mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 18:34:36 +00:00
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
parent
a743d76a01
commit
43248e5594
@ -261,6 +261,7 @@ tests = [
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"3333333",
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"33333333",
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"333333333",
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# "Cửa Việt", # llama-bpe fails on this
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chktxt,
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]
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@ -12488,7 +12488,7 @@ struct llm_tokenizer_wpm {
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continue;
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}
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code = unicode_tolower(code);
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if (type == CODEPOINT_TYPE_WHITESPACE) {
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if (type == CODEPOINT_TYPE_SEPARATOR) {
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code = ' ';
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}
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std::string s = unicode_cpt_to_utf8(code);
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@ -1,31 +1,14 @@
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import regex
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def cpt_to_utf8_str(cpt):
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if cpt <= 0xFF:
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return bytes([cpt, 0, 0, 0])
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elif cpt <= 0xFFFF:
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return bytes([cpt & 0xFF, cpt >> 8, 0, 0])
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elif cpt <= 0xFFFFFF:
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return bytes([cpt & 0xFF, (cpt >> 8) & 0xFF, (cpt >> 16) & 0xFF, 0])
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else:
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return bytes([cpt & 0xFF, (cpt >> 8) & 0xFF, (cpt >> 16) & 0xFF, cpt >> 24])
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def is_match(codepoint, regex_expr):
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try:
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res = regex.match(regex_expr, cpt_to_utf8_str(codepoint).decode('utf-32'))
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return res is not None
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except Exception:
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return False
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def get_matches(regex_expr):
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regex_expr_compiled = regex.compile(regex_expr)
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unicode_ranges = []
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current_range = None
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for codepoint in range(0x110000):
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if is_match(codepoint, regex_expr):
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char = chr(codepoint)
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if regex_expr_compiled.match(char):
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if current_range is None:
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current_range = [codepoint, codepoint]
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else:
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@ -40,27 +23,42 @@ def get_matches(regex_expr):
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return unicode_ranges
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def print_cat(cat, ranges):
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def print_cat(mode, cat, ranges):
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if mode == "range":
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print("const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_{} = {{".format(cat)) # noqa: NP100
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cnt = 0
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for start, end in ranges:
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if cnt % 4 != 0:
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print(" ", end="") # noqa: NP100
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print("{{0x{:08X}, 0x{:08X}}},".format(start, end), end="") # noqa: NP100
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if cnt % 4 == 3:
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print("") # noqa: NP100
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cnt += 1
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if cnt % 4 != 0:
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print("") # noqa: NP100
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if mode == "map":
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print("const std::map<uint32_t, uint32_t> unicode_map_{} = {{".format(cat)) # noqa: NP100
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for i, values in enumerate(ranges):
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end = ",\n" if (i % 4 == 3 or i + 1 == len(ranges)) else ", "
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values = ["0x%08X" % value for value in values]
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print("{" + ", ".join(values) + "}", end=end) # noqa: NP100
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print("};") # noqa: NP100
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print("") # noqa: NP100
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print_cat("number", get_matches(r'\p{N}'))
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print_cat("letter", get_matches(r'\p{L}'))
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print_cat("whitespace", get_matches(r'\p{Z}'))
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print_cat("accent_mark", get_matches(r'\p{M}'))
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print_cat("punctuation", get_matches(r'\p{P}'))
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print_cat("symbol", get_matches(r'\p{S}'))
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print_cat("control", get_matches(r'\p{C}'))
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print_cat("range", "number", get_matches(r'\p{N}'))
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print_cat("range", "letter", get_matches(r'\p{L}'))
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print_cat("range", "separator", get_matches(r'\p{Z}'))
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print_cat("range", "accent_mark", get_matches(r'\p{M}'))
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print_cat("range", "punctuation", get_matches(r'\p{P}'))
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print_cat("range", "symbol", get_matches(r'\p{S}'))
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print_cat("range", "control", get_matches(r'\p{C}'))
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print_cat("range", "whitespace", get_matches(r'\s'))
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map_lowercase = []
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map_uppercase = []
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for codepoint in range(0x110000):
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char = chr(codepoint)
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lower = ord(char.lower()[0])
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upper = ord(char.upper()[0])
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if codepoint != lower:
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map_lowercase.append((codepoint, lower))
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if codepoint != upper:
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map_uppercase.append((codepoint, upper))
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print_cat("map", "lowercase", map_lowercase)
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print_cat("map", "uppercase", map_uppercase)
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# TODO: generate unicode_map_nfd
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295
tests/test-tokenizer-random.py
Normal file
295
tests/test-tokenizer-random.py
Normal file
@ -0,0 +1,295 @@
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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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|
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|
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def test_compare_tokenizer(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase, generator: Iterator[str]):
|
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|
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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
|
||||
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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|
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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()
|
||||
1262
unicode-data.cpp
1262
unicode-data.cpp
File diff suppressed because it is too large
Load Diff
@ -7,6 +7,7 @@
|
||||
|
||||
extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_number;
|
||||
extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_letter;
|
||||
extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_separator;
|
||||
extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_whitespace;
|
||||
extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_accent_mark;
|
||||
extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_punctuation;
|
||||
|
||||
356
unicode.cpp
356
unicode.cpp
@ -9,6 +9,7 @@
|
||||
#include <stdexcept>
|
||||
#include <string>
|
||||
#include <unordered_map>
|
||||
#include <unordered_set>
|
||||
#include <utility>
|
||||
#include <vector>
|
||||
#include <locale>
|
||||
@ -120,9 +121,9 @@ static std::unordered_map<uint32_t, int> unicode_cpt_type_map() {
|
||||
cpt_types[i] = CODEPOINT_TYPE_LETTER;
|
||||
}
|
||||
}
|
||||
for (auto p : unicode_ranges_whitespace) {
|
||||
for (auto p : unicode_ranges_separator) {
|
||||
for (auto i = p.first; i <= p.second; ++i) {
|
||||
cpt_types[i] = CODEPOINT_TYPE_WHITESPACE;
|
||||
cpt_types[i] = CODEPOINT_TYPE_SEPARATOR;
|
||||
}
|
||||
}
|
||||
for (auto p : unicode_ranges_accent_mark) {
|
||||
@ -224,138 +225,256 @@ static std::vector<size_t> unicode_regex_split_custom_gpt2(const std::string & t
|
||||
std::vector<size_t> bpe_offsets; // store the offset of each word
|
||||
bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
|
||||
|
||||
const auto cpts = unicode_cpts_from_utf8(text);
|
||||
|
||||
size_t start = 0;
|
||||
for (auto offset : offsets) {
|
||||
const size_t offset_ini = start;
|
||||
const size_t offset_end = start + offset;
|
||||
assert(offset_end <= cpts.size());
|
||||
start = offset_end;
|
||||
|
||||
auto _get_cpt = [&] (const size_t pos) -> char32_t {
|
||||
return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0;
|
||||
};
|
||||
|
||||
auto _get_cpt_type = [&] (const size_t pos) -> int {
|
||||
return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_type(cpts[pos]) : CODEPOINT_TYPE_UNIDENTIFIED;
|
||||
};
|
||||
|
||||
size_t _prev_end = offset_ini;
|
||||
auto _add_token = [&] (const size_t end) -> size_t {
|
||||
assert(_prev_end <= end && end <= offset_end);
|
||||
size_t len = end - _prev_end;
|
||||
if (len > 0) {
|
||||
bpe_offsets.push_back(len);
|
||||
}
|
||||
_prev_end = end;
|
||||
//if (len > 0) {
|
||||
// std::string s = "";
|
||||
// for(size_t p = end-len; p < end; p++)
|
||||
// s += unicode_cpt_to_utf8(cpts[p]);
|
||||
// printf(">>> '%s'\n", s.c_str());
|
||||
//}
|
||||
return len;
|
||||
};
|
||||
|
||||
for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) {
|
||||
const char32_t cpt = _get_cpt(pos);
|
||||
const int cpt_type = _get_cpt_type(pos);
|
||||
|
||||
// regex: 's|'t|'re|'ve|'m|'ll|'d
|
||||
if (cpt == '\'' && pos+1 < offset_end) {
|
||||
char32_t cpt_next = _get_cpt(pos+1);
|
||||
if (cpt_next == 's' || cpt_next == 't' || cpt_next == 'm' || cpt_next == 'd') {
|
||||
pos += _add_token(pos+2);
|
||||
continue;
|
||||
}
|
||||
if (pos+2 < offset_end) {
|
||||
char32_t cpt_next_next = _get_cpt(pos+2);
|
||||
if ((cpt_next == 'r' && cpt_next_next == 'e') ||
|
||||
(cpt_next == 'v' && cpt_next_next == 'e') ||
|
||||
(cpt_next == 'l' && cpt_next_next == 'l')) {
|
||||
pos += _add_token(pos+3);
|
||||
continue;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
char32_t cpt2 = (cpt == ' ' ? _get_cpt(pos+1) : cpt);
|
||||
int cpt2_type = (cpt == ' ' ? _get_cpt_type(pos+1) : cpt_type);
|
||||
// regex: <space>?\p{L}+
|
||||
if (cpt2_type == CODEPOINT_TYPE_LETTER) {
|
||||
pos += (cpt == ' ');
|
||||
while (cpt2_type == CODEPOINT_TYPE_LETTER) {
|
||||
cpt2_type = _get_cpt_type(++pos);
|
||||
}
|
||||
_add_token(pos);
|
||||
continue;
|
||||
}
|
||||
// regex: <space>?\p{N}+
|
||||
if (cpt2_type == CODEPOINT_TYPE_NUMBER) {
|
||||
pos += (cpt == ' ');
|
||||
while (cpt2_type == CODEPOINT_TYPE_NUMBER) {
|
||||
cpt2_type = _get_cpt_type(++pos);
|
||||
}
|
||||
_add_token(pos);
|
||||
continue;
|
||||
}
|
||||
// regex: <space>?[^\s\p{L}\p{N}]+
|
||||
if (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) {
|
||||
pos += (cpt == ' ');
|
||||
while (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) {
|
||||
cpt2_type = _get_cpt_type(++pos);
|
||||
cpt2 = _get_cpt(pos);
|
||||
}
|
||||
_add_token(pos);
|
||||
continue;
|
||||
}
|
||||
|
||||
size_t num_whitespaces = 0;
|
||||
while (unicode_cpt_is_whitespace(_get_cpt(pos+num_whitespaces))) {
|
||||
num_whitespaces++;
|
||||
}
|
||||
|
||||
// regex: \s+(?!\S)
|
||||
if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != 0) {
|
||||
pos += num_whitespaces - 1;
|
||||
_add_token(pos);
|
||||
continue;
|
||||
}
|
||||
|
||||
// regex: \s+
|
||||
if (num_whitespaces > 0) {
|
||||
pos += num_whitespaces;
|
||||
_add_token(pos);
|
||||
continue;
|
||||
}
|
||||
|
||||
// no matches
|
||||
_add_token(++pos);
|
||||
}
|
||||
}
|
||||
|
||||
return bpe_offsets;
|
||||
}
|
||||
|
||||
// LLAMA3 system regex: "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"
|
||||
static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string & text, const std::vector<size_t> & offsets) {
|
||||
std::vector<size_t> bpe_offsets; // store the offset of each word
|
||||
bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
|
||||
|
||||
const auto cpts = unicode_cpts_from_utf8(text);
|
||||
|
||||
size_t start = 0;
|
||||
for (auto offset : offsets) {
|
||||
std::string token;
|
||||
const size_t offset_ini = start;
|
||||
const size_t offset_end = start + offset;
|
||||
assert(offset_end <= cpts.size());
|
||||
start = offset_end;
|
||||
|
||||
bool collecting_numeric = false;
|
||||
bool collecting_letter = false;
|
||||
bool collecting_special = false;
|
||||
bool collecting_whitespace_lookahead = false;
|
||||
bool collecting = false;
|
||||
auto _get_cpt = [&] (const size_t pos) -> char32_t {
|
||||
return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0;
|
||||
};
|
||||
|
||||
std::vector<std::string> text_utf;
|
||||
text_utf.reserve(offset);
|
||||
auto _get_cpt_type = [&] (const size_t pos) -> int {
|
||||
return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_type(cpts[pos]) : CODEPOINT_TYPE_UNIDENTIFIED;
|
||||
};
|
||||
|
||||
for (size_t i = start; i < start + offset; ++i) {
|
||||
text_utf.emplace_back(unicode_cpt_to_utf8(cpts[i]));
|
||||
size_t _prev_end = offset_ini;
|
||||
auto _add_token = [&] (const size_t end) -> size_t {
|
||||
assert(_prev_end <= end && end <= offset_end);
|
||||
size_t len = end - _prev_end;
|
||||
if (len > 0) {
|
||||
bpe_offsets.push_back(len);
|
||||
}
|
||||
_prev_end = end;
|
||||
//if (len > 0) {
|
||||
// std::string s = "";
|
||||
// for(size_t p = end-len; p < end; p++)
|
||||
// s += unicode_cpt_to_utf8(cpts[p]);
|
||||
// printf(">>> '%s'\n", s.c_str());
|
||||
//}
|
||||
return len;
|
||||
};
|
||||
|
||||
for (int i = 0; i < (int)text_utf.size(); i++) {
|
||||
const std::string & utf_char = text_utf[i];
|
||||
bool split_condition = false;
|
||||
int bytes_remain = text_utf.size() - i;
|
||||
for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) {
|
||||
const char32_t cpt = _get_cpt(pos);
|
||||
const int cpt_type = _get_cpt_type(pos);
|
||||
|
||||
// forward backward lookups
|
||||
const std::string & utf_char_next = (i + 1 < (int)text_utf.size()) ? text_utf[i + 1] : "";
|
||||
const std::string & utf_char_next_next = (i + 2 < (int)text_utf.size()) ? text_utf[i + 2] : "";
|
||||
|
||||
// handling contractions
|
||||
if (!split_condition && bytes_remain >= 2) {
|
||||
// 's|'t|'m|'d
|
||||
if (utf_char == "\'" && (utf_char_next == "s" || utf_char_next == "t" || utf_char_next == "m" || utf_char_next == "d")) {
|
||||
split_condition = true;
|
||||
// regex: (?i:'s|'t|'re|'ve|'m|'ll|'d) // case insensitive
|
||||
if (cpt == '\'' && pos+1 < offset_end) {
|
||||
char32_t cpt_next = unicode_tolower(_get_cpt(pos+1));
|
||||
if (cpt_next == 's' || cpt_next == 't' || cpt_next == 'm' || cpt_next == 'd') {
|
||||
pos += _add_token(pos+2);
|
||||
continue;
|
||||
}
|
||||
if (split_condition) {
|
||||
if (token.size()) {
|
||||
bpe_offsets.emplace_back(unicode_cpts_from_utf8(token).size());
|
||||
}
|
||||
token = utf_char + utf_char_next;
|
||||
bpe_offsets.emplace_back(unicode_cpts_from_utf8(token).size());
|
||||
token = "";
|
||||
i++;
|
||||
if (pos+2 < offset_end) {
|
||||
char32_t cpt_next_next = unicode_tolower(_get_cpt(pos+2));
|
||||
if ((cpt_next == 'r' && cpt_next_next == 'e') ||
|
||||
(cpt_next == 'v' && cpt_next_next == 'e') ||
|
||||
(cpt_next == 'l' && cpt_next_next == 'l')) {
|
||||
pos += _add_token(pos+3);
|
||||
continue;
|
||||
}
|
||||
}
|
||||
if (!split_condition && bytes_remain >= 3) {
|
||||
// 're|'ve|'ll
|
||||
if (utf_char == "\'" && (
|
||||
(utf_char_next == "r" && utf_char_next_next == "e") ||
|
||||
(utf_char_next == "v" && utf_char_next_next == "e") ||
|
||||
(utf_char_next == "l" && utf_char_next_next == "l"))
|
||||
) {
|
||||
split_condition = true;
|
||||
}
|
||||
if (split_condition) {
|
||||
// current token + next token can be defined
|
||||
if (token.size()) {
|
||||
bpe_offsets.emplace_back(unicode_cpts_from_utf8(token).size());
|
||||
}
|
||||
token = utf_char;
|
||||
token += utf_char_next;
|
||||
token += utf_char_next_next;
|
||||
|
||||
bpe_offsets.emplace_back(unicode_cpts_from_utf8(token).size());
|
||||
token = "";
|
||||
i += 2;
|
||||
// regex: [^\r\n\p{L}\p{N}]?\p{L}+ //####FIXME: the first \p{L} is correct?
|
||||
if (cpt != '\r' && cpt != '\n' && /*cpt_type != CODEPOINT_TYPE_LETTER &&*/ cpt_type != CODEPOINT_TYPE_NUMBER) {
|
||||
if (cpt_type == CODEPOINT_TYPE_LETTER || _get_cpt_type(pos+1) == CODEPOINT_TYPE_LETTER) { // one or more letters
|
||||
pos++;
|
||||
while (_get_cpt_type(pos) == CODEPOINT_TYPE_LETTER) {
|
||||
pos++;
|
||||
}
|
||||
_add_token(pos);
|
||||
continue;
|
||||
}
|
||||
}
|
||||
|
||||
if (!split_condition && !collecting) {
|
||||
if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || (token.empty() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) {
|
||||
collecting_letter = true;
|
||||
collecting = true;
|
||||
}
|
||||
else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_NUMBER || (token.empty() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_NUMBER)) {
|
||||
collecting_numeric = true;
|
||||
collecting = true;
|
||||
}
|
||||
else if (
|
||||
((unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_NUMBER) && (unicode_cpt_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) ||
|
||||
(token.empty() && utf_char == " " && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_NUMBER && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE)
|
||||
) {
|
||||
collecting_special = true;
|
||||
collecting = true;
|
||||
}
|
||||
else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) {
|
||||
collecting_whitespace_lookahead = true;
|
||||
collecting = true;
|
||||
}
|
||||
else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) {
|
||||
split_condition = true;
|
||||
// regex: \p{N}{1,3}
|
||||
if (cpt_type == CODEPOINT_TYPE_NUMBER) {
|
||||
size_t ini = pos;
|
||||
while (_get_cpt_type(pos) == CODEPOINT_TYPE_NUMBER) {
|
||||
if (++pos - ini >= 3 ) {
|
||||
_add_token(pos);
|
||||
ini = pos;
|
||||
}
|
||||
}
|
||||
else if (!split_condition && collecting) {
|
||||
if (collecting_letter && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER) {
|
||||
split_condition = true;
|
||||
}
|
||||
else if (collecting_numeric && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_NUMBER) {
|
||||
split_condition = true;
|
||||
}
|
||||
else if (collecting_special && (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_NUMBER || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) {
|
||||
split_condition = true;
|
||||
}
|
||||
else if (collecting_whitespace_lookahead && (unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_NUMBER)) {
|
||||
split_condition = true;
|
||||
}
|
||||
_add_token(pos);
|
||||
continue;
|
||||
}
|
||||
|
||||
if (utf_char_next == "") {
|
||||
split_condition = true; // final
|
||||
token += utf_char;
|
||||
// regex: <space>?[^\s\p{L}\p{N}]+[\r\n]*
|
||||
char32_t cpt2 = (cpt == ' ' ? _get_cpt(pos+1) : cpt);
|
||||
int cpt2_type = (cpt == ' ' ? _get_cpt_type(pos+1) : cpt_type);
|
||||
if (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) {
|
||||
pos += (cpt == ' ');
|
||||
while (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) {
|
||||
cpt2_type = _get_cpt_type(++pos);
|
||||
cpt2 = _get_cpt(pos);
|
||||
}
|
||||
while (cpt2 == '\r' || cpt2 == '\n') {
|
||||
cpt2 = _get_cpt(++pos);
|
||||
}
|
||||
_add_token(pos);
|
||||
continue;
|
||||
}
|
||||
|
||||
if (split_condition) {
|
||||
if (token.size()) {
|
||||
bpe_offsets.emplace_back(unicode_cpts_from_utf8(token).size());
|
||||
}
|
||||
token = utf_char;
|
||||
collecting = false;
|
||||
collecting_letter = false;
|
||||
collecting_numeric = false;
|
||||
collecting_special = false;
|
||||
collecting_whitespace_lookahead = false;
|
||||
}
|
||||
else {
|
||||
token += utf_char;
|
||||
size_t num_whitespaces = 0;
|
||||
size_t last_end_r_or_n = 0;
|
||||
while (unicode_cpt_is_whitespace(_get_cpt(pos+num_whitespaces))) {
|
||||
char32_t cpt2 = _get_cpt(pos+num_whitespaces);
|
||||
if (cpt2 == '\r' || cpt2 == '\n') {
|
||||
last_end_r_or_n = pos + num_whitespaces + 1;
|
||||
}
|
||||
num_whitespaces++;
|
||||
}
|
||||
|
||||
start += offset;
|
||||
// regex: \s*[\r\n]+
|
||||
if (last_end_r_or_n > 0) {
|
||||
pos = last_end_r_or_n;
|
||||
_add_token(pos);
|
||||
continue;
|
||||
}
|
||||
|
||||
// regex: \s+(?!\S)
|
||||
if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != 0) {
|
||||
pos += num_whitespaces - 1;
|
||||
_add_token(pos);
|
||||
continue;
|
||||
}
|
||||
|
||||
// regex: \s+
|
||||
if (num_whitespaces > 0) {
|
||||
pos += num_whitespaces;
|
||||
_add_token(pos);
|
||||
continue;
|
||||
}
|
||||
|
||||
// no matches
|
||||
_add_token(++pos);
|
||||
}
|
||||
}
|
||||
|
||||
return bpe_offsets;
|
||||
@ -424,14 +543,14 @@ static std::vector<size_t> unicode_regex_split_stl(const std::string & text, con
|
||||
static std::vector<size_t> unicode_regex_split_custom(const std::string & text, const std::string & regex_expr, const std::vector<size_t> & offsets) {
|
||||
std::vector<size_t> bpe_offsets;
|
||||
|
||||
(void)(text);
|
||||
(void)(regex_expr);
|
||||
(void)(offsets);
|
||||
// TODO: this implementation is actually wrong, uncomment and run:
|
||||
// make -j && ./bin/test-tokenizer-0 ../models/ggml-vocab-gpt-2.gguf
|
||||
//if (regex_expr == "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)") {
|
||||
// bpe_offsets = unicode_regex_split_custom_gpt2(text, offsets);
|
||||
//}
|
||||
if (regex_expr == "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)") {
|
||||
bpe_offsets = unicode_regex_split_custom_gpt2(text, offsets);
|
||||
} else if (
|
||||
regex_expr == "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+" ||
|
||||
regex_expr == "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+") {
|
||||
|
||||
bpe_offsets = unicode_regex_split_custom_llama3(text, offsets);
|
||||
}
|
||||
|
||||
return bpe_offsets;
|
||||
}
|
||||
@ -506,6 +625,19 @@ int unicode_cpt_type(const std::string & utf8) {
|
||||
return unicode_cpt_type(unicode_cpt_from_utf8(utf8, offset));
|
||||
}
|
||||
|
||||
bool unicode_cpt_is_whitespace(uint32_t cp) {
|
||||
static const std::unordered_set<uint32_t> is_whitespace = [] {
|
||||
std::unordered_set<uint32_t> is_whitespace;
|
||||
for (auto p : unicode_ranges_whitespace) {
|
||||
for (auto i = p.first; i <= p.second; ++i) {
|
||||
is_whitespace.insert(i);
|
||||
}
|
||||
}
|
||||
return is_whitespace;
|
||||
}();
|
||||
return (bool)is_whitespace.count(cp);
|
||||
}
|
||||
|
||||
std::string unicode_byte_to_utf8(uint8_t byte) {
|
||||
static std::unordered_map<uint8_t, std::string> map = unicode_byte_to_utf8_map();
|
||||
return map.at(byte);
|
||||
|
||||
@ -7,7 +7,7 @@
|
||||
#define CODEPOINT_TYPE_UNIDENTIFIED 0
|
||||
#define CODEPOINT_TYPE_NUMBER 1
|
||||
#define CODEPOINT_TYPE_LETTER 2
|
||||
#define CODEPOINT_TYPE_WHITESPACE 3
|
||||
#define CODEPOINT_TYPE_SEPARATOR 3
|
||||
#define CODEPOINT_TYPE_ACCENT_MARK 4
|
||||
#define CODEPOINT_TYPE_PUNCTUATION 5
|
||||
#define CODEPOINT_TYPE_SYMBOL 6
|
||||
@ -21,6 +21,8 @@ std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & c
|
||||
int unicode_cpt_type(uint32_t cp);
|
||||
int unicode_cpt_type(const std::string & utf8);
|
||||
|
||||
bool unicode_cpt_is_whitespace(uint32_t cp);
|
||||
|
||||
std::string unicode_byte_to_utf8(uint8_t byte);
|
||||
uint8_t unicode_utf8_to_byte(const std::string & utf8);
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user