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57e06d7 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 | """Frame prototype Phase 0 — instruments + full extraction (v0.1).
1. T5 walk table of C (instrument-first: entry loss needs whole-walk
census; only the wordpiece walk existed).
2. Boundary-context profiles per state (prev/next byte distributions
at attested sites) for the byte-structural codebook init.
3. Extraction v2: ALL sites, no cap. Unmasked anchor readouts
(byte-anchored first subtoken + last subtoken, k per side) AND
masked-span readouts (bert: [MASK]*k, read first mask; t5:
<extra_id_0> replacing span, read sentinel in encoder) — the
reading-vs-guessing control. CUDA, <18GB.
"""
import json
import sys
sys.path.insert(0, r"E:\mirel\geolip-bytelex")
sys.stdout.reconfigure(encoding="utf-8", errors="replace")
import numpy as np
import torch
import transformers
from transformers import AutoTokenizer, AutoModel, T5EncoderModel
import transformers.utils.logging as hlog
hlog.set_verbosity_error()
CODEX = r"E:\mirel\data\bytelex\codex_v1.txt"
WORDS = r"E:\mirel\data\bytelex\words_of_C.json"
OUTDIR = r"E:\mirel\data\bytelex\proto_frame"
SEPS = set(b" \t\n\r.,;:!?\"'()[]{}")
L_BERT, L_T5 = 8, 6
DEV = "cuda"
BATCH = 256
states = json.load(open(WORDS, encoding="utf-8"))
sid_of = {s["text"]: i for i, s in enumerate(states)}
tkB = AutoTokenizer.from_pretrained("bert-base-uncased")
tkA = AutoTokenizer.from_pretrained("google/flan-t5-small")
# ---- 1. t5 walk table of C
t5_walk = []
for s in states:
ids = tkA(s["text"], add_special_tokens=False)["input_ids"]
toks = tkA.convert_ids_to_tokens(ids)
t5_walk.append({"text": s["text"], "count": s["count"],
"k": len(ids), "seg": "|".join(toks),
"ids": ids,
"whole": len(ids) == 1})
n_whole = sum(w["whole"] for w in t5_walk)
with open(rf"{OUTDIR}\t5_walk_of_C.json", "w", encoding="utf-8") as f:
json.dump(t5_walk, f, indent=0)
print(f"[P0] t5 walk of C: {n_whole}/999 whole", flush=True)
# ---- sites (ALL, no cap) + 2. boundary-context profiles
lines = open(CODEX, "rb").read().decode("ascii").split("\n")
sites = []
prev_ctx = np.zeros((999, 256))
next_ctx = np.zeros((999, 256))
for li, ln in enumerate(lines):
if not ln:
continue
raw = ln.encode("ascii") + b" "
lo = None
for j, ch in enumerate(raw):
if ch in SEPS:
if lo is not None:
sid = sid_of.get(ln[lo:j])
if sid is not None:
sites.append((li, lo, j, sid))
prev_ctx[sid, raw[lo - 1] if lo else 32] += 1
next_ctx[sid, ch] += 1
lo = None
elif lo is None:
lo = j
np.savez_compressed(rf"{OUTDIR}\ctx_profiles.npz",
prev_ctx=prev_ctx, next_ctx=next_ctx)
print(f"[P0] {len(sites)} sites (uncapped), ctx profiles saved",
flush=True)
mB = AutoModel.from_pretrained("bert-base-uncased").to(DEV).eval()
mT = T5EncoderModel.from_pretrained("google/flan-t5-small").to(DEV).eval()
embT = mT.get_input_embeddings()
SENT = tkA.convert_tokens_to_ids("<extra_id_0>")
MASK = tkB.mask_token_id
N = len(sites)
H_B = np.zeros((N, 2, 768), dtype=np.float16) # first,last
H_T = np.zeros((N, 2, 512), dtype=np.float32)
M_B = np.zeros((N, 768), dtype=np.float16) # masked readout
M_T = np.zeros((N, 512), dtype=np.float32)
E_T = np.zeros((N, 512), dtype=np.float32) # t5 input emb (first)
KK = np.zeros((N, 2), dtype=np.int16) # k_B, k_T
# ---- unmasked pass, batched by line
by_line = {}
for k, (li, lo, hi, sid) in enumerate(sites):
by_line.setdefault(li, []).append(k)
line_ids = sorted(by_line)
tokcacheB, tokcacheT = {}, {}
with torch.no_grad():
for bs in range(0, len(line_ids), BATCH):
chunk = line_ids[bs:bs + BATCH]
texts = [lines[li] for li in chunk]
eb = tkB(texts, return_offsets_mapping=True, padding=True,
return_tensors="pt")
et = tkA(texts, return_offsets_mapping=True, padding=True,
return_tensors="pt")
hb = mB(input_ids=eb["input_ids"].to(DEV),
attention_mask=eb["attention_mask"].to(DEV),
output_hidden_states=True).hidden_states[L_BERT].cpu()
ht = mT(input_ids=et["input_ids"].to(DEV),
attention_mask=et["attention_mask"].to(DEV),
output_hidden_states=True).hidden_states[L_T5].cpu()
em = embT(et["input_ids"].to(DEV)).cpu()
for r, li in enumerate(chunk):
offB = eb["offset_mapping"][r].tolist()
offT = et["offset_mapping"][r].tolist()
idsB = eb["input_ids"][r].tolist()
idsT = et["input_ids"][r].tolist()
tokcacheB[li] = (idsB, offB)
tokcacheT[li] = (idsT, offT)
for k in by_line[li]:
_, lo, hi, sid = sites[k]
ixB = [i for i, (s, t) in enumerate(offB)
if t > s and s < hi and t > lo]
ixT = [i for i, (s, t) in enumerate(offT)
if t > s and s < hi and t > lo]
if not ixB or not ixT:
KK[k] = (0, 0)
continue
H_B[k, 0] = hb[r, ixB[0]].numpy()
H_B[k, 1] = hb[r, ixB[-1]].numpy()
H_T[k, 0] = ht[r, ixT[0]].numpy()
H_T[k, 1] = ht[r, ixT[-1]].numpy()
E_T[k] = em[r, ixT[0]].numpy()
KK[k] = (len(ixB), len(ixT))
if (bs // BATCH) % 5 == 0:
print(f"[P0-unmasked] {bs}/{len(line_ids)} lines", flush=True)
print("[P0] unmasked pass done", flush=True)
# ---- masked pass: one sequence per SITE, id-spliced
def masked_batchB(ks):
seqs, poss = [], []
for k in ks:
li, lo, hi, sid = sites[k]
idsB, offB = tokcacheB[li]
ix = [i for i, (s, t) in enumerate(offB)
if t > s and s < hi and t > lo]
pre = [idsB[i] for i, (s, t) in enumerate(offB)
if t > s and t <= lo]
post = [idsB[i] for i, (s, t) in enumerate(offB)
if t > s and s >= hi]
seqs.append([tkB.cls_token_id] + pre + [MASK] * max(len(ix), 1)
+ post + [tkB.sep_token_id])
poss.append(1 + len(pre))
return seqs, poss
def masked_batchT(ks):
seqs, poss = [], []
for k in ks:
li, lo, hi, sid = sites[k]
ln = lines[li]
src = ln[:lo] + "<extra_id_0>" + ln[hi:]
ids = tkA(src, add_special_tokens=False)["input_ids"]
try:
p = ids.index(SENT)
except ValueError:
p = 0
seqs.append(ids)
poss.append(p)
return seqs, poss
def run_masked(model, seqs, poss, layer, pad_id):
mx = max(len(s) for s in seqs)
ids = torch.full((len(seqs), mx), pad_id, dtype=torch.long)
att = torch.zeros((len(seqs), mx), dtype=torch.long)
for i, s in enumerate(seqs):
ids[i, :len(s)] = torch.tensor(s)
att[i, :len(s)] = 1
with torch.no_grad():
h = model(input_ids=ids.to(DEV), attention_mask=att.to(DEV),
output_hidden_states=True).hidden_states[layer].cpu()
return h[torch.arange(len(seqs)), torch.tensor(poss)]
order = [k for k in range(N) if KK[k, 0] > 0]
for bs in range(0, len(order), BATCH):
ks = order[bs:bs + BATCH]
sq, ps = masked_batchB(ks)
out = run_masked(mB, sq, ps, L_BERT, tkB.pad_token_id)
for j, k in enumerate(ks):
M_B[k] = out[j].numpy()
sq, ps = masked_batchT(ks)
out = run_masked(mT, sq, ps, L_T5, tkA.pad_token_id)
for j, k in enumerate(ks):
M_T[k] = out[j].numpy()
if (bs // BATCH) % 20 == 0:
print(f"[P0-masked] {bs}/{len(order)} sites", flush=True)
np.savez_compressed(
rf"{OUTDIR}\frame_dump_v2.npz",
H_B=H_B, H_T=H_T, M_B=M_B, M_T=M_T, E_T=E_T, KK=KK,
sid=np.array([s[3] for s in sites], dtype=np.int32),
line=np.array([s[0] for s in sites], dtype=np.int32),
lo=np.array([s[1] for s in sites], dtype=np.int32),
hi=np.array([s[2] for s in sites], dtype=np.int32))
meta = {"n_sites": N, "skipped": int(N - len(order)),
"layers": {"bert": L_BERT, "t5": L_T5},
"t5_whole": n_whole,
"env": {"transformers": transformers.__version__,
"torch": torch.__version__}}
with open(rf"{OUTDIR}\phase0_meta.json", "w", encoding="utf-8") as f:
json.dump(meta, f, indent=1)
print(f"[P0] COMPLETE: {len(order)}/{N} sites, "
f"vram peak {torch.cuda.max_memory_allocated()/2**30:.1f}GB",
flush=True)
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