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2f06744 | 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 | #!/usr/bin/env python3
"""Gentle final polish of the capacity model at the exact acceptance distribution.
Contingency tool (fired manually when the curriculum's self-check lands below
0.999): the level fillers were [0,0,0,4,8] but the test uses filler=14; the
diagnostic sweep showed f=8 at 0.992 vs f=14 at 0.977 -- the gap is distribution
mismatch, not mechanism. This script fine-tunes at f in {8,14} with a LOW entry
learning rate (no restart shock), probes on the exact test protocol every 250
steps, keeps the best checkpoint, and exits early on two consecutive >= 0.999
probes.
"""
from __future__ import annotations
import json
import os
import sys
import time
import torch
import torch.nn.functional as F
HERE = os.path.dirname(os.path.abspath(__file__))
ROOT = os.path.join(HERE, "..")
sys.path.insert(0, os.path.join(ROOT, "src"))
from mulattice.protocol import (SynthCfg, build_model, eval_batch, force_fast_scan, logits_at,
spread_loss, synth_batch)
DEVICE = os.environ.get("DEVICE", "cuda")
CKPT_IN = os.environ.get("RESUME", os.path.join(ROOT, "checkpoints", "capacity8k_p8192_best.pt"))
CKPT_OUT = os.environ.get("OUT", os.path.join(ROOT, "checkpoints", "capacity8k.pt"))
MAX_STEPS = int(os.environ.get("STEPS", "1600"))
LR = float(os.environ.get("LR", "2e-4"))
HEARTBEAT = os.path.join(ROOT, "logs", "polish_capacity.heartbeat")
CFG = SynthCfg()
def save(m, path, extra=None):
live_scale = float(m.blocks[0].mixer.cfg.addr_scale)
payload = {"model": m.state_dict(), "proto_cfg": {
"NK": CFG.NK, "NV": CFG.NV, "d_model": CFG.d_model, "n_heads": CFG.n_heads,
"d_k": CFG.d_k, "d_v": CFG.d_v, "addr_scale": live_scale,
"retention_init": CFG.retention_init}, "cfg": "capacity polish at acceptance distribution"}
if extra:
payload.update(extra)
torch.save(payload, path)
def main():
dev = DEVICE
ck = torch.load(CKPT_IN, map_location="cpu", weights_only=False)
cfg = SynthCfg()
if ck.get("proto_cfg", {}).get("addr_scale"):
cfg.addr_scale = float(ck["proto_cfg"]["addr_scale"])
m = build_model(cfg)
m.load_state_dict(ck["model"])
m = m.to(dev).train()
force_fast_scan(m)
print(f"polish from {CKPT_IN} (step {ck.get('step')}, acc {ck.get('acc')}, "
f"scale {cfg.addr_scale:.0f}), lr {LR}, max {MAX_STEPS} steps", flush=True)
opt = torch.optim.AdamW(m.parameters(), lr=LR)
sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=MAX_STEPS, eta_min=LR / 4)
g = torch.Generator().manual_seed(11)
best = 0.0
good = 0
t0 = time.time()
hist = []
fcyc = [8, 14, 14, 8, 14] # eval-distribution heavy
for s in range(MAX_STEPS):
f = fcyc[s % len(fcyc)]
Q = 128 if s % 3 == 0 else 64
ow = 0.25 if s % 4 == 1 else 0.0
b = synth_batch(1, 8192, Q, CFG, filler=f, overwrite_frac=ow, generator=g)
tok = b["tokens"].to(dev)
logits = logits_at(m, tok, b["ans_pos"].to(dev))
gold = b["gold"].to(dev).reshape(-1)
# DFT-style reward rectification (arXiv:2508.05629): per-position CE
# weighted by 1/p(gold), clipped, so the few answers the model still
# finds improbable dominate the gradient -- a one-line objective fix
# aimed exactly at the last 0.2 points (uniform polish wobbles at the
# optimum instead: measured 1.0000 -> 0.9980 -> 0.9961).
p_gold = torch.softmax(logits.reshape(-1, CFG.V), -1).gather(1, gold.unsqueeze(1)).squeeze(1)
w = (1.0 / p_gold.clamp_min(1e-4)).clamp(1.0, 10.0)
ce_vec = F.cross_entropy(logits.reshape(-1, CFG.V), gold, reduction="none")
ce = (w * ce_vec).sum() / w.sum()
kp = b["key_pos"]
if b["upd_key_pos"] is not None:
kp = torch.cat([kp, b["upd_key_pos"]], 1)
sl = spread_loss(m, tok, kp.to(dev), CFG, gate_subsample=1024)
loss = ce + sl
opt.zero_grad(set_to_none=True)
loss.backward()
torch.nn.utils.clip_grad_norm_(m.parameters(), 1.0)
opt.step()
sched.step()
if s % 50 == 0:
print(f" polish {s:4d} f{f} Q{Q} ce {float(ce.detach()):.4f} T{b['L']} "
f"{time.time()-t0:6.0f}s", flush=True)
with open(HEARTBEAT, "w") as fh:
fh.write(f"{s}\n")
if (s + 1) % 250 == 0 or s == MAX_STEPS - 1:
gg = torch.Generator().manual_seed(5)
bb = synth_batch(1, 8192, 512, CFG, filler=14, generator=gg)
r = eval_batch(m, bb, dev)
dense = eval_batch(m, synth_batch(1, 8192, 512, CFG, filler=0,
generator=torch.Generator().manual_seed(6)), dev)
hist.append({"s": s, "probe": round(r["acc"], 4), "dense": round(dense["acc"], 4)})
with open(os.path.join(ROOT, "results", "polish_capacity.json"), "w") as fh:
json.dump(hist, fh, indent=2)
print(f" PROBE f14 {r['acc']:.4f} ({r['correct']}/{r['n']}) "
f"dense {dense['acc']:.4f} [{time.time()-t0:.0f}s]", flush=True)
if r["acc"] >= best:
best = r["acc"]
save(m, CKPT_OUT, {"step": s, "acc": r["acc"]})
good = good + 1 if r["acc"] >= 0.999 else 0
if good >= 2:
print(f"TARGET: two consecutive probes >= 0.999 at step {s}", flush=True)
save(m, CKPT_OUT, {"step": s, "acc": r["acc"]})
return
print(f"done; best probe {best:.4f}; best model saved to {CKPT_OUT}", flush=True)
if __name__ == "__main__":
main()
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