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99297d7 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 | """vimeml-tiny-zh-v1 arm mixtures (docs/zh-CN/training-zh-v1-plan.md).
Web: every window of the arm's pool (train.tiers.bin <= max tier: P1 3, P5 2,
P20 1) once per epoch, prefix-cropped per epoch. Chat: one run-long sequence of
passes over LCCC train; pass k is a fresh permutation read through chat copy k,
a crop view with its own seed, so a repeated utterance gets a new crop whether
the arm has 1 epoch or 20. After epoch e the chat targets taken so far are
round(web targets so far * f / (1 - f)). Each epoch map lists joined window ids,
shuffled; maps are SHA256-pinned and must rebuild byte-identically.
This module does not import torch.
"""
import json
from pathlib import Path
import numpy as np
from vimeml.training.data import SentenceWindowDataset, file_sha
from vimeml.training.data_mixture import JoinedPrefixWindows, MixtureWindowDataset
from vimeml.training.data_v2 import MASK64, PrefixCropWindowDataset
from vimeml.training.v41_data import window_sentences
ROOT = Path(__file__).resolve().parents[3]
FORMAT = "vimeml_zh13_mixture_v1"
TAGS = {8192: "8k", 12288: "12k"}
POOLS = {"P1": 3, "P5": 2, "P20": 1} # max train.tiers.bin value kept
PROBE_TIER = 0
CHAT_FRACTION = 0.05
CROP_PROBABILITY = 0.30
CROP_MINIMUM = 8
CHAT_COPIES = 8
CHAT_SEED_STEP = 0x9E3779B97F4A7C15
CHUNK = 1 << 20
# arm -> (vocabulary, pool, web epochs, seed). Seeds drawn once with secrets.randbelow(2**31).
ARMS = {
"12k-fresh-s1": (12288, "P1", 1, 1591996171),
"12k-fresh-s2": (12288, "P1", 1, 136617650),
"12k-fresh-s3": (12288, "P1", 1, 1137767227),
"12k-ep5": (12288, "P5", 5, 1591996171),
"12k-ep20": (12288, "P20", 20, 1591996171),
"8k-fresh": (8192, "P1", 1, 1591996171),
"8k-ep5": (8192, "P5", 5, 1591996171),
"8k-ep20": (8192, "P20", 20, 1591996171),
}
def read(path):
return json.loads(Path(path).read_text("utf-8"))
def write_lf(path, value):
path = Path(path)
temporary = path.with_suffix(path.suffix + ".tmp")
temporary.write_bytes((json.dumps(value, ensure_ascii=False, indent=1) + "\n").encode("utf-8"))
temporary.replace(path)
def store_paths(vocab, data_root="artifacts"):
tag = TAGS[vocab]
return {"web": (f"{data_root}/token-data/zh13-web-{tag}", f"{data_root}/training-data/zh13-web-{tag}-c128"),
"chat": (f"{data_root}/token-data/zh13-chat-{tag}", f"{data_root}/training-data/zh13-chat-{tag}-c128")}
def mixture_directory(arm, data_root="artifacts"):
return f"{data_root}/training-data/zh13-{arm}-c128"
def request_for(arm, root=ROOT, data_root="artifacts"):
vocab, pool, epochs, seed = ARMS[arm]
paths = store_paths(vocab, data_root)
stores = []
for name, (tokens, index) in paths.items():
manifest = read(Path(root) / tokens / "manifest.json")
if manifest["vocab_size"] != vocab:
raise ValueError(f"{tokens} is not a {vocab} store")
record = {"name": name, "token_dir": tokens, "index_dir": index,
"token_manifest_sha256": file_sha(Path(root) / tokens / "manifest.json"),
"window_manifest_sha256": file_sha(Path(root) / index / "manifest.json"),
"tokenizer_model_sha256": manifest["tokenizer_model_sha256"]}
stores.append(record)
web, chat = stores
if web["tokenizer_model_sha256"] != chat["tokenizer_model_sha256"]:
raise ValueError("Web and chat stores use different tokenizers")
web_manifest = read(Path(root) / web["token_dir"] / "manifest.json")
return {"format": FORMAT, "arm": arm, "vocab_size": vocab, "pool": pool, "max_tier": POOLS[pool],
"epochs": epochs, "seed": seed, "chat_fraction": CHAT_FRACTION,
"crop_probability": CROP_PROBABILITY, "crop_minimum": CROP_MINIMUM, "chat_copies": CHAT_COPIES,
"chat_seed_step": CHAT_SEED_STEP, "pools_sha256": web_manifest["pools_sha256"],
"tokenizer_model_sha256": web["tokenizer_model_sha256"],
# MixtureWindowDataset checks these against the primary (web) store.
"token_manifest_sha256": web["token_manifest_sha256"], "index_dir": web["index_dir"],
"window_manifest_sha256": web["window_manifest_sha256"], "web": web, "chat": chat}
def chat_seed(seed, copy):
return (seed + (copy + 1) * CHAT_SEED_STEP) & MASK64
def joined_windows(request, root=ROOT, seed=None):
"""Web store, then chat_copies crop views of the chat store; source ids 0-1 web, 2 chat."""
seed = request["seed"] if seed is None else seed
root = Path(root)
options = {"probability": request["crop_probability"], "min_remaining_tokens": request["crop_minimum"]}
web, chat = request["web"], request["chat"]
bases = [PrefixCropWindowDataset(root / web["token_dir"], root / web["index_dir"], seed=seed, **options)]
try:
for copy in range(request["chat_copies"]):
bases.append(PrefixCropWindowDataset(root / chat["token_dir"], root / chat["index_dir"],
seed=chat_seed(seed, copy), **options))
except BaseException:
for base in bases:
base.close()
raise
return JoinedPrefixWindows(bases, 0, [{0: 0, 1: 1}] + [{0: 2}] * request["chat_copies"])
def window_tiers(base, tiers):
"""train.tiers.bin value of every web window, uint8."""
out = np.empty(len(base), dtype=np.uint8)
for start in range(0, len(base), CHUNK):
ids = np.arange(start, min(len(base), start + CHUNK), dtype=np.int64)
out[start:start + len(ids)] = tiers[window_sentences(base, ids)]
return out
def _lengths(joined, ids):
total = 0
for start in range(0, len(ids), CHUNK):
total += int(joined.window_lengths(ids[start:start + CHUNK]).sum())
return total
def _save(directory, name, array):
path = Path(directory) / name
temporary = path.with_suffix(".tmp.npy")
np.save(temporary, array, allow_pickle=False)
temporary.replace(path)
return path
def prepare_maps(directory, root=ROOT):
directory, root = Path(directory), Path(root)
request = read(directory / "request.json")
expected = read(directory / "manifest.json") if (directory / "manifest.json").exists() else None
if expected:
files = [r["file"] for r in expected["epochs"]] + [expected["probe"]["file"]]
digests = [r["sha256"] for r in expected["epochs"]] + [expected["probe"]["sha256"]]
if all((directory / f).exists() and file_sha(directory / f) == d for f, d in zip(files, digests)):
return expected
for store in (request["web"], request["chat"]):
if (file_sha(root / store["token_dir"] / "manifest.json") != store["token_manifest_sha256"]
or file_sha(root / store["index_dir"] / "manifest.json") != store["window_manifest_sha256"]):
raise ValueError(f"{store['name']} store identity changed")
web_manifest = read(root / request["web"]["token_dir"] / "manifest.json")
tiers_path = root / request["web"]["token_dir"] / "train.tiers.bin"
if file_sha(tiers_path) != web_manifest["output_sha256"]["train.tiers.bin"]:
raise ValueError("train.tiers.bin differs from its manifest")
tiers = np.fromfile(tiers_path, dtype=np.uint8)
joined = joined_windows(request, root)
try:
if len(joined) >= 1 << 32:
raise ValueError("Joined windows exceed uint32 map ids")
web_base = joined.bases[0]
if len(tiers) != web_manifest["splits"]["train"]["sentences"]:
raise ValueError("Tier count differs from web sentences")
per_window = window_tiers(web_base, tiers)
web = np.flatnonzero(per_window <= request["max_tier"]).astype("<u4")
probe = np.flatnonzero(per_window == PROBE_TIER).astype("<u4")
del per_window
probe_path = _save(directory, "probe-windows.npy", probe)
chat_windows = len(joined.bases[1])
chat_starts = joined.starts[1:]
chat_uncropped = int(SentenceWindowDataset.window_lengths(joined.bases[1], np.arange(chat_windows)).sum())
records = []
web_cumulative = chat_cumulative = 0
chat_pass, chat_position, permutation = 0, 0, None
for epoch in range(request["epochs"]):
joined.set_epoch(epoch)
web_tokens = _lengths(joined, web)
web_cumulative += web_tokens
fraction = request["chat_fraction"]
target = round(web_cumulative * fraction / (1 - fraction)) - chat_cumulative
chosen, used = [], 0
while used < target:
if chat_pass >= request["chat_copies"]:
raise ValueError("Chat quota needs more crop copies than chat_copies")
if permutation is None:
permutation = np.random.default_rng([request["seed"], chat_pass]).permutation(chat_windows)
ids = permutation[chat_position:] + chat_starts[chat_pass]
cumulative = np.cumsum(joined.window_lengths(ids), dtype=np.int64)
take = int(np.searchsorted(cumulative, target - used, side="right"))
if take:
chosen.append(ids[:take])
used += int(cumulative[take - 1])
if take < len(ids):
chat_position += take
break
chat_pass, chat_position, permutation = chat_pass + 1, 0, None
chat_cumulative += used
chosen = np.concatenate(chosen).astype("<u4") if chosen else np.empty(0, dtype="<u4")
mapping = np.concatenate((web, chosen))
np.random.default_rng([request["seed"], 1 << 20, epoch]).shuffle(mapping)
path = _save(directory, f"epoch-{epoch}.npy", mapping)
record = {"epoch": epoch, "file": path.name, "bytes": path.stat().st_size, "sha256": file_sha(path),
"windows": len(mapping), "web_windows": len(web), "chat_windows": len(chosen),
"web_tokens": web_tokens, "chat_tokens": used, "effective_tokens": web_tokens + used,
"chat_fraction": used / (web_tokens + used),
"chat_cursor_after": {"pass": chat_pass, "position": chat_position}}
if expected and record != expected["epochs"][epoch]:
raise ValueError(f"Rebuilt map differs from frozen epoch {epoch}")
records.append(record)
print(json.dumps({"map_epoch": epoch + 1, "windows": len(mapping),
"chat_fraction": round(record["chat_fraction"], 6)}), flush=True)
del mapping
train = web_manifest["splits"]["train"]
kept = [name for value, name in enumerate(("probe", "p20", "p5", "p1")) if value <= request["max_tier"]]
web_characters = sum(train["tiers"][name]["characters"] for name in kept)
result = {"format": FORMAT, "status": "complete", "request": request, "epochs": records,
"probe": {"file": probe_path.name, "sha256": file_sha(probe_path), "windows": len(probe),
"prediction_pairs": train["tiers"]["probe"]["prediction_pairs"],
"characters": train["tiers"]["probe"]["characters"],
"documents": train["tiers"]["probe"]["documents"]},
"web_tiers_kept": kept, "web_characters_per_epoch": web_characters,
"web_characters_total": web_characters * request["epochs"],
"chat_targets_total": chat_cumulative, "chat_uncropped_targets_per_pass": chat_uncropped,
"chat_passes": chat_cumulative / chat_uncropped,
"policy": ("Each pool web window once per epoch, prefix crop per epoch; chat as one run-long "
f"sequence of permuted passes, pass k through crop copy k, at {CHAT_FRACTION:.0%} "
"of cumulative cropped targets; maps SHA256-pinned")}
if expected and result != expected:
raise ValueError("Rebuilt mixture manifest differs")
if expected is None:
write_lf(directory / "manifest.json", result)
return result
finally:
joined.close()
class Zh13Mixture(MixtureWindowDataset):
"""MixtureWindowDataset over a vimeml_zh13_mixture_v1 manifest."""
def __init__(self, base, directory, root=ROOT):
self.base = base
self.directory = Path(directory)
self.manifest = read(self.directory / "manifest.json")
if self.manifest.get("status") != "complete" or self.manifest.get("format") != FORMAT:
raise ValueError("Incomplete or unsupported vimeml-tiny-zh-v1 mixture")
request = self.manifest["request"]
if (request["token_manifest_sha256"] != file_sha(Path(base.token_dir) / "manifest.json")
or request["window_manifest_sha256"] != file_sha(Path(root) / request["index_dir"] / "manifest.json")):
raise ValueError("Mixture belongs to different token data/indexes")
if (request["seed"], request["crop_probability"], request["crop_minimum"]) != (
base.seed, base.probability, base.min_remaining_tokens):
raise ValueError("Mixture crop settings differ from training")
self.context_length = base.context_length
self.split = "train"
self.epoch = 0
self._mapping = self._mapping_epoch = self._store = None
def training_dataset(directory, root=ROOT):
manifest = read(Path(directory) / "manifest.json")
joined = joined_windows(manifest["request"], root)
try:
return Zh13Mixture(joined, directory, root)
except BaseException:
joined.close()
raise
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