File size: 12,594 Bytes
fac8bb8 | 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 | """Train a byte-level BPE and build audited, streaming 1024-token LM data."""
from __future__ import annotations
import argparse
from collections import Counter
import json
import os
os.environ.setdefault('RAYON_NUM_THREADS', '4')
import random
from pathlib import Path
import time
import numpy as np
import pyarrow.parquet as pq
from tokenizers import Tokenizer, models, trainers, pre_tokenizers, decoders
from common import *
CHAT_TEMPLATE = "{% for message in messages %}{{ '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
def rows(path, seed=SEED):
pf = pq.ParquetFile(path)
groups = list(range(pf.num_row_groups))
random.Random(seed).shuffle(groups)
for group in groups:
row_offset = sum(pf.metadata.row_group(i).num_rows for i in range(group))
for batch in pf.iter_batches(batch_size=128, row_groups=[group],
columns=['source','kind','prompt','think','answer']):
for i, row in enumerate(batch.to_pylist()):
yield row_offset + i, row
row_offset += batch.num_rows
def train_tokenizer(source, manifest, out, sample_bytes):
from transformers import PreTrainedTokenizerFast
counts = Counter()
byte_counts = Counter()
def texts():
# Each source file gets the same byte allowance, avoiding a tokenizer
# dominated by the two largest distillation files.
per_file = max(65536, sample_bytes // len(manifest['files']))
for item in manifest['files']:
for _, row in rows(source / item['name']):
if split_for(row['prompt']) != 'train': continue
text = render_row(row)
if text is None: continue
yield text
counts[item['name']] += 1
byte_counts[item['name']] += len(text.encode('utf-8'))
if byte_counts[item['name']] >= per_file: break
print('TOKENIZER_SAMPLE', item['name'], counts[item['name']], flush=True)
tokenizer = Tokenizer(models.BPE())
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False, use_regex=True)
tokenizer.decoder = decoders.ByteLevel()
trainer = trainers.BpeTrainer(vocab_size=VOCAB, min_frequency=2,
special_tokens=SPECIALS,
initial_alphabet=pre_tokenizers.ByteLevel.alphabet(),
show_progress=False)
tokenizer.train_from_iterator(texts(), trainer=trainer)
if tokenizer.get_vocab_size() < VOCAB:
tokenizer.add_tokens([f'<|reserved_{i}|>' for i in range(VOCAB-tokenizer.get_vocab_size())])
assert tokenizer.get_vocab_size() == VOCAB
fast = PreTrainedTokenizerFast(tokenizer_object=tokenizer,
pad_token=SPECIALS[0], bos_token=SPECIALS[1], eos_token=SPECIALS[2],
additional_special_tokens=SPECIALS[3:], model_max_length=CONTEXT,
clean_up_tokenization_spaces=False)
fast.chat_template = CHAT_TEMPLATE
out.mkdir(parents=True, exist_ok=True)
fast.save_pretrained(out)
report = tokenizer_checks(fast)
report.update({'training_split_only': True, 'sample_rows_by_file': dict(counts),
'sample_utf8_bytes_by_file': dict(byte_counts),
'normalizer': None, 'algorithm': 'byte-level BPE',
'tokenizer_sha256': sha(out/'tokenizer.json')})
dump(out/'tokenizer_audit.json', report)
print('TOKENIZER_VERIFIED', report['vocab_size'], flush=True)
return fast
def tokenizer_checks(fast):
samples = ['Hello, world!\n Two spaces stay.',
'x² + y² = π; √2 ≈ 1.414; café — naïve 🚀',
'def add(a, b):\n return a + b\n',
r'\frac{13}{6} = 2.1667; $x \\ y$',
'<|im_start|>assistant\n<think>\nCheck.\n</think>\n42<|im_end|>']
for text in samples:
ids = fast.encode(text, add_special_tokens=False)
assert fast.decode(ids, skip_special_tokens=False, clean_up_tokenization_spaces=False) == text
assert all(0 <= t < VOCAB for t in ids)
special_ids = {s: fast.encode(s, add_special_tokens=False) for s in SPECIALS}
assert special_ids == {s:[i] for i,s in enumerate(SPECIALS)}, special_ids
assert fast.model_max_length == CONTEXT
return {'vocab_size': len(fast), 'special_token_ids': special_ids,
'round_trip_cases_passed': len(samples), 'context': CONTEXT,
'no_unknown_token': fast.unk_token_id is None}
class TokenWriter:
# 512 MB per shard: below the requested 7 GB ceiling.
def __init__(self, directory, split, shard_tokens=256_000_000):
self.directory = Path(directory); self.directory.mkdir(parents=True, exist_ok=True)
self.split = split; self.shard_tokens = shard_tokens
self.real_tokens = 0; self.total_tokens = 0; self.current_tokens = 0
self.files = []; self.handle = None; self.path = None
def add(self, ids, padding=False):
ids = np.asarray(ids, dtype='<u2')
if ids.size: assert int(ids.max()) < VOCAB
if not padding: self.real_tokens += len(ids)
pos = 0
while pos < len(ids):
if self.handle is None:
self.path = self.directory/f'{self.split}-{len(self.files):04d}.bin'
self.handle = self.path.open('wb'); self.current_tokens = 0
n = min(len(ids)-pos, self.shard_tokens-self.current_tokens)
self.handle.write(ids[pos:pos+n].tobytes())
self.current_tokens += n; self.total_tokens += n; pos += n
if self.current_tokens == self.shard_tokens: self.close_shard()
def close_shard(self):
if self.handle is None: return
self.handle.close(); self.handle = None
self.files.append({'name': self.path.name, 'tokens': self.current_tokens,
'bytes': self.path.stat().st_size, 'sha256': sha(self.path)})
def finish(self):
if self.total_tokens:
# Each sample uses 1024 inputs and the next 1024 target tokens.
# Pad only the final tail; padding targets are ignored by the loss.
self.add([0] * ((1-self.total_tokens) % CONTEXT), padding=True)
self.close_shard()
return {'real_tokens': self.real_tokens, 'stored_tokens': self.total_tokens,
'padding_tokens': self.total_tokens-self.real_tokens,
'blocks': max(0,(self.total_tokens-1)//CONTEXT), 'files': self.files}
def prepare(source, manifest_path, output, train_tokens, sample_bytes, verify=True, tokenizer_path=None):
source = Path(source); output = Path(output); output.mkdir(parents=True, exist_ok=True)
manifest = json.loads(Path(manifest_path).read_text(encoding='utf-8'))
assert manifest['total_bytes'] < MAX_SOURCE_BYTES
assert all(f['bytes'] < MAX_FILE_BYTES for f in manifest['files'])
for item in manifest['files']:
path = source/item['name']
assert path.stat().st_size == item['bytes'], path
if verify:
assert sha(path) == item['sha256'], path
print('SOURCE_HASH_OK', item['name'], flush=True)
dump(output/'source_manifest.json', manifest)
if tokenizer_path:
import shutil
from transformers import AutoTokenizer
shutil.copytree(tokenizer_path,output/'tokenizer',dirs_exist_ok=True)
fast=AutoTokenizer.from_pretrained(output/'tokenizer',local_files_only=True)
tokenizer_checks(fast)
else:
fast = train_tokenizer(source, manifest, output/'tokenizer', sample_bytes)
tokenizer = fast.backend_tokenizer
writers = {s:TokenWriter(output, s) for s in ['train','validation','test']}
selected = Counter(); kinds = Counter(); source_tokens = Counter()
sample_audit = []; duplicates = 0; scanned = 0
# Store compact hashes, not full documents. This set is bounded by selected records.
seen = set()
items = manifest['files'].copy(); random.Random(SEED).shuffle(items)
quota_basis = sum(x['bytes'] for x in items)
started = time.time()
for item in items:
quota = max(CONTEXT*4, round(train_tokens * item['bytes']/quota_basis))
file_train = 0; pending = []; pending_meta = []
def flush():
nonlocal file_train, duplicates
if not pending: return
encodings = tokenizer.encode_batch(pending, add_special_tokens=False)
for text, encoding, meta in zip(pending, encodings, pending_meta):
idx, row, split = meta
digest = __import__('hashlib').sha256(text.encode('utf-8')).digest()
if digest in seen:
duplicates += 1; continue
seen.add(digest)
ids = encoding.ids
writers[split].add(ids)
selected[split] += 1; kinds[row['kind']] += 1
source_tokens[item['name'] + '/' + split] += len(ids)
if split == 'train': file_train += len(ids)
if len(sample_audit) < 100 or split != 'train':
# No source text in audit: positions and hashes identify records.
sample_audit.append({'file':item['name'], 'row':idx, 'split':split,
'prompt_sha256':prompt_key(row['prompt']).hex(),
'record_sha256':digest.hex(), 'tokens':len(ids)})
pending.clear(); pending_meta.clear()
for idx, row in rows(source/item['name']):
scanned += 1
text = render_row(row)
if text is None: continue
split = split_for(row['prompt'])
pending.append(text); pending_meta.append((idx,row,split))
if len(pending) >= 64:
flush()
if file_train >= quota: break
flush()
print('TOKENIZED_FILE', item['name'], 'train_tokens', file_train,
'total_train', writers['train'].real_tokens, 'seconds', round(time.time()-started), flush=True)
splits = {s:w.finish() for s,w in writers.items()}
assert all(splits[s]['blocks'] > 0 for s in splits), 'Each split needs data'
assert sum(x['bytes'] for s in splits.values() for x in s['files']) < MAX_SOURCE_BYTES
assert all(x['bytes'] < MAX_FILE_BYTES for s in splits.values() for x in s['files'])
result = {'created_utc':now(), 'seed':SEED, 'dtype':'little-endian uint16',
'context':CONTEXT, 'vocab_size':VOCAB,
'source_manifest_sha256':sha(manifest_path),
'tokenizer_sha256':sha(output/'tokenizer'/'tokenizer.json'),
'requested_train_tokens':train_tokens,
'sampling':'Deterministically shuffled row groups; per-file train-token quota proportional to compressed file bytes; retain whole records including final answers. Quotas may overshoot by one encoding batch.',
'split_rule':'SHA256(NFKC/casefold/whitespace-normalized prompt) modulo 1000: validation 0..4; test 5..9; train 10..999. Split before tokenizer fitting and packing.',
'packing':'Separate continuous token streams per split; EOS separates records; long documents continue into later windows. Attention resets every 1024 tokens; cross-document attention within a window is allowed.',
'loss':'All next-token targets except pad=0; no assistant-only loss at scratch-pretraining stage.',
'rows_scanned':scanned, 'selected_rows':dict(selected), 'exact_duplicate_rows_removed':duplicates,
'source_tokens':dict(source_tokens), 'kinds':dict(kinds), 'splits':splits}
dump(output/'split_record_audit.json', sample_audit)
dump(output/'corpus_manifest.json', result)
model_config().save_pretrained(output/'model_config')
print('PREPARE_COMPLETE', json.dumps({k:v['real_tokens'] for k,v in splits.items()}), flush=True)
if __name__ == '__main__':
ap=argparse.ArgumentParser()
ap.add_argument('--source',required=True); ap.add_argument('--manifest',required=True)
ap.add_argument('--out',required=True)
ap.add_argument('--train-tokens',type=int,default=1_520_000_000)
ap.add_argument('--tokenizer-sample-bytes',type=int,default=128_000_000)
ap.add_argument('--tokenizer')
args=ap.parse_args()
prepare(args.source,args.manifest,args.out,args.train_tokens,args.tokenizer_sample_bytes,tokenizer_path=args.tokenizer)
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