296 lines
10 KiB
Python
296 lines
10 KiB
Python
# Test libllama tokenizer == AutoTokenizer.
|
||
# Brute force random tokens/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 Iterator
|
||
|
||
import cffi
|
||
from transformers import AutoTokenizer, PreTrainedTokenizerBase
|
||
|
||
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)
|
||
'<s>a' # TODO: Phi-3 fail
|
||
]
|
||
|
||
|
||
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(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(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]:
|
||
"""Brute force random text with vocab characters"""
|
||
|
||
vocab_ids = list(tokenizer.vocab.values())
|
||
vocab_text = tokenizer.decode(vocab_ids, skip_special_tokens=True)
|
||
vocab_chars = list(set(vocab_text))
|
||
del vocab_ids, vocab_text
|
||
|
||
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_tokens(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]:
|
||
"""Brute force random text from vocab tokens"""
|
||
|
||
space_id = tokenizer.encode(" ", add_special_tokens=False)[0]
|
||
vocab_ids = list(tokenizer.vocab.values())
|
||
vocab_ids = list(sorted(vocab_ids + vocab_ids))
|
||
for i in range(1, len(vocab_ids), 2):
|
||
vocab_ids[i] = space_id
|
||
vocab_tokens = tokenizer.decode(vocab_ids, skip_special_tokens=True)
|
||
vocab_tokens = vocab_tokens.split(" ")
|
||
del vocab_ids
|
||
|
||
yield from vocab_tokens
|
||
|
||
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)
|
||
tokens = rand.choices(vocab_tokens, k=k)
|
||
tokens = [t.strip(" \n\r\t") for t in tokens]
|
||
sep = rand.choice(" \n\r\t")
|
||
text.append("".join(tokens) + 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(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase, 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 = model.tokenize(text, add_special=False, parse_special=False)
|
||
ids2 = tokenizer.encode(text, add_special_tokens=False)
|
||
if ids1 != ids2:
|
||
i = find_first_mismatch(ids1, ids2)
|
||
ids1 = list(ids1)[max(0, i - 2) : i + 2 + 1]
|
||
ids2 = list(ids2)[max(0, i - 2) : i + 2 + 1]
|
||
text2 = tokenizer.decode(ids2, skip_special_tokens=True)
|
||
assert (text2 in text)
|
||
logger.info(" Text: " + repr(text2))
|
||
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))
|
||
|
||
|
||
if __name__ == "__main__":
|
||
|
||
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()
|
||
|
||
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=2048))
|
||
|
||
tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer)
|
||
|
||
test_compare_tokenizer(model, tokenizer, generator_custom_text())
|
||
test_compare_tokenizer(model, tokenizer, generator_custom_text_edge_cases())
|
||
test_compare_tokenizer(model, tokenizer, generator_random_chars(10_000))
|
||
test_compare_tokenizer(model, tokenizer, generator_random_vocab_chars(tokenizer, 10_000))
|
||
test_compare_tokenizer(model, tokenizer, generator_random_vocab_tokens(tokenizer, 10_000))
|
||
# test_compare_tokenizer(model, tokenizer, generator_random_bytes(10_000)) # FAIL
|
||
|
||
model.free()
|