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3e363e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 | """Relation-aware inductive GraphSAGE and optional GAT encoders."""
from __future__ import annotations
from dataclasses import dataclass
import torch
from torch import Tensor, nn
from torch.nn import functional
from graph.config import GraphConfig
from graph.types import GraphTensors
@dataclass(frozen=True)
class GraphEncoderOutput:
node_states: Tensor
relation_states: Tensor
edge_states: Tensor
attention_weights: Tensor | None
def _edge_query(
relation_states: Tensor, query_state: Tensor | None, edge_query: Tensor | None
) -> Tensor:
"""The query vector each edge's message is conditioned on.
``edge_query`` gives one row per edge, which is how a batch of disjoint
graphs, each with its own query, is encoded in a single pass.
"""
if edge_query is not None:
return edge_query
if query_state is None:
return torch.zeros_like(relation_states)
return query_state.unsqueeze(0).expand_as(relation_states)
class RelationAwareSAGELayer(nn.Module):
def __init__(
self,
hidden_dim: int,
edge_dim: int,
num_relations: int,
dropout: float,
residual: bool = False,
) -> None:
super().__init__()
self.residual = residual
self.self_proj = nn.Linear(hidden_dim, hidden_dim)
self.message_proj = nn.Linear(
hidden_dim * 2 + hidden_dim + edge_dim, hidden_dim
)
self.relation_embedding = nn.Embedding(num_relations, hidden_dim)
self.norm = nn.LayerNorm(hidden_dim)
self.dropout = nn.Dropout(dropout)
def _skip(self, node_states: Tensor) -> Tensor | float:
"""The identity path, when enabled; it keeps depth from oversmoothing."""
return node_states if self.residual else 0.0
def forward(
self,
node_states: Tensor,
graph: GraphTensors,
relation_ids: Tensor,
query_state: Tensor | None = None,
edge_query: Tensor | None = None,
) -> tuple[Tensor, Tensor, Tensor | None]:
source, target = graph.edge_index
source_states = node_states[source]
target_states = node_states[target]
relation_states = self.relation_embedding(relation_ids)
query_for_edges = _edge_query(relation_states, query_state, edge_query)
messages = self.message_proj(
torch.cat(
(source_states, target_states, relation_states, graph.edge_features),
dim=-1,
)
)
messages = messages + query_for_edges
aggregate = torch.zeros_like(node_states)
aggregate.index_add_(0, target, messages)
degree = torch.zeros(
(graph.num_nodes,), device=node_states.device, dtype=node_states.dtype
)
degree.index_add_(0, target, torch.ones_like(target, dtype=node_states.dtype))
aggregate = aggregate / degree.clamp_min(1.0).unsqueeze(-1)
updated = self.norm(
self._skip(node_states)
+ self.self_proj(node_states)
+ self.dropout(functional.gelu(aggregate))
)
return updated, relation_states, None
class RelationAwareGATLayer(RelationAwareSAGELayer):
"""GAT ablation that retains per-edge attention for explanation."""
def __init__(
self,
hidden_dim: int,
edge_dim: int,
num_relations: int,
dropout: float,
residual: bool = False,
) -> None:
super().__init__(hidden_dim, edge_dim, num_relations, dropout, residual)
self.attn = nn.Linear(hidden_dim * 2 + hidden_dim + edge_dim, 1)
def forward(
self,
node_states: Tensor,
graph: GraphTensors,
relation_ids: Tensor,
query_state: Tensor | None = None,
edge_query: Tensor | None = None,
) -> tuple[Tensor, Tensor, Tensor | None]:
source, target = graph.edge_index
source_states = node_states[source]
target_states = node_states[target]
relation_states = self.relation_embedding(relation_ids)
query_for_edges = _edge_query(relation_states, query_state, edge_query)
edge_input = torch.cat(
(source_states, target_states, relation_states, graph.edge_features), dim=-1
)
logits = self.attn(edge_input).squeeze(-1) + (
relation_states * query_for_edges
).sum(-1) / (node_states.shape[-1] ** 0.5)
weights = torch.zeros_like(logits)
for node in range(graph.num_nodes):
mask = target == node
if mask.any():
weights[mask] = torch.softmax(logits[mask], dim=0)
messages = self.message_proj(edge_input) * weights.unsqueeze(-1)
aggregate = torch.zeros_like(node_states)
aggregate.index_add_(0, target, messages)
updated = self.norm(
self._skip(node_states)
+ self.self_proj(node_states)
+ self.dropout(functional.gelu(aggregate))
)
return updated, relation_states, weights
class GraphEncoder(nn.Module):
"""Inductive graph encoder; no trainable closed-world node-ID table is used."""
def __init__(self, config: GraphConfig) -> None:
super().__init__()
self.config = config
self.node_type_embedding = nn.Embedding(
config.num_node_types, config.hidden_dim
)
self.node_input = nn.Linear(config.graph_input_dim, config.hidden_dim)
layer_cls = (
RelationAwareGATLayer
if config.graph_encoder == "gat" or config.use_gat
else RelationAwareSAGELayer
)
self.layers = nn.ModuleList(
[
layer_cls(
config.hidden_dim,
config.edge_input_dim,
config.num_relations,
config.dropout,
residual=config.graph_residual,
)
for _ in range(config.graph_layers)
]
)
self.edge_input = nn.Linear(config.edge_input_dim, config.hidden_dim)
self.final_norm = nn.LayerNorm(config.hidden_dim)
self.start_embedding = (
nn.Embedding(2, config.hidden_dim) if config.start_marking else None
)
def forward(
self,
graph: GraphTensors,
query_state: Tensor | None = None,
*,
edge_query: Tensor | None = None,
start_mask: Tensor | None = None,
node_query: Tensor | None = None,
) -> GraphEncoderOutput:
"""Encode ``graph``, conditioned on the query where it is given.
``edge_query``/``node_query`` give one query row per edge/node, for a
batch of graphs with different queries; otherwise ``query_state`` is
shared by the whole graph. ``start_mask`` flags the query's start
entities and is used when the encoder was built with ``start_marking``.
"""
# External graph services are open-world: a tenant may introduce more
# node/relation labels than the configured embedding tables, so the stable
# global identities are reduced into the learned vocabulary here. The
# graph itself is never rewritten: its *local* type indices are what the
# caller uses to pool per-edge states into a per-graph relation bank and to
# map a bank row back to a relation name, and overwriting them with reduced
# hashes would corrupt that bookkeeping.
node_type_ids = graph.node_type_identity.remainder(self.config.num_node_types)
edge_type_ids = graph.edge_type_identity.remainder(self.config.num_relations)
node_states = self.node_input(graph.node_features) + self.node_type_embedding(
node_type_ids
)
if self.start_embedding is not None and start_mask is not None:
marked = start_mask.to(torch.long)
node_states = node_states + self.start_embedding(marked)
if node_query is None and query_state is not None:
node_query = query_state.reshape(1, -1).expand_as(node_states)
if node_query is not None:
node_states = node_states + marked.unsqueeze(-1).to(
node_states.dtype
) * node_query.to(node_states.dtype)
all_relation_states: list[Tensor] = []
attention: Tensor | None = None
for layer in self.layers:
node_states, relation_states, layer_attention = layer(
node_states, graph, edge_type_ids, query_state, edge_query
)
if layer_attention is not None:
attention = layer_attention
all_relation_states.append(relation_states)
node_states = self.final_norm(node_states)
edge_states = self.edge_input(graph.edge_features)
relation_states = (
all_relation_states[-1]
if all_relation_states
else torch.empty((0, self.config.hidden_dim), device=node_states.device)
)
return GraphEncoderOutput(node_states, relation_states, edge_states, attention)
|