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41 lines
1.7 KiB
41 lines
1.7 KiB
from transformers import AutoTokenizer
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from datasets import load_dataset
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from tinygrad.apps.llm import SimpleTokenizer, gpt2_decode_vocab, get_llama_re
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from tinygrad.helpers import tqdm, getenv, partition
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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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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()),
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lambda e: e[1] in base_tokenizer.all_special_ids)
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inv_vocab = { tid: word for word, tid in base_tokenizer.get_vocab().items() }
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simple_tokenizer = SimpleTokenizer(get_llama_re(), gpt2_decode_vocab(dict(normal_tokens)), dict(special_tokens))
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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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ds = load_dataset("OpenAssistant/oasst1")
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allow_failed = getenv("ALLOW_FAILED", 10)
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fail_count, total = 0, 0
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for idx, el in enumerate(tqdm(ds["train"])):
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total += 1
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try: simple_tokens = tuple(simple_tokenizer.encode(el["text"]))
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except RuntimeError: simple_tokens = ()
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base_tokens = tuple(base_tokenizer.encode(el["text"], add_special_tokens=False))
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if simple_tokens != base_tokens:
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fail_count += 1
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allow_failed -= 1
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if allow_failed >= 0:
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print(f"tokens mismatch at index: {idx}.\n")
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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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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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