189 lines
6.9 KiB
Python
189 lines
6.9 KiB
Python
"""Unified merge adapter: GSM8K + MBPP, prompt-only looping, fresh init.
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One adapter, one regime (loop_mask = prompt span; generated/answer tokens
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never loop), mixed-task batches. Hardened protocol elements: per-task val
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holdouts, checkpoint every 100 steps kept separately (best-val selection and
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k chosen on val, never test).
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"""
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import argparse
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import json
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import os
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import math
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import random
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import sys
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import time
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from pathlib import Path
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import torch
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import torch.nn.functional as F
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from loop_common import (AdaptiveMergeAdapter, BandLooper, MergeAdapter,
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chat_prompt, DIRECT_SUFFIX)
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from prep_mbpp import DIRECT_SUFFIX as MBPP_SUFFIX
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from prep_mbpp import mbpp_prompt
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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from jlens.core import load_model # noqa: E402
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OUT = Path(os.environ.get("LOOP_OUT",
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Path(__file__).resolve().parent.parent / "results-loop"))
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STEPS = 800
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BATCH = 6
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LR = 1e-3
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WARMUP = 20
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MAX_TOK = 512
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K_BUCKETS = [(1, ("easy",)), (2, ("easy", "hard")), (4, ("hard",))]
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SEED = 0
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VAL_N = 16 # per task per bucket
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def lr_at(step):
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if step < WARMUP:
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return LR * (step + 1) / WARMUP
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t = (step - WARMUP) / max(1, STEPS - WARMUP)
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return 1e-4 + 0.5 * (LR - 1e-4) * (1 + math.cos(math.pi * t))
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def item_texts(tok, it):
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if it["task"] == "gsm":
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return (chat_prompt(tok, it["question"], DIRECT_SUFFIX),
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it["gold"] + "<end_of_turn>")
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return (mbpp_prompt(tok, it, MBPP_SUFFIX),
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"```python\n" + it["sol_code"] + "\n```<end_of_turn>")
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def build_batch(tok, items, device="cuda"):
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seqs, labs = [], []
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for it in items:
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ptxt, atxt = item_texts(tok, it)
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p = tok(ptxt, add_special_tokens=False)["input_ids"]
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a = tok(atxt, add_special_tokens=False)["input_ids"]
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seqs.append(p + a)
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labs.append([-100] * len(p) + a)
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T = max(len(s) for s in seqs)
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pad = tok.pad_token_id or 0
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ids = torch.full((len(seqs), T), pad, dtype=torch.long)
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lab = torch.full((len(seqs), T), -100, dtype=torch.long)
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msk = torch.zeros((len(seqs), T), dtype=torch.long)
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for i, (s, l) in enumerate(zip(seqs, labs)):
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ids[i, : len(s)] = torch.tensor(s)
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lab[i, : len(s)] = torch.tensor(l)
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msk[i, : len(s)] = 1
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lmask = (lab == -100) & (msk == 1)
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return ids.to(device), msk.to(device), lab.to(device), lmask.to(device)
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@torch.no_grad()
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def val_loss(looper, adapter, tok, items, k, feedforward=False):
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tot, n = 0.0, 0
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for i in range(0, len(items), BATCH):
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ids, msk, lab, lmask = build_batch(tok, items[i : i + BATCH])
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logits = looper.loop_logits(adapter, ids, k, attention_mask=msk,
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loop_mask=lmask, feedforward=feedforward)
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loss = F.cross_entropy(logits[:, :-1].flatten(0, 1).float(),
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lab[:, 1:].flatten(), ignore_index=-100)
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tot += loss.item() * len(ids)
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n += len(ids)
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return tot / n
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--noloop", action="store_true",
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help="feedforward control: adapter(e,e), no recurrence")
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ap.add_argument("--tasks", default="gsm,mbpp")
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ap.add_argument("--tag", default=None)
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ap.add_argument("--adaptive", action="store_true")
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args = ap.parse_args()
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tasks = args.tasks.split(",")
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tag = args.tag or ("ff" if args.noloop else "uni")
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rng = random.Random(SEED)
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torch.manual_seed(SEED)
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gsm, mbpp = [], []
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if "gsm" in tasks:
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gsm = [dict(it, task="gsm")
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for it in json.load(open(OUT / "star_data.json"))
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if it["split"] == "train" and it["label"] != "drop"]
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if "mbpp" in tasks:
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mbpp = [dict(it, task="mbpp")
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for it in json.load(open(OUT / "mbpp_data.json"))
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if it["split"] == "train" and it["label"] != "drop"
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and it.get("sol_code")]
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model, tok = load_model(dtype=torch.bfloat16)
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for p in model.parameters():
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p.requires_grad_(False)
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looper = BandLooper(model)
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cls = AdaptiveMergeAdapter if args.adaptive else MergeAdapter
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adapter = cls(
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d=model.config.get_text_config().hidden_size).cuda()
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opt = torch.optim.AdamW(adapter.parameters(), lr=LR, weight_decay=0.01)
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def fits(it):
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ptxt, atxt = item_texts(tok, it)
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return (len(tok(ptxt)["input_ids"]) + len(tok(atxt)["input_ids"])
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<= MAX_TOK)
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items = [it for it in gsm + mbpp if fits(it)]
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rng.shuffle(items)
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pool, val = {"easy": [], "hard": []}, {}
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for task in tasks:
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for lbl in ("easy", "hard"):
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sub = [it for it in items if it["task"] == task
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and it["label"] == lbl]
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val[(task, lbl)] = sub[:VAL_N]
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pool.setdefault(lbl, []).extend(sub[VAL_N:])
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print("pool: easy={} hard={} (gsm {} / mbpp {})".format(
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len(pool["easy"]), len(pool["hard"]),
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sum(it["task"] == "gsm" for l in pool.values() for it in l),
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sum(it["task"] == "mbpp" for l in pool.values() for it in l)),
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flush=True)
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log = []
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t0 = time.time()
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for step in range(STEPS):
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k, labels = K_BUCKETS[step % len(K_BUCKETS)]
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cand = [it for lbl in labels for it in pool[lbl]]
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batch = rng.sample(cand, BATCH)
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ids, msk, lab, lmask = build_batch(tok, batch)
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for g in opt.param_groups:
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g["lr"] = lr_at(step)
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logits = looper.loop_logits(adapter, ids, k, attention_mask=msk,
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use_checkpoint=True, loop_mask=lmask,
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feedforward=args.noloop)
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loss = F.cross_entropy(logits[:, :-1].flatten(0, 1).float(),
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lab[:, 1:].flatten(), ignore_index=-100)
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opt.zero_grad(set_to_none=True)
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loss.backward()
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torch.nn.utils.clip_grad_norm_(adapter.parameters(), 1.0)
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opt.step()
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log.append({"step": step, "k": k, "loss": loss.item()})
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if step % 10 == 0:
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print(f"step {step:4d} k={k} loss={loss.item():.4f} "
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f"({(time.time()-t0)/(step+1):.1f}s/step)", flush=True)
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if step % 100 == 99 or step == STEPS - 1:
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vals = {}
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kk_grid = (0, 1) if args.noloop else (0, 1, 2, 4)
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for (task, lbl), vitems in val.items():
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for kk in kk_grid:
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vals[f"{task}_{lbl}_k{kk}"] = val_loss(
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looper, adapter, tok, vitems, kk,
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feedforward=args.noloop)
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print(f" val@{step}: " + " ".join(
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f"{n}={v:.3f}" for n, v in sorted(vals.items())), flush=True)
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log.append({"step": step, "val": vals})
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torch.save(adapter.state_dict(),
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OUT / f"adapter_{tag}_e{step+1}.pt")
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json.dump(log, open(OUT / f"train_{tag}_log.json", "w"), indent=1)
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print("done", flush=True)
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if __name__ == "__main__":
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main()
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