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https://github.com/firestar5683/StarPilot.git
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firestar5683
22a8575199
Keep official openpilot ancestry and dependency licenses. Store resources directly in Git, remove LFS setup requirements, and leave the downloadable big model outside the source tree.
51 lines
2.1 KiB
Python
51 lines
2.1 KiB
Python
import functools, multiprocessing
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from transformers import AutoTokenizer
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from datasets import load_dataset
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from tinygrad.llm.cli import SimpleTokenizer
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from tinygrad.helpers import tqdm, getenv, partition
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@functools.cache
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def get_tokenizers():
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print("getting tokenizers")
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base_tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B-Instruct")
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special_tokens, normal_tokens = partition(((t, tid) for t, tid in base_tokenizer.vocab.items()), lambda e: e[1] in base_tokenizer.all_special_ids)
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simple_tokenizer = SimpleTokenizer(dict(normal_tokens), dict(special_tokens))
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return base_tokenizer, simple_tokenizer
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def test_tokenize(samp) -> bool:
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base_tokenizer, simple_tokenizer = get_tokenizers()
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idx, txt = samp
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try: simple_tokens = tuple(simple_tokenizer.encode(txt))
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except RuntimeError: simple_tokens = ()
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base_tokens = tuple(base_tokenizer.encode(txt, add_special_tokens=False))
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if simple_tokens != base_tokens:
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print(f"tokens mismatch at index: {idx}.\n")
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color_codes = [91, 92, 94, 93, 95]
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def color_tokens(tids):
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return "".join(f"\033[{color_codes[i%len(color_codes)]}m{base_tokenizer.decode([t])}" for i, t in enumerate(tids)) + "\033[0m"
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print("simple: ", color_tokens(simple_tokens))
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print("official:", color_tokens(base_tokens) + "\n")
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return False
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if simple_tokenizer.decode(simple_tokens) != txt:
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print(f"decode mismatch at {idx}")
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return False
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return True
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# use ALLOW_FAILED=-1 to go over the entire dataset without printing.
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if __name__ == "__main__":
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print("loading datasets")
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ds = load_dataset("OpenAssistant/oasst1")
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loaded_ds = [(idx, el["text"]) for idx, el in enumerate(ds["train"])]
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print(f"loaded {len(loaded_ds)}")
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allow_failed = getenv("ALLOW_FAILED", 10)
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fail_count, total = 0, 0
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with multiprocessing.Pool(16) as pool:
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for good in tqdm(pool.imap_unordered(test_tokenize, loaded_ds), total=len(loaded_ds)):
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total += 1
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if not good:
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fail_count += 1
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allow_failed -= 1
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if allow_failed == 0: break
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print(f"{fail_count}/{total} samples are inconsistent with the official tokenizer.")
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