openai/tiktoken
Publicmirrored from https://github.com/openai/tiktokenAvailable
scripts/benchmark.py
39lines · modeblame
a1a9f168Shantanu Jain3 years ago | 1 | import base64 |
| 2 | import functools | |
| 3 | import gzip | |
| 4 | import json | |
| 5 | import os | |
| 6 | import random | |
| 7 | import time | |
| 8 | from typing import Any, cast | |
| 9 | | |
| 10 | import blobfile | |
| 11 | | |
| 12 | import tiktoken | |
| 13 | | |
| 14 | | |
| 15 | def benchmark_batch(documents: list[str]) -> None: | |
| 16 | num_threads = int(os.environ["RAYON_NUM_THREADS"]) | |
| 17 | num_bytes = sum(map(len, map(str.encode, documents))) | |
| 18 | print(f"num_threads: {num_threads}, num_bytes: {num_bytes}") | |
| 19 | | |
| 20 | enc = tiktoken.get_encoding("gpt2") | |
| 21 | enc.encode("warmup") | |
| 22 | | |
| 23 | start = time.perf_counter_ns() | |
| 24 | enc.encode_ordinary_batch(documents, num_threads=num_threads) | |
| 25 | end = time.perf_counter_ns() | |
| 26 | print(f"tiktoken \t{num_bytes / (end - start) * 1e9} bytes / s") | |
| 27 | | |
| 28 | import transformers | |
| 29 | | |
| 30 | hf_enc = cast(Any, transformers).GPT2TokenizerFast.from_pretrained("gpt2") | |
| 31 | hf_enc.model_max_length = 1e30 # silence! | |
| 32 | hf_enc.encode("warmup") | |
| 33 | | |
| 34 | start = time.perf_counter_ns() | |
| 35 | hf_enc(documents) | |
| 36 | end = time.perf_counter_ns() | |
| 37 | print(f"huggingface \t{num_bytes / (end - start) * 1e9} bytes / s") | |
| 38 | | |
| 39 | |