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f930dac | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 | #!/usr/bin/env python3
"""ORACLE-STATE CONTROL: does PNS-World admit a bounded sufficient state?
The negative result says learned recurrent states never become causally useful.
A reviewer's remaining escape hatch is: "maybe the semantic target or the
output architecture is simply not learnable." This control closes it.
We hand the model the simulator's *ground-truth bounded* semantic state -
the current (entity, attribute) -> value map and the (entity, relation) ->
entity map, as at most 120 typed slots, exactly the capacity Sigma has - plus
the question, and ask the SAME query-conditioned read + enum head to answer.
S_oracle_t + e_t -> enum head -> answer
If this scores high, the task admits a bounded sufficient state and the answer
heads can exploit one, so the learned models' failure is in CONSTRUCTING or
USING state, not in the task or the readout. If it scores low, the negative
result is confounded and must be reported as such.
The slot CONTENTS are oracle; their embeddings are learned (a symbol must be
embedded to be read). Nothing about the future or the gold answer's identity
is supplied - only the world state that a perfect bounded summariser would
hold at t-.
"""
import argparse
import sys
import time
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "src"))
sys.path.insert(0, str(Path(__file__).resolve().parent))
from pns.common import atomic_write_json, eval_root # noqa: E402
from pns.model.modules import enum_legal_mask # noqa: E402
from pns.world.schema import ENUM_ID, ENUM_VOCAB, ET, Fam # noqa: E402
from pns.world.sim import LifetimeGen # noqa: E402
N_SLOT, N_ENT, N_ATTR, N_VAL = 120, 40, 24, len(ENUM_VOCAB) + 1
ATTRS = sorted({a for a in
["location", "team", "status", "phase", "priority", "destination",
"owner", "project", "vault"]})
AID = {a: i for i, a in enumerate(ATTRS)}
def build(seed, length=256):
"""Replay one lifetime; at every SEM question emit the oracle slot table
as it stands BEFORE that question, plus the query and gold."""
g = LifetimeGen(seed, length)
g.plan()
events, red, _ = g.walk()
out = []
# Derive the as-of-t bounded state directly from the event stream, exactly
# as a perfect bounded summariser would - including revert semantics.
live, rel = {}, {}
hist = {} # (ent, attr) -> [(clock, source, value), ...]
clock = 0
for t, ev in enumerate(events):
clock += ev.dt
p = ev.payload
if ev.etype == ET.QUESTION and ev.family in (int(Fam.SEM_LATEST),
int(Fam.SEM_2HOP)):
tk = g.slots[t]
d_ = tk.data if tk is not None else {}
slots = []
for (e, a), v in live.items():
if a in AID and v in ENUM_ID:
slots.append((e % N_ENT, AID[a], ENUM_ID[v] + 1, 0))
for (e, r), dst in rel.items():
if r in AID:
slots.append((e % N_ENT, AID[r], 0, dst % N_ENT + 1))
if ev.family == int(Fam.SEM_LATEST):
q_ent, q_rel, q_attr = d_.get("ent", 0) % N_ENT, 0, d_.get("attr")
else:
q_ent = d_.get("src", 0) % N_ENT
q_rel = AID.get(d_.get("rel", ""), 0) + 1
q_attr = d_.get("attr")
if q_attr not in AID:
continue
lm = np.zeros(8, np.uint8)
for _e in ev.enum_legal:
lm[_e >> 3] |= 1 << (_e & 7)
out.append(dict(slots=slots[:N_SLOT], gold=ev.enum_gold, legal=lm,
fam=ev.family, q_ent=q_ent, q_rel=q_rel,
q_attr=AID[q_attr], delay=ev.meta.get("delay", -1)))
# ---- advance the ground-truth bounded state
if ev.etype == ET.OBSERVATION:
if p.get("rel"):
rel[(p["ent"], p["attr"])] = p["ent2"]
else:
k = (p["ent"], p["attr"])
live[k] = p["value"]
hist.setdefault(k, []).append((clock, p.get("source", ""), p["value"]))
elif ev.etype == ET.CORRECTION:
k = (p["ent"], p["key"])
if p["mode"] == "replace":
if not p.get("exact") and p.get("value") is not None:
live[k] = p["value"]
hist.setdefault(k, []).append((clock, "", p["value"]))
else: # revert: restore the referenced earlier assertion
h = hist.get(k, [])
tgt = None
if p.get("ref_kind") == "source":
for c, src, v in h:
if src == p.get("ref_source"):
tgt = v
else:
for c, src, v in h:
if c == p.get("ref_clock"):
tgt = v
if tgt is not None:
live[k] = tgt
hist[k] = [x for x in h if x[0] <= (p.get("ref_clock") or c)]
return out
class OracleReader(nn.Module):
"""Query-conditioned attention read over oracle slots -> enum head.
Same shape of computation as PNSR's read path, same enum output head."""
def __init__(self, d=256):
super().__init__()
self.ent = nn.Embedding(N_ENT + 1, d)
self.attr = nn.Embedding(N_ATTR + 1, d)
self.val = nn.Embedding(N_VAL + 1, d)
self.dst = nn.Embedding(N_ENT + 2, d)
self.qe = nn.Embedding(N_ENT + 1, d)
self.qa = nn.Embedding(N_ATTR + 1, d)
self.qr = nn.Embedding(N_ATTR + 2, d)
self.rd = nn.MultiheadAttention(d, 8, batch_first=True)
self.rd2 = nn.MultiheadAttention(d, 8, batch_first=True)
self.mlp = nn.Sequential(nn.LayerNorm(d), nn.Linear(d, d), nn.GELU(),
nn.Linear(d, d))
self.enum = nn.Linear(d, len(ENUM_VOCAB))
def forward(self, se, sa, sv, sd, mask, qe, qa, qr):
s = self.ent(se) + self.attr(sa) + self.val(sv) + self.dst(sd)
q = (self.qe(qe) + self.qa(qa) + self.qr(qr)).unsqueeze(1)
# hop 1: resolve the referent (identity for 1-hop questions)
r1, _ = self.rd(q, s, s, key_padding_mask=~mask, need_weights=False)
# hop 2: read the attribute of whatever hop 1 resolved
q2 = r1 + self.qa(qa).unsqueeze(1)
r2, _ = self.rd2(q2, s, s, key_padding_mask=~mask, need_weights=False)
h = r2.squeeze(1)
return self.enum(h + self.mlp(h))
def pack(rows, dev):
B = len(rows)
se = torch.zeros(B, N_SLOT, dtype=torch.long)
sa = torch.zeros(B, N_SLOT, dtype=torch.long)
sv = torch.zeros(B, N_SLOT, dtype=torch.long)
sd = torch.zeros(B, N_SLOT, dtype=torch.long)
mk = torch.zeros(B, N_SLOT, dtype=torch.bool)
for i, r in enumerate(rows):
for j, (e, a, v, dd) in enumerate(r["slots"][:N_SLOT]):
se[i, j], sa[i, j], sv[i, j], sd[i, j], mk[i, j] = e, a, v, dd, True
qe = torch.tensor([r["q_ent"] for r in rows])
qa = torch.tensor([r["q_attr"] for r in rows])
qr = torch.tensor([r["q_rel"] for r in rows])
y = torch.tensor([r["gold"] for r in rows])
lg = torch.tensor(np.stack([r["legal"] for r in rows]))
return [x.to(dev) for x in (se, sa, sv, sd, mk, qe, qa, qr, y, lg)]
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--train-lifetimes", type=int, default=3000)
ap.add_argument("--val-lifetimes", type=int, default=400)
ap.add_argument("--steps", type=int, default=3000)
args = ap.parse_args()
dev = "cuda"
print("building oracle rows ...", flush=True)
tr = [r for s in range(args.train_lifetimes) for r in build(s)]
va = [r for s in range(1_000_000, 1_000_000 + args.val_lifetimes) for r in build(s)]
print(f"train rows {len(tr)} val rows {len(va)}", flush=True)
m = OracleReader().to(dev)
opt = torch.optim.AdamW(m.parameters(), lr=3e-4, weight_decay=0.01)
rng = np.random.default_rng(0)
t0 = time.time()
for step in range(args.steps):
idx = rng.integers(0, len(tr), 256)
se, sa, sv, sd, mk, qe, qa, qr, y, lg = pack([tr[i] for i in idx], dev)
logits = m(se, sa, sv, sd, mk, qe, qa, qr).masked_fill(~enum_legal_mask(lg), -1e9)
loss = F.cross_entropy(logits, y)
opt.zero_grad(); loss.backward(); opt.step()
if (step + 1) % 500 == 0:
m.eval()
accs, per = [], {}
with torch.no_grad():
for k in range(0, len(va), 512):
ch = va[k:k + 512]
se, sa, sv, sd, mk, qe, qa, qr, y, lg = pack(ch, dev)
p = m(se, sa, sv, sd, mk, qe, qa, qr).masked_fill(~enum_legal_mask(lg), -1e9).argmax(-1)
ok = (p == y).float().cpu().numpy()
accs.extend(ok)
for r, o in zip(ch, ok):
per.setdefault(Fam(r["fam"]).name, []).append(o)
m.train()
print(f"step {step+1} loss {float(loss):.3f} val {np.mean(accs):.4f} "
+ " ".join(f"{k} {np.mean(v):.3f}" for k, v in sorted(per.items()))
+ f" ({time.time()-t0:.0f}s)", flush=True)
rep = {"val_acc": float(np.mean(accs)), "n_val": len(va),
"per_family": {k: float(np.mean(v)) for k, v in per.items()},
"n_train_rows": len(tr)}
atomic_write_json(eval_root() / "ORACLE_STATE.json", rep)
print(rep)
if __name__ == "__main__":
main()
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