import json import os from typing import Any, Dict, List, Optional, Tuple, Union import torch from torch import nn from transformers import AutoConfig, AutoModel, AutoTokenizer, MT5Config, T5Config class Transformer(nn.Module): """Huggingface AutoModel to generate token embeddings. Loads the correct class, e.g. BERT / RoBERTa etc. Args: model_name_or_path: Huggingface models name (https://huggingface.co/models) max_seq_length: Truncate any inputs longer than max_seq_length model_args: Keyword arguments passed to the Huggingface Transformers model tokenizer_args: Keyword arguments passed to the Huggingface Transformers tokenizer config_args: Keyword arguments passed to the Huggingface Transformers config cache_dir: Cache dir for Huggingface Transformers to store/load models do_lower_case: If true, lowercases the input (independent if the model is cased or not) tokenizer_name_or_path: Name or path of the tokenizer. When None, then model_name_or_path is used """ def __init__( self, model_name_or_path: str, max_seq_length: Optional[int] = None, model_args: Optional[Dict[str, Any]] = None, tokenizer_args: Optional[Dict[str, Any]] = None, config_args: Optional[Dict[str, Any]] = None, cache_dir: Optional[str] = None, do_lower_case: bool = False, tokenizer_name_or_path: str = None, ) -> None: super(Transformer, self).__init__() self.config_keys = ["max_seq_length", "do_lower_case"] self.do_lower_case = do_lower_case if model_args is None: model_args = {} if tokenizer_args is None: tokenizer_args = {} if config_args is None: config_args = {} config = AutoConfig.from_pretrained(model_name_or_path, **config_args, cache_dir=cache_dir) self._load_model(model_name_or_path, config, cache_dir, **model_args) if max_seq_length is not None and "model_max_length" not in tokenizer_args: tokenizer_args["model_max_length"] = max_seq_length self.tokenizer = AutoTokenizer.from_pretrained( tokenizer_name_or_path if tokenizer_name_or_path is not None else model_name_or_path, cache_dir=cache_dir, **tokenizer_args, ) # No max_seq_length set. Try to infer from model if max_seq_length is None: if ( hasattr(self.auto_model, "config") and hasattr(self.auto_model.config, "max_position_embeddings") and hasattr(self.tokenizer, "model_max_length") ): max_seq_length = min(self.auto_model.config.max_position_embeddings, self.tokenizer.model_max_length) self.max_seq_length = max_seq_length if tokenizer_name_or_path is not None: self.auto_model.config.tokenizer_class = self.tokenizer.__class__.__name__ def _load_model(self, model_name_or_path, config, cache_dir, **model_args) -> None: """Loads the transformer model""" if isinstance(config, T5Config): self._load_t5_model(model_name_or_path, config, cache_dir, **model_args) elif isinstance(config, MT5Config): self._load_mt5_model(model_name_or_path, config, cache_dir, **model_args) else: self.auto_model = AutoModel.from_pretrained( model_name_or_path, config=config, cache_dir=cache_dir, **model_args ) def _load_t5_model(self, model_name_or_path, config, cache_dir, **model_args) -> None: """Loads the encoder model from T5""" from transformers import T5EncoderModel T5EncoderModel._keys_to_ignore_on_load_unexpected = ["decoder.*"] self.auto_model = T5EncoderModel.from_pretrained( model_name_or_path, config=config, cache_dir=cache_dir, **model_args ) def _load_mt5_model(self, model_name_or_path, config, cache_dir, **model_args) -> None: """Loads the encoder model from T5""" from transformers import MT5EncoderModel MT5EncoderModel._keys_to_ignore_on_load_unexpected = ["decoder.*"] self.auto_model = MT5EncoderModel.from_pretrained( model_name_or_path, config=config, cache_dir=cache_dir, **model_args ) def __repr__(self) -> str: return "Transformer({}) with Transformer model: {} ".format( self.get_config_dict(), self.auto_model.__class__.__name__ ) def forward(self, features: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: """Returns token_embeddings, cls_token""" trans_features = {"input_ids": features["input_ids"], "attention_mask": features["attention_mask"]} if "token_type_ids" in features: trans_features["token_type_ids"] = features["token_type_ids"] output_states = self.auto_model(**trans_features, return_dict=False) output_tokens = output_states[0] features.update({"token_embeddings": output_tokens, "attention_mask": features["attention_mask"]}) if self.auto_model.config.output_hidden_states: all_layer_idx = 2 if len(output_states) < 3: # Some models only output last_hidden_states and all_hidden_states all_layer_idx = 1 hidden_states = output_states[all_layer_idx] features.update({"all_layer_embeddings": hidden_states}) return features def get_word_embedding_dimension(self) -> int: return self.auto_model.config.hidden_size def tokenize( self, texts: Union[List[str], List[Dict], List[Tuple[str, str]]], padding: Union[str, bool] = True ) -> Dict[str, torch.Tensor]: """Tokenizes a text and maps tokens to token-ids""" output = {} if isinstance(texts[0], str): to_tokenize = [texts] elif isinstance(texts[0], dict): to_tokenize = [] output["text_keys"] = [] for lookup in texts: text_key, text = next(iter(lookup.items())) to_tokenize.append(text) output["text_keys"].append(text_key) to_tokenize = [to_tokenize] else: batch1, batch2 = [], [] for text_tuple in texts: batch1.append(text_tuple[0]) batch2.append(text_tuple[1]) to_tokenize = [batch1, batch2] # strip to_tokenize = [[str(s).strip() for s in col] for col in to_tokenize] # Lowercase if self.do_lower_case: to_tokenize = [[s.lower() for s in col] for col in to_tokenize] output.update( self.tokenizer( *to_tokenize, padding=padding, truncation="longest_first", return_tensors="pt", max_length=self.max_seq_length, ) ) return output def get_config_dict(self) -> Dict[str, Any]: return {key: self.__dict__[key] for key in self.config_keys} def save(self, output_path: str, safe_serialization: bool = True) -> None: self.auto_model.save_pretrained(output_path, safe_serialization=safe_serialization) self.tokenizer.save_pretrained(output_path) with open(os.path.join(output_path, "sentence_bert_config.json"), "w") as fOut: json.dump(self.get_config_dict(), fOut, indent=2) @staticmethod def load(input_path: str) -> "Transformer": # Old classes used other config names than 'sentence_bert_config.json' for config_name in [ "sentence_bert_config.json", "sentence_roberta_config.json", "sentence_distilbert_config.json", "sentence_camembert_config.json", "sentence_albert_config.json", "sentence_xlm-roberta_config.json", "sentence_xlnet_config.json", ]: sbert_config_path = os.path.join(input_path, config_name) if os.path.exists(sbert_config_path): break with open(sbert_config_path) as fIn: config = json.load(fIn) # Don't allow configs to set trust_remote_code if "model_args" in config and "trust_remote_code" in config["model_args"]: config["model_args"].pop("trust_remote_code") if "tokenizer_args" in config and "trust_remote_code" in config["tokenizer_args"]: config["tokenizer_args"].pop("trust_remote_code") if "config_args" in config and "trust_remote_code" in config["config_args"]: config["config_args"].pop("trust_remote_code") return Transformer(model_name_or_path=input_path, **config)