GSM plan-distillation control (width/depth law falsification test)

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

scripts/train_distill_gsm.py

@@ -0,0 +1,155 @@
+"""GSM plan-distillation control: KL from CoT-context teacher into FF adapter.
+
+The width/depth law's falsification test: on code, distilling the model's own
+plans into a feedforward adapter beat every recurrent variant. If the law is
+right, the same recipe FAILS on GSM — sequential arithmetic cannot be
+compressed into weight-space plan-priming. Interpretation caveat (recorded):
+GSM answer spans are ~3 tokens vs ~100 for code, so supervision density is
+also lower here; a null is law-consistent but not law-proving.
+"""
+
+import argparse
+import json
+import math
+import os
+import random
+import sys
+import time
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+
+from loop_common import (COT_SUFFIX, DIRECT_SUFFIX, BandLooper, MergeAdapter,
+                         chat_prompt, last_number, num_eq)
+from prep_mbpp import batch_generate
+
+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
+LR = 1e-3
+WARMUP = 20
+KL_T = 1.0
+
+
+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 ensure_cots(model, tok, items):
+    path = OUT / "gsm_cots.json"
+    if path.exists():
+        return json.load(open(path))
+    print(f"generating CoTs for {len(items)} items", flush=True)
+    gens = batch_generate(model, tok,
+                          [chat_prompt(tok, it["question"], COT_SUFFIX)
+                           for it in items], max_new_tokens=320,
+                          batch_size=32)
+    cots = {}
+    for it, g in zip(items, gens):
+        if num_eq(last_number(g), it["gold"]):   # keep only correct traces
+            cots[str(it["idx"])] = g
+    json.dump(cots, open(path, "w"), indent=1)
+    return cots
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--seed", type=int, default=0)
+    args = ap.parse_args()
+    rng = random.Random(args.seed)
+    torch.manual_seed(args.seed)
+
+    model, tok = load_model(dtype=torch.bfloat16)
+    tok.padding_side = "left"
+    for p in model.parameters():
+        p.requires_grad_(False)
+    looper = BandLooper(model)
+    d = model.config.get_text_config().hidden_size
+    adapter = MergeAdapter(d=d).cuda()
+    opt = torch.optim.AdamW(adapter.parameters(), lr=LR, weight_decay=0.01)
+
+    data = [it for it in json.load(open(OUT / "star_data.json"))
+            if it["split"] == "train" and it["label"] != "drop"]
+    cots = ensure_cots(model, tok, data)
+    tok.padding_side = "right"
+    train = [it for it in data if str(it["idx"]) in cots
+             and len(tok(it["question"])["input_ids"]) <= 350]
+    print(f"distill pool: {len(train)}", flush=True)
+
+    pad = tok.pad_token_id or 0
+    t0 = time.time()
+    for step in range(STEPS):
+        batch = rng.sample(train, BATCH)
+        pairs = []
+        for it in batch:
+            a = tok(it["gold"] + "<end_of_turn>",
+                    add_special_tokens=False)["input_ids"]
+            sp = tok(chat_prompt(tok, it["question"], DIRECT_SUFFIX),
+                     add_special_tokens=False)["input_ids"]
+            tp = tok(chat_prompt(tok, it["question"], COT_SUFFIX)
+                     + cots[str(it["idx"])] + "\n",
+                     add_special_tokens=False)["input_ids"]
+            pairs.append((sp, tp, a))
+        Ts = max(len(sp) + len(a) for sp, _, a in pairs)
+        Tt = max(len(tp) + len(a) for _, tp, a in pairs)
+        s_ids = torch.full((BATCH, Ts), pad, dtype=torch.long)
+        s_msk = torch.zeros((BATCH, Ts), dtype=torch.long)
+        t_ids = torch.full((BATCH, Tt), pad, dtype=torch.long)
+        t_msk = torch.zeros((BATCH, Tt), dtype=torch.long)
+        lab = torch.full((BATCH, Ts), -100, dtype=torch.long)
+        spans = []
+        for i, (sp, tp, a) in enumerate(pairs):
+            s_ids[i, : len(sp) + len(a)] = torch.tensor(sp + a)
+            s_msk[i, : len(sp) + len(a)] = 1
+            lab[i, len(sp): len(sp) + len(a)] = torch.tensor(a)
+            t_ids[i, : len(tp) + len(a)] = torch.tensor(tp + a)
+            t_msk[i, : len(tp) + len(a)] = 1
+            spans.append((len(sp), len(tp), len(a)))
+        s_ids, s_msk, lab = s_ids.cuda(), s_msk.cuda(), lab.cuda()
+        t_ids, t_msk = t_ids.cuda(), t_msk.cuda()
+        lmask = (lab == -100) & (s_msk == 1)
+
+        with torch.no_grad():
+            t_logits = model(input_ids=t_ids, attention_mask=t_msk,
+                             use_cache=False).logits
+        for g in opt.param_groups:
+            g["lr"] = lr_at(step)
+        s_logits = looper.loop_logits(adapter, s_ids, 1, attention_mask=s_msk,
+                                      loop_mask=lmask, feedforward=True,
+                                      use_checkpoint=True)
+        kl = torch.zeros((), device="cuda")
+        n_tok = 0
+        for i, (ls, lt, la) in enumerate(spans):
+            sl = s_logits[i, ls - 1: ls + la - 1].float()
+            tl = t_logits[i, lt - 1: lt + la - 1].float()
+            kl = kl + F.kl_div(F.log_softmax(sl / KL_T, -1),
+                               F.log_softmax(tl / KL_T, -1),
+                               log_target=True, reduction="sum")
+            n_tok += la
+        kl = kl / n_tok
+        ce = F.cross_entropy(s_logits[:, :-1].flatten(0, 1).float(),
+                             lab[:, 1:].flatten(), ignore_index=-100)
+        loss = kl + 0.5 * ce
+        opt.zero_grad(set_to_none=True)
+        loss.backward()
+        torch.nn.utils.clip_grad_norm_(adapter.parameters(), 1.0)
+        opt.step()
+        if step % 10 == 0:
+            print(f"step {step:4d}  kl={kl.item():.4f}  ce={ce.item():.4f}  "
+                  f"({(time.time()-t0)/(step+1):.1f}s/step)", flush=True)
+        if step % 200 == 199 or step == STEPS - 1:
+            torch.save(adapter.state_dict(),
+                       OUT / f"adapter_gsm_distill_e{step+1}.pt")
+    print("done", flush=True)
+
+
+if __name__ == "__main__":
+    main()