E1 halting-gate machinery (adapter, trainer, gated eval) + pre-registration item 18

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

scripts/train_gate_code.py

@@ -0,0 +1,120 @@
+"""E1 trainer (PLAN_SELFPACED): halting-gate merge on MBPP prompt-only loop.
+
+Same data/recipe as train_merge_code.py (STaR-verified code supervision,
+prompt-only loop_mask), but NO difficulty->depth curriculum: every batch
+mixes difficulties and runs the soft-halting loop to k_max — the GATE must
+learn the allocation the curriculum used to hand-code.
+
+Loss: CE(code tokens | s_mix) + lam * mean(E[iters]).
+"""
+
+import argparse
+import json
+import os
+import math
+import random
+import sys
+import time
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+
+from halting_common import HaltingMergeAdapter, halting_loop_logits
+from loop_common import BandLooper
+from prep_mbpp import DIRECT_SUFFIX, mbpp_prompt
+from train_merge_code import build_code_batch, MAX_TOK
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+from jlens.core import load_model  # noqa: E402
+
+OUT = Path(os.environ.get("LOOP_OUT",
+    Path(__file__).resolve().parent.parent / "results-loop"))
+STEPS = 600
+BATCH = 4
+WARMUP = 20
+
+ap = argparse.ArgumentParser()
+ap.add_argument("--seed", type=int, default=0)
+ap.add_argument("--lam", type=float, default=1e-3,
+                help="compute penalty weight on E[iterations]")
+ap.add_argument("--kmax", type=int, default=4)
+ap.add_argument("--lr", type=float, default=1e-3)
+ap.add_argument("--lam-warmup", type=int, default=100,
+                help="steps before the penalty ramps in (anti-collapse)")
+ARGS = ap.parse_args()
+LR = ARGS.lr
+TAG = (f"gate_l{ARGS.lam:g}" + (f"_s{ARGS.seed}" if ARGS.seed else ""))
+
+
+def lr_at(step):
+    if step < WARMUP:
+        return LR * (step + 1) / WARMUP
+    t = (step - WARMUP) / max(1, STEPS - WARMUP)
+    return 1e-4 + 0.5 * (LR - 1e-4) * (1 + math.cos(math.pi * t))
+
+
+def lam_at(step):
+    return ARGS.lam * min(1.0, max(0.0, (step - ARGS.lam_warmup) / 100))
+
+
+def main():
+    rng = random.Random(ARGS.seed)
+    torch.manual_seed(ARGS.seed)
+    data = json.load(open(OUT / "mbpp_data.json"))
+    model, tok = load_model(dtype=torch.bfloat16)
+    for p in model.parameters():
+        p.requires_grad_(False)
+    looper = BandLooper(model)
+    adapter = HaltingMergeAdapter(
+        d=model.config.get_text_config().hidden_size).cuda()
+    opt = torch.optim.AdamW(adapter.parameters(), lr=LR, weight_decay=0.01)
+
+    train = [it for it in data if it["split"] == "train"
+             and it["label"] != "drop" and it["sol_code"]]
+    train = [it for it in train
+             if len(tok(mbpp_prompt(tok, it, DIRECT_SUFFIX))["input_ids"])
+             + len(tok(it["sol_code"])["input_ids"]) + 12 <= MAX_TOK]
+    val = train[:24]
+    pool = train[24:]
+    print(f"pool={len(pool)} val={len(val)}  lam={ARGS.lam} kmax={ARGS.kmax}",
+          flush=True)
+
+    log = []
+    t0 = time.time()
+    for step in range(STEPS):
+        batch = rng.sample(pool, BATCH)
+        ids, msk, lab, lmask = build_code_batch(tok, batch)
+        # last prompt position = last position where label == -100 & mask==1
+        pl = (lmask.long().cumsum(-1).argmax(-1))
+        for g in opt.param_groups:
+            g["lr"] = lr_at(step)
+        logits, q, ei = halting_loop_logits(
+            looper, adapter, ids, ARGS.kmax, msk, pl, loop_mask=lmask)
+        ce = F.cross_entropy(logits[:, :-1].flatten(0, 1).float(),
+                             lab[:, 1:].flatten(), ignore_index=-100)
+        loss = ce + lam_at(step) * ei.mean()
+        opt.zero_grad(set_to_none=True)
+        loss.backward()
+        torch.nn.utils.clip_grad_norm_(adapter.parameters(), 1.0)
+        opt.step()
+        hard_b = [it["label"] == "hard" for it in batch]
+        log.append({"step": step, "ce": ce.item(), "ei": ei.mean().item(),
+                    "ei_hard": (ei[torch.tensor(hard_b)].mean().item()
+                                if any(hard_b) else None),
+                    "ei_easy": (ei[~torch.tensor(hard_b)].mean().item()
+                                if not all(hard_b) else None)})
+        if step % 10 == 0:
+            r = log[-1]
+            print(f"step {step:4d}  ce={r['ce']:.4f}  E[k]={r['ei']:.2f}  "
+                  f"(hard {r['ei_hard']} / easy {r['ei_easy']})  "
+                  f"({(time.time()-t0)/(step+1):.1f}s/step)", flush=True)
+        if step % 100 == 99 or step == STEPS - 1:
+            torch.save(adapter.state_dict(),
+                       OUT / f"adapter_{TAG}_e{step+1}.pt")
+            json.dump(log, open(OUT / f"train_{TAG}_log.json", "w"))
+    print("done", flush=True)
+
+
+if __name__ == "__main__":
+    main()