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969c687 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | """Long-memory bench (docs/MIX_DATA_LONG_MEMORY.md §3): fresh ATOM, same per-tick training logic as
docs/artifacts/decfix_bptt/memorize_bptt.py (AdamW wd .01, clip 1, stabilize after each step, atom flush
every 64, field loss .05, last-atom dropout .5, efference off, --field-bptt window rule), 1 thread.
task recall : an endless stream of episodes "Nom: <NAME>.\n<filler of G bytes>\nQui ? <NAME>.\n\n"
(16 names with distinct first letters, G uniform in [8, 160], filler = random slices of the
mixed training corpus). The first name byte after "Qui ? " is only predictable from the
name seen G+~15 bytes earlier: chance = ln 16 = 2.77 nats / 6.25 % acc. Eval on 256 fixed
held-out episodes (other filler, other seed) bucketed by distance.
task mem2k : the 2 KB memorisation test (root_cause/memorize_2k.txt, greedy regen past a 32-byte prefix).
usage: longmem_bench.py NAME --task recall|mem2k --minutes M [--k K] [--slow-block F] [--slow-cap C]
[--slow-gain ema|none] [--lr 2e-3] [--leak .05]
"""
import argparse, json, math, random, sys, time
from pathlib import Path
import torch
HERE = Path(__file__).resolve().parent
ROOT = HERE.parents[2]
sys.path.insert(0, str(ROOT)); sys.path.insert(0, str(ROOT / "tools")); sys.path.insert(0, str(HERE.parent / "decfix_bptt"))
from src.atom_native import AtomNativeModel # noqa: E402
from src.io.atomizer import Atomizer # noqa: E402
import memorize_bptt as MB # noqa: E402 (flush, tf_ce, regen)
NAMES = ["Alice", "Bruno", "Cyril", "Denis", "Emile", "Fanny", "Gaston", "Hugo",
"Irene", "Jules", "Karim", "Louise", "Marc", "Nadia", "Oscar", "Paul"]
OUTDIR = ROOT / "checkpoints/mix_long_memory_bench"
def filler_source():
raw = b"".join(p.read_bytes() for p in sorted((ROOT / "data").glob("corpus_fr_mix_[0-9]*.txt")))
return raw.decode("utf-8", "ignore").replace("\n", " ")
def episode(rng, src, gmin=8, gmax=160, gap=None):
name = rng.choice(NAMES); g = gap if gap is not None else rng.randint(gmin, gmax)
o = rng.randrange(0, len(src) - 400); fill = src[o:o + g]
head = f"Nom: {name}.\n"; q = "\nQui ? "
text = head + fill + q + name + ".\n\n"
ans = len((head + fill + q).encode()) # byte index of the first answer byte
dist = ans - len("Nom: ".encode()) # bytes between the name's first byte and the answer
return text.encode(), ans, dist, name
def build(a):
torch.manual_seed(a.seed)
m = AtomNativeModel(d_model=32, n_modes=32, n_atoms_max=64, max_payload_bytes=1,
atomizer=Atomizer(max_span_bytes=1), field_max_rms=3.0, enable_merge=False,
field_loss_weight=0.05, field_obligatory_hard=True, last_atom_readout=True,
field_leak=a.leak)
m.field_leak = float(a.leak)
m.set_field_leak_learnable(True, init=float(a.leak))
if a.slow_block > 0:
m.set_field_leak_slow_block(a.slow_block, cap=a.slow_cap, gain=a.slow_gain, ref=a.leak)
m.set_field_bptt(int(a.k))
m.surface.payload_enabled = False; m.surface.last_atom_dropout = a.ladrop; m.field_loss_weight = 0.05
m._efference_every = 0
return m
@torch.no_grad()
def eval_recall(m, eps):
m.eval(); m.reset_state(reset_atomizer=True)
buckets = {}; tick = 0
for text, ans, dist, name in eps:
pk = m.atomizer.encode_bytes(text, reset=False)
for i in range(len(pk) - 1):
out = m.forward_packet(pk[i]); tick += 1
if tick % 64 == 0: MB.flush(m)
if i + 1 == ans:
lp = torch.log_softmax(out["surface"]["byte_logits"][0], -1)
tgt = text[ans]
b = "d<32" if dist < 32 else "d32-63" if dist < 64 else "d64-127" if dist < 128 else "d128+"
r = buckets.setdefault(b, {"n": 0, "ce": 0.0, "acc": 0})
r["n"] += 1; r["ce"] += float(-lp[tgt]); r["acc"] += int(int(lp.argmax()) == tgt)
m.forward_packet(pk[-1]); tick += 1
m.train()
return {b: {"n": r["n"], "ce": round(r["ce"] / r["n"], 4), "acc": round(r["acc"] / r["n"], 4)} for b, r in sorted(buckets.items())}
def main():
ap = argparse.ArgumentParser()
ap.add_argument("name"); ap.add_argument("--task", default="recall"); ap.add_argument("--minutes", type=float, default=45)
ap.add_argument("--k", type=int, default=128); ap.add_argument("--leak", type=float, default=0.05)
ap.add_argument("--slow-block", type=float, default=0.0); ap.add_argument("--slow-cap", type=float, default=0.02)
ap.add_argument("--slow-gain", default="ema"); ap.add_argument("--lr", type=float, default=2e-3)
ap.add_argument("--ladrop", type=float, default=0.5); ap.add_argument("--seed", type=int, default=20260913)
ap.add_argument("--eval-min", type=float, default=10.0)
a = ap.parse_args()
torch.set_num_threads(1)
m = build(a); m.train()
params = [p for p in m.parameters() if p.requires_grad]
opt = torch.optim.AdamW(params, lr=a.lr, weight_decay=0.01)
t0 = time.time(); step = 0; updates = 0; capped = 0; log = []; nan = False
if a.task == "recall":
src = filler_source(); half = len(src) // 2
train_src, eval_src = src[:half], src[half:]
erng = random.Random(777)
eval_eps = [episode(erng, eval_src) for _ in range(256)]
rng = random.Random(a.seed)
stream = iter(lambda: episode(rng, train_src)[0], None)
r0 = {"min": 0, "recall": eval_recall(m, eval_eps)}; log.append(r0); print(json.dumps(r0), flush=True)
else:
data = MB.TEXT.read_bytes()
r0 = {"min": 0, "tf_ce": MB.tf_ce(m, data)}; log.append(r0); print(json.dumps(r0), flush=True)
next_eval = a.eval_min * 60; acc = None; n = 0
m.reset_state(reset_atomizer=True)
def tick(cur, tgt):
nonlocal step, updates, capped, acc, n, nan
step += 1
loss, info = m.transition_loss(cur, tgt)
capped += info["field_scale"] < 0.999999
acc = loss if acc is None else acc + loss; n += 1
if n >= a.k:
window_step()
if step % 64 == 0: MB.flush(m)
def window_step():
nonlocal updates, acc, n, nan
if acc is None: return
opt.zero_grad(set_to_none=True); (acc / n).backward()
gn = torch.nn.utils.clip_grad_norm_(params, 1.0)
if not math.isfinite(float(gn)): nan = True; raise FloatingPointError("nan grad")
opt.step(); updates += 1; m.detach_field_graph(); acc = None; n = 0
m.stabilize_dynamics_parameters()
def leak_stats():
lam = m.learnable_leak_rates().detach().reshape(-1)
out = {"leak_min_mean_max": [round(float(lam.min()), 4), round(float(lam.mean()), 4), round(float(lam.max()), 4)]}
if m.field_leak_slow_block is not None:
out["slow_leak"] = [round(float(lam[i]), 4) for i in m.field_leak_slow_block["modes"]]
return out
try:
while time.time() - t0 < a.minutes * 60:
if a.task == "recall":
chunk = b"".join(next(stream) for _ in range(8))
pk = m.atomizer.encode_bytes(chunk, reset=False)
prev = getattr(m, "_bench_prev", None)
if prev is not None: tick(prev, pk[0])
for i in range(len(pk) - 1): tick(pk[i], pk[i + 1])
m._bench_prev = pk[-1]
else:
m.reset_state(reset_atomizer=True); pk = m.atomizer.encode_bytes(data, reset=True)
for i in range(len(pk) - 1): tick(pk[i], pk[i + 1])
window_step()
if time.time() - t0 >= next_eval:
window_step(); next_eval += a.eval_min * 60
r = {"min": round((time.time() - t0) / 60, 1), "step": step, "updates": updates, "cap_frac": round(capped / step, 4), **leak_stats()}
if a.task == "recall":
saved = m.core.state.alpha.clone(); r["recall"] = eval_recall(m, eval_eps)
m.reset_state(reset_atomizer=True); m._bench_prev = None
else:
r["tf_ce"] = MB.tf_ce(m, data); r["regen"] = MB.regen(m, data)[0] - 32
log.append(r); print(json.dumps(r), flush=True)
except FloatingPointError as exc:
print("STOP:", exc, flush=True)
window_step()
res = {"name": a.name, "args": vars(a), "steps": step, "updates": updates, "minutes": round((time.time() - t0) / 60, 1),
"field_cap_fraction": capped / max(step, 1), "nan": nan, **leak_stats(),
"dynamics": {k: v for k, v in m.stabilize_dynamics_parameters().items() if k in ("energy_decay", "coupling_scale", "phase_sync")}}
if a.task == "recall":
res["final_recall"] = eval_recall(m, eval_eps)
else:
same, head = MB.regen(m, data)
res.update(final_tf_ce=MB.tf_ce(m, data), regen_bytes_past_prefix=same - 32, regen_head=head[:300])
res["log"] = log
OUTDIR.mkdir(parents=True, exist_ok=True)
ck = OUTDIR / f"{a.name}.pt"
if not ck.exists():
m.save(ck); res["checkpoint"] = str(ck.relative_to(ROOT))
(HERE / f"bench_{a.name}.json").write_text(json.dumps(res, indent=1, ensure_ascii=False))
print(json.dumps({k: v for k, v in res.items() if k != "log"}, ensure_ascii=False), flush=True)
if __name__ == "__main__":
main()
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