"""Unified data structure for input and ouput of all the stages in loading process."""
from dataclasses import dataclass
from typing import Dict, List, Tuple, Union
import torch
import dgl
from dgl.utils import recursive_apply
from .base import etype_str_to_tuple
from .internal import get_attributes
from .sampled_subgraph import SampledSubgraph
__all__ = ["MiniBatch"]
[docs]@dataclass
class MiniBatch:
r"""A composite data class for data structure in the graphbolt.
It is designed to facilitate the exchange of data among different components
involved in processing data. The purpose of this class is to unify the
representation of input and output data across different stages, ensuring
consistency and ease of use throughout the loading process."""
seed_nodes: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
"""
Representation of seed nodes used for sampling in the graph.
- If `seed_nodes` is a tensor: It indicates the graph is homogeneous.
- If `seed_nodes` is a dictionary: The keys should be node type and the
value should be corresponding heterogeneous node ids.
"""
node_pairs: Union[
Tuple[torch.Tensor, torch.Tensor],
Dict[str, Tuple[torch.Tensor, torch.Tensor]],
] = None
"""
Representation of seed node pairs utilized in link prediction tasks.
- If `node_pairs` is a tuple: It indicates a homogeneous graph where each
tuple contains two tensors representing source-destination node pairs.
- If `node_pairs` is a dictionary: The keys should be edge type, and the
value should be a tuple of tensors representing node pairs of the given
type.
"""
labels: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
"""
Labels associated with seed nodes / node pairs in the graph.
- If `labels` is a tensor: It indicates the graph is homogeneous. The value
should be corresponding labels to given 'seed_nodes' or 'node_pairs'.
- If `labels` is a dictionary: The keys should be node or edge type and the
value should be corresponding labels to given 'seed_nodes' or 'node_pairs'.
"""
negative_srcs: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
"""
Representation of negative samples for the head nodes in the link
prediction task.
- If `negative_srcs` is a tensor: It indicates a homogeneous graph.
- If `negative_srcs` is a dictionary: The key should be edge type, and the
value should correspond to the negative samples for head nodes of the
given type.
"""
negative_dsts: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
"""
Representation of negative samples for the tail nodes in the link
prediction task.
- If `negative_dsts` is a tensor: It indicates a homogeneous graph.
- If `negative_dsts` is a dictionary: The key should be edge type, and the
value should correspond to the negative samples for head nodes of the
given type.
"""
sampled_subgraphs: List[SampledSubgraph] = None
"""A list of 'SampledSubgraph's, each one corresponding to one layer,
representing a subset of a larger graph structure.
"""
input_nodes: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
"""A representation of input nodes in the outermost layer. Conatins all nodes
in the 'sampled_subgraphs'.
- If `input_nodes` is a tensor: It indicates the graph is homogeneous.
- If `input_nodes` is a dictionary: The keys should be node type and the
value should be corresponding heterogeneous node id.
"""
node_features: Union[
Dict[str, torch.Tensor], Dict[Tuple[str, str], torch.Tensor]
] = None
"""A representation of node features.
- If keys are single strings: It means the graph is homogeneous, and the
keys are feature names.
- If keys are tuples: It means the graph is heterogeneous, and the keys
are tuples of '(node_type, feature_name)'.
"""
edge_features: List[
Union[Dict[str, torch.Tensor], Dict[Tuple[str, str], torch.Tensor]]
] = None
"""Edge features associated with the 'sampled_subgraphs'.
- If keys are single strings: It means the graph is homogeneous, and the
keys are feature names.
- If keys are tuples: It means the graph is heterogeneous, and the keys
are tuples of '(edge_type, feature_name)'. Note, edge type is single
string of format 'str:str:str'.
"""
compacted_node_pairs: Union[
Tuple[torch.Tensor, torch.Tensor],
Dict[str, Tuple[torch.Tensor, torch.Tensor]],
] = None
"""
Representation of compacted node pairs corresponding to 'node_pairs', where
all node ids inside are compacted.
"""
compacted_negative_srcs: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
"""
Representation of compacted nodes corresponding to 'negative_srcs', where
all node ids inside are compacted.
"""
compacted_negative_dsts: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
"""
Representation of compacted nodes corresponding to 'negative_dsts', where
all node ids inside are compacted.
"""
def __repr__(self) -> str:
return _minibatch_str(self)
[docs] def node_ids(self) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
"""A representation of input nodes in the outermost layer. Contains all
nodes in the `sampled_subgraphs`.
- If `input_nodes` is a tensor: It indicates the graph is homogeneous.
- If `input_nodes` is a dictionary: The keys should be node type and the
value should be corresponding heterogeneous node id.
"""
return self.input_nodes
[docs] def num_layers(self) -> int:
"""Return the number of layers."""
if self.sampled_subgraphs is None:
return 0
return len(self.sampled_subgraphs)
[docs] def edge_ids(
self, layer_id: int
) -> Union[Dict[str, torch.Tensor], torch.Tensor]:
"""Get the edge ids of a layer."""
return self.sampled_subgraphs[layer_id].original_edge_ids
[docs] def set_node_features(
self,
node_features: Union[
Dict[str, torch.Tensor], Dict[Tuple[str, str], torch.Tensor]
],
) -> None:
"""Set node features."""
self.node_features = node_features
[docs] def set_edge_features(
self,
edge_features: List[
Union[Dict[str, torch.Tensor], Dict[Tuple[str, str], torch.Tensor]]
],
) -> None:
"""Set edge features."""
self.edge_features = edge_features
@property
def blocks(self):
"""Extracts DGL blocks from `MiniBatch` to construct a graphical
structure and ID mappings.
"""
if not self.sampled_subgraphs:
return None
is_heterogeneous = isinstance(
self.sampled_subgraphs[0].sampled_csc, Dict
)
blocks = []
for subgraph in self.sampled_subgraphs:
original_row_node_ids = subgraph.original_row_node_ids
assert (
original_row_node_ids is not None
), "Missing `original_row_node_ids` in sampled subgraph."
original_column_node_ids = subgraph.original_column_node_ids
assert (
original_column_node_ids is not None
), "Missing `original_column_node_ids` in sampled subgraph."
if is_heterogeneous:
sampled_csc = {
etype_str_to_tuple(etype): (
"csc",
(
v.indptr,
v.indices,
torch.arange(
0,
len(v.indices),
device=v.indptr.device,
dtype=v.indptr.dtype,
),
),
)
for etype, v in subgraph.sampled_csc.items()
}
num_src_nodes = {
ntype: nodes.size(0)
for ntype, nodes in original_row_node_ids.items()
}
num_dst_nodes = {
ntype: nodes.size(0)
for ntype, nodes in original_column_node_ids.items()
}
else:
sampled_csc = subgraph.sampled_csc
sampled_csc = (
"csc",
(
sampled_csc.indptr,
sampled_csc.indices,
torch.arange(
0,
len(sampled_csc.indices),
device=sampled_csc.indptr.device,
dtype=sampled_csc.indptr.dtype,
),
),
)
num_src_nodes = original_row_node_ids.size(0)
num_dst_nodes = original_column_node_ids.size(0)
blocks.append(
dgl.create_block(
sampled_csc,
num_src_nodes=num_src_nodes,
num_dst_nodes=num_dst_nodes,
)
)
if is_heterogeneous:
# Assign reverse node ids to the outermost layer's source nodes.
for node_type, reverse_ids in self.sampled_subgraphs[
0
].original_row_node_ids.items():
blocks[0].srcnodes[node_type].data[dgl.NID] = reverse_ids
# Assign reverse edges ids.
for block, subgraph in zip(blocks, self.sampled_subgraphs):
if subgraph.original_edge_ids:
for (
edge_type,
reverse_ids,
) in subgraph.original_edge_ids.items():
block.edges[etype_str_to_tuple(edge_type)].data[
dgl.EID
] = reverse_ids
else:
blocks[0].srcdata[dgl.NID] = self.sampled_subgraphs[
0
].original_row_node_ids
# Assign reverse edges ids.
for block, subgraph in zip(blocks, self.sampled_subgraphs):
if subgraph.original_edge_ids is not None:
block.edata[dgl.EID] = subgraph.original_edge_ids
return blocks
@property
def positive_node_pairs(self):
"""`positive_node_pairs` is a representation of positive graphs used for
evaluating or computing loss in link prediction tasks.
- If `positive_node_pairs` is a tuple: It indicates a homogeneous graph
containing two tensors representing source-destination node pairs.
- If `positive_node_pairs` is a dictionary: The keys should be edge type,
and the value should be a tuple of tensors representing node pairs of the
given type.
"""
return self.compacted_node_pairs
@property
def negative_node_pairs(self):
"""`negative_node_pairs` is a representation of negative graphs used for
evaluating or computing loss in link prediction tasks.
- If `negative_node_pairs` is a tuple: It indicates a homogeneous graph
containing two tensors representing source-destination node pairs.
- If `negative_node_pairs` is a dictionary: The keys should be edge type,
and the value should be a tuple of tensors representing node pairs of the
given type.
"""
# Build negative graph.
if (
self.compacted_negative_srcs is not None
and self.compacted_negative_dsts is not None
):
# For homogeneous graph.
if isinstance(self.compacted_negative_srcs, torch.Tensor):
negative_node_pairs = (
self.compacted_negative_srcs,
self.compacted_negative_dsts,
)
# For heterogeneous graph.
else:
negative_node_pairs = {
etype: (
neg_src,
self.compacted_negative_dsts[etype],
)
for etype, neg_src in self.compacted_negative_srcs.items()
}
elif (
self.compacted_negative_srcs is not None
and self.compacted_node_pairs is not None
):
# For homogeneous graph.
if isinstance(self.compacted_negative_srcs, torch.Tensor):
negative_ratio = self.compacted_negative_srcs.size(1)
negative_node_pairs = (
self.compacted_negative_srcs,
self.compacted_node_pairs[1]
.repeat_interleave(negative_ratio)
.view(-1, negative_ratio),
)
# For heterogeneous graph.
else:
negative_ratio = list(self.compacted_negative_srcs.values())[
0
].size(1)
negative_node_pairs = {
etype: (
neg_src,
self.compacted_node_pairs[etype][1]
.repeat_interleave(negative_ratio)
.view(-1, negative_ratio),
)
for etype, neg_src in self.compacted_negative_srcs.items()
}
elif (
self.compacted_negative_dsts is not None
and self.compacted_node_pairs is not None
):
# For homogeneous graph.
if isinstance(self.compacted_negative_dsts, torch.Tensor):
negative_ratio = self.compacted_negative_dsts.size(1)
negative_node_pairs = (
self.compacted_node_pairs[0]
.repeat_interleave(negative_ratio)
.view(-1, negative_ratio),
self.compacted_negative_dsts,
)
# For heterogeneous graph.
else:
negative_ratio = list(self.compacted_negative_dsts.values())[
0
].size(1)
negative_node_pairs = {
etype: (
self.compacted_node_pairs[etype][0]
.repeat_interleave(negative_ratio)
.view(-1, negative_ratio),
neg_dst,
)
for etype, neg_dst in self.compacted_negative_dsts.items()
}
else:
negative_node_pairs = None
return negative_node_pairs
@property
def node_pairs_with_labels(self):
"""Get a node pair tensor and a label tensor from MiniBatch. They are
used for evaluating or computing loss. For homogeneous graph, it will
return `(node_pairs, labels)` as result; for heterogeneous graph, the
`node_pairs` and `labels` will both be a dict with etype as the key.
- If it's a link prediction task, `node_pairs` will contain both
negative and positive node pairs and `labels` will consist of 0 and 1,
indicating whether the corresponding node pair is negative or positive.
- If it's an edge classification task, this function will directly
return `compacted_node_pairs` for each etype and the corresponding
`labels`.
- Otherwise it will return None.
"""
if self.labels is None:
# Link prediction.
positive_node_pairs = self.positive_node_pairs
negative_node_pairs = self.negative_node_pairs
if positive_node_pairs is None or negative_node_pairs is None:
return None
if isinstance(positive_node_pairs, Dict):
# Heterogeneous graph.
node_pairs_by_etype = {}
labels_by_etype = {}
for etype in positive_node_pairs:
pos_src, pos_dst = positive_node_pairs[etype]
neg_src, neg_dst = negative_node_pairs[etype]
neg_src, neg_dst = neg_src.view(-1), neg_dst.view(-1)
node_pairs_by_etype[etype] = (
torch.cat((pos_src, neg_src), dim=0),
torch.cat((pos_dst, neg_dst), dim=0),
)
pos_label = torch.ones_like(pos_src)
neg_label = torch.zeros_like(neg_src)
labels_by_etype[etype] = torch.cat(
[pos_label, neg_label], dim=0
)
return (node_pairs_by_etype, labels_by_etype)
else:
# Homogeneous graph.
pos_src, pos_dst = positive_node_pairs
neg_src, neg_dst = negative_node_pairs
neg_src, neg_dst = neg_src.view(-1), neg_dst.view(-1)
node_pairs = (
torch.cat((pos_src, neg_src), dim=0),
torch.cat((pos_dst, neg_dst), dim=0),
)
pos_label = torch.ones_like(pos_src)
neg_label = torch.zeros_like(neg_src)
labels = torch.cat([pos_label, neg_label], dim=0)
return (node_pairs, labels.float())
elif self.compacted_node_pairs is not None:
# Edge classification.
return (self.compacted_node_pairs, self.labels)
else:
return None
[docs] def to(self, device: torch.device): # pylint: disable=invalid-name
"""Copy `MiniBatch` to the specified device using reflection."""
def _to(x, device):
return x.to(device) if hasattr(x, "to") else x
def apply_to(x, device):
return recursive_apply(x, lambda x: _to(x, device))
if self.seed_nodes is not None and self.compacted_node_pairs is None:
# Node related tasks.
transfer_attrs = [
"labels",
"sampled_subgraphs",
"node_features",
"edge_features",
]
if self.labels is None:
# Layerwise inference
transfer_attrs.append("seed_nodes")
elif self.seed_nodes is None and self.compacted_node_pairs is not None:
# Link/edge related tasks.
transfer_attrs = [
"labels",
"compacted_node_pairs",
"compacted_negative_srcs",
"compacted_negative_dsts",
"sampled_subgraphs",
"node_features",
"edge_features",
]
else:
# Otherwise copy all the attributes to the device.
transfer_attrs = get_attributes(self)
for attr in transfer_attrs:
# Only copy member variables.
try:
# For read-only attributes such as blocks and
# node_pairs_with_labels, setattr will throw an AttributeError.
# We catch these exceptions and skip those attributes.
setattr(self, attr, apply_to(getattr(self, attr), device))
except AttributeError:
continue
return self
def _minibatch_str(minibatch: MiniBatch) -> str:
final_str = ""
# Get all attributes in the class except methods.
attributes = get_attributes(minibatch)
attributes.reverse()
# Insert key with its value into the string.
for name in attributes:
val = getattr(minibatch, name)
def _add_indent(_str, indent):
lines = _str.split("\n")
lines = [lines[0]] + [
" " * (indent + 10) + line for line in lines[1:]
]
return "\n".join(lines)
# Let the variables in the list occupy one line each, and adjust the
# indentation on top of the original if the original data output has
# line feeds.
if isinstance(val, list):
val = [str(val_str) for val_str in val]
val = "[" + ",\n".join(val) + "]"
elif isinstance(val, tuple):
val = [str(val_str) for val_str in val]
val = "(" + ",\n".join(val) + ")"
else:
val = str(val)
final_str = (
final_str + f"{name}={_add_indent(val, len(name)+1)},\n" + " " * 10
)
return "MiniBatch(" + final_str[:-3] + ")"