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808f43d | 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 | """Output classifier: streaming harmfulness head, per paper §3.4.
A value head scores harmfulness of the FULL sequence from every prefix, so a
streaming guard can cut generation mid-stream. Loss (paper Eq. 1):
L = lam * L_NTP(t_1:T) + sum_{T'=1..T} BCE(y, p(y=1 | t_1:T'))
p = (1-omega) * sigmoid(z_{T'}) + omega * max_{tau<=T'} sigmoid(z_tau)
omega ramps linearly 0 -> 1, reaching 1 at 3/4 of training (optimization
stability). lam and exact LR are NOT published in the paper; defaults here are
our choice and are flagged in the README.
Run:
python scripts/train_output_classifier.py \
--base_model Qwen/Qwen2.5-1.5B \
--train data/train.jsonl --val data/val.jsonl \
--hub_model_id aytsrndl/cc-repro-output-classifier
"""
import argparse, json, math, os, torch, torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset
from transformers import (AutoModelForCausalLM, AutoTokenizer, Trainer,
TrainingArguments, TrainerCallback)
class StreamClassifier(nn.Module):
"""Causal LM + linear score head; forward returns the paper's loss."""
def __init__(self, model_name):
super().__init__()
self.lm = AutoModelForCausalLM.from_pretrained(model_name)
self.score_head = nn.Linear(self.lm.config.hidden_size, 1)
self.omega = 0.0 # updated each step by OmegaRamp
self.lam = None # set from args in the script
def forward(self, input_ids, attention_mask, ntp_labels, harm_labels):
out = self.lm(input_ids=input_ids, attention_mask=attention_mask,
labels=ntp_labels, output_hidden_states=True)
h = out.hidden_states[-1] # (B, T, H) last-layer states
z = self.score_head(h.float()).squeeze(-1) # (B, T) harmfulness logits
p_tok = torch.sigmoid(z)
cummax, _ = torch.cummax(p_tok, dim=1) # max_{tau<=T'} sigma(z_tau)
p = (1 - self.omega) * p_tok + self.omega * cummax
y = harm_labels.unsqueeze(1).expand_as(p).float()
bce = F.binary_cross_entropy(p, y, reduction="none")
harm_loss = (bce * attention_mask).sum() / attention_mask.sum().clamp(min=1)
ntp_loss = out.loss if out.loss is not None else torch.tensor(0.0, device=z.device)
loss = self.lam * ntp_loss + harm_loss
return {"loss": loss, "harm_loss": harm_loss.detach(), "ntp_loss": ntp_loss.detach()}
class PairDataset(Dataset):
"""Conversation = template(user)+assistant; label = 1 if harmful."""
def __init__(self, path, tokenizer, max_length):
self.rows = [json.loads(l) for l in open(path)]
self.tok = tokenizer
self.max_length = max_length
def __len__(self):
return len(self.rows)
def __getitem__(self, i):
r = self.rows[i]
text = f"Request: {r['user']}\n\nAssistant response: {r['assistant']}"
ids = self.tok(text, truncation=True, max_length=self.max_length)
return {"input_ids": ids["input_ids"], "harm": float(r["label"])}
def collate(batch, pad_id):
maxlen = max(len(b["input_ids"]) for b in batch)
input_ids, attn, ntp, harm = [], [], [], []
for b in batch:
n, pad = len(b["input_ids"]), maxlen - len(b["input_ids"])
input_ids.append(b["input_ids"] + [pad_id] * pad)
attn.append([1] * n + [0] * pad)
ntp.append(b["input_ids"] + [-100] * pad) # NTP regularizer on real tokens
harm.append(b["harm"])
return {"input_ids": torch.tensor(input_ids), "attention_mask": torch.tensor(attn),
"ntp_labels": torch.tensor(ntp), "harm_labels": torch.tensor(harm)}
class OmegaRamp(TrainerCallback):
"""omega: 0 -> 1 linearly, reaching 1 at 3/4 of training."""
def __init__(self, model, total_steps):
self.model, self.total = model, total_steps
def on_step_end(self, args, state, control, **kw):
frac = state.global_step / max(1, math.floor(0.75 * self.total))
self.model.omega = min(1.0, frac)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--base_model", default="Qwen/Qwen2.5-1.5B")
ap.add_argument("--train", default="data/train.jsonl")
ap.add_argument("--val", default="data/val.jsonl")
ap.add_argument("--max_length", type=int, default=1024)
ap.add_argument("--lr", type=float, default=1e-5, help="NOT published in the paper; our default")
ap.add_argument("--lam", type=float, default=0.5, help="NTP regularizer weight; NOT published")
ap.add_argument("--epochs", type=float, default=1.0)
ap.add_argument("--per_device_bs", type=int, default=8)
ap.add_argument("--grad_accum", type=int, default=4)
ap.add_argument("--hub_model_id", required=True)
args = ap.parse_args()
tok = AutoTokenizer.from_pretrained(args.base_model)
if tok.pad_token is None:
tok.pad_token = tok.eos_token
model = StreamClassifier(args.base_model)
model.lam = args.lam
train_ds = PairDataset(args.train, tok, args.max_length)
val_ds = PairDataset(args.val, tok, args.max_length)
steps = math.ceil(len(train_ds) / (args.per_device_bs * args.grad_accum) * args.epochs)
targs = TrainingArguments(
output_dir="out/output-classifier",
hub_model_id=args.hub_model_id,
push_to_hub=False, # custom class: saved manually below
num_train_epochs=args.epochs,
learning_rate=args.lr,
per_device_train_batch_size=args.per_device_bs,
per_device_eval_batch_size=args.per_device_bs,
gradient_accumulation_steps=args.grad_accum,
gradient_checkpointing=True,
bf16=True,
eval_strategy="steps",
eval_steps=100,
logging_steps=10,
save_strategy="no",
remove_unused_columns=False, # our collate emits nonstandard keys
report_to=["trackio"],
seed=7,
)
trainer = Trainer(model=model, args=targs, train_dataset=train_ds, eval_dataset=val_ds,
data_collator=lambda b: collate(b, tok.pad_token_id),
callbacks=[OmegaRamp(model, steps)])
trainer.train()
metrics = trainer.evaluate()
print("eval:", metrics)
# Persist: LM weights + score head + loader, then push (custom class can't be
# auto-loaded by AutoModel, so we ship the pieces).
os.makedirs("out/final", exist_ok=True)
model.lm.save_pretrained("out/final")
tok.save_pretrained("out/final")
torch.save(model.score_head.state_dict(), "out/final/score_head.pt")
with open("out/final/loss_config.json", "w") as f:
json.dump({"lam": args.lam, "omega_end": 1.0, "paper": "arXiv:2501.18837 §3.4"}, f)
from huggingface_hub import HfApi
api = HfApi()
api.create_repo(args.hub_model_id, exist_ok=True)
api.upload_folder(folder_path="out/final", repo_id=args.hub_model_id)
print(f"pushed -> https://huggingface.co/{args.hub_model_id}")
if __name__ == "__main__":
main()
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