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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 | """Training/eval loaders.
LifetimeBatcher: batches of whole lifetimes for the streaming PNSR trainer
(TBPTT chunks are cut by the trainer; the batcher provides full arrays).
WindowSampler: (window, position) samples for the transcript baselines.
Both materialize the live cache map from the packed per-event deltas - the
same artifact certified at generation time (no independent replay logic).
"""
from __future__ import annotations
import threading
from queue import Queue
import numpy as np
from ..data.view import Shard, shard_paths
from ..world.reducer import N_PTR_SLOTS
T_EV = 48
VAL_T, KEY_T = 12, 14
def _lifetime_arrays(sh: Shard, i: int, with_meta: bool = False) -> dict: # noqa: C901
lo, hi = int(sh.lt_off[i]), int(sh.lt_off[i + 1])
rlo, rhi = int(sh.rec_lt_off[i]), int(sh.rec_lt_off[i + 1])
L = hi - lo
tok = np.zeros((L, T_EV), np.uint16)
for e in range(lo, hi):
ids = sh.tokens[sh.ev_tok_off[e]:sh.ev_tok_off[e + 1]][:T_EV]
tok[e - lo, :len(ids)] = ids
live = np.full((L, N_PTR_SLOTS), -1, np.int16)
cur = np.full(N_PTR_SLOTS, -1, np.int16)
for e in range(lo, hi):
for rid in sh.evict_rid[sh.evict_off[e]:sh.evict_off[e + 1]]:
cur[sh.rec_slot[rlo + int(rid)]] = -1
for rid in sh.add_rid[sh.add_off[e]:sh.add_off[e + 1]]:
cur[sh.rec_slot[rlo + int(rid)]] = rid # rid = local row
live[e - lo] = cur
out = dict(
tok=tok, live=live,
etype=sh.etype[lo:hi], dt=sh.dt[lo:hi],
mode_gold=sh.mode_gold[lo:hi], family=sh.family[lo:hi],
enum_gold=sh.enum_gold[lo:hi], enum_legal=sh.enum_legal[lo:hi],
ptr_gold_slot=sh.ptr_gold_slot[lo:hi], op_gold=sh.op_gold[lo:hi],
op_arg_slots=sh.op_arg_slots[lo:hi],
rec_val_toks=sh.rec_val_toks[rlo:rhi], rec_key_toks=sh.rec_key_toks[rlo:rhi],
rec_store=sh.rec_store[rlo:rhi], rec_kind=sh.rec_kind[rlo:rhi],
rec_key=sh.rec_key[rlo:rhi], rec_ent=sh.rec_ent[rlo:rhi],
rec_birth=sh.rec_birth_ev[rlo:rhi] - lo, # lifetime-local
rec_slot=sh.rec_slot[rlo:rhi],
)
# binding addresses exist only in corpora packed after E3 (the original
# corpus predates them); consumers that need them require an E3 split.
if hasattr(sh, "bind_write"):
out.update(bind_write=sh.bind_write[lo:hi], bind_read=sh.bind_read[lo:hi],
bind_slot_ent=sh.bind_slot_ent[i],
bind_slot_attr=sh.bind_slot_attr[i])
if with_meta:
out.update(delay=sh.delay[lo:hi], kappa=sh.kappa[lo:hi],
corrected=sh.corrected[lo:hi], reverted=sh.reverted[lo:hi],
gold_val_hash=sh.gold_val_hash[lo:hi],
rec_val_hash=sh.rec_val_hash[rlo:rhi], seed=sh.lt_seed[i])
return out
def _collate(lts: list[dict], with_meta: bool = False) -> dict:
B = len(lts)
L = max(x["etype"].shape[0] for x in lts)
R = max(x["rec_store"].shape[0] for x in lts)
assert all(x["etype"].shape[0] == L for x in lts), "homogeneous L required"
b = {}
b["tok"] = np.stack([x["tok"] for x in lts])
b["live"] = np.stack([x["live"] for x in lts])
for k in ("etype", "dt", "mode_gold", "family", "enum_gold", "ptr_gold_slot",
"op_gold"):
b[k] = np.stack([x[k] for x in lts])
for k in ("bind_write", "bind_read", "bind_slot_ent", "bind_slot_attr"):
if k in lts[0]:
b[k] = np.stack([x[k] for x in lts])
b["enum_legal"] = np.stack([x["enum_legal"] for x in lts])
b["op_arg_slots"] = np.stack([x["op_arg_slots"] for x in lts])
def padded(key, dtype, extra=()):
out = np.zeros((B, R) + extra, dtype)
for j, x in enumerate(lts):
r = x[key].shape[0]
out[j, :r] = x[key]
return out
b["rec_val_toks"] = padded("rec_val_toks", np.uint16, (VAL_T,))
b["rec_key_toks"] = padded("rec_key_toks", np.uint16, (KEY_T,))
for k in ("rec_store", "rec_kind", "rec_key", "rec_ent"):
b[k] = padded(k, np.int16)
b["rec_birth"] = padded("rec_birth", np.int32)
b["rec_slot"] = padded("rec_slot", np.int16)
if with_meta:
for k in ("delay", "kappa", "corrected", "reverted"):
b[k] = np.stack([x[k] for x in lts])
b["gold_val_hash"] = np.stack([x["gold_val_hash"] for x in lts])
b["rec_val_hash"] = padded("rec_val_hash", np.uint64)
b["seed"] = np.array([x["seed"] for x in lts])
return b
class LifetimeBatcher:
def __init__(self, split: str, root, batch: int, seed: int = 0,
with_meta: bool = False, epochs: int = 1000, prefetch: int = 2,
max_lifetimes: int | None = None):
self.paths = shard_paths(split, root)
assert self.paths, f"no shards for {split} under {root}"
self.batch, self.seed, self.with_meta = batch, seed, with_meta
self.epochs = epochs
self.max_lifetimes = max_lifetimes
self.q: Queue = Queue(maxsize=prefetch)
self._t = threading.Thread(target=self._work, daemon=True)
self._t.start()
def _work(self):
rng = np.random.default_rng(self.seed)
counts = [Shard(p).n_lifetimes for p in self.paths]
index = [(pi, li) for pi, c in enumerate(counts) for li in range(c)]
if self.max_lifetimes:
index = index[: self.max_lifetimes]
for _ in range(self.epochs):
order = rng.permutation(len(index))
cache: dict[int, Shard] = {}
for k in range(0, len(order) - self.batch + 1, self.batch):
lts = []
for j in order[k:k + self.batch]:
pi, li = index[j]
if pi not in cache:
cache.clear()
cache[pi] = Shard(self.paths[pi])
lts.append(_lifetime_arrays(cache[pi], li, self.with_meta))
self.q.put(_collate(lts, self.with_meta))
self.q.put(None)
def __iter__(self):
while True:
item = self.q.get()
if item is None:
return
yield item
def iter_eval_batches(split: str, root, batch: int, limit: int | None = None):
"""Deterministic order, with meta, no shuffling; loads shard by shard."""
n = 0
for p in shard_paths(split, root):
sh = Shard(p)
for k in range(0, sh.n_lifetimes, batch):
idx = range(k, min(k + batch, sh.n_lifetimes))
yield _collate([_lifetime_arrays(sh, i, True) for i in idx], True)
n += len(idx)
if limit and n >= limit:
return
class WindowSampler:
"""Supervised (window, position) samples for TX models."""
def __init__(self, split, root, batch, window, seed=0, noout_frac=0.15,
epochs=1000, prefetch=2):
self.paths = shard_paths(split, root)
self.batch, self.window, self.noout_frac = batch, window, noout_frac
self.seed, self.epochs = seed, epochs
self.q: Queue = Queue(maxsize=prefetch)
threading.Thread(target=self._work, daemon=True).start()
def _work(self):
rng = np.random.default_rng(self.seed + 7)
for _ in range(self.epochs):
for p in rng.permutation(self.paths):
sh = Shard(p)
rows = []
for i in range(sh.n_lifetimes):
lo, hi = int(sh.lt_off[i]), int(sh.lt_off[i + 1])
sup = np.nonzero(sh.mode_gold[lo:hi] > 0)[0]
non = np.nonzero(sh.mode_gold[lo:hi] == 0)[0]
keep = non[rng.random(len(non)) < self.noout_frac]
for e in np.concatenate([sup, keep]):
rows.append((i, int(e)))
rng.shuffle(rows)
for k in range(0, len(rows) - self.batch + 1, self.batch):
chunk = sorted(rows[k:k + self.batch])
self.q.put(self._build(sh, chunk))
self.q.put(None)
def _build(self, sh, chunk):
W = self.window
toks = np.zeros((len(chunk), W), np.uint16)
lts: dict[int, dict] = {}
picks = []
for j, (i, e) in enumerate(chunk):
if i not in lts:
if len(lts) > 64:
lts.clear()
lts[i] = _lifetime_arrays(sh, i, with_meta=True)
lt = lts[i]
lo = int(sh.lt_off[i])
t_end = int(sh.ev_tok_off[lo + e + 1])
t_start = max(int(sh.ev_tok_off[lo]), t_end - W)
seg = sh.tokens[t_start:t_end]
toks[j, W - len(seg):] = seg
picks.append((lt, e))
b = dict(tok=toks)
for key, dt_ in (("etype", np.uint8), ("mode_gold", np.uint8),
("family", np.int8), ("enum_gold", np.int16),
("ptr_gold_slot", np.int16), ("op_gold", np.int8),
("delay", np.int32), ("corrected", np.int8),
("reverted", np.int8)):
b[key] = np.array([lt[key][e] for lt, e in picks], dt_)
b["enum_legal"] = np.stack([lt["enum_legal"][e] for lt, e in picks])
b["op_arg_slots"] = np.stack([lt["op_arg_slots"][e] for lt, e in picks])
b["gold_val_hash"] = np.array([lt["gold_val_hash"][e] for lt, e in picks],
np.uint64)
b["lt_seed"] = np.array([int(lt["seed"]) for lt, _ in picks], np.int64)
# per-sample live bank (variable R -> pad)
R = max(lt["rec_store"].shape[0] for lt, _ in picks)
for key, dt_, extra in (("rec_val_toks", np.uint16, (VAL_T,)),
("rec_key_toks", np.uint16, (KEY_T,)),
("rec_store", np.int16, ()), ("rec_kind", np.int16, ()),
("rec_key", np.int16, ()), ("rec_ent", np.int16, ()),
("rec_birth", np.int32, ()), ("rec_slot", np.int16, ()),
("rec_val_hash", np.uint64, ())):
arr = np.zeros((len(picks), R) + extra, dt_)
for j, (lt, _) in enumerate(picks):
r = lt[key].shape[0]
arr[j, :r] = lt[key]
b[key] = arr
b["live"] = np.stack([lt["live"][e] for lt, e in picks])
b["ev_idx"] = np.array([e for _, e in picks], np.int32)
return b
def __iter__(self):
while True:
item = self.q.get()
if item is None:
return
yield item
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