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tokenization_hy.py

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+# coding=utf-8
+# Copyright 2024 The Tencent Inc. HunYuan Team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+import base64
+import logging
+import tiktoken
+import unicodedata
+from transformers import PreTrainedTokenizer, AddedToken
+from typing import Collection, Dict, List, Set, Tuple, Union
+
+
+logger = logging.getLogger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "hy.tiktoken"}
+PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|""" \
+          r"""[^\r\n\p{L}\p{N}]?\p{L}+|""" \
+          r"""\p{N}|""" \
+          r""" ?[^\s\p{L}\p{N}]+[\r\n]*|""" \
+          r"""\s*[\r\n]+|""" \
+          r"""\s+(?!\S)|""" \
+          r"""\s+"""
+# default eod_token and bod_token of our base model
+ENDOFTEXT = "<|endoftext|>"
+STARTOFTEXT = "<|startoftext|>"
+
+# extra flag token for other training
+BOSTOKEN = "<|bos|>"
+EOSTOKEN = "<|eos|>"
+
+PADTOKEN = "<|pad|>"
+
+# extra special tokens for the tokenizer
+# as the default behavior is changed to allow special tokens in
+# regular texts, the surface forms of special tokens need to be
+# as different as possible to minimize the impact
+EXTRAS = tuple((f"<|extra_{i}|>" for i in range(204)))
+
+SPECIAL_START_ID = 127957
+
+
+def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
+    dic = {}
+    rank = 0
+    for i, line in enumerate(open(tiktoken_bpe_file, "rb")):
+        if line:
+            token, _ = line.split()
+            # skip duplicated tokens, this should not happen
+            if base64.b64decode(token) in dic:
+                raise ValueError(f"!ERROR: duplicated token {token} in your vocab file")
+            dic[base64.b64decode(token)] = int(rank)
+            rank += 1
+    return dic
+
+
+# special tokens for pretrain and finetune models
+SPECIAL_TOKENS = tuple(
+    enumerate(
+        (
+            (
+                ENDOFTEXT,
+                STARTOFTEXT,
+                BOSTOKEN,
+                EOSTOKEN,
+                PADTOKEN,
+            )
+            + EXTRAS
+        ),
+        start=SPECIAL_START_ID,
+    )
+)
+
+SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)
+
+
+class HYTokenizer(PreTrainedTokenizer):
+    """
+    HunYuan Tokenizer Initialization. We extend `tiktoken` vocab and
+        the default EOD & BOD special tokens are used for base model.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+
+        errors (`str`):
+            How to handle errors in decoding UTF-8 byte sequences.
+            use ignore if you are in streaming inference
+
+        bod_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""<|startoftext|>""`):
+            The beginning of document token that was used for training. can be modified by your task.
+            default to be `<|startoftext|>` for released base model.
+
+        eod_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""<|endoftext|>""`):
+            The end of document token that was used for training. can be modified by your task.
+            default to be `<|endoftext|>` for released base model.
+
+        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `None`):
+            The start or sep special token that was used for some training tasks.
+            default to be `<|startoftext|>` for released base model.
+            It can be set to `<|bos|>` when you training for some other task
+
+        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `None`):
+            The end or sep special token that was used for some training tasks.
+            default to be `<|endoftext|>` for released base model.
+            It can be set to `<|eos|>` when you training for some other task
+
+        pad_token (`str` or `tokenizers.AddedToken`, *optional*):
+            A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
+            attention mechanisms or loss computation.
+
+        special_vocab_file (str, *optional*):
+            Customed special extra vocab file, same format with hy.tiktoken.
+            **Be careful** to use the extra special vocab, it will may cause the model loading collapse.
+            The data line be like:
+                `PHxhYmN8Pg== 0`
+            the id followed `base64.encode(str)` is unused, we will reset them in case of collision
+
+        add_bod_token (`bool`, *optional*, defaults to `True`):
+            Whether or not to add an `bos_token` at the start of documents.
+            This will effect `build_inputs_with_special_tokens` method
+
+        add_eod_token (`bool`, *optional*, defaults to `False`):
+            Whether or not to add an `eos_token` at the end of documents.
+            This will effect `build_inputs_with_special_tokens` method
+
+    """
+    vocab_files_names = VOCAB_FILES_NAMES
+
+    def __init__(
+        self,
+        vocab_file,
+        errors="replace",
+        bod_token="<|startoftext|>",
+        eod_token="<|endoftext|>",
+        bos_token="<|startoftext|>",
+        eos_token="<|endoftext|>",
+        pad_token="<|pad|>",
+        add_bod_token=True,
+        add_eod_token=True,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.errors = errors
+        self.mergeable_ranks = _load_tiktoken_bpe(vocab_file)  # type: Dict[bytes, int]
+        self.special_tokens = {
+            token: index
+            for index, token in SPECIAL_TOKENS
+        }
+
+        enc = tiktoken.Encoding(
+            "HunYuan",
+            pat_str=PAT_STR,
+            mergeable_ranks=self.mergeable_ranks,
+            special_tokens=self.special_tokens,
+        )
+        assert (
+            len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
+        ), f"{len(self.mergeable_ranks)} + {len(self.special_tokens)} != {enc.n_vocab} in encoding"
+
+        self.decoder = {
+            v: k for k, v in self.mergeable_ranks.items()
+        }  # type: dict[int, bytes|str]
+        self.decoder.update({v: k for k, v in self.special_tokens.items()})
+
+        self.tokenizer = enc
+
+        self.bod_token, self.bod_id = bod_token, self.special_tokens[bod_token]
+        self.eod_token, self.eod_id = eod_token, self.special_tokens[eod_token]
+        self.bos_token, self.bos_id = bos_token, self.special_tokens[bos_token]
+        self.eos_token, self.eos_id = eos_token, self.special_tokens[eos_token]
+        self.pad_token, self.pad_id = pad_token, self.special_tokens[pad_token]
+
+        self._num_special_token = len(self.special_tokens)
+
+        self.add_bod_token = add_bod_token
+        self.add_eod_token = add_eod_token
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        del state["tokenizer"]
+        return state
+
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+        enc = tiktoken.Encoding(
+            "HunYuan",
+            pat_str=PAT_STR,
+            mergeable_ranks=self.mergeable_ranks,
+            special_tokens=self.special_tokens,
+        )
+        self.tokenizer = enc
+
+    def __len__(self) -> int:
+        return self.tokenizer.n_vocab
+
+    def get_vocab(self) -> Dict[bytes, int]:
+        """Return the vocabulary as a dictionary, without special tokens."""
+        return self.mergeable_ranks
+
+    def convert_tokens_to_ids(
+        self, tokens: Union[bytes, str, List[Union[bytes, str]]]
+    ) -> List[int]:
+        ids = []
+        if isinstance(tokens, (str, bytes)):
+            if tokens in self.special_tokens:
+                return self.special_tokens[tokens]
+            else:
+                return self.mergeable_ranks.get(tokens)
+        for token in tokens:
+            if token in self.special_tokens:
+                ids.append(self.special_tokens[token])
+            else:
+                ids.append(self.mergeable_ranks.get(token))
+        return ids
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        bod_token_id = [self.bod_id] if self.add_bod_token else []
+        eod_token_id = [self.eod_id] if self.add_eod_token else []
+        output = bod_token_id + token_ids_0 + eod_token_id
+        if token_ids_1 is not None:
+            output = output + bod_token_id + token_ids_1 + eod_token_id
+        return output
+
+    def _add_tokens(
+        self,
+        new_tokens: Union[List[str], List[AddedToken]],
+        special_tokens: bool = False,
+    ) -> List[Tuple[int, str]]:
+        """do not support adding tokens"""
+        if not special_tokens and new_tokens:
+            raise ValueError("Adding regular tokens is not supported")
+        for token in new_tokens:
+            surface_form = token.content if isinstance(token, AddedToken) else token
+            if surface_form not in SPECIAL_TOKENS_SET:
+                raise ValueError("Adding unknown special tokens is not supported")
+        return 0
+
+    def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
+        """
+        Save only the vocabulary of the tokenizer (vocabulary).
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        file_path = os.path.join(save_directory, "hy.tiktoken")
+        with open(file_path, "w", encoding="utf8") as w:
+            for k, v in self.mergeable_ranks.items():
+                line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
+                w.write(line)
+        return (file_path,)
+
+    def tokenize(
+        self,
+        text: str,
+        allowed_special: Union[Set, str] = "all",
+        disallowed_special: Union[Collection, str] = (),
+        **kwargs,
+    ) -> List[Union[bytes, str]]:
+        """
+        Converts a string in a sequence of tokens.
+        Args:
+            text (`str`):
+                The sequence to be encoded.
+            allowed_special (`Literal["all"]` or `set`):
+                The surface forms of the tokens to be encoded as special tokens in regular texts.
+                Default to "all".
+            disallowed_special (`Literal["all"]` or `Collection`):
+                The surface forms of the tokens that should not be in regular texts and trigger errors.
+                Default to an empty tuple.
+            kwargs (additional keyword arguments, *optional*):
+                Will be passed to the underlying model specific encode method.
+        Returns:
+            `List[bytes|str]`: The list of tokens.
+        """
+        tokens = []
+        text = unicodedata.normalize("NFC", text)
+
+        # this implementation takes a detour: text -> token id -> token surface forms
+        for t in self.tokenizer.encode(
+            text, allowed_special=allowed_special, disallowed_special=disallowed_special
+        ):
+            tokens.append(self.decoder[t])
+        return tokens
+
+    def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
+        """
+        Converts a sequence of tokens in a single string.
+        """
+        text = ""
+        temp = b""
+        for t in tokens:
+            if isinstance(t, str):
+                if temp:
+                    text += temp.decode("utf-8", errors=self.errors)
+                    temp = b""
+                text += t
+            elif isinstance(t, bytes):
+                temp += t
+            else:
+                raise TypeError("token should only be of type types or str")
+        if temp:
+            text += temp.decode("utf-8", errors=self.errors)
+        return text
+
+    @property
+    def vocab_size(self):
+        return self.tokenizer.n_vocab
+
+    def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
+        """Converts an id to a token, special tokens included"""
+        if index in self.decoder:
+            return self.decoder[index]
+        raise ValueError("unknown ids")
+
+    def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
+        """Converts a token to an id using the vocab, special tokens included"""
+        if token in self.special_tokens:
+            return self.special_tokens[token]
+        if token in self.mergeable_ranks:
+            return self.mergeable_ranks[token]
+        raise ValueError("unknown token")
+
+    def _tokenize(self, text: str, **kwargs):
+        """
+        Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
+        vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
+        Do NOT take care of added tokens.
+        """
+        raise NotImplementedError
+
+    def _decode(
+        self,
+        token_ids: Union[int, List[int]],
+        skip_special_tokens: bool = False,
+        errors: str = None,
+        **kwargs,
+    ) -> str:
+        if isinstance(token_ids, int):
+            token_ids = [token_ids]
+        if skip_special_tokens:
+            token_ids = [i for i in token_ids if i < self.eod_id]
+        return self.tokenizer.decode(token_ids, errors=errors or self.errors)