375 lines
14 KiB
Python
375 lines
14 KiB
Python
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import regex as re
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import base64
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import os
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import json
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import tiktoken
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import torch
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from torch import TensorType
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from typing import List, Optional, Union, Dict, Any
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from torchvision import transforms
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from transformers import PreTrainedTokenizer
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from transformers.utils import PaddingStrategy
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from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
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class ChatGLM4Tokenizer(PreTrainedTokenizer):
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vocab_files_names = {"vocab_file": "tokenizer.model"}
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model_input_names = ["input_ids", "attention_mask", "position_ids"]
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def __init__(
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self,
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vocab_file,
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padding_side="left",
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clean_up_tokenization_spaces=False,
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encode_special_tokens=False,
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image_size=None,
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**kwargs,
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):
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self.name = "GLM4Tokenizer"
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self.vocab_file = vocab_file
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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+"
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self.pat_str = re.compile(pat_str)
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self.encode_special_tokens = encode_special_tokens
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self.image_size = image_size
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mergeable_ranks = {}
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with open(vocab_file) as f:
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for line in f:
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token, rank = line.strip().split()
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rank = int(rank)
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token = base64.b64decode(token)
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mergeable_ranks[token] = rank
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self.mergeable_ranks = mergeable_ranks
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self.tokenizer = tiktoken.Encoding(
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name="my_tokenizer",
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pat_str=pat_str,
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mergeable_ranks=mergeable_ranks,
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special_tokens={},
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)
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self.decoder = {rank: token for token, rank in mergeable_ranks.items()}
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self.n_words = len(self.decoder)
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super().__init__(
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padding_side=padding_side,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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**kwargs,
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)
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@property
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def vocab_size(self):
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return self.n_words
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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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vocab.update(self.added_tokens_encoder)
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return vocab
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def convert_tokens_to_string(self, tokens: List[Union[bytes, str, int]]) -> str:
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"""
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Converts a sequence of tokens in a single string.
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"""
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text = ""
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temp = b""
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for t in tokens:
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if isinstance(t, int):
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t = chr(t)
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if isinstance(t, str):
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if temp:
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text += temp.decode("utf-8", errors="replace")
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elif isinstance(t, bytes):
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temp += t
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else:
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raise TypeError("token should only be of type int, bytes or str")
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if temp:
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text += temp.decode("utf-8", errors="replace")
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return text
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def _tokenize(self, text, **kwargs):
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tokens = []
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ids = self.tokenizer.encode(text)
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for t in ids:
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tokens.append(self.decoder[t])
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return tokens
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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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return self.mergeable_ranks[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.decoder.get(index, "")
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def save_vocabulary(self, save_directory, filename_prefix=None):
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"""
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Save the vocabulary and special tokens file to a directory.
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Args:
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save_directory (`str`):
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The directory in which to save the vocabulary.
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filename_prefix (`str`, *optional*):
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An optional prefix to add to the named of the saved files.
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Returns:
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`Tuple(str)`: Paths to the files saved.
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"""
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if os.path.isdir(save_directory):
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vocab_file = os.path.join(
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save_directory, self.vocab_files_names["vocab_file"]
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)
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else:
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vocab_file = save_directory
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with open(self.vocab_file, "rb") as fin:
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proto_str = fin.read()
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with open(vocab_file, "wb") as writer:
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writer.write(proto_str)
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return (vocab_file,)
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def get_prefix_tokens(self):
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prefix_tokens = [
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self.convert_tokens_to_ids("[gMASK]"),
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self.convert_tokens_to_ids("<sop>"),
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]
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return prefix_tokens
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def build_single_message(
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self, role, metadata, message, tokenize=True, message_prefix=None
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):
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assert role in ["system", "user", "assistant", "observation"], role
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if tokenize:
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role_tokens = [
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self.convert_tokens_to_ids(f"<|{role}|>")
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] + self.tokenizer.encode(f"{metadata}\n", disallowed_special=())
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message_tokens = self.tokenizer.encode(message, disallowed_special=())
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if message_prefix is not None:
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message_tokens = message_prefix + message_tokens
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tokens = role_tokens + message_tokens
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return tokens
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else:
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return str(f"<|{role}|>{metadata}\n{message}")
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def apply_chat_template(
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self,
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conversation: Union[
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List[Dict[str, str]], List[List[Dict[str, str]]], "Conversation"
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],
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add_generation_prompt: bool = False,
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tokenize: bool = True,
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padding: bool = False,
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truncation: bool = False,
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max_length: Optional[int] = None,
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return_tensors: Optional[Union[str, TensorType]] = None,
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return_dict: bool = False,
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tokenizer_kwargs: Optional[Dict[str, Any]] = None,
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add_special_tokens: bool = True,
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**kwargs,
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) -> Union[str, List[int], List[str], List[List[int]], BatchEncoding]:
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if return_dict and not tokenize:
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raise ValueError(
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"`return_dict=True` is incompatible with `tokenize=False`, because there is no dict "
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"of tokenizer outputs to return."
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)
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def handle_single_conversation(conversation):
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input_ids = self.get_prefix_tokens() if add_special_tokens else []
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input_message = "[gMASK]<sop>" if add_special_tokens else ""
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input_image = None
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transform = transforms.Compose(
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[
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transforms.Resize(
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(self.image_size, self.image_size),
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interpolation=transforms.InterpolationMode.BICUBIC,
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),
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transforms.ToTensor(),
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transforms.Normalize(
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(0.48145466, 0.4578275, 0.40821073),
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(0.26862954, 0.26130258, 0.27577711),
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),
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]
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)
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for item in conversation:
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message = ""
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message_prefix = None
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if item.get("image"):
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assert input_image is None, "Multiple images are not supported"
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input_image = transform(item["image"])
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message_prefix = self.convert_tokens_to_ids(
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["<|begin_of_image|>", "<|endoftext|>", "<|end_of_image|>"]
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)
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if item.get("content"):
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message += item["content"]
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if message or message_prefix:
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input = self.build_single_message(
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item["role"],
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item.get("metadata", ""),
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message,
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tokenize=tokenize,
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message_prefix=message_prefix,
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)
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if tokenize:
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input_ids.extend(input)
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else:
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input_message += input
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if add_generation_prompt:
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if tokenize:
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input_ids.extend([self.convert_tokens_to_ids("<|assistant|>"), 198]) # 198 is \n in the vocab
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else:
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input_message += "<|assistant|>\n"
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return {
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"input": input_ids if tokenize else input_message,
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"image": input_image,
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}
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# Main logic to handle different conversation formats
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if isinstance(conversation, list) and all(
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isinstance(i, dict) for i in conversation
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):
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result = handle_single_conversation(conversation)
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input_ids = result["input"]
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input_images = [result["image"]]
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elif isinstance(conversation, list) and all(
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isinstance(i, list) for i in conversation
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):
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results = [handle_single_conversation(c) for c in conversation]
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input_ids = [item["input"] for item in results]
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input_images = [item["image"] for item in results]
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elif hasattr(conversation, "messages"):
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result = handle_single_conversation(conversation.messages)
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input_ids = result["input"]
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input_images = [result["image"]]
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else:
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raise ValueError("Invalid conversation format")
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if tokenize:
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output = self.batch_encode_plus(
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[input_ids] if isinstance(input_ids[0], int) else input_ids,
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padding=padding,
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truncation=truncation,
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max_length=max_length,
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return_tensors=return_tensors,
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is_split_into_words=True,
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add_special_tokens=False,
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)
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if return_dict:
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found_image = False
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for image in input_images:
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if image is not None:
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found_image = True
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break
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if found_image:
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output["images"] = torch.stack(input_images)
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return output
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else:
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return output["input_ids"]
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else:
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return input_ids
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def build_inputs_with_special_tokens(
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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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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens. A BERT sequence has the following format:
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- single sequence: `[CLS] X [SEP]`
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- pair of sequences: `[CLS] A [SEP] B [SEP]`
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Args:
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token_ids_0 (`List[int]`):
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List of IDs to which the special tokens will be added.
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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 [input IDs](../glossary#input-ids) with the appropriate special tokens.
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"""
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prefix_tokens = self.get_prefix_tokens()
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token_ids_0 = prefix_tokens + token_ids_0
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if token_ids_1 is not None:
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token_ids_0 = (
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token_ids_0 + token_ids_1 + [self.convert_tokens_to_ids("<eos>")]
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)
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return token_ids_0
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def _pad(
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self,
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encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
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max_length: Optional[int] = None,
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padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
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pad_to_multiple_of: Optional[int] = None,
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padding_side: Optional[str] = None,
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return_attention_mask: Optional[bool] = None,
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) -> dict:
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"""
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Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
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Args:
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encoded_inputs:
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Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
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max_length: maximum length of the returned list and optionally padding length (see below).
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Will truncate by taking into account the special tokens.
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padding_strategy: PaddingStrategy to use for padding.
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- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
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- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
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- PaddingStrategy.DO_NOT_PAD: Do not pad
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The tokenizer padding sides are defined in self.padding_side:
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- 'left': pads on the left of the sequences
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- 'right': pads on the right of the sequences
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pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
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This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
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`>= 7.5` (Volta).
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return_attention_mask:
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(optional) Set to False to avoid returning attention mask (default: set to model specifics)
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"""
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# Load from model defaults
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assert self.padding_side == "left"
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required_input = encoded_inputs[self.model_input_names[0]]
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seq_length = len(required_input)
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if padding_strategy == PaddingStrategy.LONGEST:
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max_length = len(required_input)
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if (
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max_length is not None
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and pad_to_multiple_of is not None
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and (max_length % pad_to_multiple_of != 0)
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):
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max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
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needs_to_be_padded = (
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padding_strategy != PaddingStrategy.DO_NOT_PAD
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and len(required_input) != max_length
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)
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# Initialize attention mask if not present.
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if "attention_mask" not in encoded_inputs:
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encoded_inputs["attention_mask"] = [1] * seq_length
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if "position_ids" not in encoded_inputs:
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encoded_inputs["position_ids"] = list(range(seq_length))
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if needs_to_be_padded:
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difference = max_length - len(required_input)
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if "attention_mask" in encoded_inputs:
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encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs[
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"attention_mask"
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]
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if "position_ids" in encoded_inputs:
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|
|
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
|