bpe_regex.py

"""
Minimal (byte-level) Byte Pair Encoding tokenizer.

Algorithmically follows along the GPT tokenizer:
https://github.com/openai/gpt-2/blob/master/src/encoder.py

Unlike bpe_basic.py, this file also handles the regex splitting pattern.

But:
- Does not handle any special tokens.
"""

import regex as re

# the main GPT text split patterns, see
# https://github.com/openai/tiktoken/blob/main/tiktoken_ext/openai_public.py
GPT2_SPLIT_PATTERN = r"""'(?:[sdmt]|ll|ve|re)| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
GPT4_SPLIT_PATTERN = r"""'(?i:[sdmt]|ll|ve|re)|[^\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+"""


def get_stats(ids):
    """
    Given a list of integers, return a dictionary of counts of consecutive pairs
    Example: [1, 2, 3, 1, 2] -> {(1, 2): 2, (2, 3): 1, (3, 1): 1}
    """
    counts = {}
    for pair in zip(ids, ids[1:]): # iterate consecutive elements
        counts[pair] = counts.get(pair, 0) + 1
    return counts


def merge(ids, pair, idx):
    """
    In the list of integers (ids), replace all consecutive occurrences
    of pair with the new integer token idx
    Example: ids=[1, 2, 3, 1, 2], pair=(1, 2), idx=4 -> [4, 3, 4]
    """
    newids = []
    i = 0
    while i < len(ids):
        # if not at the very last position AND the pair matches, replace it
        if ids[i] == pair[0] and i < len(ids) - 1 and ids[i+1] == pair[1]:
            newids.append(idx)
            i += 2
        else:
            newids.append(ids[i])
            i += 1
    return newids


class Tokenizer:

    def __init__(self):
        # default to vocab size of 256 (all bytes), no merges and gpt-4 pattern
        self.merges = {}
        self.vocab = {idx: bytes([idx]) for idx in range(256)}
        self.pattern = re.compile(GPT4_SPLIT_PATTERN)
        # ugly optional part, only needed for GPT-4 tiktoken compatibility
        self.byte_shuffle = None
        self.inverse_byte_shuffle = None

    def train(self, text, vocab_size, verbose=False):
        assert vocab_size >= 256
        num_merges = vocab_size - 256

        # split the text up into text chunks
        text_chunks = re.findall(self.pattern, text)

        # input text preprocessing
        ids = [list(ch.encode("utf-8")) for ch in text_chunks]

        # iteratively merge the most common pairs to create new tokens
        merges = {} # (int, int) -> int
        vocab = {idx: bytes([idx]) for idx in range(256)} # idx -> bytes
        for i in range(num_merges):
            # count up the number of times every consecutive pair appears
            chunk_stats = [get_stats(chunk_ids) for chunk_ids in ids]
            # combine the pair counts from all chunks by summing them up
            stats = {}
            for chstat in chunk_stats:
                for pair, count in chstat.items():
                    stats[pair] = stats.get(pair, 0) + count
            # find the pair with the highest count
            pair = max(stats, key=stats.get)
            # mint a new token: assign it the next available id
            idx = 256 + i
            # replace all occurences of pair in ids with idx
            ids = [merge(chunk_ids, pair, idx) for chunk_ids in ids]
            # save the merge
            merges[pair] = idx
            vocab[idx] = vocab[pair[0]] + vocab[pair[1]]
            # prints
            if verbose:
                print(f"merge {i+1}/{num_merges}: {pair} -> {idx} ({vocab[idx]}) had {stats[pair]} occurrences")

        # save class variables
        self.merges = merges # used in encode()
        self.vocab = vocab   # used in decode()

    def decode(self, ids):
        # given ids (list of integers), return Python string
        text_bytes = b"".join(self.vocab[idx] for idx in ids)
        if self.inverse_byte_shuffle is not None:
            text_bytes = bytes(self.inverse_byte_shuffle[b] for b in text_bytes)
        text = text_bytes.decode("utf-8", errors="replace")
        return text

    def _encode_chunk(self, text):
        # given a string text, return the token ids
        text_bytes = text.encode("utf-8") # raw bytes
        # needed to repro GPT-4 because OpenAI shuffles its 1-byte tokens order
        if self.byte_shuffle is not None:
            text_bytes = bytes(self.byte_shuffle[b] for b in text_bytes)
        # let's begin. first, convert all bytes to integers in range 0..255
        ids = list(text_bytes)
        while len(ids) >= 2:
            # find the pair with the lowest merge index
            stats = get_stats(ids)
            pair = min(stats, key=lambda p: self.merges.get(p, float("inf")))
            # subtle: if there are no more merges available, the key will
            # result in an inf for every single pair, and the min will be
            # just the first pair in the list, arbitrarily
            # we can detect this terminating case by a membership check
            if pair not in self.merges:
                break # nothing else can be merged anymore
            # otherwise let's merge the best pair (lowest merge index)
            idx = self.merges[pair]
            ids = merge(ids, pair, idx)
        return ids

    def encode(self, text):
        # split text into chunks of text by categories defined in regex pattern
        text_chunks = re.findall(self.pattern, text)
        # all chunks of text are encoded separately, then results are joined
        ids = []
        for chunk in text_chunks:
            chunk_ids = self._encode_chunk(chunk)
            ids.extend(chunk_ids)
        return ids


if __name__ == "__main__":

    """
    Quick unit test, following along the Wikipedia example:
    https://en.wikipedia.org/wiki/Byte_pair_encoding

    According to Wikipedia, running bpe on the the input string:
    "aaabdaaabac"

    for 3 merges will result in string:
    "XdXac"

    where:
    X=ZY
    Y=ab
    Z=aa

    Keep in mind that for us a=97, b=98, c=99, d=100 (ASCII values)
    so Z will be 256, Y will be 257, X will be 258.

    So we expect the output list of ids to be [258, 100, 258, 97, 99]
    """

    text = "aaabdaaabac"
    tokenizer = Tokenizer()

    # we do 3 merges
    tokenizer.train(text, 256 + 3)

    # verify the correct expected result
    ids = tokenizer.encode(text)
    print("OK" if ids == [258, 100, 258, 97, 99] else "FAIL")

    # verify that decode(encode(x)) == x
    print("OK" if tokenizer.decode(tokenizer.encode(text)) == text else "FAIL")

    # take a bit more complex piece of text and train the tokenizer, chosen at random
    text = """
The llama (/ˈlɑːmə/; Spanish pronunciation: [ˈʎama] or [ˈʝama]) (Lama glama) is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.
Llamas are social animals and live with others as a herd. Their wool is soft and contains only a small amount of lanolin.[2] Llamas can learn simple tasks after a few repetitions. When using a pack, they can carry about 25 to 30% of their body weight for 8 to 13 km (5–8 miles).[3] The name llama (in the past also spelled "lama" or "glama") was adopted by European settlers from native Peruvians.[4]
The ancestors of llamas are thought to have originated from the Great Plains of North America about 40 million years ago, and subsequently migrated to South America about three million years ago during the Great American Interchange. By the end of the last ice age (10,000–12,000 years ago), camelids were extinct in North America.[3] As of 2007, there were over seven million llamas and alpacas in South America and over 158,000 llamas and 100,000 alpacas, descended from progenitors imported late in the 20th century, in the United States and Canada.[5]
In Aymara mythology, llamas are important beings. The Heavenly Llama is said to drink water from the ocean and urinates as it rains.[6] According to Aymara eschatology, llamas will return to the water springs and ponds where they come from at the end of time.[6]
""".strip()

    # do 64 merges
    tokenizer.train(text, 256 + 64)

    # verify that decode(encode(x)) == x
    print("OK" if tokenizer.decode(tokenizer.encode(text)) == text else "FAIL")

    # for fun if you like
    # print(tokenizer.vocab)