tokenization_binaryllm.py

#!/usr/bin/env python3
# tokenization_binaryllm.py
# ============================================================
# BinaryLLMTokenizer (AutoTokenizer compatible) — EXACTEMENT la même
# tokenisation/decodage que llmTalk (mode base=65536) + infer_tagged12/11:
#
# - Base: 65536
# - IDs radix: 0..65535
# - BOS: 65536
# - EOS: 65537
# - UNK: alias EOS (65537) (pas de nouveau token dans la base)
# - Encodage: UTF-8 bytes -> digits base65536 BIG-ENDIAN (chunks 2 bytes)
#   * si longueur impaire: dernier byte encodé en valeur 0..255 (1 digit)
# - Décodage: digits -> bytes BIG-ENDIAN -> UTF-8 (errors="replace")
#
# Important:
# - build_inputs_with_special_tokens: [BOS] + seq + [EOS] (comme HF classique)
# - encode(..., add_special_tokens=False) renvoie UNIQUEMENT les digits base65536
# - encode(..., add_special_tokens=True) ajoute BOS/EOS via build_inputs...
#
# Ce fichier suffit pour `trust_remote_code=True` côté repo HF.
# ============================================================

from __future__ import annotations

import json
import os
import re
from typing import Dict, List, Optional, Tuple, Any

from transformers import PreTrainedTokenizer


class BinaryLLMTokenizer(PreTrainedTokenizer):
    model_input_names = ["input_ids", "attention_mask"]

    TOKEN_RE = re.compile(r"^<U([0-9A-Fa-f]{4})>$")

    def __init__(
        self,
        bos_token: str = "<BOS>",
        eos_token: str = "<EOS>",
        unk_token: str = "<UNK>",
        pad_token: Optional[str] = None,
        **kwargs: Any,
    ):
        # radix strict
        self._base_vocab_size = 65536

        # specials strict: base + 0/1
        self._bos_id = 65536
        self._eos_id = 65537

        # UNK alias EOS (pas de token additionnel)
        self._unk_id = self._eos_id

        self._bos_str = bos_token
        self._eos_str = eos_token
        self._unk_str = unk_token
        self._pad_str = pad_token

        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            pad_token=pad_token,
            **kwargs,
        )

    # ---------- vocab / ids ----------

    @property
    def vocab_size(self) -> int:
        # 65536 + BOS + EOS
        return 65538

    def get_vocab(self) -> Dict[str, int]:
        # IMPORTANT: ne jamais appeler self.unk_token_id ici (boucle)
        v = {
            self._bos_str: self._bos_id,
            self._eos_str: self._eos_id,
            self._unk_str: self._unk_id,
        }
        if self.pad_token is not None:
            v[self.pad_token] = self._convert_token_to_id(self.pad_token)
        return v

    def _id_to_token_base(self, i: int) -> str:
        return f"<U{i:04X}>"

    # ---------- core encode/decode (même logique que infer_tagged / llmTalk base) ----------

    def _encode_to_base65536_big_endian(self, text: str) -> List[int]:
        b = bytearray(text.encode("utf-8", errors="strict"))
        if len(b) == 0:
            return [0]

        out: List[int] = []
        i = 0
        n = len(b)

        while i + 1 < n:
            # 2 bytes -> 1 digit base65536 big-endian
            out.append((b[i] << 8) | b[i + 1])
            i += 2

        if i < n:
            # dernier byte seul -> digit 0..255
            out.append(int(b[i]))

        return out

    def _decode_from_base65536_big_endian(self, ids: List[int]) -> str:
        bb = bytearray()
        for x in ids:
            xi = int(x) & 0xFFFFFFFF
            if 0 <= xi <= 255:
                bb.append(xi)
            else:
                bb.append((xi >> 8) & 0xFF)
                bb.append(xi & 0xFF)
        return bytes(bb).decode("utf-8", errors="replace")

    # ---------- HF tokenizer API overrides ----------

    def _tokenize(self, text: str) -> List[str]:
        ids = self._encode_to_base65536_big_endian(text)
        return [self._id_to_token_base(i) for i in ids]

    def _convert_token_to_id(self, token: str) -> int:
        if token == self._bos_str:
            return self._bos_id
        if token == self._eos_str:
            return self._eos_id
        if token == self._unk_str:
            return self._unk_id

        if self.pad_token is not None and token == self.pad_token:
            # pas de PAD dédié => alias EOS (compatible avec ton cadre)
            if self.pad_token == self._eos_str:
                return self._eos_id
            return self._eos_id

        m = self.TOKEN_RE.match(token)
        if m:
            return int(m.group(1), 16)

        return self._unk_id

    def _convert_id_to_token(self, index: int) -> str:
        if index == self._bos_id:
            return self._bos_str
        if index == self._eos_id:
            return self._eos_str
        if index == self._unk_id:
            return self._unk_str

        if self.pad_token is not None and index == self.pad_token_id:
            return self.pad_token

        if 0 <= index < self._base_vocab_size:
            return self._id_to_token_base(index)

        return self._unk_str

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        ids: List[int] = []
        for t in tokens:
            if t in (self._bos_str, self._eos_str, self._unk_str):
                continue
            if self.pad_token is not None and t == self.pad_token:
                continue
            m = self.TOKEN_RE.match(t)
            if m:
                ids.append(int(m.group(1), 16))
        return self._decode_from_base65536_big_endian(ids)

    def build_inputs_with_special_tokens(
        self,
        token_ids_0: List[int],
        token_ids_1: Optional[List[int]] = None,
    ) -> List[int]:
        # HF-style (simple): [BOS] seq [EOS]
        # Pair: [BOS] seq0 [EOS] seq1 [EOS]
        if token_ids_1 is None:
            return [self._bos_id] + token_ids_0 + [self._eos_id]
        return [self._bos_id] + token_ids_0 + [self._eos_id] + token_ids_1 + [self._eos_id]

    def get_special_tokens_mask(
        self,
        token_ids_0: List[int],
        token_ids_1: Optional[List[int]] = None,
        already_has_special_tokens: bool = False,
    ) -> List[int]:
        pad_id = self.pad_token_id if self.pad_token is not None else -1

        if already_has_special_tokens:
            return [
                1 if t in (self._bos_id, self._eos_id, self._unk_id, pad_id) else 0
                for t in token_ids_0
            ]

        if token_ids_1 is None:
            return [1] + [0] * len(token_ids_0) + [1]
        return [1] + [0] * len(token_ids_0) + [1] + [0] * len(token_ids_1) + [1]

    def create_token_type_ids_from_sequences(
        self,
        token_ids_0: List[int],
        token_ids_1: Optional[List[int]] = None,
    ) -> List[int]:
        if token_ids_1 is None:
            return [0] * (len(token_ids_0) + 2)
        return [0] * (len(token_ids_0) + len(token_ids_1) + 3)

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        if not os.path.isdir(save_directory):
            os.makedirs(save_directory, exist_ok=True)

        name = (filename_prefix + "-" if filename_prefix else "") + "binaryllm_vocab.json"
        path = os.path.join(save_directory, name)

        data = {
            "base_vocab_size": 65536,
            "vocab_size": 65538,
            "bos_token": self._bos_str,
            "bos_token_id": self._bos_id,
            "eos_token": self._eos_str,
            "eos_token_id": self._eos_id,
            "unk_token": self._unk_str,
            "unk_token_id": self._unk_id,
            "pad_token": self.pad_token,
            "pad_token_id": self.pad_token_id,
            "encoding": "utf-8",
            "radix": 65536,
            "endianness": "big",
            "odd_length_rule": "last_byte_as_single_digit_0_255",
        }

        with open(path, "w", encoding="utf-8") as f:
            json.dump(data, f, ensure_ascii=False, indent=2)

        return (path,)