mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 21:39:52 +00:00
3b38d48609
* Add per token attributes enum
* Using phi-3 for testing 'rstrip'
* Using jina-v2 for testing 'lstrip'
* Brute force test for 'lstrip' and 'rstrip'
* Implement 'rstrip' and 'lstrip'
* Update phi-3 GGUF file (obsolete since 917dc8c)
* Replace llama_token_type with llama_token_attribs
360 lines
13 KiB
Python
360 lines
13 KiB
Python
# Test libllama tokenizer == AutoTokenizer.
|
||
# Brute force random words/text generation.
|
||
#
|
||
# Sample usage:
|
||
#
|
||
# python3 tests/test-tokenizer-random.py ./models/ggml-vocab-llama-bpe.gguf ./models/tokenizers/llama-bpe
|
||
#
|
||
|
||
import time
|
||
import logging
|
||
import argparse
|
||
import subprocess
|
||
import random
|
||
|
||
from typing import Callable, Iterator
|
||
|
||
import cffi
|
||
from transformers import AutoTokenizer
|
||
|
||
logger = logging.getLogger("test-tokenizer-random-bpe")
|
||
|
||
|
||
class LibLlama:
|
||
|
||
DEFAULT_PATH_LLAMA_H = "./llama.h"
|
||
DEFAULT_PATH_LIBLLAMA = "./build/libllama.so" # CMakeLists.txt: BUILD_SHARED_LIBS ON
|
||
|
||
def __init__(self, path_llama_h: str = None, path_libllama: str = None):
|
||
path_llama_h = path_llama_h or self.DEFAULT_PATH_LLAMA_H
|
||
path_libllama = path_libllama or self.DEFAULT_PATH_LIBLLAMA
|
||
(self.ffi, self.lib) = self._load_libllama_cffi(path_llama_h, path_libllama)
|
||
self.lib.llama_backend_init()
|
||
|
||
def _load_libllama_cffi(self, path_llama_h: str, path_libllama: str):
|
||
cmd = ["gcc", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)=", path_llama_h]
|
||
res = subprocess.run(cmd, stdout=subprocess.PIPE)
|
||
assert (res.returncode == 0)
|
||
source = res.stdout.decode()
|
||
ffi = cffi.FFI()
|
||
if True: # workarounds for pycparser
|
||
source = "typedef struct { } __builtin_va_list;" + "\n" + source
|
||
source = source.replace("sizeof (int)", str(ffi.sizeof("int")))
|
||
source = source.replace("sizeof (void *)", str(ffi.sizeof("void*")))
|
||
source = source.replace("sizeof (size_t)", str(ffi.sizeof("size_t")))
|
||
source = source.replace("sizeof(int32_t)", str(ffi.sizeof("int32_t")))
|
||
ffi.cdef(source, override=True)
|
||
lib = ffi.dlopen(path_libllama)
|
||
return (ffi, lib)
|
||
|
||
def model_default_params(self, **kwargs):
|
||
mparams = self.lib.llama_model_default_params()
|
||
for k, v in kwargs.items():
|
||
setattr(mparams, k, v)
|
||
return mparams
|
||
|
||
def context_default_params(self, **kwargs):
|
||
cparams = self.lib.llama_context_default_params()
|
||
for k, v in kwargs.items():
|
||
setattr(cparams, k, v)
|
||
return cparams
|
||
|
||
|
||
class LibLlamaModel:
|
||
|
||
def __init__(self, libllama: LibLlama, path_model: str, mparams={}, cparams={}):
|
||
self.lib = libllama.lib
|
||
self.ffi = libllama.ffi
|
||
if isinstance(mparams, dict):
|
||
mparams = libllama.model_default_params(**mparams)
|
||
self.model = self.lib.llama_load_model_from_file(path_model.encode(), mparams)
|
||
if not self.model:
|
||
raise RuntimeError("error: failed to load model '%s'" % path_model)
|
||
if isinstance(cparams, dict):
|
||
cparams = libllama.context_default_params(**cparams)
|
||
self.ctx = self.lib.llama_new_context_with_model(self.model, cparams)
|
||
if not self.ctx:
|
||
raise RuntimeError("error: failed to create context for model '%s'" % path_model)
|
||
n_tokens_max = self.lib.llama_n_ctx(self.ctx)
|
||
self.token_ids = self.ffi.new("llama_token[]", n_tokens_max)
|
||
|
||
def free(self):
|
||
if self.ctx:
|
||
self.lib.llama_free(self.ctx)
|
||
if self.model:
|
||
self.lib.llama_free_model(self.model)
|
||
self.ctx = None
|
||
self.model = None
|
||
self.lib = None
|
||
|
||
def tokenize(self, text: str, n_tokens_max: int = 0, add_special: bool = False, parse_special: bool = False) -> list[int]:
|
||
n_tokens_max = n_tokens_max if n_tokens_max > 0 else len(self.token_ids)
|
||
text = text.encode("utf-8")
|
||
num = self.lib.llama_tokenize(self.model, text, len(text), self.token_ids, n_tokens_max, add_special, parse_special)
|
||
if num < 0:
|
||
return []
|
||
return list(self.token_ids[0:num])
|
||
|
||
|
||
def generator_custom_text() -> Iterator[str]:
|
||
"""General tests"""
|
||
yield from [
|
||
"",
|
||
" ",
|
||
" ",
|
||
" ",
|
||
"\t",
|
||
"\n",
|
||
"\n\n",
|
||
"\n\n\n",
|
||
"\t\n",
|
||
"Hello world",
|
||
" Hello world",
|
||
"Hello World",
|
||
" Hello World",
|
||
" Hello World!",
|
||
"Hello, world!",
|
||
" Hello, world!",
|
||
" this is 🦙.cpp",
|
||
"w048 7tuijk dsdfhu",
|
||
"нещо на Български",
|
||
"កាន់តែពិសេសអាចខលចេញ",
|
||
"🚀 (normal) 😶🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
|
||
"Hello",
|
||
" Hello",
|
||
" Hello",
|
||
" Hello",
|
||
" Hello",
|
||
" Hello\n Hello",
|
||
" (",
|
||
"\n =",
|
||
"' era",
|
||
"Hello, y'all! How are you 😁 ?我想在apple工作1314151天～",
|
||
"3",
|
||
"33",
|
||
"333",
|
||
"3333",
|
||
"33333",
|
||
"333333",
|
||
"3333333",
|
||
"33333333",
|
||
"333333333",
|
||
]
|
||
|
||
|
||
def generator_custom_text_edge_cases() -> Iterator[str]:
|
||
"""Edge cases found while debugging"""
|
||
yield from [
|
||
'\x1f-a', # unicode_ranges_control, {0x00001C, 0x00001F}
|
||
'¼-a', # unicode_ranges_digit, 0x00BC
|
||
'½-a', # unicode_ranges_digit, 0x00BD
|
||
'¾-a', # unicode_ranges_digit, 0x00BE
|
||
'a 〇b', # unicode_ranges_digit, 0x3007
|
||
'Ⅵ-a', # unicode_ranges_digit, {0x00002150, 0x0000218F} // Number Forms
|
||
'\uFEFF//', # unicode_ranges_control, 0xFEFF (BOM)
|
||
'Cửa Việt', # llama-3, ignore_merges = true
|
||
'<s>a', # Phi-3 fail
|
||
'<unk><|endoftext|><s>', # Phi-3 fail
|
||
'a\na', # TODO: Bert fail
|
||
'a </s> b', # rstrip phi-3
|
||
'a <mask> b', # lstrip jina-v2
|
||
]
|
||
|
||
|
||
def generator_vocab_words(vocab: list[str]) -> Iterator[str]:
|
||
"""Brute force check all vocab words"""
|
||
yield from vocab
|
||
|
||
|
||
def generator_added_lr_strip(tokenizer) -> Iterator[str]:
|
||
WHITESPACES = ["", " ", " ", " "]
|
||
special_tokens = list(tokenizer.all_special_tokens)
|
||
added_tokens = list(tokenizer.added_tokens_encoder)
|
||
all_tokens = list(sorted(set(special_tokens + added_tokens)))
|
||
for token in all_tokens:
|
||
for lstrip in WHITESPACES:
|
||
for rstrip in WHITESPACES:
|
||
yield lstrip + token + rstrip
|
||
yield "a" + lstrip + token + rstrip
|
||
yield lstrip + token + rstrip + "z"
|
||
yield "a" + lstrip + token + rstrip + "z"
|
||
|
||
|
||
def generator_random_added_tokens(tokenizer, iterations=100) -> Iterator[str]:
|
||
special_tokens = list(tokenizer.all_special_tokens)
|
||
added_tokens = list(tokenizer.added_tokens_encoder)
|
||
separations = [" ", "\n", "\t", "-", "!", "one", "1", "<s>", "</s>"]
|
||
all_tokens = list(sorted(set(special_tokens + added_tokens + separations)))
|
||
rand = random.Random()
|
||
for m in range(iterations):
|
||
rand.seed(m)
|
||
words = rand.choices(all_tokens, k=500)
|
||
if words[0] == tokenizer.bos_token: # skip spam warning of double BOS
|
||
while len(words) > 1 and words[1] == tokenizer.bos_token: # leave one starting BOS
|
||
words.pop(0)
|
||
if tokenizer.add_bos_token: # drop all starting BOS
|
||
words.pop(0)
|
||
yield "".join(words)
|
||
|
||
|
||
def generator_random_chars(iterations=100) -> Iterator[str]:
|
||
"""Brute force random text with simple characters"""
|
||
|
||
WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
|
||
CHARS = list(sorted(set("""
|
||
ABCDEFGHIJKLMNOPQRSTUVWXYZ
|
||
abcdefghijklmnopqrstuvwxyz
|
||
ÁÉÍÓÚÀÈÌÒÙÂÊÎÔÛÄËÏÖÜ
|
||
áéíóúàèìòùâêîôûäëïöü
|
||
.-,*/-+ª!"·$%&/()=?¿[]{}<>\\|@#~½¬~;:_
|
||
""")))
|
||
|
||
rand = random.Random()
|
||
for m in range(iterations):
|
||
rand.seed(m)
|
||
text = []
|
||
num_words = rand.randint(300, 400)
|
||
for i in range(num_words):
|
||
k = rand.randint(1, 7)
|
||
word = rand.choices(CHARS, k=k)
|
||
space = rand.choice(WHITESPACES)
|
||
text.append("".join(word) + space)
|
||
yield "".join(text)
|
||
|
||
|
||
def generator_random_vocab_chars(vocab: list[str], iterations=100) -> Iterator[str]:
|
||
"""Brute force random text with vocab characters"""
|
||
|
||
vocab_chars = set()
|
||
for word in vocab:
|
||
vocab_chars.update(word)
|
||
vocab_chars = list(sorted(vocab_chars))
|
||
|
||
rand = random.Random()
|
||
for m in range(iterations):
|
||
rand.seed(m)
|
||
text = rand.choices(vocab_chars, k=1024)
|
||
yield "".join(text)
|
||
|
||
|
||
def generator_random_vocab_words(vocab: list[str], iterations=100) -> Iterator[str]:
|
||
"""Brute force random text from vocab words"""
|
||
|
||
vocab = [w.strip() for w in vocab]
|
||
yield from vocab
|
||
|
||
rand = random.Random()
|
||
for m in range(iterations):
|
||
rand.seed(m)
|
||
text = []
|
||
num_words = rand.randint(300, 400)
|
||
for i in range(num_words):
|
||
k = rand.randint(1, 3)
|
||
words = rand.choices(vocab, k=k)
|
||
sep = rand.choice(" \n\r\t")
|
||
text.append("".join(words) + sep)
|
||
yield "".join(text)
|
||
|
||
|
||
def generator_random_bytes(iterations=100) -> Iterator[str]:
|
||
"""Brute force random bytes"""
|
||
|
||
WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
|
||
|
||
rand = random.Random()
|
||
for m in range(iterations):
|
||
rand.seed(m)
|
||
text = []
|
||
num_words = rand.randint(300, 400)
|
||
for i in range(num_words):
|
||
k = rand.randint(1, 8)
|
||
word = [chr(r) for r in rand.randbytes(k) if r]
|
||
word.append(rand.choice(WHITESPACES))
|
||
text.append("".join(word))
|
||
yield "".join(text)
|
||
|
||
|
||
def test_compare_tokenizer(func_tokenize1: Callable, func_tokenize2: Callable, generator: Iterator[str]):
|
||
|
||
def find_first_mismatch(ids1: list[int], ids2: list[int]):
|
||
for i, (a, b) in enumerate(zip(ids1, ids2)):
|
||
if a != b:
|
||
return i
|
||
if len(ids1) == len(ids2):
|
||
return -1
|
||
return min(len(ids1), len(ids2))
|
||
|
||
t0 = time.perf_counter()
|
||
logger.info("%s: %s" % (generator.__name__, "ini"))
|
||
for text in generator:
|
||
ids1 = func_tokenize1(text)
|
||
ids2 = func_tokenize2(text)
|
||
if ids1 != ids2:
|
||
i = find_first_mismatch(ids1, ids2)
|
||
ids1 = list(ids1)[max(0, i - 2) : i + 5 + 1]
|
||
ids2 = list(ids2)[max(0, i - 2) : i + 5 + 1]
|
||
logger.info(" TokenIDs: " + str(ids1))
|
||
logger.info(" Expected: " + str(ids2))
|
||
raise Exception()
|
||
t1 = time.perf_counter()
|
||
logger.info("%s: end, time: %.3f secs" % (generator.__name__, t1 - t0))
|
||
|
||
|
||
def main(argv: list[str] = None):
|
||
parser = argparse.ArgumentParser()
|
||
parser.add_argument("vocab_file", help="path to vocab 'gguf' file")
|
||
parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
|
||
parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
|
||
args = parser.parse_args(argv)
|
||
|
||
logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
|
||
|
||
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_tokenize1(text: str):
|
||
return model.tokenize(text, add_special=True, parse_special=True)
|
||
|
||
def func_tokenize2(text: str):
|
||
return tokenizer.encode(text, add_special_tokens=True)
|
||
|
||
ids = func_tokenize2("a")
|
||
assert 1 <= len(ids) <= 3
|
||
add_bos_token = len(ids) > 1 and tokenizer.bos_token_id == ids[0]
|
||
tokenizer.add_bos_token = getattr(tokenizer, "add_bos_token", add_bos_token)
|
||
|
||
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_vocab_words(vocab))
|
||
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_added_lr_strip(tokenizer))
|
||
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_added_tokens(tokenizer, 10_000))
|
||
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))
|
||
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_words(vocab, 5_000))
|
||
# test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_bytes(10_000)) # FAIL
|
||
|
||
model.free()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# main()
|
||
|
||
path_tokenizers = "./models/tokenizers/"
|
||
path_vocab_format = "./models/ggml-vocab-%s.gguf"
|
||
|
||
# import os
|
||
# tokenizers = os.listdir(path_tokenizers)
|
||
tokenizers = [
|
||
"llama-spm", # SPM
|
||
"phi-3", # SPM
|
||
"jina-v2-en", # WPM
|
||
"bert-bge", # WPM
|
||
]
|
||
|
||
for tokenizer in tokenizers:
|
||
print("\n" + "=" * 50 + "\n" + tokenizer + "\n") # noqa
|
||
vocab_file = path_vocab_format % tokenizer
|
||
dir_tokenizer = path_tokenizers + "/" + tokenizer
|
||
main([vocab_file, dir_tokenizer, "--verbose"])
|