tokenization_chatglm.py · THUDM/chatglm3-6b at main (original) (raw)
Update tokenization_chatglm.py
e9e0406 verified 5 months ago
| import json |
|---|
| import os |
| import re |
| from typing import List, Optional, Union, Dict |
| from sentencepiece import SentencePieceProcessor |
| from transformers import PreTrainedTokenizer |
| from transformers.utils import logging, PaddingStrategy |
| from transformers.tokenization_utils_base import EncodedInput, BatchEncoding |
| logger = logging.get_logger(__name__) |
| class SPTokenizer: |
| def __init__(self, model_path: str): |
| # reload tokenizer |
| assert os.path.isfile(model_path), model_path |
| self.sp_model = SentencePieceProcessor(model_file=model_path) |
| # BOS / EOS token IDs |
| self.n_words: int = self.sp_model.vocab_size() |
| self.bos_id: int = self.sp_model.bos_id() |
| self.eos_id: int = self.sp_model.eos_id() |
| self.pad_id: int = self.sp_model.unk_id() |
| assert self.sp_model.vocab_size() == self.sp_model.get_piece_size() |
| role_special_tokens = ["<|system |
| special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens |
| self.special_tokens = {} |
| self.index_special_tokens = {} |
| for token in special_tokens: |
| self.special_tokens[token] = self.n_words |
| self.index_special_tokens[self.n_words] = token |
| self.n_words += 1 |
| self.role_special_token_expression = "|".join([re.escape(token) for token in special_tokens]) # for apply_chat_template |
| def tokenize(self, s: str, encode_special_tokens=False): |
| if encode_special_tokens: |
| last_index = 0 |
| t = [] |
| for match in re.finditer(self.role_special_token_expression, s): |
| if last_index < match.start(): |
| t.extend(self.sp_model.EncodeAsPieces(s[last_index:match.start()])) |
| t.append(s[match.start():match.end()]) |
| last_index = match.end() |
| if last_index < len(s): |
| t.extend(self.sp_model.EncodeAsPieces(s[last_index:])) |
| return t |
| else: |
| return self.sp_model.EncodeAsPieces(s) |
| def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]: |
| assert type(s) is str |
| t = self.sp_model.encode(s) |
| if bos: |
| t = [self.bos_id] + t |
| if eos: |
| t = t + [self.eos_id] |
| return t |
| def decode(self, t: List[int]) -> str: |
| text, buffer = "", [] |
| for token in t: |
| if token in self.index_special_tokens: |
| if buffer: |
| text += self.sp_model.decode(buffer) |
| buffer = [] |
| text += self.index_special_tokens[token] |
| else: |
| buffer.append(token) |
| if buffer: |
| text += self.sp_model.decode(buffer) |
| return text |
| def decode_tokens(self, tokens: List[str]) -> str: |
| text = self.sp_model.DecodePieces(tokens) |
| return text |
| def convert_token_to_id(self, token): |
| """ Converts a token (str) in an id using the vocab. """ |
| if token in self.special_tokens: |
| return self.special_tokens[token] |
| return self.sp_model.PieceToId(token) |
| def convert_id_to_token(self, index): |
| """Converts an index (integer) in a token (str) using the vocab.""" |
| if index in self.index_special_tokens: |
| return self.index_special_tokens[index] |
| if index in [self.eos_id, self.bos_id, self.pad_id] or index < 0 or index > self.sp_model.vocab_size(): |
| return "" |
| return self.sp_model.IdToPiece(index) |
| class ChatGLMTokenizer(PreTrainedTokenizer): |
| vocab_files_names = {"vocab_file": "tokenizer.model"} |
| model_input_names = ["input_ids", "attention_mask", "position_ids"] |
| def __init__( |
| self, |
| vocab_file, |
| padding_side="left", |
| clean_up_tokenization_spaces=False, |
| encode_special_tokens=False, |
| **kwargs |
| ): |
| self.name = "GLMTokenizer" |
| self.vocab_file = vocab_file |
| self.tokenizer = SPTokenizer(vocab_file) |
| self.special_tokens = { |
| "": self.tokenizer.bos_id, |
| "": self.tokenizer.eos_id, |
| "": self.tokenizer.pad_id, |
| "": self.tokenizer.pad_id |
| } |
| self.encode_special_tokens = encode_special_tokens |
| super().__init__( |
| padding_side=padding_side, |
| clean_up_tokenization_spaces=clean_up_tokenization_spaces, |
| **kwargs |
| ) |
| def get_command(self, token): |
| if token in self.special_tokens: |
| return self.special_tokens[token] |
| assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}" |
| return self.tokenizer.special_tokens[token] |
| @property |
| def unk_token(self) -> str: |
| return self.tokenizer.sp_model.IdToPiece(self.get_command("")) |
| @property |
| def pad_token(self) -> str: |
| return self.tokenizer.sp_model.IdToPiece(self.get_command("")) |
| @property |
| def eos_token(self) -> str: |
| return self.tokenizer.sp_model.IdToPiece(self.get_command("")) |
| @property |
| def unk_token_id(self) -> int: |
| return self.get_command("") |
| @property |
| def pad_token_id(self) -> int: |
| return self.get_command("") |
| @property |
| def eos_token_id(self): |
| return self.get_command("") |
| @unk_token.setter |
| def unk_token(self, value): |
| logger.warning("Setting unk_token is not supported, use the default one.") |
| @pad_token.setter |
| def pad_token(self, value): |
| logger.warning("Setting pad_token is not supported, use the default one.") |
| @eos_token.setter |
| def eos_token(self, value): |
| logger.warning("Setting eos_token is not supported, use the default one.") |
| @property |
| def vocab_size(self): |
| return self.tokenizer.n_words |
| def get_vocab(self): |
| """ Returns vocab as a dict """ |
| vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)} |
| vocab.update(self.added_tokens_encoder) |
| return vocab |
| def _tokenize(self, text, **kwargs): |
| return self.tokenizer.tokenize(text, encode_special_tokens=self.encode_special_tokens) |
| def _convert_token_to_id(self, token): |
| """ Converts a token (str) in an id using the vocab. """ |
| return self.tokenizer.convert_token_to_id(token) |
| def _convert_id_to_token(self, index): |
| """Converts an index (integer) in a token (str) using the vocab.""" |
| return self.tokenizer.convert_id_to_token(index) |
| def convert_tokens_to_string(self, tokens: List[str]) -> str: |
| return self.tokenizer.decode_tokens(tokens) |
| def save_vocabulary(self, save_directory, filename_prefix=None): |
| """ |
| Save the vocabulary and special tokens file to a directory. |
| Args: |
| save_directory (`str`): |
| The directory in which to save the vocabulary. |
| filename_prefix (`str`, *optional*): |
| An optional prefix to add to the named of the saved files. |
| Returns: |
| `Tuple(str)`: Paths to the files saved. |
| """ |
| if os.path.isdir(save_directory): |
| vocab_file = os.path.join( |
| save_directory, self.vocab_files_names["vocab_file"] |
| ) |
| else: |
| vocab_file = save_directory |
| with open(self.vocab_file, 'rb') as fin: |
| proto_str = fin.read() |
| with open(vocab_file, "wb") as writer: |
| writer.write(proto_str) |
| return (vocab_file,) |
| def get_prefix_tokens(self): |
| prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")] |
| return prefix_tokens |
| def build_single_message(self, role, metadata, message): |
| assert role in ["system", "user", "assistant", "observation"], role |
| role_tokens = [self.get_command(f"<|{role} |
| message_tokens = self.tokenizer.encode(message) |
| tokens = role_tokens + message_tokens |
| return tokens |
| def build_chat_input(self, query, history=None, role="user"): |
| if history is None: |
| history = [] |
| input_ids = [] |
| for item in history: |
| content = item["content"] |
| if item["role"] == "system" and "tools" in item: |
| content = content + "\n" + json.dumps(item["tools"], indent=4, ensure_ascii=False) |
| input_ids.extend(self.build_single_message(item["role"], item.get("metadata", ""), content)) |
| input_ids.extend(self.build_single_message(role, "", query)) |
| input_ids.extend([self.get_command("<|assistant |
| return self.batch_encode_plus([input_ids], return_tensors="pt", is_split_into_words=True) |
| def build_inputs_with_special_tokens( |
| self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None |
| ) -> List[int]: |
| """ |
| Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and |
| adding special tokens. A BERT sequence has the following format: |
| - single sequence: `[CLS] X [SEP]` |
| - pair of sequences: `[CLS] A [SEP] B [SEP]` |
| Args: |
| token_ids_0 (`List[int]`): |
| List of IDs to which the special tokens will be added. |
| token_ids_1 (`List[int]`, *optional*): |
| Optional second list of IDs for sequence pairs. |
| Returns: |
| `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. |
| """ |
| prefix_tokens = self.get_prefix_tokens() |
| token_ids_0 = prefix_tokens + token_ids_0 |
| if token_ids_1 is not None: |
| token_ids_0 = token_ids_0 + token_ids_1 + [self.get_command("")] |
| return token_ids_0 |
| def _pad( |
| self, |
| encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], |
| max_length: Optional[int] = None, |
| padding_side: str = "left", |
| padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, |
| pad_to_multiple_of: Optional[int] = None, |
| return_attention_mask: Optional[bool] = None, |
| ) -> dict: |
| """ |
| Pad encoded inputs (on left/right and up to predefined length or max length in the batch) |
| Args: |
| encoded_inputs: |
| Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). |
| max_length: maximum length of the returned list and optionally padding length (see below). |
| Will truncate by taking into account the special tokens. |
| padding_strategy: PaddingStrategy to use for padding. |
| - PaddingStrategy.LONGEST Pad to the longest sequence in the batch |
| - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) |
| - PaddingStrategy.DO_NOT_PAD: Do not pad |
| The tokenizer padding sides are defined in self.padding_side: |
| - 'left': pads on the left of the sequences |
| - 'right': pads on the right of the sequences |
| pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. |
| This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability |
| `>= 7.5` (Volta). |
| return_attention_mask: |
| (optional) Set to False to avoid returning attention mask (default: set to model specifics) |
| """ |
| # Load from model defaults |
| assert self.padding_side == "left" |
| assert padding_side is None or padding_side == "left" |
| required_input = encoded_inputs[self.model_input_names[0]] |
| seq_length = len(required_input) |
| if padding_strategy == PaddingStrategy.LONGEST: |
| max_length = len(required_input) |
| if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): |
| max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of |
| needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length |
| # Initialize attention mask if not present. |
| if "attention_mask" not in encoded_inputs: |
| encoded_inputs["attention_mask"] = [1] * seq_length |
| if "position_ids" not in encoded_inputs: |
| encoded_inputs["position_ids"] = list(range(seq_length)) |
| if needs_to_be_padded: |
| difference = max_length - len(required_input) |
| if "attention_mask" in encoded_inputs: |
| encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] |
| if "position_ids" in encoded_inputs: |
| encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"] |
| encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input |
| return encoded_inputs |