File size: 7,976 Bytes
468c4c2 | 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 | """Encoder fine-tuning for multi-label ATT&CK technique classification.
A plain PyTorch loop rather than ``Trainer``: this repo's whole claim is that
its numbers are reproducible, and a visible loop with an explicit loss, an
explicit schedule and an explicit best-checkpoint rule is easier to audit than
a config dict spread across a framework whose argument names drift between
releases.
Class imbalance is handled with per-class ``pos_weight`` in the BCE loss. 79%
of sentences carry no label at all and the rarest retained technique has ~20
examples, so unweighted BCE converges to predicting nothing.
"""
from __future__ import annotations
import json
import math
from pathlib import Path
import numpy as np
import torch
from torch.utils.data import DataLoader, Dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup
from . import config, evaluate
def device() -> torch.device:
return torch.device("cuda" if torch.cuda.is_available() else "cpu")
class SentenceDataset(Dataset):
def __init__(self, records: list[dict], labels: list[str], tokenizer, max_length: int):
self.texts = [r["sentence"] for r in records]
self.Y = evaluate.to_matrix(records, labels).astype(np.float32)
self.tok = tokenizer
self.max_length = max_length
def __len__(self) -> int:
return len(self.texts)
def __getitem__(self, i: int) -> dict:
enc = self.tok(
self.texts[i],
truncation=True,
max_length=self.max_length,
padding="max_length",
return_tensors="pt",
)
item = {k: v.squeeze(0) for k, v in enc.items()}
item["labels"] = torch.from_numpy(self.Y[i])
return item
def compute_pos_weight(Y: np.ndarray, cap: float = 50.0) -> torch.Tensor:
"""``(negatives / positives)`` per class, capped so rare classes don't explode."""
pos = Y.sum(axis=0)
neg = Y.shape[0] - pos
with np.errstate(divide="ignore", invalid="ignore"):
w = np.where(pos > 0, neg / np.maximum(pos, 1), 1.0)
return torch.tensor(np.clip(w, 1.0, cap), dtype=torch.float32)
@torch.no_grad()
def predict_scores(model, loader, dev, amp: bool = True) -> np.ndarray:
model.eval()
out = []
for batch in loader:
labels = batch.pop("labels", None)
batch = {k: v.to(dev) for k, v in batch.items()}
with torch.autocast("cuda", dtype=torch.bfloat16,
enabled=amp and dev.type == "cuda"):
logits = model(**batch).logits
out.append(torch.sigmoid(logits.float()).cpu().numpy())
return np.concatenate(out, axis=0)
def train(
model_key: str,
scheme: str,
train_records: list[dict],
dev_records: list[dict],
labels: list[str],
output_dir: Path,
epochs: int = config.EPOCHS,
seed: int = config.SEED,
) -> dict:
torch.manual_seed(seed)
np.random.seed(seed)
dev_ = device()
model_name = config.BASE_MODELS[model_key]
amp = model_key not in config.FP32_ONLY_MODELS
print(f"\n base model : {model_name}")
print(f" device : {dev_} ({torch.cuda.get_device_name(0) if dev_.type == 'cuda' else 'cpu'})")
print(f" precision : {'bf16 autocast' if amp else 'fp32 (autocast disabled for this model)'}")
tok = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(
model_name,
num_labels=len(labels),
problem_type="multi_label_classification",
id2label={i: l for i, l in enumerate(labels)},
label2id={l: i for i, l in enumerate(labels)},
).to(dev_)
ds_tr = SentenceDataset(train_records, labels, tok, config.MAX_LENGTH)
ds_dv = SentenceDataset(dev_records, labels, tok, config.MAX_LENGTH)
dl_tr = DataLoader(ds_tr, batch_size=config.BATCH_SIZE, shuffle=True, drop_last=False)
dl_dv = DataLoader(ds_dv, batch_size=config.BATCH_SIZE * 2, shuffle=False)
pos_weight = compute_pos_weight(ds_tr.Y).to(dev_)
loss_fn = torch.nn.BCEWithLogitsLoss(pos_weight=pos_weight)
decay = [p for n, p in model.named_parameters() if not any(x in n for x in ("bias", "LayerNorm.weight", "norm.weight"))]
no_decay = [p for n, p in model.named_parameters() if any(x in n for x in ("bias", "LayerNorm.weight", "norm.weight"))]
optim = torch.optim.AdamW(
[{"params": decay, "weight_decay": config.WEIGHT_DECAY},
{"params": no_decay, "weight_decay": 0.0}],
lr=config.LEARNING_RATE,
)
steps_per_epoch = math.ceil(len(dl_tr) / config.GRAD_ACCUM)
total_steps = steps_per_epoch * epochs
sched = get_linear_schedule_with_warmup(
optim, int(total_steps * config.WARMUP_RATIO), total_steps)
Ydv = ds_dv.Y.astype(np.int8)
best = {"macro_f1": -1.0, "epoch": -1, "threshold": 0.5}
output_dir.mkdir(parents=True, exist_ok=True)
for epoch in range(1, epochs + 1):
model.train()
running, nb = 0.0, 0
optim.zero_grad(set_to_none=True)
for step, batch in enumerate(dl_tr):
y = batch.pop("labels").to(dev_)
batch = {k: v.to(dev_) for k, v in batch.items()}
with torch.autocast("cuda", dtype=torch.bfloat16,
enabled=amp and dev_.type == "cuda"):
logits = model(**batch).logits
loss = loss_fn(logits.float(), y) / config.GRAD_ACCUM
# Fail loudly. A silent nan run previously trained for six full
# epochs, saved a checkpoint and reported macro-F1 0.0000 as though
# it were a legitimate result.
if not torch.isfinite(loss):
raise RuntimeError(
f"non-finite loss at epoch {epoch} step {step} for "
f"{model_name!r} (amp={'bf16' if amp else 'off'}). Training "
f"aborted rather than reporting a meaningless score. If this "
f"model is new, add it to config.FP32_ONLY_MODELS."
)
loss.backward()
running += loss.item() * config.GRAD_ACCUM
nb += 1
if (step + 1) % config.GRAD_ACCUM == 0 or step + 1 == len(dl_tr):
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optim.step()
sched.step()
optim.zero_grad(set_to_none=True)
scores = predict_scores(model, dl_dv, dev_, amp=amp)
thr, dev_macro = evaluate.tune_global_threshold(Ydv, scores)
print(f" epoch {epoch}/{epochs} loss={running / max(nb, 1):.4f} "
f"dev macro-F1={dev_macro:.4f} (t={thr})")
if dev_macro > best["macro_f1"]:
best = {"macro_f1": dev_macro, "epoch": epoch, "threshold": thr}
model.save_pretrained(output_dir)
tok.save_pretrained(output_dir)
(output_dir / "labels.json").write_text(
json.dumps(labels, indent=2), encoding="utf-8")
(output_dir / "training_meta.json").write_text(json.dumps({
"base_model": model_name,
"split_scheme": scheme,
"epochs": epochs,
"best_epoch": best["epoch"],
"best_dev_macro_f1": round(best["macro_f1"], 4),
"best_dev_global_threshold": best["threshold"],
"max_length": config.MAX_LENGTH,
"batch_size": config.BATCH_SIZE,
"grad_accum": config.GRAD_ACCUM,
"learning_rate": config.LEARNING_RATE,
"seed": seed,
"n_train": len(train_records),
"n_dev": len(dev_records),
"n_labels": len(labels),
}, indent=2), encoding="utf-8")
print(f" best: epoch {best['epoch']}, dev macro-F1 {best['macro_f1']:.4f}")
return best
def load_for_inference(model_dir: str | Path):
tok = AutoTokenizer.from_pretrained(str(model_dir))
model = AutoModelForSequenceClassification.from_pretrained(str(model_dir))
model.eval()
return model, tok
|