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e962f8e | 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 | """trainable span Head와 분리된 BIO/boundary/constrained decoding."""
from __future__ import annotations
from dataclasses import dataclass
from itertools import product
from typing import Iterable, Sequence
import torch
@dataclass(frozen=True, slots=True)
class DecodedSpan:
sentence_index: int
token_start: int
token_end: int
label: str
score: float
character_start: int | None = None
character_end: int | None = None
@property
def boundary(self) -> tuple[int, int, int]:
return self.sentence_index, self.token_start, self.token_end
@dataclass(frozen=True, slots=True)
class BoundaryDecodeResult:
"""Boundary threshold를 통과한 pair 수와 deterministic 선택 결과."""
spans: tuple[DecodedSpan, ...]
candidate_count: int
class BIOFlatDecoder:
"""BIO argmax를 flat/non-overlap span으로 복원한다."""
def __init__(self, labels: Sequence[str]) -> None:
if not labels or labels[0] != "O":
raise ValueError("BIO labels must start with O")
self.labels = tuple(labels)
def decode(
self,
logits: torch.Tensor,
token_mask: torch.BoolTensor,
) -> list[list[list[DecodedSpan]]]:
if logits.ndim != 4 or token_mask.shape != logits.shape[:3]:
raise ValueError("BIO decoder expects logits [B,S,T,C]")
probabilities = torch.softmax(logits, dim=-1)
ids = probabilities.argmax(dim=-1)
output: list[list[list[DecodedSpan]]] = []
for batch_index in range(logits.shape[0]):
article = []
for sentence_index in range(logits.shape[1]):
spans: list[DecodedSpan] = []
active_label: str | None = None
start = 0
scores: list[float] = []
valid_positions = torch.nonzero(
token_mask[batch_index, sentence_index], as_tuple=False
).flatten().tolist()
for offset in range(len(valid_positions) + 1):
token_index = (
valid_positions[-1] + 1
if offset == len(valid_positions) and valid_positions
else (0 if offset == len(valid_positions) else valid_positions[offset])
)
label = "O" if offset == len(valid_positions) else self.labels[int(ids[batch_index, sentence_index, token_index])]
prefix, _, kind = label.partition("-")
if active_label is not None and (prefix != "I" or kind != active_label):
spans.append(DecodedSpan(sentence_index, start, token_index, active_label, sum(scores) / len(scores)))
active_label, scores = None, []
if prefix == "B" or (prefix == "I" and active_label is None):
active_label, start = kind, token_index
scores = [float(probabilities[batch_index, sentence_index, token_index, int(ids[batch_index, sentence_index, token_index])])]
elif prefix == "I" and kind == active_label:
scores.append(float(probabilities[batch_index, sentence_index, token_index, int(ids[batch_index, sentence_index, token_index])]))
article.append(spans)
output.append(article)
return output
class BoundaryProposalDecoder:
"""독립 start/end probability에서 폭 제한 proposal을 만든다."""
def __init__(self, labels: Sequence[str], threshold: float = 0.5, max_width: int = 96) -> None:
self.labels = tuple(labels)
self.threshold = threshold
self.max_width = max_width
def decode(self, start_logits: torch.Tensor, end_logits: torch.Tensor, mask: torch.BoolTensor) -> list[DecodedSpan]:
return list(self.decode_with_diagnostics(start_logits, end_logits, mask).spans)
def decode_with_diagnostics(
self,
start_logits: torch.Tensor,
end_logits: torch.Tensor,
mask: torch.BoolTensor,
) -> BoundaryDecodeResult:
starts = torch.sigmoid(start_logits).detach()
ends = torch.sigmoid(end_logits).detach()
proposals = []
for sentence in range(start_logits.shape[0]):
for label_index, label in enumerate(self.labels):
start_ids = torch.nonzero((starts[sentence, :, label_index] >= self.threshold) & mask[sentence], as_tuple=False).flatten().tolist()
end_ids = torch.nonzero((ends[sentence, :, label_index] >= self.threshold) & mask[sentence], as_tuple=False).flatten().tolist()
for start, end_inclusive in product(start_ids, end_ids):
if start <= end_inclusive and end_inclusive - start + 1 <= self.max_width:
score = float((starts[sentence, start, label_index] * ends[sentence, end_inclusive, label_index]).sqrt())
proposals.append(DecodedSpan(sentence, start, end_inclusive + 1, label, score))
return BoundaryDecodeResult(tuple(proposals), len(proposals))
class GreedyOneToOneBoundaryDecoder:
"""Confidence 순으로 start/end를 한 번씩만 쓰는 historical-style decoder.
Exact duplicate는 endpoint 1:1 제약으로 제거한다. 서로 다른 endpoint를 사용하는 span의
overlap/nesting은 허용하며, label·문장별 출력 cap으로 무제한 조합을 막는다.
"""
def __init__(
self,
labels: Sequence[str],
threshold: float = 0.5,
max_width: int = 64,
max_outputs_per_label_sentence: int = 4,
) -> None:
if max_width <= 0 or max_outputs_per_label_sentence <= 0:
raise ValueError("Greedy boundary decoder limits must be positive")
self.labels = tuple(labels)
self.threshold = threshold
self.max_width = max_width
self.max_outputs_per_label_sentence = max_outputs_per_label_sentence
def decode(
self,
start_logits: torch.Tensor,
end_logits: torch.Tensor,
mask: torch.BoolTensor,
) -> list[DecodedSpan]:
return list(self.decode_with_diagnostics(start_logits, end_logits, mask).spans)
def decode_with_diagnostics(
self,
start_logits: torch.Tensor,
end_logits: torch.Tensor,
mask: torch.BoolTensor,
) -> BoundaryDecodeResult:
starts = torch.sigmoid(start_logits).detach()
ends = torch.sigmoid(end_logits).detach()
selected: list[DecodedSpan] = []
candidate_count = 0
for sentence in range(start_logits.shape[0]):
for label_index, label in enumerate(self.labels):
start_ids = torch.nonzero(
(starts[sentence, :, label_index] >= self.threshold)
& mask[sentence],
as_tuple=False,
).flatten().tolist()
end_ids = torch.nonzero(
(ends[sentence, :, label_index] >= self.threshold)
& mask[sentence],
as_tuple=False,
).flatten().tolist()
candidates: list[DecodedSpan] = []
for start in start_ids:
for end_inclusive in end_ids:
width = end_inclusive - start + 1
if 1 <= width <= self.max_width:
score = float(
(
starts[sentence, start, label_index]
* ends[sentence, end_inclusive, label_index]
).sqrt()
)
candidates.append(
DecodedSpan(
sentence,
start,
end_inclusive + 1,
label,
score,
)
)
candidate_count += len(candidates)
used_starts: set[int] = set()
used_ends: set[int] = set()
for candidate in sorted(
candidates,
key=lambda item: (
-item.score,
item.token_start,
item.token_end,
),
):
end_inclusive = candidate.token_end - 1
if (
candidate.token_start in used_starts
or end_inclusive in used_ends
):
continue
selected.append(candidate)
used_starts.add(candidate.token_start)
used_ends.add(end_inclusive)
if (
len(used_starts)
>= self.max_outputs_per_label_sentence
):
break
return BoundaryDecodeResult(tuple(selected), candidate_count)
@dataclass(frozen=True, slots=True)
class SemanticConstraintConfig:
threshold: float = 0.5
max_outputs_per_sentence: int = 12
allow_cross_label_exact_boundary: bool = True
allow_nested: bool = True
containment_margin: float = 0.08
overlap_margin: float = 0.05
class SemanticConstraintDecoder:
"""confidence 순으로 prefix/suffix/merged proposition 중복을 제한한다."""
def __init__(self, config: SemanticConstraintConfig | None = None) -> None:
self.config = config or SemanticConstraintConfig()
def decode(self, candidates: Iterable[DecodedSpan]) -> list[DecodedSpan]:
selected: list[DecodedSpan] = []
counts: dict[int, int] = {}
for candidate in sorted(candidates, key=lambda item: (-item.score, item.token_start, item.token_end, item.label)):
if candidate.score < self.config.threshold:
continue
if counts.get(candidate.sentence_index, 0) >= self.config.max_outputs_per_sentence:
continue
if any(self._conflicts(candidate, accepted) for accepted in selected):
continue
selected.append(candidate)
counts[candidate.sentence_index] = counts.get(candidate.sentence_index, 0) + 1
return sorted(selected, key=lambda item: (item.sentence_index, item.token_start, item.token_end, item.label))
def _conflicts(self, candidate: DecodedSpan, accepted: DecodedSpan) -> bool:
if candidate.sentence_index != accepted.sentence_index:
return False
if candidate.boundary == accepted.boundary:
return not (
self.config.allow_cross_label_exact_boundary and candidate.label != accepted.label
)
overlap = max(candidate.token_start, accepted.token_start) < min(candidate.token_end, accepted.token_end)
if not overlap:
return False
candidate_contains = candidate.token_start <= accepted.token_start and accepted.token_end <= candidate.token_end
accepted_contains = accepted.token_start <= candidate.token_start and candidate.token_end <= accepted.token_end
if candidate_contains or accepted_contains:
if self.config.allow_nested and candidate.label != accepted.label:
return False
return candidate.score + self.config.containment_margin < accepted.score
return candidate.score <= accepted.score + self.config.overlap_margin
def attach_character_offsets(
spans: Iterable[DecodedSpan],
token_offsets: torch.LongTensor,
sentence_offsets: torch.LongTensor,
) -> list[DecodedSpan]:
output = []
for span in spans:
local_start = int(token_offsets[span.sentence_index, span.token_start, 0])
local_end = int(token_offsets[span.sentence_index, span.token_end - 1, 1])
article_start = int(sentence_offsets[span.sentence_index, 0]) + local_start
article_end = int(sentence_offsets[span.sentence_index, 0]) + local_end
output.append(
DecodedSpan(
span.sentence_index,
span.token_start,
span.token_end,
span.label,
span.score,
article_start,
article_end,
)
)
return output
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