File size: 9,065 Bytes
97c39f2 | 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 | """Guarded SFT for TinyLiquid: chat+SOP+forensic mix with TinyStories retention.
Differs from train_sft.py:
* supports raw full-loss retention examples ({"raw": text})
* evals BOTH masked SFT holdout loss AND TinyStories val PPL (coherence guard)
* keeps best.pt (min sft_val_loss while val_ppl < 90) and best_ppl.pt (min ppl)
Usage:
.venv/bin/python train/train_sft2.py --base ckpt/nlp --data data/sft_mix_v2.jsonl \
--ckpt ckpt/v2 --epochs 3 --lr 2e-5
"""
import argparse, json, math, random, time
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
from model.config import TinyLiquidConfig, CONFIGS
from model.utils import latest_ckpt
from model.tiny_liquid import TinyLiquid
from data.tokenizer import load_tokenizer
USER_T, ASST_T, EOT_T = "<|user|>", "<|assistant|>", "<|endoftext|>"
PERSONA_T = {"analyst": "<|analyst|>", "skeptic": "<|skeptic|>", "spock": "<|analyst|>", "none": ""}
P_IDS = {"analyst": 1, "skeptic": 2, "spock": 1, "none": 0}
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument("--base", default="ckpt/nlp")
ap.add_argument("--resume", default="", help="resume from latest ckpt in this dir")
ap.add_argument("--data", default="data/sft_mix_v2.jsonl")
ap.add_argument("--tok", default="data/tokenizer.json")
ap.add_argument("--ckpt", default="ckpt/v2")
ap.add_argument("--val-bin", default="data/valid.bin")
ap.add_argument("--epochs", type=int, default=3)
ap.add_argument("--batch", type=int, default=8)
ap.add_argument("--seq", type=int, default=256)
ap.add_argument("--lr", type=float, default=2e-5)
ap.add_argument("--eval-every", type=int, default=25)
ap.add_argument("--log-every", type=int, default=25)
ap.add_argument("--ppl-guard", type=float, default=90.0)
ap.add_argument("--val-batches", type=int, default=2)
ap.add_argument("--seed", type=int, default=7)
ap.add_argument("--threads", type=int, default=8)
return ap.parse_args()
def tokenize_example(tok, ex, seq, u_id, a_id, eot_id):
if "raw" in ex:
ids = tok.encode(ex["raw"]).ids + [eot_id]
x = torch.tensor(ids[:-1], dtype=torch.long)
y = torch.tensor(ids[1:], dtype=torch.long)
mask = torch.ones_like(y, dtype=torch.bool)
return x[:seq], y[:seq], mask[:seq], 0
persona = PERSONA_T.get(ex.get("persona", "analyst"), PERSONA_T["analyst"])
p_id = P_IDS.get(ex.get("persona"), 1)
user_ids = tok.encode(ex["user"]).ids
asst_ids = tok.encode(ex["assistant"]).ids
if persona:
p_ids_ = tok.encode(persona).ids
ids = p_ids_ + [u_id] + user_ids + [a_id] + asst_ids + [eot_id]
asst_start = len(p_ids_) + 1 + len(user_ids) + 1
else:
ids = [u_id] + user_ids + [a_id] + asst_ids + [eot_id]
asst_start = 1 + len(user_ids) + 1
if len(ids) > seq:
ids = ids[:seq - 1] + [eot_id]
x = torch.tensor(ids[:-1], dtype=torch.long)
y = torch.tensor(ids[1:], dtype=torch.long)
mask = torch.zeros_like(y, dtype=torch.bool)
mask[asst_start - 1:] = True
return x[:seq], y[:seq], mask[:seq], p_id
def collate(items, seq):
xs, ys, ms, ps = [], [], [], []
for x, y, m, p in items:
xs.append(F.pad(x, (0, seq - x.shape[0]), value=0))
ys.append(F.pad(y, (0, seq - y.shape[0]), value=0))
ms.append(F.pad(m, (0, seq - m.shape[0]), value=False))
ps.append(p)
return torch.stack(xs), torch.stack(ys), torch.stack(ms), torch.tensor(ps, dtype=torch.long)
@torch.no_grad()
def val_ppl(model, val_bin, batch=4, seq=64, n_batches=2, seed=0):
mm = np.memmap(val_bin, dtype=np.uint16, mode="r")
total, cnt = 0.0, 0
rng = np.random.RandomState(seed)
n = (len(mm) - 1) // seq
for b in range(n_batches):
s = int(rng.randint(0, n - batch))
buf = torch.stack([torch.from_numpy(mm[s * seq + i * seq: s * seq + i * seq + seq].astype(np.int64))
for i in range(batch)])
x, y = buf[:, :-1], buf[:, 1:]
loss = F.cross_entropy(model(x).reshape(-1, 8192), y.reshape(-1))
total += loss.item() * y.numel(); cnt += y.numel()
return float(np.exp(total / cnt))
def main():
args = parse_args()
torch.set_num_threads(args.threads)
torch.manual_seed(args.seed); random.seed(args.seed)
rng = random.Random(args.seed)
tok = load_tokenizer(args.tok)
u_id, a_id, eot_id = tok.token_to_id(USER_T), tok.token_to_id(ASST_T), tok.token_to_id(EOT_T)
assert None not in (u_id, a_id, eot_id)
exs = [json.loads(l) for l in open(args.data, encoding="utf-8") if l.strip()]
rng.shuffle(exs)
n_eval = min(128, max(8, len(exs) // 12))
eval_ex, train_ex = exs[:n_eval], exs[n_eval:]
print(f"train {len(train_ex)} eval {len(eval_ex)}", flush=True)
base_path = latest_ckpt(args.resume or args.base)
base = torch.load(base_path, map_location="cpu")
base_cfg = base.get("config") or CONFIGS["tiny10m"]
cfg = TinyLiquidConfig(vocab_size=tok.get_vocab_size(),
**{k: v for k, v in base_cfg.items() if k != "vocab_size"})
model = TinyLiquid(cfg)
model.load_state_dict(base["model"])
opt = torch.optim.AdamW(model.parameters(), lr=args.lr, betas=(0.9, 0.95), weight_decay=0.05)
print(f"loaded base {base_path.name}", flush=True)
out = Path(args.ckpt); out.mkdir(parents=True, exist_ok=True)
steps_per_epoch = max(1, len(train_ex) // args.batch)
total_steps = steps_per_epoch * args.epochs
def make_items(exs_):
return [tokenize_example(tok, e, args.seq, u_id, a_id, eot_id) for e in exs_]
eval_items = make_items(eval_ex)
def run_eval():
model.eval()
total, n = 0.0, 0
for i in range(0, len(eval_items), args.batch):
x, y, m, p = collate(eval_items[i:i + args.batch], args.seq)
with torch.no_grad():
logits = model(x, persona_ids=p).reshape(-1, 8192)
loss = F.cross_entropy(logits, y.reshape(-1), reduction="none")
loss = (loss * m.reshape(-1)).sum() / m.sum()
total += loss.item() * m.sum().item(); n += m.sum().item()
sft_vl = total / n
ppl = val_ppl(model, args.val_bin, n_batches=args.val_batches)
model.train()
return sft_vl, ppl
best_guard, best_ppl = float("inf"), float("inf")
t0 = time.time(); step = 0
for ep in range(args.epochs):
rng.shuffle(train_ex)
items = make_items(train_ex)
for i in range(0, len(items) - len(items) % args.batch, args.batch):
step += 1
x, y, m, p = collate(items[i:i + args.batch], args.seq)
opt.zero_grad(set_to_none=True)
logits = model(x, persona_ids=p).reshape(-1, 8192)
loss = F.cross_entropy(logits, y.reshape(-1), reduction="none")
loss = (loss * m.reshape(-1)).sum() / m.sum()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
opt.step()
if step % args.log_every == 0:
print(f"step {step}/{total_steps} loss {loss.item():.4f} "
f"{args.batch*args.seq*args.log_every/(time.time()-t0):.0f} tok/s", flush=True)
t0 = time.time()
if step % args.eval_every == 0:
sft_vl, ppl = run_eval()
try:
sp = tok.encode("<|analyst|><|user|>Find discrepancies between: Account A: The meeting ended at 11am. Account B: The meeting ended at noon.<|assistant|>").ids
with torch.no_grad():
sout = tok.decode(model.generate(tok, sp, persona_id=1, max_new=50, temperature=0.35,
top_k=20, repetition_penalty=1.25,
no_repeat_ngram_size=4)[len(sp):]).replace("\n", " ").strip()[:180]
print(f" sample: {sout}", flush=True)
except Exception:
pass
tag = ""
if ppl < args.ppl_guard and sft_vl < best_guard:
best_guard = sft_vl
torch.save({"model": model.state_dict(), "step": step, "config": cfg.__dict__}, out / "best.pt")
tag += " [new best]"
if ppl < best_ppl:
best_ppl = ppl
torch.save({"model": model.state_dict(), "step": step, "config": cfg.__dict__}, out / "best_ppl.pt")
tag += " [best ppl]"
torch.save({"model": model.state_dict(), "step": step, "config": cfg.__dict__}, out / f"model_{step}.pt")
print(f" [eval {step}] sft_val_loss {sft_vl:.4f} val_ppl {ppl:.2f}{tag}", flush=True)
torch.save({"model": model.state_dict(), "step": step, "config": cfg.__dict__}, out / "model_final.pt")
print(f"done -> {out} best_guard_sft_loss={best_guard:.4f} best_ppl={best_ppl:.2f}", flush=True)
if __name__ == "__main__":
main()
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