File size: 10,914 Bytes
5952424 | 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 | """Collate shard records into model-ready batches: noise, then bin-pack.
Pipeline per record: raw planes (chars/boundary/dia/cap/punct) -> noise_sequence (elastic
patterns change length) -> pack B records into fixed-width rows, building seg_id for
doc-masked attention (multiple documents per row, ~99% fill vs ~20% one-per-row).
Aux-head labels (boundary/dia/cap/punct) are supervised only where the character was masked
OR the corresponding input channel was dropped for that position — elsewhere it would just
be copy-through, teaching the model nothing.
"""
from __future__ import annotations
import numpy as np
import torch
from train.noising import NoiseConfig, noise_sequence
UNK_BND, UNK_DIA, UNK_PUNCT = 3, 48, 6
# Metadata-conditioning ids (insc/data/meta_vocab.py's UNK_REGION/UNK_CENTURY) -- kept as
# plain constants here, not imported, so base pretraining (which never sets these) doesn't
# pick up a dependency on the insc-only package. Records without region_id/century_id
# (every non-insc shard) fall back to these UNK rows; CharBertEncoder only reads them at
# all when its optional e_region/e_century tables are enabled (n_region/n_century > 0).
UNK_REGION, UNK_CENTURY = 14, 15
def _dropped_region_century(rec, cfg, g):
"""Per-DOCUMENT metadata dropout (not per-position): with probability p_region_none /
p_century_none, force UNK regardless of whether the true value is known -- so the model
is trained under both "metadata given" and "metadata withheld" conditions (a real
fragment's provenance/date is often genuinely unknown at inference too). Independent
draws: knowing a find-spot doesn't imply knowing the date or vice versa."""
region_id = rec.get("region_id", UNK_REGION)
century_id = rec.get("century_id", UNK_CENTURY)
if cfg.p_region_none > 0 and torch.rand(1, generator=g).item() < cfg.p_region_none:
region_id = UNK_REGION
if cfg.p_century_none > 0 and torch.rand(1, generator=g).item() < cfg.p_century_none:
century_id = UNK_CENTURY
return region_id, century_id
def pack_batch(record_iter, cfg: NoiseConfig, T_char, rows, g):
"""Greedy sequence packing: pull records, noise them, bin-pack into `rows` rows of width
T_char. Each doc within a row gets a distinct seg id (1,2,..) so attention never crosses
a document boundary (block-diagonal, not a context discount)."""
B = rows
ids = torch.full((B, T_char), cfg.pad_id, dtype=torch.long)
bnd_in = torch.full((B, T_char), UNK_BND, dtype=torch.long)
dia_in = torch.full((B, T_char), UNK_DIA, dtype=torch.long)
punct_in = torch.full((B, T_char), UNK_PUNCT, dtype=torch.long)
region_in = torch.full((B, T_char), UNK_REGION, dtype=torch.long)
century_in = torch.full((B, T_char), UNK_CENTURY, dtype=torch.long)
seg = torch.zeros(B, T_char, dtype=torch.long)
labels = torch.full((B, T_char), -100, dtype=torch.long)
loss_w = torch.zeros(B, T_char)
bnd_lab = torch.full((B, T_char), -100, dtype=torch.long)
dia_lab = torch.full((B, T_char), -100, dtype=torch.long)
cap_lab = torch.full((B, T_char), -100, dtype=torch.long)
punct_lab = torch.full((B, T_char), -100, dtype=torch.long)
for b in range(B):
cpos = 0 # char cursor in this row
doc = 0 # doc id within row
misses = 0 # consecutive records that didn't fit the tail
while True:
rec = next(record_iter)
chars = torch.from_numpy(rec["chars"].astype(np.int64))
boundary = torch.from_numpy(rec["boundary"].astype(np.int64))
real_lac = (torch.from_numpy(rec["is_real_lacuna"]) if "is_real_lacuna" in rec
else None)
out = noise_sequence(chars, boundary.to(torch.uint8), cfg, g, is_real_lacuna=real_lac)
L = out["input_ids"].numel()
if L > T_char: # over-long single doc: truncate to fit an empty row
L = T_char
if cpos + L > T_char:
if cpos == 0: # row empty: force-place a truncated copy so we never stall
L = T_char
else: # try a few more (smaller) records before giving up on this row
misses += 1
if misses >= 6:
break
continue
misses = 0
doc += 1
sl = slice(cpos, cpos + L)
ids[b, sl] = out["input_ids"][:L]
labels[b, sl] = out["labels"][:L]
loss_w[b, sl] = out["loss_w"][:L]
seg[b, sl] = doc
region_id, century_id = _dropped_region_century(rec, cfg, g)
region_in[b, sl] = region_id
century_in[b, sl] = century_id
kb = out["keep_bnd_mask"][:L]; kd = out["keep_dia_mask"][:L]; kp = out["keep_punct_mask"][:L]
bnd_true = out["boundary"][:L].long().clamp(max=2)
bnd_in[b, sl] = torch.where(kb, bnd_true, torch.full_like(bnd_true, UNK_BND))
if not out["rebuilt"]:
dia_true = torch.from_numpy(rec["dia"].astype(np.int64))[:L]
dia_in[b, sl] = torch.where(kd, dia_true, torch.full_like(dia_true, UNK_DIA))
punct_true = torch.from_numpy(rec["punct"].astype(np.int64))[:L]
punct_in[b, sl] = torch.where(kp, punct_true, torch.full_like(punct_true, UNK_PUNCT))
masked = ids[b, sl] == cfg.mask_id
if not out["rebuilt"]:
Lc = min(L, chars.numel())
# real-lacuna positions have no ground truth for ANY channel (not just chars)
# -- exclude them from aux supervision too, same as the char loss.
not_lac = (~real_lac[:Lc] if real_lac is not None
else torch.ones(Lc, dtype=torch.bool))
sup_b = (masked[:Lc] | (~kb[:Lc])) & not_lac
sup_d = (masked[:Lc] | (~kd[:Lc])) & not_lac
sup_p = (masked[:Lc] | (~kp[:Lc])) & not_lac
sup_c = masked[:Lc] & not_lac
bnd_t = boundary[:Lc]; dia_t = torch.from_numpy(rec["dia"].astype(np.int64))[:Lc]
cap_t = torch.from_numpy(rec["cap"].astype(np.int64))[:Lc]
punct_t = torch.from_numpy(rec["punct"].astype(np.int64))[:Lc]
bnd_lab[b, cpos:cpos+Lc] = torch.where(sup_b, bnd_t, torch.full_like(bnd_t, -100))
dia_lab[b, cpos:cpos+Lc] = torch.where(sup_d, dia_t, torch.full_like(dia_t, -100))
cap_lab[b, cpos:cpos+Lc] = torch.where(sup_c, cap_t, torch.full_like(cap_t, -100))
punct_lab[b, cpos:cpos+Lc] = torch.where(sup_p, punct_t, torch.full_like(punct_t, -100))
cpos += L
if cpos >= T_char - 8:
break
return dict(
input_ids=ids, boundary=bnd_in, dia=dia_in, punct=punct_in, seg_id=seg,
region=region_in, century=century_in,
labels=labels, loss_w=loss_w, bnd_lab=bnd_lab, dia_lab=dia_lab, cap_lab=cap_lab,
punct_lab=punct_lab,
)
def collate(records, cfg: NoiseConfig, T_char, g):
"""records: list of dicts with chars/boundary/dia/cap/punct (np.uint8 arrays). One doc
per row (no packing) — used for eval batches where records are already pre-selected."""
B = len(records)
ids = torch.full((B, T_char), cfg.pad_id, dtype=torch.long)
bnd_in = torch.full((B, T_char), UNK_BND, dtype=torch.long)
dia_in = torch.full((B, T_char), UNK_DIA, dtype=torch.long)
punct_in = torch.full((B, T_char), UNK_PUNCT, dtype=torch.long)
region_in = torch.full((B, T_char), UNK_REGION, dtype=torch.long)
century_in = torch.full((B, T_char), UNK_CENTURY, dtype=torch.long)
seg = torch.zeros(B, T_char, dtype=torch.long)
labels = torch.full((B, T_char), -100, dtype=torch.long)
loss_w = torch.zeros(B, T_char)
bnd_lab = torch.full((B, T_char), -100, dtype=torch.long)
dia_lab = torch.full((B, T_char), -100, dtype=torch.long)
cap_lab = torch.full((B, T_char), -100, dtype=torch.long)
punct_lab = torch.full((B, T_char), -100, dtype=torch.long)
for b, rec in enumerate(records):
chars = torch.from_numpy(rec["chars"].astype(np.int64))
boundary = torch.from_numpy(rec["boundary"].astype(np.int64))
real_lac = (torch.from_numpy(rec["is_real_lacuna"]) if "is_real_lacuna" in rec
else None)
out = noise_sequence(chars, boundary.to(torch.uint8), cfg, g, is_real_lacuna=real_lac)
seqlen = min(out["input_ids"].numel(), T_char)
ids[b, :seqlen] = out["input_ids"][:seqlen]
labels[b, :seqlen] = out["labels"][:seqlen]
loss_w[b, :seqlen] = out["loss_w"][:seqlen]
seg[b, :seqlen] = b + 1
region_id, century_id = _dropped_region_century(rec, cfg, g)
region_in[b, :seqlen] = region_id
century_in[b, :seqlen] = century_id
kb = out["keep_bnd_mask"][:seqlen]; kd = out["keep_dia_mask"][:seqlen]
kp = out["keep_punct_mask"][:seqlen]
bnd_true = out["boundary"][:seqlen].long().clamp(max=2)
bnd_in[b, :seqlen] = torch.where(kb, bnd_true, torch.full_like(bnd_true, UNK_BND))
if not out["rebuilt"]:
dia_true = torch.from_numpy(rec["dia"].astype(np.int64))[:seqlen]
dia_in[b, :seqlen] = torch.where(kd, dia_true, torch.full_like(dia_true, UNK_DIA))
punct_true = torch.from_numpy(rec["punct"].astype(np.int64))[:seqlen]
punct_in[b, :seqlen] = torch.where(kp, punct_true, torch.full_like(punct_true, UNK_PUNCT))
masked = ids[b] == cfg.mask_id
if not out["rebuilt"]:
L = min(seqlen, chars.numel())
not_lac = (~real_lac[:L] if real_lac is not None
else torch.ones(L, dtype=torch.bool))
sup_b = (masked[:L] | (~kb[:L])) & not_lac
sup_d = (masked[:L] | (~kd[:L])) & not_lac
sup_p = (masked[:L] | (~kp[:L])) & not_lac
sup_c = masked[:L] & not_lac
cap_t = torch.from_numpy(rec["cap"].astype(np.int64))
bnd_t = boundary.clone()
dia_t = torch.from_numpy(rec["dia"].astype(np.int64))
punct_t = torch.from_numpy(rec["punct"].astype(np.int64))
bnd_lab[b, :L] = torch.where(sup_b, bnd_t[:L], torch.full_like(bnd_t[:L], -100))
dia_lab[b, :L] = torch.where(sup_d, dia_t[:L], torch.full_like(dia_t[:L], -100))
cap_lab[b, :L] = torch.where(sup_c, cap_t[:L], torch.full_like(cap_t[:L], -100))
punct_lab[b, :L] = torch.where(sup_p, punct_t[:L], torch.full_like(punct_t[:L], -100))
return dict(
input_ids=ids, boundary=bnd_in, dia=dia_in, punct=punct_in, seg_id=seg,
region=region_in, century=century_in,
labels=labels, loss_w=loss_w, bnd_lab=bnd_lab, dia_lab=dia_lab, cap_lab=cap_lab,
punct_lab=punct_lab,
)
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