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

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

scripts/eval_gate_code.py

@@ -0,0 +1,114 @@
+"""E1 eval (PLAN_SELFPACED): halting-gate MBPP eval.
+
+Per item: deploy-time halting picks k* (1..kmax); generation then uses the
+frozen-prompt path at that k (items grouped by k* for batching). Reports
+pass@1 by label, the k* distribution by label, mean compute, and the
+gate-difficulty point-biserial correlation.
+"""
+
+import argparse
+import json
+import os
+import sys
+import time
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+
+import torch
+
+from halting_common import HaltingMergeAdapter, halted_k_per_item
+from loop_common import BandLooper
+from prep_mbpp import DIRECT_SUFFIX, extract_code, mbpp_prompt, run_tests
+
+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"))
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--adapter", required=True)
+    ap.add_argument("--tag", required=True)
+    ap.add_argument("--kmax", type=int, default=4)
+    ap.add_argument("--n", type=int, default=250)
+    ap.add_argument("--batch", type=int, default=8)
+    args = ap.parse_args()
+
+    model, tok = load_model(dtype=torch.bfloat16)
+    tok.padding_side = "left"
+    looper = BandLooper(model)
+    adapter = HaltingMergeAdapter(
+        d=model.config.get_text_config().hidden_size).cuda()
+    adapter.load_state_dict(torch.load(args.adapter, map_location="cuda"))
+    adapter.eval()
+
+    items = [it for it in json.load(open(OUT / "mbpp_data.json"))
+             if it["split"] == "test"][: args.n]
+    print(f"[{args.tag}] gated MBPP eval on {len(items)}, kmax={args.kmax}",
+          flush=True)
+
+    # phase 1: per-item k*
+    kstars = []
+    with torch.no_grad():
+        for i in range(0, len(items), args.batch):
+            chunk = items[i : i + args.batch]
+            enc = tok([mbpp_prompt(tok, it, DIRECT_SUFFIX) for it in chunk],
+                      return_tensors="pt", padding=True,
+                      add_special_tokens=False).to("cuda")
+            pl = enc["attention_mask"].sum(-1) - 1  # left-pad: last position
+            pl = torch.full_like(pl, enc["input_ids"].shape[1] - 1)
+            ks = halted_k_per_item(looper, adapter, enc["input_ids"],
+                                   args.kmax, enc["attention_mask"], pl)
+            kstars.extend(ks.tolist())
+    t0 = time.time()
+
+    # phase 2: generate grouped by k*
+    codes = [None] * len(items)
+    for kval in sorted(set(kstars)):
+        idxs = [i for i, kk in enumerate(kstars) if kk == kval]
+        for j in range(0, len(idxs), args.batch):
+            grp = idxs[j : j + args.batch]
+            enc = tok([mbpp_prompt(tok, items[i], DIRECT_SUFFIX) for i in grp],
+                      return_tensors="pt", padding=True,
+                      add_special_tokens=False).to("cuda")
+            gen = looper.generate_frozen_prompt(
+                adapter, tok, enc["input_ids"], kval, max_new_tokens=220,
+                attention_mask=enc["attention_mask"])
+            for gi, i in enumerate(grp):
+                txt = tok.decode(gen[gi, enc["input_ids"].shape[1]:],
+                                 skip_special_tokens=True)
+                codes[i] = extract_code(txt)
+    with ThreadPoolExecutor(8) as ex:
+        oks = list(ex.map(lambda ci: run_tests(ci[0], ci[1]),
+                          zip(codes, items)))
+
+    per_label, kdist = {}, {}
+    for it, ok, kk in zip(items, oks, kstars):
+        d = per_label.setdefault(it["label"], [0, 0, 0.0])
+        d[0] += ok; d[1] += 1; d[2] += kk
+        kdist.setdefault(it["label"], []).append(kk)
+    acc = sum(oks) / len(items)
+    by_label = {l: c / n for l, (c, n, _) in per_label.items()}
+    mean_k = {l: sum(v) / len(v) for l, v in kdist.items()}
+    hard = torch.tensor([it["label"] == "hard" for it in items], dtype=torch.float)
+    kk = torch.tensor(kstars, dtype=torch.float)
+    r = ((kk - kk.mean()) * (hard - hard.mean())).mean() / (kk.std() * hard.std() + 1e-9)
+    print(f"pass@1={acc:.3f} by_label={ {l: round(v,3) for l,v in by_label.items()} }")
+    print(f"mean k* by label: { {l: round(v,2) for l,v in mean_k.items()} }  "
+          f"overall E[k]={sum(kstars)/len(kstars):.2f}  "
+          f"gate-difficulty r={r:.3f}  ({time.time()-t0:.0f}s)", flush=True)
+
+    json.dump({"tag": args.tag, "acc": acc, "by_label": by_label,
+               "mean_kstar": mean_k, "corr_hard": r.item(),
+               "kstars": kstars,
+               "per_item": [{"task_id": it["task_id"], "ok": bool(o),
+                             "kstar": kk_} for it, o, kk_ in
+                            zip(items, oks, kstars)]},
+              open(OUT / f"eval_code_{args.tag}.json", "w"), indent=1)
+    print("wrote", OUT / f"eval_code_{args.tag}.json")
+
+
+if __name__ == "__main__":
+    main()