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 <>
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8 changed files with 1468 additions and 549 deletions
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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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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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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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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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