computing the representation of given
nodes for a node classification/regression task.
Parameters
----------
items : list[int] or list[tuple[str, int]]
Either a list of node IDs (for homogeneous graphs), or a list of node type-ID
pairs (for heterogeneous graphs).
Returns
-------
input_nodes : Tensor or dict[ntype, Tensor]
The input nodes necessary for computation in this minibatch.
If the original graph has multiple node types, return a dictionary of
node type names and node ID tensors. Otherwise, return a single tensor.
output_nodes : Tensor or dict[ntype, Tensor]
The nodes whose representations are to be computed in this minibatch.
If the original graph has multiple node types, return a dictionary of
node type names and node ID tensors. Otherwise, return a single tensor.
MFGs : list[DGLGraph]
The list of MFGs necessary for computing the representation.
"""
if isinstance(items[0], tuple):
# returns a list of pairs: group them by node types into a dict
items = utils.group_as_dict(items)
items = _prepare_tensor_dict(self.g, items, 'items', self._is_distributed)
else:
items = _prepare_tensor(self.g, items, 'items', self._is_distributed)
blocks = self.block_sampler.sample_blocks(self.g, items)
output_nodes = blocks[-1].dstdata[NID]
input_nodes = blocks[0].srcdata[NID]
return input_nodes, output_nodes, blocks
class EdgeCollator(Collator):
"""DGL collator to combine edges and their computation dependencies within a minibatch for
training edge classification, edge regression, or link prediction on a single graph
with neighborhood sampling.
Given a set of edges, the collate function will yield
* A tensor of input nodes necessary for computing the representation on edges, or
a dictionary of node type names and such tensors.
* A subgraph that contains only the edges in the minibatch and their incident nodes.
Note that the graph has an identical metagraph with the original graph.
* If a negative sampler is given, another graph that contains the "negative edges",
connecting the source and destination nodes yielded from the given negative sampler.
* A list of MFGs necessary for computing the representation of the incident nodes
of the edges in the minibatch.
Parameters
----------
g : DGLGraph
The graph from which the edges are iterated in minibatches and the subgraphs
are generated.
eids : Tensor or dict[etype, Tensor]
The edge set in graph :attr:`g` to compute outputs.
block_sampler : dgl.dataloading.BlockSampler
The neighborhood sampler.
g_sampling : DGLGraph, optional
The graph where neighborhood sampling and message passing is performed.
Note that this is not necessarily the same as :attr:`g`.
If None, assume to be the same as :attr:`g`.
exclude : str, optional
Whether and how to exclude dependencies related to the sampled edges in the
minibatch. Possible values are
* None, which excludes nothing.
* ``'reverse_id'``, which excludes the reverse edges of the sampled edges. The said
reverse edges have the same edge type as the sampled edges. Only works
on edge types whose source node type is the same as its destination node type.
* ``'reverse_types'``, which excludes the reverse edges of the sampled edges. The
said reverse edges have different edge types from the sampled edges.
If ``g_sampling`` is given, ``exclude`` is ignored and will be always ``None``.
reverse_eids : Tensor or dict[etype, Tensor], optional
A tensor of reverse edge ID mapping. The i-th element indicates the ID of
the i-th edge's reverse edge.
If the graph is heterogeneous, this argument requires a dictionary of edge
types and the reverse edge ID mapping tensors.
Required and only used when ``exclude`` is set to ``reverse_id``.
For heterogeneous graph this will be a dict of edge type and edge IDs. Note that
only the edge types whose source node type is the same as destination node type
are needed.
reverse_etypes : dict[etype, etype], optional
The mapping from the edge type to its reverse edge type.
Required and only used when ``exclude`` is set to ``reverse_types``.
negative_sampler : callable, optional
The negative sampler. Can be omitted if no negative sampling is needed.
The negative sampler must be a callable that takes in the following arguments:
* The original (heterogeneous) graph.
* The ID array of sampled edges in the minibatch, or the dictionary of edge
types and ID array of sampled edges in the minibatch if the graph is
heterogeneous.
It should return
* A pair of source and destination node ID arrays as negative samples,
or a dictionary of edge types and such pairs if the graph is heterogenenous.
A set of builtin negative samplers are provided in
:ref:`the negative sampling module <api-dataloading-negative-sampling>`.
Examples
--------
The following example shows how to train a 3-layer GNN for edge classification on a
set of edges ``train_eid`` on a homogeneous undirected graph. Each node takes
messages from all neighbors.
Say that you have an array of source node IDs ``src`` and another array of destination
node IDs ``dst``. One can make it bidirectional by adding another set of edges
that connects from ``dst`` to ``src``:
>>> g = dgl.graph((torch.cat([src, dst]), torch.cat([dst, src])))
One can then know that the ID difference of an edge and its reverse edge is ``|E|``,
where ``|E|`` is the length of your source/destination array. The reverse edge
mapping can be obtained by
>>> E = len(src)
>>> reverse_eids = torch.cat([torch.arange(E, 2 * E), torch.arange(0, E)])
Note that the sampled edges as well as their reverse edges are removed from
computation dependencies of the incident nodes. This is a common trick to avoid
information leakage.
>>> sampler = dgl.dataloading.MultiLayerNeighborSampler([15, 10, 5])
>>> collator = dgl.dataloading.EdgeCollator(
... g, train_eid, sampler, exclude='reverse_id',
... reverse_eids=reverse_eids)
>>> dataloader = torch.utils.data.DataLoader(
... collator.dataset, collate_fn=collator.collate,
... batch_size=1024, shuffle=True, drop_last=False, num_workers=4)
>>> for input_nodes, pair_graph, blocks in dataloader:
... train_on(input_nodes, pair_graph, blocks)
To train a 3-layer GNN for link prediction on a set of edges ``train_eid`` on a
homogeneous graph where each node takes messages from all neighbors (assume the
backend is PyTorch), with 5 uniformly chosen negative samples per edge:
>>> sampler = dgl.dataloading.MultiLayerNeighborSampler([15, 10, 5])
>>> neg_sampler = dgl.dataloading.negative_sampler.Uniform(5)
>>> collator = dgl.dataloading.EdgeCollator(
... g, train_eid, sampler, exclude='reverse_id',
... reverse_eids=reverse_eids, negative_sampler=neg_sampler,
>>> dataloader = torch.utils.data.DataLoader(
... collator.dataset, collate_fn=collator.collate,
... batch_size=1024, shuffle=True, drop_last=False, num_workers=4)
>>> for input_nodes, pos_pair_graph, neg_pair_graph, blocks in dataloader:
... train_on(input_nodse, pair_graph, neg_pair_graph, blocks)
For heterogeneous graphs, the reverse of an edge may have a different edge type
from the original edge. For instance, consider that you have an array of
user-item clicks, representated by a user array ``user`` and an item array ``item``.
You may want to build a heterogeneous graph with a user-click-item relation and an
item-clicked-by-user relation.
>>> g = dgl.heterograph({
... ('user', 'click', 'item'): (user, item),
... ('item', 'clicked-by', 'user'): (item, user)})
To train a 3-layer GNN for edge classification on a set of edges ``train_eid`` with
type ``click``, you can write
>>> sampler = dgl.dataloading.MultiLayerNeighborSampler([15, 10, 5])
>>> collator = dgl.dataloading.EdgeCollator(
... g, {'click': train_eid}, sampler, exclude='reverse_types',
... reverse_etypes={'click': 'clicked-by', 'clicked-by': 'click'})
>>> dataloader = torch.utils.data.DataLoader(
... collator.dataset, collate_fn=collator.collate,
... batch_size=1024, shuffle=True, drop_last=False, num_workers=4)
>>> for input_nodes, pair_graph, blocks in dataloader:
... train_on(input_nodes, pair_graph, blocks)
To train a 3-layer GNN for link prediction on a set of edges ``train_eid`` with type
``click``, you can write
>>> sampler = dgl.dataloading.MultiLayerNeighborSampler([15, 10, 5])
>>> neg_sampler = dgl.dataloading.negative_sampler.Uniform(5)
>>> collator = dgl.dataloading.EdgeCollator(
... g, train_eid, sampler, exclude='reverse_types',
... reverse_etypes={'click': 'clicked-by', 'clicked-by': 'click'},
... negative_sampler=neg_sampler)
>>> dataloader = torch.utils.data.DataLoader(
... collator.dataset, collate_fn=collator.collate,
... batch_size=1024, shuffle=True, drop_last=False, num_workers=4)
>>> for input_nodes, pos_pair_graph, neg_pair_graph, blocks in dataloader:
... train_on(input_nodes, pair_graph, neg_pair_graph, 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, g, eids, block_sampler, g_sampling=None, exclude=None,
reverse_eids=None, reverse_etypes=None, negative_sampler=None):
self.g = g
self._is_distributed = isinstance(g, DistGraph)
if not isinstance(eids, Mapping):
assert len(g.etypes) == 1, \
"eids should be a dict of etype and ids for graph with multiple etypes"
self.block_sampler = block_sampler
# One may wish to iterate over the edges in one graph while perform sampling in
# another graph. This may be the case for iterating over validation and test
# edge set while perform neighborhood sampling on the graph formed by only
# the training edge set.
# See GCMC for an example usage.
if g_sampling is not None:
self.g_sampling = g_sampling
self.exclude = None
else:
self.g_sampling = self.g
self.exclude = exclude
self.reverse_eids = reverse_eids
self.reverse_etypes = reverse_etypes
self.negative_sampler = negative_sampler
if isinstance(eids, Mapping):
self.eids = _prepare_tensor_dict(g, eids, 'eids', self._is_distributed)
self._dataset = utils.FlattenedDict(self.eids)
else:
self.eids = _prepare_tensor(g, eids, 'eids', self._is_distributed)
self._dataset = self.eids
@property
def dataset(self):
return self._dataset
def _collate(self, items):
if isinstance(items[0], tuple):
# returns a list of pairs: group them by node types into a dict
items = utils.group_as_dict(items)
items = _prepare_tensor_dict(self.g_sampling, items, 'items', self._is_distributed)
else:
items = _prepare_tensor(self.g_sampling, items, 'items', self._is_distributed)
pair_graph = self.g.edge_subgraph(items)
seed_nodes = pair_graph.ndata[NID]
exclude_eids = _find_exclude_eids(
self.g,
self.exclude,
items,
reverse_eid_map=self.reverse_eids,
reverse_etype_map=self.reverse_etypes)
blocks = self.block_sampler.sample_blocks(
self.g_sampling, seed_nodes, exclude_eids=exclude_eids)
input_nodes = blocks[0].srcdata[NID]
return input_nodes, pair_graph, blocks
def _collate_with_negative_sampling(self, items):
if isinstance(items[0], tuple):
# returns a list of pairs: group them by node types into a dict
items = utils.group_as_dict(items)
items = _prepare_tensor_dict(self.g_sampling, items, 'items', self._is_distributed)
else:
items = _prepare_tensor(self.g_sampling, items, 'items', self._is_distributed)
pair_graph = self.g.edge_subgraph(items, preserve_nodes=True)
induced_edges = pair_graph.edata[EID]
neg_srcdst = self.negative_sampler(self.g, items)
if not isinstance(neg_srcdst, Mapping):
assert len(self.g.etypes) == 1, \
'graph has multiple or no edge types; '\
'please return a dict in negative sampler.'
neg_srcdst = {self.g.canonical_etypes[0]: neg_srcdst}
# Get dtype from a tuple of tensors
dtype = F.dtype(list(neg_srcdst.values())[0][0])
neg_edges = {
etype: neg_srcdst.get(etype, (F.tensor([], dtype), F.tensor([], dtype)))
for etype in self.g.canonical_etypes}
neg_pair_graph = heterograph(
neg_edges, {ntype: self.g.number_of_nodes(ntype) for ntype in self.g.ntypes})
pair_graph, neg_pair_graph = transform.compact_graphs([pair_graph, neg_pair_graph])
pair_graph.edata[EID] = induced_edges
seed_nodes = pair_graph.ndata[NID]
exclude_eids = _find_exclude_eids(
self.g,
self.exclude,
items,
reverse_eid_map=self.reverse_eids,
reverse_etype_map=self.reverse_etypes)
blocks = self.block_sampler.sample_blocks(
self.g_sampling, seed_nodes, exclude_eids=exclude_eids)
input_nodes = blocks[0].srcdata[NID]
return input_nodes, pair_graph, neg_pair_graph, blocks
def collate(self, items):
"""Combines the sampled edges into a minibatch for edge classification, edge
regression, and link prediction tasks.
Parameters
----------
items : list[int] or list[tuple[str, int]]
Either a list of edge IDs (for homogeneous graphs), or a list of edge type-ID
pairs (for heterogeneous graphs).
Returns
-------
Either ``(input_nodes, pair_graph, blocks)``, or
``(input_nodes, pair_graph, negative_pair_graph, blocks)`` if negative sampling is
enabled.
input_nodes : Tensor or dict[ntype, Tensor]
The input nodes necessary for computation in this minibatch.
If the original graph has multiple node types, return a dictionary of
node type names and node ID tensors. Otherwise, return a single tensor.
pair_graph : DGLGraph
The graph that contains only the edges in the minibatch as well as their incident
nodes.
Note that the metagraph of this graph will be identical to that of the original
graph.
negative_pair_graph : DGLGraph
The graph that contains only the edges connecting the source and destination nodes
yielded from the given negative sampler, if negative sampling is enabled.
Note that the metagraph of this graph will be identical to that of the original
graph.
blocks : list[DGLGraph]
The list of MFGs necessary for computing the representation of the edges.
"""
if self.negative_sampler is None:
return self._collate(items)
else:
return self._collate_with_negative_sampling(items)
class GraphCollator(object):
"""Given a set of graphs as well as their graph-level data, the collate function will batch the
graphs into a batched graph, and stack the tensors into a single bigger tensor. If the
example is a container (such as sequences or mapping), the collate function preserves
the structure and collates each of the elements recursively.
If the set of graphs has no graph-level data, the collate function will yield a batched graph.
Examples
--------
To train a GNN for graph classification on a set of graphs in ``dataset`` (assume
the backend is PyTorch):
>>> dataloader = dgl.dataloading.GraphDataLoader(
... dataset, batch_size=1024, shuffle=True, drop_last=False, num_workers=4)
>>> for batched_graph, labels in dataloader:
... train_on(batched_graph, labels)
"""
def __init__(self):
self.graph_collate_err_msg_format = (
"graph_collate: batch must contain DGLGraph, tensors, numpy arrays, "
"numbers, dicts or lists; found {}")
self.np_str_obj_array_pattern = re.compile(r'[SaUO]')
#This implementation is based on torch.utils.data._utils.collate.default_collate
def collate(self, items):
"""This function is similar to ``torch.utils.data._utils.collate.default_collate``.
It combines the sampled graphs and corresponding graph-level data
into a batched graph and tensors.
Parameters
----------
items : list of data points or tuples
Elements in the list are expected to have the same length.
Each sub-element will be batched as a batched graph, or a
batched tensor correspondingly.
Returns
-------
A tuple of the batching results.
"""
elem = items[0]
elem_type = type(elem)
if isinstance(elem, DGLGraph):
batched_graphs = batch(items)
return batched_graphs
elif F.is_tensor(elem):
return F.stack(items, 0)
elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
and elem_type.__name__ != 'string_':
if elem_type.__name__ == 'ndarray' or elem_type.__name__ == 'memmap':
# array of string classes and object
if self.np_str_obj_array_pattern.search(elem.dtype.str) is not None:
raise TypeError(self.graph_collate_err_msg_format.format(elem.dtype))
return self.collate([F.tensor(b) for b in items])
elif elem.shape == (): # scalars
return F.tensor(items)
elif isinstance(elem, float):
return F.tensor(items, dtype=F.float64)
elif isinstance(elem, int):
return F.tensor(items)
elif isinstance(elem, (str, bytes)):
return items
elif isinstance(elem, Mapping):
return {key: self.collate([d[key] for d in items]) for key in elem}
elif isinstance(elem, tuple) and hasattr(elem, '_fields'): # namedtuple
return elem_type(*(self.collate(samples) for samples in zip(*items)))
elif isinstance(elem, Sequence):
# check to make sure that the elements in batch have consistent size
item_iter = iter(items)
elem_size = len(next(item_iter))
if not all(len(elem) == elem_size for elem in item_iter):
raise RuntimeError('each element in list of batch should be of equal size')
transposed = zip(*items)
return [self.collate(samples) for samples in transposed]
raise TypeError(self.graph_collate_err_msg_format.format(elem_type))
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