File size: 7,556 Bytes
10d4526 | 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 | """v0.2 sequence entry: position-specific linear maps G_0..G_3 emit
the length-matched embedding sequence for ANY state (k<=4), opening
entry from 149 whole-walk states to the full inventory. Same
generalization discipline: fit on train states, probe with UNSEEN
states; counterfactual restricted to SAME-k pairs (length confound
excluded); random-codebook control retained (Gate 4)."""
import json
import sys
import zlib
sys.path.insert(0, r"E:\mirel\geolip-bytelex")
sys.stdout.reconfigure(encoding="utf-8", errors="replace")
import numpy as np
import torch
import torch.nn.functional as F
import transformers
from transformers import AutoTokenizer, T5EncoderModel
import transformers.utils.logging as hlog
hlog.set_verbosity_error()
D = r"E:\mirel\data\bytelex\proto_frame"
DEV = "cuda"
SEED = zlib.crc32(b"frame-proto-v01") & 0xFFFFFFFF
L_T5 = 6
KMAX = 4
torch.manual_seed(SEED)
from geolip.bytelex.frame import (apply_whitening, fit_whitening,
split_sites)
dump = np.load(rf"{D}\frame_dump_v2.npz")
anch = np.load(rf"{D}\frame_anchors_v01.npz")
walk = json.load(open(rf"{D}\t5_walk_of_C.json", encoding="utf-8"))
sid = dump["sid"].astype(np.int64)
KK = dump["KK"]
ok = KK[:, 0] > 0
sp = split_sites(sid[ok], seed=SEED)
gix = {k: np.flatnonzero(ok)[v] for k, v in sp.items()}
kk = np.array([w["k"] for w in walk])
ids_of = [w["ids"] for w in walk]
usable = np.flatnonzero((kk >= 1) & (kk <= KMAX))
KMAX = int(kk[usable].max())
print(f"[v02e] observed KMAX={KMAX}, k census: "
f"{np.bincount(kk[usable]).tolist()}", flush=True)
rngw = np.random.default_rng(SEED + 41)
perm = rngw.permutation(len(usable))
gfit = usable[perm[:int(0.6 * len(usable))]]
gprobe = usable[perm[int(0.6 * len(usable)):]]
print(f"[v02e] usable {len(usable)}/999 (k<=4) -> fit {len(gfit)} / "
f"probe {len(gprobe)}", flush=True)
tkA = AutoTokenizer.from_pretrained("google/flan-t5-small")
mT = T5EncoderModel.from_pretrained("google/flan-t5-small").to(DEV)
mT.eval()
for p in mT.parameters():
p.requires_grad_(False)
emb = mT.get_input_embeddings().weight.detach()
E_C = torch.tensor(anch["E_C"], dtype=torch.float32, device=DEV)
E_Cn = F.normalize(E_C, dim=-1)
W_S = torch.tensor(anch["W_S"], dtype=torch.float32, device=DEV)
H_T = dump["H_T"][:, 0].astype(np.float64)
muT, wTw = fit_whitening(H_T[gix["fit"]])
tmu = torch.tensor(muT, dtype=torch.float32, device=DEV)
tw = torch.tensor(wTw, dtype=torch.float32, device=DEV)
def frame_readout(h):
return F.normalize(((h - tmu) @ tw) @ W_S, dim=-1)
def fit_seq_g(E_use):
"""G_i: 256->512 per piece position, lstsq on fit states with
k > i. Returns (list of G, per-position held-out residual)."""
Gs, res = [], []
for i in range(KMAX):
fs = [int(u) for u in gfit if kk[u] > i]
ps = [int(u) for u in gprobe if kk[u] > i]
a = E_use[torch.tensor(fs, device=DEV)].double().cpu().numpy()
b = emb[torch.tensor([ids_of[u][i] for u in fs], device=DEV)
].double().cpu().numpy()
g, *_ = np.linalg.lstsq(a, b, rcond=None)
if ps:
ah = E_use[torch.tensor(ps, device=DEV)
].double().cpu().numpy()
bh = emb[torch.tensor([ids_of[u][i] for u in ps],
device=DEV)].double().cpu().numpy()
res.append(round(float(np.linalg.norm(ah @ g - bh)
/ np.linalg.norm(bh)), 4))
Gs.append(torch.tensor(g, dtype=torch.float32, device=DEV))
return Gs, res
G_byte, res_byte = fit_seq_g(E_C)
E_rand = F.normalize(torch.randn_like(E_C), dim=-1)
G_rand, res_rand = fit_seq_g(E_rand)
print(f"[v02e] held-out residuals byte={res_byte} rand={res_rand}",
flush=True)
lines = open(r"E:\mirel\data\bytelex\codex_v1.txt",
"rb").read().decode("ascii").split("\n")
line_tok = {}
def enc_line(li):
if li not in line_tok:
e = tkA(lines[li], return_offsets_mapping=True)
line_tok[li] = (e["input_ids"], e["offset_mapping"])
return line_tok[li]
probe_by_k = {}
for u in gprobe:
probe_by_k.setdefault(int(kk[u]), []).append(int(u))
ev_cand = [k for k in gix["eval"]
if int(kk[sid[k]]) in probe_by_k
and len(probe_by_k[int(kk[sid[k]])]) > 1
and KK[k, 1] == int(kk[sid[k]])]
rng = np.random.default_rng(SEED + 31)
ev_cand = rng.permutation(ev_cand)[:1200]
print(f"[v02e] probe sites: {len(ev_cand)}", flush=True)
def probe(Gs, E_use, tag):
flips = stuck = n = 0
div = 0.0
nb = 0
rngp = np.random.default_rng(SEED + 37)
BS = 48
for i in range(0, len(ev_cand), BS):
seqs, spans, alts, poss = [], [], [], []
for k in ev_cand[i:i + BS]:
li = int(dump["line"][k])
ids, off = enc_line(li)
lo, hi = int(dump["lo"][k]), int(dump["hi"][k])
ix = [j for j, (s, t) in enumerate(off)
if t > s and s < hi and t > lo]
u = int(sid[k])
cand = probe_by_k[int(kk[u])]
up = cand[int(rngp.integers(0, len(cand)))]
while up == u:
up = cand[int(rngp.integers(0, len(cand)))]
if len(ix) != int(kk[u]):
continue
seqs.append(ids)
spans.append(ix)
alts.append(up)
poss.append(ix[0])
if not seqs:
continue
mx = max(len(s) for s in seqs)
idt = torch.full((len(seqs), mx), tkA.pad_token_id,
dtype=torch.long)
att = torch.zeros((len(seqs), mx), dtype=torch.long)
for j, s in enumerate(seqs):
idt[j, :len(s)] = torch.tensor(s)
att[j, :len(s)] = 1
idt, att = idt.to(DEV), att.to(DEV)
with torch.no_grad():
clean = mT(input_ids=idt, attention_mask=att,
output_hidden_states=True).hidden_states[L_T5]
x = emb[idt].clone()
for j, (ix, up) in enumerate(zip(spans, alts)):
for pi, p in enumerate(ix):
x[j, p] = E_use[up] @ Gs[pi]
subh = mT(inputs_embeds=x, attention_mask=att,
output_hidden_states=True).hidden_states[L_T5]
div += float((((subh - clean) ** 2).sum(-1) * att).sum()
/ att.sum() / (clean ** 2).sum(-1).mean())
nb += 1
zr = frame_readout(subh[torch.arange(len(seqs)),
torch.tensor(poss, device=DEV)])
pred = (zr @ E_Cn.T).argmax(-1).cpu().numpy()
flips += int((pred == np.array(alts)).sum())
stuck += int((pred == np.array(
[sid[k] for k in ev_cand[i:i + BS]][:len(seqs)])).sum())
n += len(seqs)
return {"n": n, "flip_to_injected": round(flips / max(n, 1), 4),
"stuck_on_true": round(stuck / max(n, 1), 4),
"rel_divergence": round(div / max(nb, 1), 4)}
res = {"byte_codebook": probe(G_byte, E_C, "byte"),
"random_codebook_control": probe(G_rand, E_rand, "rand"),
"heldout_residuals": {"byte": res_byte, "random": res_rand},
"g_split": {"usable": int(len(usable)), "fit": int(len(gfit)),
"probe": int(len(gprobe))},
"_env": {"transformers": transformers.__version__,
"torch": torch.__version__}}
with open(rf"{D}\v02_entry_ledger.json", "w", encoding="utf-8") as f:
json.dump(res, f, indent=1)
print(json.dumps(res, indent=1), flush=True)
print("[v02e] SEQUENCE ENTRY COMPLETE", flush=True)
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