jzzliu
f7567f7d7a
Lossless on any string (256-byte base vocab), regex pre-tokenization so merges never cross word boundaries, incremental pair-count training with deterministic tie-breaks, special tokens, and JSON persistence. The trainer gains --tokenizer bpe and reports bits/byte so tokenizers are comparable; old character checkpoints still load. Tests check the incremental trainer against a naive reference, round-trips on random unicode, and merge-priority semantics; mutation checks confirmed they fail on broken code.
43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
"""BPE tokenizer: training time, compression on held-out text, and encode throughput.
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python scripts/bench_bpe.py --data data/shakespeare.txt
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"""
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from __future__ import annotations
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import argparse
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import time
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from pathlib import Path
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from microgpt.bpe import BPETokenizer
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def main() -> None:
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p = argparse.ArgumentParser()
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p.add_argument("--data", default="data/shakespeare.txt")
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args = p.parse_args()
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text = Path(args.data).read_text(encoding="utf-8")
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cut = int(0.9 * len(text))
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train_text, val_text = text[:cut], text[cut:]
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val_bytes = len(val_text.encode("utf-8"))
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print(f"{len(train_text):,} train chars, {val_bytes:,} held-out bytes\n")
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print("| vocab | train time (s) | bytes/token (held-out) | encode (MB/s) | round-trip |")
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print("|---:|---:|---:|---:|:-:|")
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for vocab in (300, 512, 1024, 2048):
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t0 = time.perf_counter()
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tok = BPETokenizer.train(train_text, vocab)
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train_s = time.perf_counter() - t0
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t0 = time.perf_counter()
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ids = tok.encode(val_text)
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enc_s = time.perf_counter() - t0
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ok = tok.decode(ids) == val_text
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print(
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f"| {vocab} | {train_s:.1f} | {val_bytes / len(ids):.2f} "
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f"| {val_bytes / enc_s / 1e6:.2f} | {'yes' if ok else 'NO'} |"
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)
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if __name__ == "__main__":
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main()
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