Detokenizer fixes (#8039)
* Add llama_detokenize():
- Update header files location
- UNKNOWN and CONTROL are 'special pieces'
- Remove space after UNKNOWN and CONTROL
- Refactor llama_token_to_piece()
- Add flag: clean_up_tokenization_spaces
- Symmetric params for llama_tokenize() and llama_detokenize()
* Update and fix tokenizer tests:
- Using llama_detokenize()
- Unexpected vocab type as test fail instead of error
- Useful when automating tests:
- If you don't know in advance the vocab type
- Differenciate other loading errors
- Skip unicode surrogaes and undefined
- Gracefully exit threads
- Using exit() is throwing random exceptions
- Clean old known problematic codepoints
- Minor: confusing hexadecimal codepoint
* Update bruteforce random tests
- Add detokenizer checks
- New generator: ascii_lr_strip
- New generator: apostrophe
- Add more vocabs files
- Detokenize special tokens.
- Replace errors with '\uFFFD' when detokenizing to 'utf-8'
- More edge cases
- Better detokenization results check
* Fix add_space_prefix, set false by default
* Better leading space removal
* Do not remove space when decoding special tokens
* Bugfix: custom regexs splits undefined unicode codepoints
* 'viking' detokenizer clean spaces
This commit is contained in:
jaime-m-p
GitHub
parent
be20e7f49d
commit
213701b51a
11 changed files with 499 additions and 265 deletions
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@ -13,7 +13,7 @@ import subprocess
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import random
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import unicodedata
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from typing import Callable, Iterator
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from typing import Iterator
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import cffi
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from transformers import AutoTokenizer
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@ -24,17 +24,20 @@ logger = logging.getLogger("test-tokenizer-random")
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class LibLlama:
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DEFAULT_PATH_LLAMA_H = "./llama.h"
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DEFAULT_PATH_LIBLLAMA = "./build/libllama.so" # CMakeLists.txt: BUILD_SHARED_LIBS ON
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DEFAULT_PATH_LLAMA_H = "./include/llama.h"
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DEFAULT_PATH_INCLUDES = ["./ggml/include/", "./include/"]
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DEFAULT_PATH_LIBLLAMA = "./build/src/libllama.so" # CMakeLists.txt: BUILD_SHARED_LIBS ON
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def __init__(self, path_llama_h: str = None, path_libllama: str = None):
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def __init__(self, path_llama_h: str = None, path_includes: list[str] = [], path_libllama: str = None):
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path_llama_h = path_llama_h or self.DEFAULT_PATH_LLAMA_H
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path_includes = path_includes or self.DEFAULT_PATH_INCLUDES
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path_libllama = path_libllama or self.DEFAULT_PATH_LIBLLAMA
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(self.ffi, self.lib) = self._load_libllama_cffi(path_llama_h, path_libllama)
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(self.ffi, self.lib) = self._load_libllama_cffi(path_llama_h, path_includes, path_libllama)
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self.lib.llama_backend_init()
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def _load_libllama_cffi(self, path_llama_h: str, path_libllama: str):
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cmd = ["gcc", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)=", path_llama_h]
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def _load_libllama_cffi(self, path_llama_h: str, path_includes: list[str], path_libllama: str):
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cmd = ["gcc", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)="]
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cmd += ["-I" + path for path in path_includes] + [path_llama_h]
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res = subprocess.run(cmd, stdout=subprocess.PIPE)
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assert (res.returncode == 0)
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source = res.stdout.decode()
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@ -79,6 +82,7 @@ class LibLlamaModel:
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raise RuntimeError("error: failed to create context for model '%s'" % path_model)
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n_tokens_max = self.lib.llama_n_ctx(self.ctx)
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self.token_ids = self.ffi.new("llama_token[]", n_tokens_max)
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self.text_buff = self.ffi.new("uint8_t[]", 1024)
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def free(self):
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if self.ctx:
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@ -89,14 +93,78 @@ class LibLlamaModel:
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self.model = None
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self.lib = None
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def tokenize(self, text: str, n_tokens_max: int = 0, add_special: bool = False, parse_special: bool = False) -> list[int]:
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n_tokens_max = n_tokens_max if n_tokens_max > 0 else len(self.token_ids)
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def tokenize(self, text: str, add_special: bool = False, parse_special: bool = False) -> list[int]:
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text = text.encode("utf-8")
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num = self.lib.llama_tokenize(self.model, text, len(text), self.token_ids, n_tokens_max, add_special, parse_special)
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if num < 0:
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return []
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num = self.lib.llama_tokenize(self.model, text, len(text), self.token_ids, len(self.token_ids), add_special, parse_special)
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while num < 0 and len(self.token_ids) < (16 << 20):
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self.token_ids = self.ffi.new("llama_token[]", -2 * num)
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num = self.lib.llama_tokenize(self.model, text, len(text), self.token_ids, len(self.token_ids), add_special, parse_special)
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return list(self.token_ids[0:num])
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def detokenize(self, ids: list[int], remove_special: bool = False, unparse_special: bool = False) -> str:
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if len(self.token_ids) < len(ids):
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self.token_ids = self.ffi.new("llama_token[]", 2 * len(ids))
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for i, id in enumerate(ids):
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self.token_ids[i] = id
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num = self.lib.llama_detokenize(self.model, self.token_ids, len(ids), self.text_buff, len(self.text_buff), remove_special, unparse_special)
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while num < 0 and len(self.text_buff) < (16 << 20):
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self.text_buff = self.ffi.new("uint8_t[]", -2 * num)
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num = self.lib.llama_detokenize(self.model, self.token_ids, len(ids), self.text_buff, len(self.text_buff), remove_special, unparse_special)
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return str(self.ffi.buffer(self.text_buff, num), encoding="utf-8", errors="replace") # replace errors with '\uFFFD'
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class Tokenizer:
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def encode(self, text: str) -> list[int]:
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raise NotImplementedError
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def decode(self, ids: list[int]) -> str:
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raise NotImplementedError
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class TokenizerGroundtruth (Tokenizer):
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def __init__(self, dir_tokenizer: str):
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self.model = AutoTokenizer.from_pretrained(dir_tokenizer)
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# guess BOS and EOS
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ids = self.encode("a")
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assert 1 <= len(ids) <= 3
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add_bos_token = len(ids) > 1 and self.model.bos_token_id == ids[0]
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add_eos_token = len(ids) > 1 and self.model.eos_token_id == ids[-1]
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self.add_bos_token = getattr(self.model, "add_bos_token", add_bos_token)
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self.add_eos_token = getattr(self.model, "add_eos_token", add_eos_token)
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# build vocab
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tokens = list(self.model.get_vocab().values())
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self.vocab = self.model.batch_decode(tokens, skip_special_tokens=True)
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self.vocab = list(sorted(self.vocab))
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# tokens and lists
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self.special_tokens = list(self.model.all_special_tokens)
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self.added_tokens = list(self.model.added_tokens_encoder)
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self.bos_token = self.model.bos_token
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self.eos_token = self.model.eos_token
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def encode(self, text: str) -> list[int]:
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return self.model.encode(text, add_special_tokens=True)
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def decode(self, ids: list[int]) -> str:
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return self.model.decode(ids, skip_special_tokens=False)
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class TokenizerLlamaCpp (Tokenizer):
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libllama: LibLlama = None
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def __init__(self, vocab_file: str):
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if not self.libllama:
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self.libllama = LibLlama()
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self.model = LibLlamaModel(self.libllama, vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096))
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def encode(self, text: str) -> list[int]:
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return self.model.tokenize(text, add_special=True, parse_special=True)
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def decode(self, ids: list[int]) -> str:
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return self.model.detokenize(ids, remove_special=False, unparse_special=True)
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def generator_custom_text() -> Iterator[str]:
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"""General tests"""
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@ -165,19 +233,48 @@ def generator_custom_text_edge_cases() -> Iterator[str]:
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'a </s> b', # rstrip phi-3
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'a <mask> b', # lstrip jina-v2
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'\xa0aC', # deepseek
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'\u2029 \uA3E4', # deepseek-llm
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"a ?",
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'å', # mpt
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'\U000ac517', # utf-8 encode error, falcon
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'\U000522f4', # utf-8 encode error, starcoder
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"<s><s><unk><s>a<s>b<s>c<unk>d<unk></s>",
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"<s> <s> <unk><s>a<s>b<s>c<unk>d<unk></s>",
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]
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def generator_vocab_words(vocab: list[str]) -> Iterator[str]:
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def generator_vocab_words(tokenizer: TokenizerGroundtruth) -> Iterator[str]:
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"""Brute force check all vocab words"""
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yield from vocab
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yield from tokenizer.vocab
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def generator_added_lr_strip(tokenizer) -> Iterator[str]:
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WHITESPACES = ["", " ", " ", " "]
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special_tokens = list(tokenizer.all_special_tokens)
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added_tokens = list(tokenizer.added_tokens_encoder)
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all_tokens = list(sorted(set(special_tokens + added_tokens)))
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def generator_ascii_lr_strip() -> Iterator[str]:
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WHITESPACES = ["", " ", " "]
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CHARACTERS = list(chr(i) for i in range(1, 0x80)) + [""]
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for char1 in CHARACTERS:
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for char2 in CHARACTERS:
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for lstrip in WHITESPACES:
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for rstrip in WHITESPACES:
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yield lstrip + char1 + char2 + rstrip
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yield lstrip + char1 + rstrip + char2
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yield char1 + lstrip + char2 + rstrip
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def generator_apostrophe() -> Iterator[str]:
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WHITESPACES = ["", " ", " "]
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CHARACTERS = list(chr(i) for i in range(1, 0x80)) + [""]
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for char1 in CHARACTERS:
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for char2 in CHARACTERS:
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for lstrip in WHITESPACES:
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for rstrip in WHITESPACES:
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yield char1 + lstrip + "'" + rstrip + char2
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yield char1 + char2 + lstrip + "'" + rstrip + "z"
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yield "a" + lstrip + "'" + rstrip + char1 + char2
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def generator_added_lr_strip(tokenizer: TokenizerGroundtruth) -> Iterator[str]:
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WHITESPACES = ["", " ", " ", "\n", "\r\n", "\n\n", "\t", "\t\t"]
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all_tokens = list(sorted(set(tokenizer.special_tokens + tokenizer.added_tokens)))
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for token in all_tokens:
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for lstrip in WHITESPACES:
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for rstrip in WHITESPACES:
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@ -187,11 +284,9 @@ def generator_added_lr_strip(tokenizer) -> Iterator[str]:
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yield "a" + lstrip + token + rstrip + "z"
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def generator_random_added_tokens(tokenizer, iterations=100) -> Iterator[str]:
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special_tokens = list(tokenizer.all_special_tokens)
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added_tokens = list(tokenizer.added_tokens_encoder)
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separations = [" ", "\n", "\t", "-", "!", "one", "1", "<s>", "</s>"]
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all_tokens = list(sorted(set(special_tokens + added_tokens + separations)))
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def generator_random_added_tokens(tokenizer: TokenizerGroundtruth, iterations=100) -> Iterator[str]:
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separations = [" ", "\n", "\t", "-", "!", "one", "1", "<s>", "</s>"]
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all_tokens = list(sorted(set(tokenizer.special_tokens + tokenizer.added_tokens + separations)))
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rand = random.Random()
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for m in range(iterations):
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rand.seed(m)
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@ -242,13 +337,13 @@ def generator_unicodes() -> Iterator[str]:
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def _valid(cpt):
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if cpt >= 0x30000: # unassigned and supplementary
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return False
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if 0x00D800 <= cpt <= 0x00F8FF: # Surrogates
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return False
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if unicodedata.category(chr(cpt)) == "Cn":
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# if cpt == 0x2029: # deepseek-llm
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# return False
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if unicodedata.category(chr(cpt)) in ("Cn", "Cs", "Co"): # undefined, surrogates, private
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return False
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return True
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characters = [chr(cpt) for cpt in range(1, MAX_CODEPOINTS) if _valid(cpt)]
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characters = [chr(cpt) for cpt in range(0, MAX_CODEPOINTS) if _valid(cpt)]
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yield from characters
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@ -273,11 +368,11 @@ def generator_random_unicodes(iterations=100) -> Iterator[str]:
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yield "".join(text)
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def generator_random_vocab_chars(vocab: list[str], iterations=100) -> Iterator[str]:
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def generator_random_vocab_chars(tokenizer: TokenizerGroundtruth, iterations=100) -> Iterator[str]:
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"""Brute force random text with vocab characters"""
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vocab_chars = set()
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for word in vocab:
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for word in tokenizer.vocab:
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vocab_chars.update(word)
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vocab_chars = list(sorted(vocab_chars))
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@ -288,10 +383,10 @@ def generator_random_vocab_chars(vocab: list[str], iterations=100) -> Iterator[s
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yield "".join(text)
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def generator_random_vocab_words(vocab: list[str], iterations=100) -> Iterator[str]:
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def generator_random_vocab_words(tokenizer: TokenizerGroundtruth, iterations=100) -> Iterator[str]:
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"""Brute force random text from vocab words"""
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vocab = [w.strip() for w in vocab]
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vocab = [w.strip() for w in tokenizer.vocab]
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yield from vocab
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rand = random.Random()
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@ -307,7 +402,7 @@ def generator_random_vocab_words(vocab: list[str], iterations=100) -> Iterator[s
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yield "".join(text)
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def compare_tokenizers(func_tokenize1: Callable, func_tokenize2: Callable, generator: Iterator[str]):
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def compare_tokenizers(tokenizer1: TokenizerGroundtruth, tokenizer2: TokenizerLlamaCpp, generator: Iterator[str]):
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def find_first_mismatch(ids1: list[int], ids2: list[int]):
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for i, (a, b) in enumerate(zip(ids1, ids2)):
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@ -317,34 +412,67 @@ def compare_tokenizers(func_tokenize1: Callable, func_tokenize2: Callable, gener
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return -1
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return min(len(ids1), len(ids2))
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t_tokenizer1 = 0
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t_tokenizer2 = 0
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def check_detokenizer(text: str, text1: str, text2: str) -> bool:
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if text1 == text2: # equal to TokenizerGroundtruth?
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return True
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# equal to source text?
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if tokenizer1.add_bos_token: # remove BOS
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if text2.startswith(tokenizer1.bos_token):
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text2 = text2[len(tokenizer1.bos_token):]
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if tokenizer1.add_eos_token: # remove EOS
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if text2.endswith(tokenizer1.eos_token):
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text2 = text2[:-len(tokenizer1.eos_token)]
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return text == text2
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t_encode1 = 0
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t_encode2 = 0
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t_decode1 = 0
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t_decode2 = 0
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t_start = time.perf_counter()
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num_errors = 10
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encode_errors = 0
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decode_errors = 0
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MAX_ERRORS = 10
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logger.info("%s: %s" % (generator.__name__, "ini"))
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for text in generator:
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# print(repr(text), text.encode())
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# print(repr(text), hex(ord(text[0])), text.encode())
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t0 = time.perf_counter()
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ids1 = func_tokenize1(text)
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ids1 = tokenizer1.encode(text)
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t1 = time.perf_counter()
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ids2 = func_tokenize2(text)
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ids2 = tokenizer2.encode(text)
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t2 = time.perf_counter()
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t_tokenizer1 += t1 - t0
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t_tokenizer2 += t2 - t1
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if ids1 != ids2:
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text1 = tokenizer1.decode(ids1)
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t3 = time.perf_counter()
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text2 = tokenizer2.decode(ids1)
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t4 = time.perf_counter()
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t_encode1 += t1 - t0
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t_encode2 += t2 - t1
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t_decode1 += t3 - t2
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t_decode2 += t4 - t3
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if encode_errors < MAX_ERRORS and ids1 != ids2:
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i = find_first_mismatch(ids1, ids2)
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ids1 = list(ids1)[max(0, i - 2) : i + 5 + 1]
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ids2 = list(ids2)[max(0, i - 2) : i + 5 + 1]
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logger.error(" TokenIDs: " + str(ids1))
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logger.error(" Expected: " + str(ids2))
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logger.error(" Expected: " + str(ids1))
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logger.error(" Result: " + str(ids2))
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encode_errors += 1
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logger.error(f" {encode_errors=}")
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if decode_errors < MAX_ERRORS and not check_detokenizer(text, text1, text2):
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i = find_first_mismatch(text1, text2)
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text1 = list(text1[max(0, i - 2) : i + 5 + 1])
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text2 = list(text2[max(0, i - 2) : i + 5 + 1])
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logger.error(" Expected: " + " ".join(hex(ord(x)) for x in text1))
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logger.error(" Result: " + " ".join(hex(ord(x)) for x in text2))
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decode_errors += 1
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logger.error(f" {decode_errors=}")
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if encode_errors >= MAX_ERRORS and decode_errors >= MAX_ERRORS:
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logger.error(f" EXIT: {encode_errors=} {decode_errors=}")
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# raise Exception()
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num_errors += 1
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if num_errors > 10:
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break
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break
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t_total = time.perf_counter() - t_start
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logger.info("%s: end, tok1: %.3f tok2: %.3f total: %.3f" % (generator.__name__, t_tokenizer1, t_tokenizer2, t_total))
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logger.info(f"{generator.__name__}: end, {t_encode1=:.3f} {t_encode2=:.3f} {t_decode1=:.3f} {t_decode2=:.3f} {t_total=:.3f}")
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def main(argv: list[str] = None):
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@ -357,74 +485,76 @@ def main(argv: list[str] = None):
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logging.basicConfig(level = logging.DEBUG if args.verbose else logging.INFO)
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logger.info(f"VOCABFILE: '{args.vocab_file}'")
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model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096))
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tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer)
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tokenizer1 = TokenizerGroundtruth(args.dir_tokenizer)
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tokenizer2 = TokenizerLlamaCpp(args.vocab_file)
|
||||
|
||||
def func_tokenize1(text: str):
|
||||
return model.tokenize(text, add_special=True, parse_special=True)
|
||||
# compare_tokenizers(tokenizer1, tokenizer2, generator_custom_text())
|
||||
# compare_tokenizers(tokenizer1, tokenizer2, generator_custom_text_edge_cases())
|
||||
compare_tokenizers(tokenizer1, tokenizer2, generator_ascii_lr_strip())
|
||||
compare_tokenizers(tokenizer1, tokenizer2, generator_apostrophe())
|
||||
compare_tokenizers(tokenizer1, tokenizer2, generator_unicodes())
|
||||
compare_tokenizers(tokenizer1, tokenizer2, generator_vocab_words(tokenizer1))
|
||||
compare_tokenizers(tokenizer1, tokenizer2, generator_added_lr_strip(tokenizer1))
|
||||
# compare_tokenizers(tokenizer1, tokenizer2, generator_random_added_tokens(tokenizer1, 10_000))
|
||||
# compare_tokenizers(tokenizer1, tokenizer2, generator_random_chars(10_000))
|
||||
# compare_tokenizers(tokenizer1, tokenizer2, generator_random_unicodes(10_000))
|
||||
# compare_tokenizers(tokenizer1, tokenizer2, generator_random_vocab_chars(tokenizer1, 10_000))
|
||||
# compare_tokenizers(tokenizer1, tokenizer2, generator_random_vocab_words(tokenizer1, 5_000))
|
||||
|
||||
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]
|
||||
add_eos_token = len(ids) > 1 and tokenizer.eos_token_id == ids[-1]
|
||||
tokenizer.add_bos_token = getattr(tokenizer, "add_bos_token", add_bos_token)
|
||||
tokenizer.add_eos_token = getattr(tokenizer, "add_eos_token", add_eos_token)
|
||||
|
||||
vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True)))
|
||||
|
||||
compare_tokenizers(func_tokenize1, func_tokenize2, generator_custom_text())
|
||||
compare_tokenizers(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases())
|
||||
compare_tokenizers(func_tokenize1, func_tokenize2, generator_unicodes())
|
||||
compare_tokenizers(func_tokenize1, func_tokenize2, generator_vocab_words(vocab))
|
||||
compare_tokenizers(func_tokenize1, func_tokenize2, generator_added_lr_strip(tokenizer))
|
||||
compare_tokenizers(func_tokenize1, func_tokenize2, generator_random_added_tokens(tokenizer, 10_000))
|
||||
compare_tokenizers(func_tokenize1, func_tokenize2, generator_random_chars(10_000))
|
||||
compare_tokenizers(func_tokenize1, func_tokenize2, generator_random_unicodes(10_000))
|
||||
compare_tokenizers(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000))
|
||||
compare_tokenizers(func_tokenize1, func_tokenize2, generator_random_vocab_words(vocab, 5_000))
|
||||
|
||||
model.free()
|
||||
tokenizer2.model.free()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# main()
|
||||
|
||||
if True:
|
||||
logging.basicConfig(
|
||||
level = logging.DEBUG,
|
||||
format = "%(asctime)s.%(msecs)03d %(name)s %(levelname)s %(message)s",
|
||||
datefmt = "%Y-%m-%d %H:%M:%S",
|
||||
filename = logger.name + ".log",
|
||||
filemode = "a"
|
||||
)
|
||||
logging.basicConfig(
|
||||
level = logging.DEBUG,
|
||||
format = "%(asctime)s.%(msecs)03d %(name)s %(levelname)s %(message)s",
|
||||
datefmt = "%Y-%m-%d %H:%M:%S",
|
||||
filename = logger.name + ".log",
|
||||
filemode = "a"
|
||||
format = "%(levelname)s %(message)s",
|
||||
)
|
||||
|
||||
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
|
||||
# "bert-bge", # WPM
|
||||
# "jina-v2-en", # WPM
|
||||
"gpt-2", # BPE
|
||||
"llama-spm", # SPM
|
||||
"phi-3", # SPM
|
||||
"gemma", # SPM
|
||||
"gemma-2", # SPM
|
||||
"baichuan", # SPM
|
||||
"bert-bge", # WPM
|
||||
"jina-v2-en", # WPM
|
||||
"llama-bpe", # BPE
|
||||
"phi-2", # BPE
|
||||
"deepseek-llm", # BPE
|
||||
"deepseek-coder", # BPE
|
||||
"falcon", # BPE
|
||||
"mpt", # BPE
|
||||
"starcoder", # BPE
|
||||
"gpt-2", # BPE
|
||||
"stablelm2", # BPE
|
||||
"refact", # BPE
|
||||
"qwen2", # BPE
|
||||
"olmo", # BPE
|
||||
"jina-v2-es", # BPE
|
||||
"jina-v2-de", # BPE
|
||||
"jina-v2-code", # BPE
|
||||
"smaug-bpe", # BPE
|
||||
"phi-2", # BPE
|
||||
"deepseek-coder", # BPE
|
||||
"deepseek-llm", # BPE
|
||||
"poro-chat", # BPE
|
||||
"jina-v2-code", # BPE
|
||||
"viking", # BPE
|
||||
"jais", # BPE
|
||||
]
|
||||
|
||||
logger.info("=" * 50)
|
||||
for tokenizer in tokenizers:
|
||||
logger.info("=" * 50)
|
||||
logger.info("-" * 50)
|
||||
logger.info(f"TOKENIZER: '{tokenizer}'")
|
||||
vocab_file = path_vocab_format % tokenizer
|
||||
dir_tokenizer = path_tokenizers + "/" + tokenizer
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue