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https://github.com/ggml-org/llama.cpp.git
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llama3 custom regex split (#6965)
* merged the changes from deepseeker models to main branch
* Moved regex patterns to unicode.cpp and updated unicode.h
* Moved header files
* Resolved issues
* added and refactored unicode_regex_split and related functions
* Updated/merged the deepseek coder pr
* Refactored code
* Adding unicode regex mappings
* Adding unicode regex function
* Added needed functionality, testing remains
* Fixed issues
* Fixed issue with gpt2 regex custom preprocessor
* unicode : fix? unicode_wstring_to_utf8
* lint : fix whitespaces
* tests : add tokenizer tests for numbers
* unicode : remove redundant headers
* tests : remove and rename tokenizer test scripts
* tests : add sample usage
* gguf-py : reader prints warnings on duplicate keys
* llama : towards llama3 tokenization support (wip)
* unicode : shot in the dark to fix tests on Windows
* unicode : first try custom implementations
* convert : add "tokenizer.ggml.pre" GGUF KV (wip)
* llama : use new pre-tokenizer type
* convert : fix pre-tokenizer type writing
* lint : fix
* make : add test-tokenizer-0-llama-v3
* wip
* models : add llama v3 vocab file
* llama : adapt punctuation regex + add llama 3 regex
* minor
* unicode : set bomb
* unicode : set bomb
* unicode : always use std::wregex
* unicode : support \p{N}, \p{L} and \p{P} natively
* unicode : try fix windows
* unicode : category support via std::regex
* unicode : clean-up
* unicode : simplify
* llama3 custom regex split
* convert : add convert-hf-to-gguf-update.py
ggml-ci
* lint : update
* convert : add falcon
ggml-ci
* unicode : normalize signatures
* lint : fix
* lint : fix
* convert : remove unused functions
* convert : add comments
* convert : exercise contractions
ggml-ci
* Using char32_t for codepoints
* lint : fix
* already exists unicode_tolower()
* Typing
* Restore BOM
* cmake : refactor test targets
* tests : refactor vocab tests
ggml-ci
* tests : add more vocabs and tests
ggml-ci
* unicode : cleanup
* scripts : ignore new update script in check-requirements.sh
* Fix merge
* models : add phi-3, mpt, gpt-2, starcoder
* tests : disable obsolete
ggml-ci
* tests : use faster bpe test
ggml-ci
* llama : more prominent warning for old BPE models
* tests : disable test-tokenizer-1-bpe due to slowness
ggml-ci
* Move unused variable value
* GPT2 custom regex split
* Add alternative regex for custom aplit llama3
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Style
* Add bruteforce random tests for token encoding
* wip: fixing unicode codepoint ranges
* Fix merge
* Unicode tables: separator, lowercase, uppercase and whitespace
* llama3 custom regex split: fix \s
* Restore BOM
* Style
* wip: generate NDF table
* Ignore special tokens for testing
* Clean gen-unicode-data.py
* Refactor random tokenizer test
* lint : fix
* tests : add fail test for llama-bpe
---------
Co-authored-by: Jaggzh <jaggz.h@gmail.com>
Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: jaime-m-p <>
This commit is contained in:
jaime-m-p
GitHub
295
tests/test-tokenizer-random.py
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295
tests/test-tokenizer-random.py
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# Test libllama tokenizer == AutoTokenizer.
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# Brute force random tokens/text generation.
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#
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# Sample usage:
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#
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# python3 tests/test-tokenizer-random.py ./models/ggml-vocab-llama-bpe.gguf ./models/tokenizers/llama-bpe
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#
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import time
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import logging
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import argparse
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import subprocess
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import random
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from typing import Iterator
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import cffi
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from transformers import AutoTokenizer, PreTrainedTokenizerBase
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logger = logging.getLogger("test-tokenizer-random-bpe")
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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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def __init__(self, path_llama_h: str = None, 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_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.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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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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ffi = cffi.FFI()
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if True: # workarounds for pycparser
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source = "typedef struct { } __builtin_va_list;" + "\n" + source
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source = source.replace("sizeof (int)", str(ffi.sizeof("int")))
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source = source.replace("sizeof (void *)", str(ffi.sizeof("void*")))
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source = source.replace("sizeof (size_t)", str(ffi.sizeof("size_t")))
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source = source.replace("sizeof(int32_t)", str(ffi.sizeof("int32_t")))
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ffi.cdef(source, override=True)
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lib = ffi.dlopen(path_libllama)
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return (ffi, lib)
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def model_default_params(self, **kwargs):
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mparams = self.lib.llama_model_default_params()
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for k, v in kwargs.items():
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setattr(mparams, k, v)
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return mparams
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def context_default_params(self, **kwargs):
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cparams = self.lib.llama_context_default_params()
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for k, v in kwargs.items():
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setattr(cparams, k, v)
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return cparams
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class LibLlamaModel:
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def __init__(self, libllama: LibLlama, path_model: str, mparams={}, cparams={}):
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self.lib = libllama.lib
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self.ffi = libllama.ffi
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if isinstance(mparams, dict):
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mparams = libllama.model_default_params(**mparams)
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self.model = self.lib.llama_load_model_from_file(path_model.encode(), mparams)
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if not self.model:
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raise RuntimeError("error: failed to load model '%s'" % path_model)
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if isinstance(cparams, dict):
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cparams = libllama.context_default_params(**cparams)
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self.ctx = self.lib.llama_new_context_with_model(self.model, cparams)
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if not self.ctx:
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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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def free(self):
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if self.ctx:
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self.lib.llama_free(self.ctx)
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if self.model:
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self.lib.llama_free_model(self.model)
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self.ctx = None
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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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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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return list(self.token_ids[0:num])
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def generator_custom_text() -> Iterator[str]:
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"""General tests"""
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yield from [
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"",
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" ",
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" ",
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" ",
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"\t",
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"\n",
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"\n\n",
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"\n\n\n",
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"\t\n",
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"Hello world",
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" Hello world",
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"Hello World",
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" Hello World",
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" Hello World!",
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"Hello, world!",
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" Hello, world!",
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" this is 🦙.cpp",
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"w048 7tuijk dsdfhu",
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"нещо на Български",
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"កាន់តែពិសេសអាចខលចេញ",
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"🚀 (normal) 😶🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
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"Hello",
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" Hello",
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" Hello",
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" Hello",
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" Hello",
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" Hello\n Hello",
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" (",
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"\n =",
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"' era",
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"Hello, y'all! How are you 😁 ?我想在apple工作1314151天～",
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"3",
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"33",
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"333",
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"3333",
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"33333",
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"333333",
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"3333333",
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"33333333",
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"333333333",
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]
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def generator_custom_text_edge_cases() -> Iterator[str]:
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"""Edge cases found while debugging"""
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yield from [
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'\x1f-a', # unicode_ranges_control, {0x00001C, 0x00001F}
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'¼-a', # unicode_ranges_digit, 0x00BC
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'½-a', # unicode_ranges_digit, 0x00BD
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'¾-a', # unicode_ranges_digit, 0x00BE
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'a 〇b', # unicode_ranges_digit, 0x3007
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'Ⅵ-a', # unicode_ranges_digit, {0x00002150, 0x0000218F} // Number Forms
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'\uFEFF//', # unicode_ranges_control, 0xFEFF (BOM)
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'<s>a' # TODO: Phi-3 fail
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]
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def generator_random_chars(iterations = 100) -> Iterator[str]:
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"""Brute force random text with simple characters"""
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WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
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CHARS = list(set("""
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ABCDEFGHIJKLMNOPQRSTUVWXYZ
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abcdefghijklmnopqrstuvwxyz
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ÁÉÍÓÚÀÈÌÒÙÂÊÎÔÛÄËÏÖÜ
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áéíóúàèìòùâêîôûäëïöü
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.-,*/-+ª!"·$%&/()=?¿[]{}<>\\|@#~½¬~;:_
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"""))
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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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text = []
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num_words = rand.randint(300, 400)
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for i in range(num_words):
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k = rand.randint(1, 7)
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word = rand.choices(CHARS, k=k)
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space = rand.choice(WHITESPACES)
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text.append("".join(word) + space)
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yield "".join(text)
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def generator_random_vocab_chars(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]:
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"""Brute force random text with vocab characters"""
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vocab_ids = list(tokenizer.vocab.values())
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vocab_text = tokenizer.decode(vocab_ids, skip_special_tokens=True)
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vocab_chars = list(set(vocab_text))
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del vocab_ids, vocab_text
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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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text = rand.choices(vocab_chars, k=1024)
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yield "".join(text)
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def generator_random_vocab_tokens(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]:
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"""Brute force random text from vocab tokens"""
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space_id = tokenizer.encode(" ", add_special_tokens=False)[0]
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vocab_ids = list(tokenizer.vocab.values())
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vocab_ids = list(sorted(vocab_ids + vocab_ids))
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for i in range(1, len(vocab_ids), 2):
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vocab_ids[i] = space_id
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vocab_tokens = tokenizer.decode(vocab_ids, skip_special_tokens=True)
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vocab_tokens = vocab_tokens.split(" ")
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del vocab_ids
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yield from vocab_tokens
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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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text = []
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num_words = rand.randint(300, 400)
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for i in range(num_words):
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k = rand.randint(1, 3)
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tokens = rand.choices(vocab_tokens, k=k)
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tokens = [t.strip(" \n\r\t") for t in tokens]
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sep = rand.choice(" \n\r\t")
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text.append("".join(tokens) + sep)
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yield "".join(text)
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def generator_random_bytes(iterations = 100) -> Iterator[str]:
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"""Brute force random bytes"""
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WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
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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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text = []
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num_words = rand.randint(300, 400)
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for i in range(num_words):
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k = rand.randint(1, 8)
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word = [chr(r) for r in rand.randbytes(k) if r]
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word.append(rand.choice(WHITESPACES))
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text.append("".join(word))
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yield "".join(text)
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def test_compare_tokenizer(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase, 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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if a != b:
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return i
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if len(ids1) == len(ids2):
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return -1
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return min(len(ids1), len(ids2))
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t0 = time.perf_counter()
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logger.info("%s: %s" % (generator.__name__, "ini"))
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for text in generator:
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ids1 = model.tokenize(text, add_special=False, parse_special=False)
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ids2 = tokenizer.encode(text, add_special_tokens=False)
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if ids1 != ids2:
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i = find_first_mismatch(ids1, ids2)
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ids1 = list(ids1)[max(0, i - 2) : i + 2 + 1]
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ids2 = list(ids2)[max(0, i - 2) : i + 2 + 1]
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text2 = tokenizer.decode(ids2, skip_special_tokens=True)
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assert (text2 in text)
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logger.info(" Text: " + repr(text2))
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logger.info(" TokenIDs: " + str(ids1))
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logger.info(" Expected: " + str(ids2))
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raise Exception()
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t1 = time.perf_counter()
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logger.info("%s: end, time: %.3f secs" % (generator.__name__, t1 - t0))
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("vocab_file", help="path to vocab 'gguf' file")
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parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
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parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
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args = parser.parse_args()
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logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
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model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=2048))
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tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer)
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test_compare_tokenizer(model, tokenizer, generator_custom_text())
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test_compare_tokenizer(model, tokenizer, generator_custom_text_edge_cases())
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test_compare_tokenizer(model, tokenizer, generator_random_chars(10_000))
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test_compare_tokenizer(model, tokenizer, generator_random_vocab_chars(tokenizer, 10_000))
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test_compare_tokenizer(model, tokenizer, generator_random_vocab_tokens(tokenizer, 10_000))
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# test_compare_tokenizer(model, tokenizer, generator_random_bytes(10_000)) # FAIL
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model.free()
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