vbatts/llama.cpp
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Tokenizer SPM fixes for phi-3 and llama-spm (bugfix) (#7425)

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* Update brute force test: add_special
* Update brute force test: default values for add_bos_token and add_eos_token
* Enable rtrim when pre-inserting BOS

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Revert "server : fix test regexes"
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jaime-m-p 2024-05-21 14:39:48 +02:00 • committed by GitHub
parent 917dc8cfa6
commit d7e852c1bc
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5 changed files with 28 additions and 23 deletions
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22
tests/test-tokenizer-random.py
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@ -154,19 +154,22 @@ def generator_custom_text_edge_cases() -> Iterator[str]:
'\uFEFF//', # unicode_ranges_control, 0xFEFF (BOM)
'Cửa Việt', # llama-3, ignore_merges = true
'<s>a', # Phi-3 fail
'<unk><|endoftext|><s>' # Phi-3 fail
'<unk><|endoftext|><s>', # Phi-3 fail
'a\na', # TODO: Bert fail
]
def generator_random_special_tokens(special_tokens:list[str], iterations=100) -> Iterator[str]:
special_tokens = set(special_tokens)
def generator_random_special_tokens(tokenizer, iterations=100) -> Iterator[str]:
special_tokens = set(tokenizer.all_special_tokens)
special_tokens.update([" ", "\n", "\t", "-", "!", "one", "1", "<s>", "</s>"])
special_tokens = list(sorted(special_tokens))
rand = random.Random()
for m in range(iterations):
rand.seed(m)
words = rand.choices(special_tokens, k=500)
if tokenizer.add_bos_token: # skip spam warning of double BOS
while words and words[0] == tokenizer.bos_token:
words.pop(0)
yield "".join(words)
@ -290,18 +293,19 @@ def main(argv: list[str] = None):
model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096))
tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer)
def func_tokenize2(text: str):
return tokenizer.encode(text, add_special_tokens=False)
parse_special = all(len(func_tokenize2(t)) == 1 for t in tokenizer.all_special_tokens)
tokenizer.add_bos_token = getattr(tokenizer, "add_bos_token", True)
tokenizer.add_eos_token = getattr(tokenizer, "add_eos_token", False)
def func_tokenize1(text: str):
return model.tokenize(text, add_special=False, parse_special=parse_special)
return model.tokenize(text, add_special=True, parse_special=True)
def func_tokenize2(text: str):
return tokenizer.encode(text, add_special_tokens=True)
vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True)))
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text())
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases())
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_special_tokens(tokenizer.all_special_tokens, 10_000))
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_special_tokens(tokenizer, 10_000))
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_vocab_words(vocab))
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_chars(10_000))
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000))