Delete tiktoken.py

tiktoken.py

@@ -1,359 +0,0 @@
-# Copyright 2022 MosaicML LLM Foundry authors
-# SPDX-License-Identifier: Apache-2.0
-from functools import lru_cache
-from typing import Any, Dict, List, Optional, Tuple
-
-from transformers import PreTrainedTokenizer
-
-DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible."""
-
-
-# Taken from
-# https://github.com/huggingface/transformers/blob/8aca43bdb3cb9a5020f6d57589d85679dc873b1c/src/transformers/models/gpt2/tokenization_gpt2.py#L62-L84
-@lru_cache()
-def bytes_to_unicode():
-    """Returns list of utf-8 byte and a mapping to unicode strings.
-
-    We specifically avoids mapping to whitespace/control characters the bpe code
-    barfs on.
-
-    The reversible bpe codes work on unicode strings. This means you need a
-    large # of unicode characters in your vocab if you want to avoid UNKs. When
-    you're at something like a 10B token dataset you end up needing around 5K
-    for decent coverage. This is a significant percentage of your normal, say,
-    32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and
-    unicode strings.
-    """
-    bs = (list(range(ord('!'),
-                     ord('~') + 1)) + list(range(ord('¡'),
-                                                 ord('¬') + 1)) +
-          list(range(ord('®'),
-                     ord('ÿ') + 1)))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8 + n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-class TiktokenTokenizerWrapper(PreTrainedTokenizer):
-    """A thin wrapper around tiktoken to make it compatible with Hugging Face.
-
-    tokenizers.
-
-    See HuggingFace for further documentation on general tokenizer methods.
-    """
-
-    model_input_names = ['input_ids', 'attention_mask']
-
-    def __init__(self,
-                 model_name: Optional[str] = None,
-                 encoding_name: Optional[str] = None,
-                 add_bos_token: bool = False,
-                 add_eos_token: bool = False,
-                 use_default_system_prompt: bool = False,
-                 unk_token: Optional[str] = '<|endoftext|>',
-                 eos_token: Optional[str] = '<|endoftext|>',
-                 bos_token: Optional[str] = '<|endoftext|>',
-                 pad_token: Optional[str] = None,
-                 errors: str = 'replace',
-                 **kwargs: Any):
-        """Constructor creates a tiktoken tokenizer to use as the underlying.
-
-        tokenizer.
-
-        Args:
-            model_name (Optional[str], optional): The name of the model to load from tiktoken. Defaults to None.
-                Either model_name or encoding_name must be set, but not both.
-            encoding_name (Optional[str], optional): The name of the encoding to load from tiktoken. Defaults to None.
-                Either model_name or encoding_name must be set, but not both.
-            add_bos_token (bool, optional): Whether to add bos tokens. Defaults to False.
-            add_eos_token (bool, optional): Whether to add eos tokens. Defaults to False.
-            use_default_system_prompt (bool, optional): Use the default system prompt or not. Defaults to False.
-            unk_token (Optional[str], optional): The unk token. Defaults to '<|endoftext|>'.
-            eos_token (Optional[str], optional): The eos token. Defaults to '<|endoftext|>'.
-            bos_token (Optional[str], optional): The bos token. Defaults to '<|endoftext|>'.
-            pad_token (Optional[str], optional): The pad token. Defaults to None.
-            errors (str, optional): Paradigm to follow when decoding bytes to UTF-8. See
-                [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
-                Defaults to `"replace"`.
-        """
-        try:
-            import tiktoken
-        except:
-            raise ImportError(
-                'You need to install tiktoken to use TiktokenTokenizerWrapper.')
-
-        # Workaround to make tiktokenizer picklable.
-        # https://github.com/huggingface/datasets/issues/5536#issuecomment-1682309347
-        # There is an open PR from HF to add this to tiktoken: https://github.com/openai/tiktoken/pull/181
-        import copyreg
-        import functools
-
-        from tiktoken import Encoding  # type: ignore (thirdParty)
-
-        def pickle_Encoding(enc: Encoding):
-            return (functools.partial(Encoding,
-                                      enc.name,
-                                      pat_str=enc._pat_str,
-                                      mergeable_ranks=enc._mergeable_ranks,
-                                      special_tokens=enc._special_tokens), ())
-
-        copyreg.pickle(Encoding, pickle_Encoding)
-
-        if model_name is not None and encoding_name is not None:
-            raise ValueError(
-                'You need to specify either model_name or encoding_name, not both.'
-            )
-
-        self.model_name = model_name
-        self.encoding_name = encoding_name
-
-        if self.model_name is not None:
-            self.encoding = tiktoken.encoding_for_model(  # type: ignore (thirdParty)
-                self.model_name)
-        elif self.encoding_name is not None:
-            self.encoding = tiktoken.get_encoding(  # type: ignore (thirdParty)
-                self.encoding_name)
-        else:
-            raise ValueError(
-                'You need to specify either model_name or encoding_name.')
-
-        self.add_bos_token = add_bos_token
-        self.add_eos_token = add_eos_token
-        self.use_default_system_prompt = use_default_system_prompt
-
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        self.errors = errors
-
-        self.decoder: Dict[int, str] = {}
-        for i in range(self.encoding.n_vocab):
-            try:
-                self.encoding.decode_single_token_bytes(i)
-            except KeyError:
-                continue
-            # Taken from
-            # https://gist.github.com/xenova/a452a6474428de0182b17605a98631ee
-            decoding = ''.join([
-                bytes_to_unicode()[ord(char)] for char in
-                self.encoding.decode_single_token_bytes(i).decode('latin-1')
-            ])
-            self.decoder[i] = decoding
-
-        self.encoder: Dict[str, int] = {}
-        for i in range(self.encoding.n_vocab):
-            if i in self.decoder:
-                self.encoder[self.decoder[i]] = i
-
-        super().__init__(model_name=model_name,
-                         encoding_name=encoding_name,
-                         add_bos_token=add_bos_token,
-                         add_eos_token=add_eos_token,
-                         use_default_system_prompt=use_default_system_prompt,
-                         unk_token=unk_token,
-                         eos_token=eos_token,
-                         bos_token=bos_token,
-                         pad_token=pad_token,
-                         errors=errors,
-                         **kwargs)
-
-    @property
-    def vocab_size(self) -> int:
-        """Returns vocab size."""
-        return self.encoding.n_vocab
-
-    @property
-    def is_fast(self) -> bool:
-        return False
-
-    @property
-    def default_chat_template(self):
-        """Chat ML Template for User/Assistant.
-
-        Pinning default Chat ML template in case defaults change.
-        """
-        template = (
-            "{% if messages[0]['role'] == 'system' %}"
-            '{% set loop_messages = messages[1:] %}'
-            "{% set system_message = messages[0]['content'] %}"
-            "{% elif USE_DEFAULT_PROMPT == true and not 'system' in messages[0]['role'] %}"
-            '{% set loop_messages = messages %}'
-            "{% set system_message = 'DEFAULT_SYSTEM_PROMPT' %}"
-            '{% else %}'
-            '{% set loop_messages = messages %}'
-            '{% set system_message = false %}'
-            '{% endif %}'
-            '{% for message in loop_messages %}'
-            '{% if loop.index0 == 0 %}'
-            '{% if system_message != false %}'
-            "{{ '<|im_start|>system\n' + system_message.strip() + '<|im_end|>\n'}}"
-            '{% endif %}'
-            "{{ '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' }}"
-            '{% else %}'
-            "{{ '\n' + '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' }}"
-            '{% endif %}'
-            '{% if (add_generation_prompt == true and loop.last) %}'
-            "{{ '\n' + '<|im_start|>' + 'assistant' + '\n' }}"
-            '{% endif %}'
-            '{% endfor %}')
-        template = template.replace(
-            'USE_DEFAULT_PROMPT',
-            'true' if self.use_default_system_prompt else 'false')
-        template = template.replace('DEFAULT_SYSTEM_PROMPT',
-                                    DEFAULT_SYSTEM_PROMPT)
-        return template
-
-    def get_vocab(self) -> Dict[str, int]:
-        """Returns vocab as a dict."""
-        # As far as I can tell, we don't require get_vocab to completely work,
-        # but when using additional_special_tokens, Hugging Face determines the next
-        # token index to add with len(self.get_vocab()) so we need the _size_ of this dictionary to be correct.
-        vocab_clone = self.encoder.copy()
-        extra_id_index = 0
-        candidate_extra_id = f'<extra_id_{extra_id_index}>'
-        indices_to_fill_in = {i for i in range(self.vocab_size)} - set(
-            vocab_clone.values())
-
-        # Add enough indices to make get_vocab() the right length
-        for index_to_add in indices_to_fill_in:
-            # Make sure we don't overwrite a token that already exists
-            while candidate_extra_id in vocab_clone:
-                extra_id_index += 1
-                candidate_extra_id = f'<extra_id_{extra_id_index}>'
-
-            # Get an index to add and add the item
-            vocab_clone[candidate_extra_id] = index_to_add
-
-        return vocab_clone
-
-    def _tokenize(self, text: str) -> List[str]:
-        """Returns a tokenized string."""
-        if not isinstance(text, str):
-            raise ValueError(
-                f'Expected a string input to _tokenize but got {type(text)}.')
-
-        tokens = [
-            self.decoder[t]
-            for t in self.encoding.encode(text, allowed_special='all')
-        ]
-
-        return tokens
-
-    def _convert_token_to_id(self, token: str) -> Optional[int]:
-        """Converts a token (str) in an id using the vocab."""
-        return self.encoder.get(token, self.encoder.get(self.unk_token))
-
-    def _convert_id_to_token(self, index: int) -> Optional[str]:
-        """Converts an index (integer) in a token (str) using the vocab."""
-        # For tokens in either the gap in ids in the tokenizer, or beyond the range of the tokenizer,
-        # we return empty string. This matches the behavior of Hugging Face fast tokenizers,
-        # but not slow tokenizers.
-        return self.decoder.get(index, '')
-
-    def convert_tokens_to_string(self, tokens: List[str]) -> str:
-        """Converts a sequence of tokens (string) in a single string."""
-        text = ''.join(tokens)
-        text = bytearray([self.byte_decoder[c] for c in text
-                         ]).decode('utf-8', errors=self.errors)
-        return text
-
-    def build_inputs_with_special_tokens(
-            self,
-            token_ids_0: List[int],
-            token_ids_1: Optional[List[int]] = None) -> List[int]:
-        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
-        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
-
-        output = bos_token_id + token_ids_0 + eos_token_id
-
-        if token_ids_1 is not None:
-            output = output + bos_token_id + token_ids_1 + eos_token_id
-
-        return output
-
-    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]:
-        """Retrieves sequence ids from a token list that has no special tokens.
-
-        Function copied from
-        https://github.com/huggingface/transformers/blob/e3a4bd2bee212a2d0fd9f03b27fe7bfc1debe42d/src/transformers/models/gpt2/tokenization_gpt2.py#L265-L295
-
-        added. This method is called when adding special tokens using the
-        tokenizer `prepare_for_model` or `encode_plus` methods.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-        if already_has_special_tokens:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0,
-                token_ids_1=token_ids_1,
-                already_has_special_tokens=True)
-
-        bos_token_id = [1] if self.add_bos_token else []
-        eos_token_id = [1] if self.add_eos_token else []
-
-        if token_ids_1 is None:
-            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
-        return (bos_token_id + ([0] * len(token_ids_0)) + eos_token_id +
-                bos_token_id + ([0] * len(token_ids_1)) + eos_token_id)
-
-    def create_token_type_ids_from_sequences(
-            self,
-            token_ids_0: List[int],
-            token_ids_1: Optional[List[int]] = None) -> List[int]:
-        sep = [self.sep_token_id]
-
-        if token_ids_1 is None:
-            return len(token_ids_0 + sep) * [0]
-        return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
-
-    def save_vocabulary(self,
-                        save_directory: str,
-                        filename_prefix: Optional[str] = None) -> Tuple[str]:
-
-        # ignore the below type to keep the original signature
-        # we are knowingly breaking the signature here, although not 100% certain
-        # it doesn't have side effects
-        # There is some code in huggingface that calls this function to get the vocab files,
-        # but it doesn't seem to access them (or at least checks for their existence
-        # before accessing them)
-        return (None, None)  # type: ignore
-
-    def sanitize_special_tokens(self) -> int:
-        """Make sure that all the special tokens attributes of the tokenizer.
-
-        (`tokenizer.mask_token`, `tokenizer.cls_token`, etc.) are in the
-        vocabulary.
-
-        Add the missing ones to the vocabulary if needed.
-
-        Return:
-            `int`: The number of tokens added in the vocabulary during the operation.
-        """
-        actual_new_tokens = []
-        for token in self.all_special_tokens_extended:
-            encoded = self.encoding.encode(token, allowed_special='all')
-            if len(encoded) > 1:
-                actual_new_tokens.append(token)
-
-        return self.add_tokens(actual_new_tokens, special_tokens=True)
-
-
-TiktokenTokenizerWrapper.register_for_auto_class()