update(tokenizer): convert to `GPT2Tokenizer`

README.md

@@ -41,7 +41,7 @@ This format is also available through the tokenizer's `apply_chat_template` meth
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-2-zephyr-1_6b', trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-2-zephyr-1_6b')
 model = AutoModelForCausalLM.from_pretrained(
     'stabilityai/stablelm-2-zephyr-1_6b',
     device_map="auto"

special_tokens_map.json

@@ -1,5 +1,40 @@
 {
+  "additional_special_tokens": [
+    "<|reg_extra|>",
+    "<|endoftext|>",
+    "<|fim_prefix|>",
+    "<|fim_middle|>",
+    "<|fim_suffix|>",
+    "<|fim_pad|>",
+    "<gh_stars>",
+    "<filename>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<empty_output>",
+    "<commit_before>",
+    "<commit_msg>",
+    "<commit_after>",
+    "<reponame>",
+    "<|endofprompt|>",
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|pause|>",
+    "<|reg0|>",
+    "<|reg1|>",
+    "<|reg2|>",
+    "<|reg3|>",
+    "<|reg4|>",
+    "<|reg5|>",
+    "<|reg6|>",
+    "<|reg7|>",
+    "<|extra0|>"
+  ],
   "bos_token": "<|endoftext|>",
   "eos_token": "<|endoftext|>",
-  "pad_token": "<|endoftext|>"
-}
+  "unk_token": "<|endoftext|>"
+}

tokenization_arcade100k.py

@@ -1,292 +0,0 @@
-# coding=utf-8
-# Copyright (c) 2023 Alibaba Cloud & Stability AI.
-#
-# Tongyi Qianwen LICENSE AGREEMENT:
-# https://github.com/QwenLM/Qwen/blob/5aa84bdfd3237b37f01bc88cd49b3279b9a71d0b/Tongyi%20Qianwen%20LICENSE%20AGREEMENT
-"""Tokenization classes for Arcade100k."""
-
-import base64
-import os
-import unicodedata
-from typing import Collection, Dict, List, Set, Tuple, Union
-
-import tiktoken
-from transformers.utils import logging
-from transformers import PreTrainedTokenizer, AddedToken
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "arcade100k.tiktoken"}
-NAME = "arcade100k"
-
-
-def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
-    with open(tiktoken_bpe_file, "rb") as f:
-        contents = f.read()
-    return {
-        base64.b64decode(token): int(rank)
-        for token, rank in (line.split() for line in contents.splitlines() if line)
-    }
-
-
-ENDOFTEXT = "<|endoftext|>"
-FIM = [
-    "<|fim_prefix|>",
-    "<|fim_middle|>",
-    "<|fim_suffix|>",
-    "<|fim_pad|>",
-]
-# `StarCoder` Tokens
-CODE = [
-    "<gh_stars>",
-    "<filename>",
-    "<issue_start>",
-    "<issue_comment>",
-    "<issue_closed>",
-    "<jupyter_start>",
-    "<jupyter_text>",
-    "<jupyter_code>",
-    "<jupyter_output>",
-    "<empty_output>",
-    "<commit_before>",
-    "<commit_msg>",
-    "<commit_after>",
-    "<reponame>",
-]
-CHAT = [
-    "<|im_start|>",  # Chat: Input message start
-    "<|im_end|>",  # Chat: Input message end
-]
-PAUSE = "<|pause|>"  # Think before you speak (https://arxiv.org/abs/2310.02226)
-REGISTERS = [
-    f"<|reg{i}|>" for i in range(0, 8)
-]  # Register 0 sink token (https://arxiv.org/abs/2309.17453)
-ENDOFPROMPT = "<|endofprompt|>"
-SPECIAL_TOKENS_NAMES = (
-    [ENDOFTEXT]
-    + FIM
-    + CODE
-    + [ENDOFPROMPT]
-    + CHAT
-    + [PAUSE]
-    + REGISTERS
-    + ["<|extra0|>"]
-)
-START_ID = 100257
-SPECIAL_TOKENS = {t: START_ID + i for i, t in enumerate(SPECIAL_TOKENS_NAMES)}
-
-
-def _arcade100k(vocab_file: str):
-    mergeable_ranks = _load_tiktoken_bpe(vocab_file)
-
-    return {
-        "name": NAME,
-        "pat_str": r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+""",
-        "mergeable_ranks": mergeable_ranks,
-        "special_tokens": SPECIAL_TOKENS,
-    }
-
-
-class Arcade100kTokenizer(PreTrainedTokenizer):
-    """
-    Construct a Arcade100k tokenizer backed by `tiktoken`.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        errors (`str`, *optional*, defaults to `"replace"`):
-            How to handle errors in decoding UTF-8 byte sequences.
-            WARNING: the default behaviour of this function is lossy, since decoded bytes are not
-            guaranteed to be valid UTF-8. You can control this behaviour using the `errors` parameter,
-            for instance, setting `errors=strict`.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    model_input_names = ["input_ids", "attention_mask"]
-
-    def __init__(
-        self,
-        vocab_file: str,
-        errors: str = "replace",
-        **kwargs,
-    ):
-        super().__init__(errors=errors, **kwargs)
-        self.errors = errors
-
-        self._tiktoken_config = _arcade100k(vocab_file)
-        self.tokenizer = tiktoken.Encoding(**self._tiktoken_config)
-
-        # TODO: Remove this assertion
-        assert (
-            len(self.tokenizer._mergeable_ranks)
-            + len(self.tokenizer._special_tokens)
-            + 1
-            == self.tokenizer.n_vocab
-        ), f"{len(self.tokenizer._mergeable_ranks) + len(self.tokenizer._special_tokens)} != {self.tokenizer.n_vocab} in encoding"
-
-        self.decoder = {i: n for n, i in self.tokenizer._mergeable_ranks.items()}
-        self.decoder.update({i: n for n, i in self.tokenizer._special_tokens.items()})
-        # Provide default `eos_token` and `pad_token`
-        if self.eos_token is None:
-            self.eos_token = self.decoder[self.tokenizer.eot_token]
-        if self.pad_token is None:
-            self.pad_token = self.decoder[self.tokenizer.pad_token]
-
-        # Expose for convenience
-        self.mergeable_ranks = self.tokenizer._mergeable_ranks
-        self.special_tokens = self.tokenizer._special_tokens
-
-    def __len__(self):
-        return self.tokenizer.n_vocab
-
-    def __getstate__(self):
-        # Required for `pickle` support
-        state = self.__dict__.copy()
-        del state["tokenizer"]
-        return state
-
-    def __setstate__(self, state):
-        self.__dict__.update(state)
-        self.tokenizer = tiktoken.Encoding(**self._tiktoken_config)
-
-    @property
-    def vocab_size(self):
-        return self.tokenizer.n_vocab
-
-    def get_vocab(self) -> Dict[bytes, int]:
-        return self.tokenizer._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.tokenizer._special_tokens:
-                return self.tokenizer._special_tokens[tokens]
-            else:
-                return self.tokenizer._mergeable_ranks.get(tokens)
-        for token in tokens:
-            if token in self.tokenizer._special_tokens:
-                ids.append(self.tokenizer._special_tokens[token])
-            else:
-                ids.append(self.tokenizer._mergeable_ranks.get(token))
-        return ids
-
-    def _add_tokens(
-        self,
-        new_tokens: Union[List[str], List[AddedToken]],
-        special_tokens: bool = False,
-    ) -> int:
-        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:
-                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, "arcade100k.tiktoken")
-        with open(file_path, "w", encoding="utf8") as w:
-            for k, v in self.tokenizer._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
-
-    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.tokenizer._special_tokens:
-            return self.tokenizer._special_tokens[token]
-        if token in self.tokenizer._mergeable_ranks:
-            return self.tokenizer._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.tokenizer.eot_token]
-        return self.tokenizer.decode(token_ids)

tokenizer_config.json

@@ -1,17 +1,46 @@
 {
-  "added_tokens_decoder": {},
-  "auto_map": {
-    "AutoTokenizer": [
-      "tokenization_arcade100k.Arcade100kTokenizer",
-      null
-    ]
-  },
+  "add_prefix_space": false,
+  "additional_special_tokens": [
+    "<|reg_extra|>",
+    "<|endoftext|>",
+    "<|fim_prefix|>",
+    "<|fim_middle|>",
+    "<|fim_suffix|>",
+    "<|fim_pad|>",
+    "<gh_stars>",
+    "<filename>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<empty_output>",
+    "<commit_before>",
+    "<commit_msg>",
+    "<commit_after>",
+    "<reponame>",
+    "<|endofprompt|>",
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|pause|>",
+    "<|reg0|>",
+    "<|reg1|>",
+    "<|reg2|>",
+    "<|reg3|>",
+    "<|reg4|>",
+    "<|reg5|>",
+    "<|reg6|>",
+    "<|reg7|>",
+    "<|extra0|>"
+  ],
   "bos_token": "<|endoftext|>",
   "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
   "clean_up_tokenization_spaces": true,
   "eos_token": "<|endoftext|>",
-  "errors": "replace",
+  "tokenizer_class": "GPT2Tokenizer",
   "model_max_length": 2048,
   "pad_token": "<|endoftext|>",
-  "tokenizer_class": "Arcade100kTokenizer"
+  "unk_token": "<|endoftext|>"
 }