File size: 7,063 Bytes
20a371d | 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 | import json
import os
import sys
from collections import Counter, defaultdict
from multiprocessing import Pool
import numpy as np
import regex
VOCAB_SIZE = 4096
SPECIALS = ["<|endoftext|>", "<|user|>", "<|assistant|>", "<|end|>"]
SAMPLE_MB = 30
STRIDE = 64
OUT = "data"
# GPT-4's pre-split pattern: text is cut into chunks first, so no merge can
# span a word boundary.
PAT = regex.compile(
r"""'(?i:[sdmt]|ll|ve|re)|[^\r\n\p{L}\p{N}]?+\p{L}+|\p{N}{1,3}"""
r"""| ?[^\s\p{L}\p{N}]++[\r\n]*|\s*[\r\n]|\s+(?!\S)|\s+"""
)
SPECIAL_PAT = regex.compile("(" + "|".join(regex.escape(s) for s in SPECIALS) + ")")
def merge(ids, pair, new):
"""Replace every occurrence of pair in ids with the single token new."""
out, i = [], 0
while i < len(ids):
if i + 1 < len(ids) and (ids[i], ids[i + 1]) == pair:
out.append(new)
i += 2
else:
out.append(ids[i])
i += 1
return out
def train(text, n_merges):
"""Learn n_merges pairs, in the order learned."""
# Chunks are deduplicated and weighted by count: identical chunks always
# merge identically, and 30MB holds only ~50k distinct ones.
freq = Counter(PAT.findall(text))
words = [list(w.encode("utf-8")) for w in freq]
reps = list(freq.values())
count = defaultdict(int) # pair -> how often it occurs
where = defaultdict(set) # pair -> which words contain it
for i, w in enumerate(words):
for p, k in Counter(zip(w, w[1:])).items():
count[p] += k * reps[i]
where[p].add(i)
merges = []
for new in range(256, 256 + n_merges):
best = max(count, key=lambda p: (count[p], -p[0], -p[1])) # ties: smallest pair
merges.append(best)
# Only the words containing best change: drop their pairs, merge, put
# the new pairs back.
for i in where.pop(best):
w, r = words[i], reps[i]
for p, k in Counter(zip(w, w[1:])).items():
count[p] -= k * r
if count[p] == 0:
del count[p]
words[i] = w = merge(w, best, new)
for p, k in Counter(zip(w, w[1:])).items():
count[p] += k * r
where[p].add(i)
return merges
class Tokenizer:
def __init__(self, merges):
self.ranks = {p: 256 + i for i, p in enumerate(merges)}
self.vocab = {i: bytes([i]) for i in range(256)}
for i, (a, b) in enumerate(merges):
self.vocab[256 + i] = self.vocab[a] + self.vocab[b]
base = 256 + len(merges)
self.special = {s: base + i for i, s in enumerate(SPECIALS)}
for s, i in self.special.items():
self.vocab[i] = s.encode("utf-8")
self.cache = {} # chunk -> ids, looked up ~400M times over the corpus
def encode_chunk(self, chunk):
"""Apply the merges to one chunk, earliest-learned pair first."""
ids = self.cache.get(chunk)
if ids is not None:
return ids
ids = list(chunk.encode("utf-8"))
while len(ids) > 1:
pair = min(zip(ids, ids[1:]), key=lambda p: self.ranks.get(p, 1 << 30))
if pair not in self.ranks:
break
ids = merge(ids, pair, self.ranks[pair])
self.cache[chunk] = ids
return ids
def encode(self, text):
# Specials are split out first, so BPE never sees them and cannot
# shred one into pieces.
ids = []
for part in SPECIAL_PAT.split(text):
if part in self.special:
ids.append(self.special[part])
else:
ids += [i for c in PAT.findall(part) for i in self.encode_chunk(c)]
return ids
def decode(self, ids):
return b"".join(self.vocab[i] for i in ids).decode("utf-8", errors="replace")
def save(merges, path):
with open(path, "w") as f:
f.writelines(f"{a} {b}\n" for a, b in merges)
def load(path=f"{OUT}/tokenizer.model"):
with open(path) as f:
return Tokenizer([tuple(int(x) for x in line.split()) for line in f])
def sample(path, mb, stride):
"""Every stride-th story, up to mb megabytes, so the sample spans the corpus."""
parts, n = [], 0
with open(path, encoding="utf-8") as f:
for i, line in enumerate(f):
if i % stride:
continue
parts.append(json.loads(line)["text"])
n += len(parts[-1])
if n > mb * 1e6:
break
return "\n".join(parts)
def batches(path, n=2000):
"""Stories from a .jsonl file, n at a time."""
batch = []
with open(path, encoding="utf-8") as f:
for line in f:
batch.append(json.loads(line)["text"])
if len(batch) == n:
yield batch
batch = []
if batch:
yield batch
enc = None # one Tokenizer per worker process
def init_worker(merges):
global enc
enc = Tokenizer(merges)
def encode_batch(batch):
eot = enc.special["<|endoftext|>"]
ids, nbytes = [], 0
for s in batch:
story = enc.encode(s)
assert enc.decode(story) == s, s[:80] # lossless only if every char lands in a chunk
ids += story + [eot]
nbytes += len(s.encode("utf-8"))
return np.array(ids, dtype=np.uint16), nbytes, len(ids) - len(batch)
def tokenize(name, merges, out):
"""Write <out>/<name>.bin, return its byte and token counts."""
ntok = nbytes = 0
with Pool(initializer=init_worker, initargs=(merges,)) as pool:
with open(f"{out}/{name}.bin", "wb") as f:
for arr, nb, nt in pool.imap(encode_batch, batches(f"{OUT}/{name}.jsonl")):
f.write(arr.tobytes())
nbytes += nb
ntok += nt
return nbytes, ntok
def main():
# Any other vocab size goes in its own directory, next to the same jsonl.
vocab = int(sys.argv[1]) if len(sys.argv) > 1 else VOCAB_SIZE
out = OUT if vocab == VOCAB_SIZE else f"{OUT}/vocab{vocab}"
os.makedirs(out, exist_ok=True)
n_merges = vocab - 256 - len(SPECIALS)
text = sample(f"{OUT}/train.jsonl", SAMPLE_MB, STRIDE)
print(f"training on {len(text) / 1e6:.1f}MB, {len(set(PAT.findall(text))):,} chunks")
merges = train(text, n_merges)
save(merges, f"{out}/tokenizer.model")
tok = load(f"{out}/tokenizer.model")
assert len(tok.vocab) == vocab, len(tok.vocab)
word = tok.encode("story")
for s in SPECIALS:
assert tok.encode(f"story{s}story") == word + [tok.special[s]] + word, s
step = n_merges // 18
print("vocab:", ", ".join(repr(tok.vocab[i].decode("utf-8", "replace"))
for i in range(256, 256 + n_merges, step)))
for name in ("val", "train"):
nbytes, ntok = tokenize(name, merges, out)
print(f"{name}: {ntok:,} tokens, {nbytes / ntok:.3f} bytes/token")
ids = np.memmap(f"{out}/train.bin", dtype=np.uint16, mode="r")[:220]
print("\n" + tok.decode(ids.tolist()))
if __name__ == "__main__":
main()
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