File size: 10,272 Bytes
c43c544 | 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 | """mDeBERTa-v3 cross-encoder: fine-tuned on the ce split (disjoint from stack/val, so its scores are
leak-free features downstream). Resumable training; sharded multi-GPU inference."""
import math
import os
import subprocess
import sys
import time
import numpy as np
import pandas as pd
import torch
from common import Progress, get_logger, gpu_patient, load_json, save_json, save_npy, save_parquet, shard_path
from config import CFG, PATHS, WORK
from prep import load_prep, load_splits
from retrieve_dense import n_shards
log = get_logger("ce")
CE = WORK / "ce"
os.environ.setdefault("HF_HOME", str(PATHS["hf_cache"]))
def _texts(split, side):
d = load_prep(split, side, ["raw_name", "raw_addr"])
return (d.raw_name + " | " + d.raw_addr).values
def _amp_dtype():
"""bf16 on Ampere+ (A100/L4/H100, same as the server run); fp16 + loss scaling on T4/V100."""
return torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
def _tok_model():
from transformers import AutoModelForSequenceClassification, AutoTokenizer
return AutoTokenizer, AutoModelForSequenceClassification
def build_pairs():
path = CE / "pairs.parquet"
if path.exists():
return pd.read_parquet(path)
sp = load_splits()
K = load_json(WORK / "reports" / "k_choice.json")["K"]
parts = []
for i in range(n_shards("train")):
d = pd.read_parquet(shard_path("block", "train", i), columns=["q", "c", "y", "rrf_rank"])
parts.append(d[np.isin(d.q.values, sp["g_ce"]) & (d.rrf_rank < K)])
d = pd.concat(parts, ignore_index=True)
rng = np.random.default_rng(CFG["seed"])
pos = d[d.y == 1]
neg = d[d.y == 0].sort_values(["q", "rrf_rank"])
hard = neg.groupby("q").head(CFG["ce_neg_hard"])
rest = neg.drop(hard.index)
rnd = rest.assign(r=rng.random(len(rest))).sort_values(["q", "r"]).groupby("q").head(CFG["ce_neg_rand"])
pairs = pd.concat([pos, hard, rnd[pos.columns]], ignore_index=True)[["q", "c", "y"]]
pairs = pairs.sample(frac=1.0, random_state=CFG["seed"]).reset_index(drop=True)
if len(pairs) > CFG["ce_max_pairs"]: # shared GPU budget: a uniform sample keeps the pos/neg mix
pairs = pairs.head(CFG["ce_max_pairs"])
pairs["dev"] = ((pairs.q.values.astype(np.int64) * 2654435761 % 2 ** 32) % 30 == 0)
save_parquet(pairs, path)
log.info(f"ce pairs: {len(pairs)} (pos {int(pairs.y.sum())}), dev {int(pairs.dev.sum())}")
return pairs
def _lr_lambda(total, warm):
return lambda s: min(1.0, (s + 1) / max(1, warm)) * max(0.0, (total - s) / max(1, total - warm))
def run_ce_train():
final = CE / "final"
if (final / "config.json").exists():
return
AutoTokenizer, AutoModel = _tok_model()
pairs = build_pairs()
tr, dv = pairs[~pairs.dev].reset_index(drop=True), pairs[pairs.dev].reset_index(drop=True)
ta, tb = _texts("train", "s1"), _texts("train", "pool")
tok = AutoTokenizer.from_pretrained(CFG["ce_model"])
# transformers>=5 loads the stored dtype (fp16 for mDeBERTa) by default; fp16 weights + AdamW -> NaN after 1 step
model = AutoModel.from_pretrained(CFG["ce_model"], num_labels=1, dtype=torch.float32)
model = gpu_patient(lambda: model.cuda(), log, "load cross-encoder")
B = CFG["ce_batch"]
spe = math.ceil(len(tr) / B)
total = spe * CFG["ce_epochs"]
if CFG["ce_max_steps"]:
total = min(total, CFG["ce_max_steps"])
opt = torch.optim.AdamW(model.parameters(), lr=CFG["ce_lr"], weight_decay=0.01)
sched = torch.optim.lr_scheduler.LambdaLR(opt, _lr_lambda(total, int(0.05 * total)))
ck = CE / "ckpt.pt"
amp = _amp_dtype()
scaler = torch.amp.GradScaler("cuda", enabled=amp == torch.float16)
log.info(f"cross-encoder mixed precision: {amp}")
step = 0
if ck.exists():
st = torch.load(ck, map_location="cuda", weights_only=False)
model.load_state_dict(st["model"]); opt.load_state_dict(st["opt"]); sched.load_state_dict(st["sched"])
if st.get("scaler") and scaler.is_enabled():
scaler.load_state_dict(st["scaler"])
step = st["step"]
log.info(f"resumed cross-encoder from step {step}/{total}")
lossf = torch.nn.BCEWithLogitsLoss()
model.train()
t0, run = time.time(), []
perms = {}
while step < total:
e, b = divmod(step, spe)
if e not in perms:
perms = {e: np.random.default_rng(CFG["seed"] + e).permutation(len(tr))}
idx = perms[e][b * B:(b + 1) * B]
q, c, y = tr.q.values[idx], tr.c.values[idx], tr.y.values[idx]
swap = ((q.astype(np.int64) + c + e) % 2) == 1
a, bb = ta[q], tb[c]
x1, x2 = np.where(swap, bb, a).tolist(), np.where(swap, a, bb).tolist()
try:
enc = tok(x1, x2, truncation=True, max_length=CFG["ce_max_len"], padding=True, return_tensors="pt").to("cuda")
with torch.autocast("cuda", dtype=amp):
logits = model(**enc).logits.squeeze(-1)
loss = lossf(logits.float(), torch.tensor(y, dtype=torch.float32, device="cuda"))
scaler.scale(loss).backward()
except torch.cuda.OutOfMemoryError: # shared GPU filled up: drop this attempt, wait, redo the step
opt.zero_grad(set_to_none=True)
enc = logits = loss = None
torch.cuda.empty_cache()
log.warning(f"OOM at step {step}; waiting 60s for the shared GPU")
time.sleep(60)
continue
scaler.unscale_(opt)
gn = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
if not torch.isfinite(gn) and not scaler.is_enabled():
raise RuntimeError(f"non-finite gradient at step {step}; refusing to save a corrupted checkpoint")
scaler.step(opt) # with fp16 an overflowing step is skipped and the scale lowered
scaler.update(); sched.step(); opt.zero_grad(set_to_none=True)
if not all(torch.isfinite(p_).all() for p_ in (model.classifier.weight,)):
raise RuntimeError(f"weights became non-finite at step {step}")
step += 1
run.append(loss.item())
if step % 100 == 0:
log.info(f"ce step {step}/{total} loss {np.mean(run[-100:]):.4f} {(time.time() - t0) / 60:.1f}m")
if step % CFG["ce_ckpt_every"] == 0 or step == total:
tmp = ck.with_suffix(".tmp")
torch.save({"model": model.state_dict(), "opt": opt.state_dict(), "sched": sched.state_dict(),
"scaler": scaler.state_dict(), "step": step}, tmp)
os.replace(tmp, ck)
model.eval()
p = _score(model, tok, ta[dv.q.values], tb[dv.c.values])
from sklearn.metrics import roc_auc_score
auc = roc_auc_score(dv.y.values, p) if dv.y.nunique() > 1 else float("nan")
tmpd = CE / "final.tmp"
model.save_pretrained(tmpd); tok.save_pretrained(tmpd)
os.replace(tmpd, final)
save_json({"dev_auc": auc, "steps": total, "n_train": len(tr)}, WORK / "reports" / "ce_train.json")
log.info(f"cross-encoder dev AUC {auc:.5f}")
@torch.no_grad()
def _score(model, tok, a, b, bs=None):
bs = bs or CFG["ce_infer_batch"]
order = np.argsort([len(x) + len(y) for x, y in zip(a, b)])
out = np.empty(len(a), dtype=np.float32)
i = 0
while i < len(order):
idx = order[i:i + bs]
try:
enc = tok([a[j] for j in idx], [b[j] for j in idx], truncation=True, max_length=CFG["ce_max_len"],
padding=True, return_tensors="pt").to("cuda")
with torch.autocast("cuda", dtype=_amp_dtype()):
out[idx] = model(**enc).logits.squeeze(-1).float().cpu().numpy()
i += bs
except torch.cuda.OutOfMemoryError:
torch.cuda.empty_cache()
bs = max(8, bs // 2)
log.warning(f"OOM -> infer batch {bs}")
return out
def _n_ce_shards(split):
n = len(pd.read_parquet(WORK / "s2" / f"{split}.parquet", columns=["q"]))
return n, max(1, math.ceil(n / CFG["ce_shard_pairs"]))
def run_ce_infer_worker(split, rank, world):
AutoTokenizer, AutoModel = _tok_model()
n, ns = _n_ce_shards(split)
todo = [i for i in range(ns) if i % world == rank and not shard_path("ce_scores", split, i, "npy").exists()]
if not todo:
return
s2 = pd.read_parquet(WORK / "s2" / f"{split}.parquet", columns=["q", "c"])
ta, tb = _texts(split, "s1"), _texts(split, "pool")
tok = AutoTokenizer.from_pretrained(CE / "final")
model = AutoModel.from_pretrained(CE / "final", dtype=torch.float32)
model = gpu_patient(lambda: model.cuda().eval(), log, "load cross-encoder")
prog = Progress(log, len(todo), f"ce infer {split} rank {rank}")
for i in todo:
if shard_path("ce_scores", split, i, "npy").exists(): # finished by another worker/helper meanwhile
prog.step()
continue
sl = slice(i * CFG["ce_shard_pairs"], (i + 1) * CFG["ce_shard_pairs"])
q, c = s2.q.values[sl], s2.c.values[sl]
save_npy(shard_path("ce_scores", split, i, "npy"), _score(model, tok, ta[q], tb[c]))
prog.step()
def run_ce_infer(split):
gpus = [g for g in os.environ.get("M4_ASSIGNED_GPUS", "0").split(",") if g != ""]
if len(gpus) <= 1:
run_ce_infer_worker(split, 0, 1)
else:
procs = []
for r, g in enumerate(gpus):
env = dict(os.environ, CUDA_VISIBLE_DEVICES=g)
procs.append(subprocess.Popen([sys.executable, "-u", __file__.replace("cross_encoder.py", "stages.py"),
"ce_infer_worker", split, str(r), str(len(gpus))], env=env))
codes = [p.wait() for p in procs]
if any(codes):
raise RuntimeError(f"ce inference workers failed: {codes}")
n, ns = _n_ce_shards(split)
missing = [i for i in range(ns) if not shard_path("ce_scores", split, i, "npy").exists()]
if missing:
raise RuntimeError(f"missing ce shards {missing[:10]}")
def load_ce_scores(split):
s2 = pd.read_parquet(WORK / "s2" / f"{split}.parquet", columns=["q", "c"])
n, ns = _n_ce_shards(split)
ce = np.concatenate([np.load(shard_path("ce_scores", split, i, "npy")) for i in range(ns)])
assert len(ce) == len(s2)
return s2.assign(ce=ce)
|