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7c5e40e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 | """First-class token-anchored graph-object utilities.
The v1 graph object is intentionally minimal and dense:
* ``node_type`` / ``node_mask`` over token positions.
* ``edge_type`` / ``edge_mask`` over ``[query_token, key_token]`` pairs.
This mirrors the current UD/SRL supervision contract and gives the model an
explicit graph source that can later be compared under typed, untyped,
relation-permuted, random, span-corrupted, and zero-object modes at matched
tensor budget.
"""
from __future__ import annotations
from typing import Literal, TypedDict
import torch
from strata.data.srl_labels import NONE_LOCAL, SRL_BASE
from strata.data.ud_labels import IGNORE_INDEX
IGNORE_GRAPH_LABEL = -100
GraphObjectIntervention = Literal[
"none",
"typed_graph_object",
"untyped_same_topology",
"relation_permuted",
"random_same_degree",
"span_corrupted",
"batch_shuffled_graph",
"zero_graph_object",
]
GRAPH_OBJECT_INTERVENTIONS: tuple[str, ...] = (
"none",
"typed_graph_object",
"untyped_same_topology",
"relation_permuted",
"random_same_degree",
"span_corrupted",
"batch_shuffled_graph",
"zero_graph_object",
)
class GraphObject(TypedDict):
node_type: torch.Tensor # [B, S], long, IGNORE_GRAPH_LABEL when absent
node_mask: torch.Tensor # [B, S], bool
edge_type: torch.Tensor # [B, S, S], long, IGNORE_GRAPH_LABEL when absent
edge_mask: torch.Tensor # [B, S, S], bool
edge_src: torch.Tensor # [B, E], long token index
edge_dst: torch.Tensor # [B, E], long token index
edge_rel: torch.Tensor # [B, E], long relation id
edge_slot_mask: torch.Tensor # [B, E], bool
def graph_object_from_batch(batch: dict[str, torch.Tensor]) -> GraphObject | None:
"""Extract a graph object from a collated batch if present."""
required = (
"graph_node_type",
"graph_node_mask",
"graph_edge_type",
"graph_edge_mask",
"graph_edge_src",
"graph_edge_dst",
"graph_edge_rel",
"graph_edge_slot_mask",
)
if not all(key in batch for key in required):
return None
return {
"node_type": batch["graph_node_type"],
"node_mask": batch["graph_node_mask"],
"edge_type": batch["graph_edge_type"],
"edge_mask": batch["graph_edge_mask"],
"edge_src": batch["graph_edge_src"],
"edge_dst": batch["graph_edge_dst"],
"edge_rel": batch["graph_edge_rel"],
"edge_slot_mask": batch["graph_edge_slot_mask"],
}
def graph_object_from_targets(batch: dict[str, torch.Tensor]) -> GraphObject | None:
"""Build a graph object from collated UD/SRL supervision tensors.
This is used by eval loaders that do not go through ``collate_graph`` but do
have gold arc/role targets. It preserves duplicate relations through edge
slots while also filling a dense compatibility matrix.
"""
if "input_ids" not in batch:
return None
input_ids = batch["input_ids"]
b, s = input_ids.shape
device = input_ids.device
node_type = torch.full((b, s), IGNORE_GRAPH_LABEL, dtype=torch.long, device=device)
node_mask = torch.zeros((b, s), dtype=torch.bool, device=device)
dense_edge_type = torch.full((b, s, s), IGNORE_GRAPH_LABEL, dtype=torch.long, device=device)
dense_edge_mask = torch.zeros((b, s, s), dtype=torch.bool, device=device)
edge_lists: list[list[tuple[int, int, int]]] = [[] for _ in range(b)]
if "node_type" in batch:
node_type = batch["node_type"].clone()
node_mask |= node_type != IGNORE_INDEX
if "candidate_head" in batch:
node_mask |= batch["candidate_head"].to(torch.bool)
if "arc_head" in batch and "rel" in batch:
arc_head = batch["arc_head"]
rel = batch["rel"]
for bi in range(b):
supervised = ((arc_head[bi] != IGNORE_INDEX) & (rel[bi] != IGNORE_INDEX)).nonzero(as_tuple=False).flatten()
for dep_t in supervised.tolist():
head_t = int(arc_head[bi, dep_t].item())
rel_t = int(rel[bi, dep_t].item())
if 0 <= head_t < s:
edge_lists[bi].append((dep_t, head_t, rel_t))
dense_edge_type[bi, dep_t, head_t] = rel_t
dense_edge_mask[bi, dep_t, head_t] = True
node_mask[bi, dep_t] = True
node_mask[bi, head_t] = True
if "predicate_mask" in batch:
node_mask |= batch["predicate_mask"].to(torch.bool)
if "role_target" in batch:
role_target = batch["role_target"]
for bi in range(b):
active = ((role_target[bi] != IGNORE_INDEX) & (role_target[bi] != NONE_LOCAL)).nonzero(as_tuple=False)
for arg_t, pred_t in active.tolist():
rel_t = SRL_BASE + int(role_target[bi, arg_t, pred_t].item())
edge_lists[bi].append((arg_t, pred_t, rel_t))
dense_edge_type[bi, arg_t, pred_t] = rel_t
dense_edge_mask[bi, arg_t, pred_t] = True
node_mask[bi, arg_t] = True
node_mask[bi, pred_t] = True
max_edges = max(1, max((len(edges) for edges in edge_lists), default=0))
edge_src = torch.zeros((b, max_edges), dtype=torch.long, device=device)
edge_dst = torch.zeros((b, max_edges), dtype=torch.long, device=device)
edge_rel = torch.full((b, max_edges), IGNORE_GRAPH_LABEL, dtype=torch.long, device=device)
edge_slot_mask = torch.zeros((b, max_edges), dtype=torch.bool, device=device)
for bi, edges in enumerate(edge_lists):
for ei, (src, dst, rel_t) in enumerate(edges):
edge_src[bi, ei] = src
edge_dst[bi, ei] = dst
edge_rel[bi, ei] = rel_t
edge_slot_mask[bi, ei] = True
if not bool(edge_slot_mask.any()):
return None
return {
"node_type": node_type,
"node_mask": node_mask,
"edge_type": dense_edge_type,
"edge_mask": dense_edge_mask,
"edge_src": edge_src,
"edge_dst": edge_dst,
"edge_rel": edge_rel,
"edge_slot_mask": edge_slot_mask,
}
def _clone_graph_object(graph_object: GraphObject) -> GraphObject:
return {
"node_type": graph_object["node_type"].clone(),
"node_mask": graph_object["node_mask"].clone(),
"edge_type": graph_object["edge_type"].clone(),
"edge_mask": graph_object["edge_mask"].clone(),
"edge_src": graph_object["edge_src"].clone(),
"edge_dst": graph_object["edge_dst"].clone(),
"edge_rel": graph_object["edge_rel"].clone(),
"edge_slot_mask": graph_object["edge_slot_mask"].clone(),
}
def _valid_lengths(graph_object: GraphObject) -> list[int]:
"""Infer valid token prefix lengths from node/edge occupancy."""
node_mask = graph_object["node_mask"].to(torch.bool)
edge_mask = graph_object["edge_mask"].to(torch.bool)
lengths: list[int] = []
for i in range(node_mask.shape[0]):
occupied = node_mask[i].clone()
slots = graph_object["edge_slot_mask"][i].to(torch.bool)
if bool(slots.any()):
edge_src = graph_object["edge_src"][i, slots]
edge_dst = graph_object["edge_dst"][i, slots]
occupied[edge_src.clamp(0, occupied.numel() - 1)] = True
occupied[edge_dst.clamp(0, occupied.numel() - 1)] = True
if bool(edge_mask[i].any()):
occupied |= edge_mask[i].any(dim=0)
occupied |= edge_mask[i].any(dim=1)
nz = occupied.nonzero(as_tuple=False).flatten()
lengths.append(int(nz.max().item()) + 1 if nz.numel() else 0)
return lengths
def _permute_graph_nodes(graph_object: GraphObject, permutations: list[torch.Tensor]) -> GraphObject:
out = _clone_graph_object(graph_object)
for batch_index, perm in enumerate(permutations):
if perm.numel() <= 1:
continue
device = out["node_type"].device
perm = perm.to(device)
inv = torch.empty_like(perm)
inv[perm] = torch.arange(perm.numel(), device=device)
out["node_type"][batch_index, : perm.numel()] = graph_object["node_type"][batch_index, perm]
out["node_mask"][batch_index, : perm.numel()] = graph_object["node_mask"][batch_index, perm]
sub_edge_type = graph_object["edge_type"][batch_index, : perm.numel(), : perm.numel()]
sub_edge_mask = graph_object["edge_mask"][batch_index, : perm.numel(), : perm.numel()]
out["edge_type"][batch_index, : perm.numel(), : perm.numel()] = sub_edge_type[perm][:, perm]
out["edge_mask"][batch_index, : perm.numel(), : perm.numel()] = sub_edge_mask[perm][:, perm]
slots = graph_object["edge_slot_mask"][batch_index].to(torch.bool)
if bool(slots.any()):
src = graph_object["edge_src"][batch_index, slots].clamp(0, perm.numel() - 1)
dst = graph_object["edge_dst"][batch_index, slots].clamp(0, perm.numel() - 1)
out["edge_src"][batch_index, slots] = inv[src]
out["edge_dst"][batch_index, slots] = inv[dst]
return out
def apply_graph_object_intervention(
graph_object: GraphObject | None,
*,
intervention: str = "none",
relation_vocab_size: int,
) -> GraphObject | None:
"""Return an intervened graph object without changing tensor shapes."""
if graph_object is None:
return None
if intervention in {"none", "typed_graph_object"}:
return graph_object
if intervention not in GRAPH_OBJECT_INTERVENTIONS:
raise ValueError(f"unknown graph object intervention {intervention!r}")
out = _clone_graph_object(graph_object)
edge_mask = out["edge_mask"]
if intervention == "zero_graph_object":
out["node_mask"].zero_()
out["edge_mask"].zero_()
out["edge_slot_mask"].zero_()
out["node_type"].fill_(IGNORE_GRAPH_LABEL)
out["edge_type"].fill_(IGNORE_GRAPH_LABEL)
out["edge_rel"].fill_(IGNORE_GRAPH_LABEL)
return out
if intervention == "untyped_same_topology":
out["edge_type"] = out["edge_type"].masked_fill(edge_mask, 0)
out["edge_rel"] = out["edge_rel"].masked_fill(out["edge_slot_mask"], 0)
return out
if intervention == "relation_permuted":
typed = out["edge_type"].clamp_min(0)
out["edge_type"] = out["edge_type"].masked_scatter(
edge_mask,
((typed[edge_mask] + 1) % relation_vocab_size).to(out["edge_type"].dtype),
)
slot_mask = out["edge_slot_mask"]
typed_slots = out["edge_rel"].clamp_min(0)
out["edge_rel"] = out["edge_rel"].masked_scatter(
slot_mask,
((typed_slots[slot_mask] + 1) % relation_vocab_size).to(out["edge_rel"].dtype),
)
return out
if intervention == "batch_shuffled_graph":
if out["node_type"].shape[0] > 1:
return {
"node_type": torch.roll(out["node_type"], shifts=1, dims=0),
"node_mask": torch.roll(out["node_mask"], shifts=1, dims=0),
"edge_type": torch.roll(out["edge_type"], shifts=1, dims=0),
"edge_mask": torch.roll(out["edge_mask"], shifts=1, dims=0),
"edge_src": torch.roll(out["edge_src"], shifts=1, dims=0),
"edge_dst": torch.roll(out["edge_dst"], shifts=1, dims=0),
"edge_rel": torch.roll(out["edge_rel"], shifts=1, dims=0),
"edge_slot_mask": torch.roll(out["edge_slot_mask"], shifts=1, dims=0),
}
intervention = "span_corrupted"
if intervention == "span_corrupted":
lengths = _valid_lengths(out)
permutations = [
torch.roll(torch.arange(max(1, n), device=out["node_type"].device), shifts=1)
for n in lengths
]
return _permute_graph_nodes(graph_object, permutations)
if intervention == "random_same_degree":
permutations: list[torch.Tensor] = []
for i, n in enumerate(_valid_lengths(out)):
if n <= 1:
permutations.append(torch.arange(max(1, n), device=out["node_type"].device))
continue
generator = torch.Generator(device="cpu")
generator.manual_seed(92821 + i * 1_000_003 + n * 131)
permutations.append(torch.randperm(n, generator=generator))
return _permute_graph_nodes(graph_object, permutations)
raise AssertionError(f"unhandled graph object intervention {intervention!r}")
__all__ = [
"GRAPH_OBJECT_INTERVENTIONS",
"IGNORE_GRAPH_LABEL",
"GraphObject",
"GraphObjectIntervention",
"apply_graph_object_intervention",
"graph_object_from_batch",
"graph_object_from_targets",
]
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