File size: 7,246 Bytes
e69b72a | 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 | """Zero-shot task adapters + metrics for the fetched eval sets.
Two task shapes, both scored purely by LM likelihood:
* Minimal pairs (BLiMP): accuracy = fraction where the grammatical sentence gets
a higher total log-probability than the ungrammatical one.
* Multiple choice (COPA / XCOPA): accuracy = fraction where the gold choice gets
the highest (length-normalised) continuation log-probability.
Loaders read the immutable raw parquet directly (pyarrow), so they do not depend
on dataset loading scripts.
"""
from __future__ import annotations
import glob
from dataclasses import dataclass, field
from pathlib import Path
import torch
from strata.eval.scoring import encode_with_continuation, score_sequences
# Cause/effect connectors for building COPA/XCOPA prompts, per language.
_CONNECTORS = {
"en": {"cause": "because", "effect": "so"},
"zh": {"cause": "因为", "effect": "所以"},
}
@dataclass(frozen=True, slots=True)
class MinimalPairExample:
good: str
bad: str
tag: str = ""
@dataclass(frozen=True, slots=True)
class MultipleChoiceExample:
context: str
choices: tuple[str, ...]
gold: int
@dataclass(slots=True)
class TaskResult:
task: str
metric: str
value: float
n_examples: int
subscores: dict[str, float] = field(default_factory=dict)
def to_dict(self) -> dict[str, object]:
return {
"task": self.task,
"metric": self.metric,
"value": self.value,
"n_examples": self.n_examples,
"subscores": self.subscores,
}
# ---------------------------------------------------------------------------
# Evaluation drivers
# ---------------------------------------------------------------------------
def evaluate_minimal_pairs(
model,
tokenizer,
examples: list[MinimalPairExample],
*,
task: str,
device: torch.device,
pad_id: int,
batch_size: int = 16,
precision: str = "bf16",
predicate_memory_intervention: str = "none",
predicate_memory_residual_scale: float | None = None,
graph_object_residual_scale: float | None = None,
) -> TaskResult:
"""Total-log-prob comparison of good vs. bad sentences."""
if not examples:
raise ValueError(f"{task}: no examples to evaluate")
seqs: list[list[int]] = []
ctx: list[int] = []
for ex in examples:
for text in (ex.good, ex.bad):
ids = list(tokenizer.encode(text, add_bos=True).input_ids)
seqs.append(ids)
ctx.append(1) # score every real token (position 0 is BOS)
scores = score_sequences(
model, seqs, ctx, device=device, pad_id=pad_id, batch_size=batch_size, precision=precision,
predicate_memory_intervention=predicate_memory_intervention,
predicate_memory_residual_scale=predicate_memory_residual_scale,
graph_object_residual_scale=graph_object_residual_scale,
)
correct = 0
per_tag: dict[str, list[int]] = {}
for i, ex in enumerate(examples):
good_score, bad_score = scores[2 * i], scores[2 * i + 1]
hit = int(good_score > bad_score)
correct += hit
if ex.tag:
per_tag.setdefault(ex.tag, []).append(hit)
subscores = {tag: sum(v) / len(v) for tag, v in sorted(per_tag.items())}
return TaskResult(task, "accuracy", correct / len(examples), len(examples), subscores)
def evaluate_multiple_choice(
model,
tokenizer,
examples: list[MultipleChoiceExample],
*,
task: str,
device: torch.device,
pad_id: int,
batch_size: int = 16,
precision: str = "bf16",
length_normalize: bool = True,
predicate_memory_intervention: str = "none",
predicate_memory_residual_scale: float | None = None,
graph_object_residual_scale: float | None = None,
) -> TaskResult:
"""Pick the choice with the highest (length-normalised) continuation log-prob."""
if not examples:
raise ValueError(f"{task}: no examples to evaluate")
seqs: list[list[int]] = []
ctx: list[int] = []
spans: list[tuple[int, int]] = [] # (start, end) index into seqs per example
for ex in examples:
start = len(seqs)
for choice in ex.choices:
ids, context_len = encode_with_continuation(tokenizer, ex.context, choice)
seqs.append(ids)
ctx.append(context_len)
spans.append((start, len(seqs)))
scores = score_sequences(
model, seqs, ctx, device=device, pad_id=pad_id, batch_size=batch_size,
precision=precision, length_normalize=length_normalize,
predicate_memory_intervention=predicate_memory_intervention,
predicate_memory_residual_scale=predicate_memory_residual_scale,
graph_object_residual_scale=graph_object_residual_scale,
)
correct = 0
for ex, (start, end) in zip(examples, spans):
choice_scores = scores[start:end]
pred = max(range(len(choice_scores)), key=lambda k: choice_scores[k])
correct += int(pred == ex.gold)
return TaskResult(task, "accuracy", correct / len(examples), len(examples))
# ---------------------------------------------------------------------------
# Loaders (read raw parquet directly)
# ---------------------------------------------------------------------------
def _read_parquet_rows(path: str) -> list[dict]:
import pyarrow.parquet as pq
return pq.ParquetFile(path).read().to_pylist()
def load_blimp(raw_dir: Path, *, configs: list[str] | None = None, max_per_config: int | None = None) -> list[MinimalPairExample]:
examples: list[MinimalPairExample] = []
paths = sorted(glob.glob(str(raw_dir / "*" / "*.parquet")))
if not paths:
raise FileNotFoundError(f"no BLiMP parquet under {raw_dir}")
for path in paths:
uid = Path(path).parent.name
if configs is not None and uid not in configs:
continue
rows = _read_parquet_rows(path)
if max_per_config is not None:
rows = rows[:max_per_config]
for row in rows:
examples.append(MinimalPairExample(good=row["sentence_good"], bad=row["sentence_bad"], tag=uid))
return examples
def _copa_context(premise: str, question: str, language: str) -> str:
premise = premise.rstrip().rstrip(".。").strip()
connectors = _CONNECTORS.get(language, _CONNECTORS["en"])
connector = connectors.get(str(question), connectors["effect"])
joiner = "" if language == "zh" else " "
return f"{premise}{joiner}{connector}{joiner}"
def load_copa(parquet_path: str, *, language: str = "en", max_examples: int | None = None) -> list[MultipleChoiceExample]:
rows = _read_parquet_rows(parquet_path)
rows = [r for r in rows if int(r.get("label", -1)) in (0, 1)] # drop unlabeled test rows
if max_examples is not None:
rows = rows[:max_examples]
examples: list[MultipleChoiceExample] = []
for row in rows:
context = _copa_context(row["premise"], row["question"], language)
examples.append(
MultipleChoiceExample(
context=context,
choices=(str(row["choice1"]), str(row["choice2"])),
gold=int(row["label"]),
)
)
return examples
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