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"""Tokenization classes for CodeGen2.5.""" |
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from typing import List, Optional |
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from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer |
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from transformers.utils import logging |
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try: |
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import tiktoken |
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except ModuleNotFoundError as e: |
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raise ModuleNotFoundError("CodeGen2.5 requires the installation of tiktoken. Please install it via `pip install tiktoken`.") from e |
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logger = logging.get_logger(__name__) |
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MAX_MODEL_INPUT_SIZES = { |
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"Salesforce/codegen25-7b-multi": 2048, |
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"Salesforce/codegen25-7b-mono": 2048, |
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"Salesforce/codegen25-7b-instruct": 2048, |
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} |
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def tiktoken_tokenizer(base="gpt2", pad_token=None, add_special=True): |
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if not add_special: |
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return tiktoken.get_encoding(base) |
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def include_whitespace(n_min=2, n_max=20): |
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whitespaces = [" " * n for n in reversed(range(n_min, n_max))] |
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return whitespaces |
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def include_tabs(n_min=2, n_max=20): |
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tabs = ["\t" * n for n in reversed(range(n_min, n_max))] |
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return tabs |
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def include_fim_tokens(): |
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fim_tokens = [ |
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"<fim_prefix>", |
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"<fim_middle>", |
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"<fim_suffix>", |
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"<fim_pad>", |
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"<filename>", |
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"<gh_stars>", |
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"<issue_start>", |
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"<issue_comment>", |
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"<issue_closed>", |
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"<jupyter_start>", |
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"<jupyter_text>", |
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"<jupyter_code>", |
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"<jupyter_output>", |
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"<empty_output>", |
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"<commit_before>", |
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"<commit_msg>", |
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"<commit_after>", |
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"<reponame>" |
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] |
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return fim_tokens |
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def include_codegen2_tokens(): |
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tokens = [] |
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tokens += [f"<dummy_{i}>" for i in range(4)] |
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tokens.append("<sep>") |
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tokens.append("<eom>") |
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tokens += [f"<mask_{i}>" for i in reversed(range(1, 51199-50318+1))] |
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return tokens |
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add_whitespaces = include_whitespace(n_min=2, n_max=32) |
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add_tabs = include_tabs(n_min=2, n_max=10) |
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fim_tokens = include_fim_tokens() |
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codegen2_tokens = include_codegen2_tokens() |
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tokenizer = tiktoken.get_encoding(base) |
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idx = tokenizer.n_vocab |
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bpe_ranks = tokenizer._mergeable_ranks |
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for wsp in add_whitespaces: |
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bpe_ranks[bytes(wsp, 'ascii')] = idx |
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idx += 1 |
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for t in add_tabs: |
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bpe_ranks[bytes(t, 'ascii')] = idx |
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idx += 1 |
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special_tokens = dict() |
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for sp in fim_tokens: |
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special_tokens[sp] = idx |
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idx += 1 |
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for sp in codegen2_tokens: |
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special_tokens[sp] = idx |
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idx += 1 |
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if pad_token and pad_token not in tokenizer._special_tokens and pad_token not in special_tokens: |
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special_tokens[pad_token] = idx |
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idx += 1 |
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enc = tiktoken.Encoding( |
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name=base.replace("base", "im"), |
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pat_str=tokenizer._pat_str, |
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mergeable_ranks=bpe_ranks, |
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special_tokens={ |
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**tokenizer._special_tokens, |
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**special_tokens |
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} |
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) |
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return enc |
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class CodeGen25Tokenizer(PreTrainedTokenizer): |
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""" |
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Construct a CodeGen2.5 tokenizer. Based on byte-level Byte-Pair-Encoding. |
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Args: |
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vocab_file (`str`): |
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Path to the vocabulary file. |
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""" |
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max_model_input_sizes = MAX_MODEL_INPUT_SIZES |
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model_input_names = ["input_ids", "attention_mask"] |
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def __init__( |
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self, |
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pad_token=None, |
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eos_token="<|endoftext|>", |
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add_eos_token=False, |
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add_special_tokens=True, |
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**kwargs, |
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): |
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pad_token_added = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token |
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eos_token_added = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token |
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super().__init__( |
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pad_token=pad_token_added, |
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eos_token=eos_token_added, |
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add_eos_token=add_eos_token, |
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add_special_tokens=add_special_tokens, |
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**kwargs, |
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) |
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self.add_eos_token = add_eos_token |
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self.encoder = tiktoken_tokenizer(base="gpt2", pad_token=pad_token, add_special=add_special_tokens) |
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@property |
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def vocab_size(self): |
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"""Returns vocab size""" |
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return self.encoder.n_vocab |
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def get_vocab(self): |
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"""Returns vocab as a dict""" |
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vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)} |
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return vocab |
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def _tokenize(self, text, **kwargs): |
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"""Returns a tokenized string.""" |
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return self.encoder.encode(text, allowed_special="all") |
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def _convert_token_to_id(self, token): |
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"""Converts a token (str) in an id using the vocab.""" |
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if isinstance(token, str): |
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return self.encoder.encode_single_token(token) |
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else: |
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return token |
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def _convert_id_to_token(self, index): |
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"""Converts an index (integer) in a token (str) using the vocab.""" |
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return self.encoder.decode_single_token_bytes(index).decode("utf-8") |
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def _decode(self, token_ids: List[int], skip_special_tokens: bool = False, **kwargs): |
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if skip_special_tokens: |
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token_ids = [t for t in token_ids if t not in self.all_special_ids] |
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return self.encoder.decode(token_ids) |
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]: |
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"""Build model inputs from a sequence by appending eos_token_id.""" |
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eos_token_id = [self.eos_token_id] if self.add_eos_token else [] |
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output = token_ids_0 + eos_token_id |
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if token_ids_1 is not None: |
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output = output + token_ids_1 + eos_token_id |
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return output |
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def get_special_tokens_mask( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, |
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already_has_special_tokens: bool = False |
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) -> List[int]: |
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""" |
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding |
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special tokens using the tokenizer `prepare_for_model` method. |
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Args: |
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token_ids_0 (`List[int]`): |
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List of IDs. |
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token_ids_1 (`List[int]`, *optional*): |
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Optional second list of IDs for sequence pairs. |
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already_has_special_tokens (`bool`, *optional*, defaults to `False`): |
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Whether the token list is already formatted with special tokens for the model. |
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Returns: |
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. |
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""" |
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if already_has_special_tokens: |
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return super().get_special_tokens_mask( |
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True |
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) |
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eos_token_id = [1] if self.add_eos_token else [] |
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if token_ids_1 is None: |
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return ([0] * len(token_ids_0)) + eos_token_id |
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return ([0] * len(token_ids_0)) + eos_token_id + ([0] * len(token_ids_1)) + eos_token_id |
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def create_token_type_ids_from_sequences( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None |
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) -> List[int]: |
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""" |
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Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT |
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sequence pair mask has the following format: |
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``` |
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0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 |
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| first sequence | second sequence | |
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``` |
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if token_ids_1 is None, only returns the first portion of the mask (0s). |
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Args: |
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token_ids_0 (`List[int]`): |
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List of ids. |
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token_ids_1 (`List[int]`, *optional*): |
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Optional second list of IDs for sequence pairs. |
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Returns: |
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`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). |
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""" |
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eos_token_id = [self.eos_token_id] if self.add_eos_token else [] |
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output = [0] * len(token_ids_0 + eos_token_id) |
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if token_ids_1 is not None: |
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output += [1] * len(token_ids_1 + eos_token_id) |
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return output |
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None): |
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return () |
