| import regex as re |
| import base64 |
| import os |
| import json |
| import tiktoken |
| from transformers import PreTrainedTokenizer |
| from typing import List, Optional, Union, Dict |
| from transformers import PreTrainedTokenizer |
| from transformers.utils import logging, PaddingStrategy |
| from transformers.tokenization_utils_base import EncodedInput, BatchEncoding |
|
|
|
|
| class ChatGLM4Tokenizer(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 |
| pat_str = "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+" |
| self.pat_str = re.compile(pat_str) |
| self.encode_special_tokens = encode_special_tokens |
|
|
| mergeable_ranks = {} |
| with open(vocab_file) as f: |
| for line in f: |
| token, rank = line.strip().split() |
| rank = int(rank) |
| token = base64.b64decode(token) |
| mergeable_ranks[token] = rank |
|
|
| self.mergeable_ranks = mergeable_ranks |
| self.special_tokens = ["<|endoftext|>", "[MASK]", "[gMASK]", "[sMASK]", "<sop>", "<eop>", "<|system|>", |
| "<|user|>", "<|assistant|>", "<|observation|>", "<|begin_of_image|>", "<|end_of_image|>", |
| "<|begin_of_video|>", "<|end_of_video|>"] |
|
|
| self.special_tokens = { |
| token: idx for idx, token in enumerate(self.special_tokens, start=len(mergeable_ranks)) |
| } |
| self.special_token_ids = {idx: token for token, idx in self.special_tokens.items()} |
|
|
| self.tokenizer = tiktoken.Encoding( |
| name="my_tokenizer", |
| pat_str=pat_str, |
| mergeable_ranks=mergeable_ranks, |
| special_tokens=self.special_tokens |
| ) |
| self.decoder = {rank: token for token, rank in mergeable_ranks.items()} |
| self.n_words = len(self.decoder) + len(self.special_tokens) |
|
|
| super().__init__( |
| padding_side=padding_side, |
| clean_up_tokenization_spaces=clean_up_tokenization_spaces, |
| **kwargs |
| ) |
|
|
| def get_command(self, token): |
| assert token in self.special_tokens |
| return self.special_tokens[token] |
|
|
| @property |
| def vocab_size(self): |
| return self.n_words |
|
|
| @property |
| def eos_token_id(self): |
| return self.get_command("<|endoftext|>") |
|
|
| 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 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="replace") |
| 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="replace") |
| return text |
|
|
| def _tokenize(self, text, **kwargs): |
| tokens = [] |
| if self.encode_special_tokens: |
| ids = self.tokenizer.encode(text, allowed_special="all") |
| else: |
| ids = self.tokenizer.encode(text, disallowed_special=()) |
| for t in ids: |
| tokens.append(self.decoder[t]) |
| return tokens |
|
|
| 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.mergeable_ranks[token] |
|
|
| def _convert_id_to_token(self, index): |
| """Converts an index (integer) in a token (str) using the vocab.""" |
| if index in self.special_token_ids: |
| return self.special_token_ids[index] |
| return self.decoder[index] |
|
|
| 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}|>")] + self.tokenizer.encode(f"{metadata}\n") |
| message_tokens = self.tokenizer.encode(message, disallowed_special=()) |
| 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: |
| for function in item["tools"]: |
| content += f"\n\n## {function['name']}\n\n{json.dumps(function, ensure_ascii=False, indent=4)}" |
| content += "\n在调用上述函数时，请使用 Json 格式表示调用的参数。" |
| 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("<eos>")] |
| return token_ids_0 |
|
|
| def _pad( |
| self, |
| encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], |
| max_length: Optional[int] = None, |
| padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, |
| pad_to_multiple_of: Optional[int] = None, |
| return_attention_mask: Optional[bool] = None, |
| padding_side: Optional[str] = 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) |
| """ |
| |
| assert self.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 |
|
|
| |
| 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 |
|
|
