| import json
|
| import logging
|
| import os
|
| from io import BytesIO
|
| from typing import Any, Dict, List, Optional, Tuple, Union
|
|
|
| import torch
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| from torch import nn
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| from transformers import AutoConfig, AutoModel, AutoTokenizer
|
|
|
| logger = logging.getLogger(__name__)
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|
|
|
|
| class Transformer(nn.Module):
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| """Huggingface AutoModel to generate token embeddings.
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| Loads the correct class, e.g. BERT / RoBERTa etc.
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|
|
| Args:
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| model_name_or_path: Huggingface models name
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| (https://huggingface.co/models)
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| max_seq_length: Truncate any inputs longer than max_seq_length
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| model_args: Keyword arguments passed to the Huggingface
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| Transformers model
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| tokenizer_args: Keyword arguments passed to the Huggingface
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| Transformers tokenizer
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| config_args: Keyword arguments passed to the Huggingface
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| Transformers config
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| cache_dir: Cache dir for Huggingface Transformers to store/load
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| models
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| do_lower_case: If true, lowercases the input (independent if the
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| model is cased or not)
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| tokenizer_name_or_path: Name or path of the tokenizer. When
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| None, then model_name_or_path is used
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| """
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|
|
| save_in_root: bool = True
|
|
|
| def __init__(
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| self,
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| model_name_or_path: str,
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| max_seq_length: int = None,
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| model_args: Dict[str, Any] = None,
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| tokenizer_args: Dict[str, Any] = None,
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| config_args: Dict[str, Any] = None,
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| cache_dir: str = None,
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| do_lower_case: bool = False,
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| tokenizer_name_or_path: str = None,
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| **kwargs,
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| ) -> None:
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| super().__init__()
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| self.config_keys = ["max_seq_length", "do_lower_case"]
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| self.do_lower_case = do_lower_case
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| if model_args is None:
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| model_args = {}
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| if tokenizer_args is None:
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| tokenizer_args = {}
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| if config_args is None:
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| config_args = {}
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|
|
| if kwargs.get("backend", "torch") != "torch":
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| logger.warning(
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| f'"jinaai/jina-embeddings-v3" is currently not compatible with the {kwargs["backend"]} backend. '
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| 'Continuing with the "torch" backend.'
|
| )
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|
|
| self.config = AutoConfig.from_pretrained(model_name_or_path, **config_args, cache_dir=cache_dir)
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|
|
| self._lora_adaptations = self.config.lora_adaptations
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| if (
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| not isinstance(self._lora_adaptations, list)
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| or len(self._lora_adaptations) < 1
|
| ):
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| raise ValueError(
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| f"`lora_adaptations` must be a list and contain at least one element"
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| )
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| self._adaptation_map = {
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| name: idx for idx, name in enumerate(self._lora_adaptations)
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| }
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|
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| self.default_task = model_args.pop('default_task', None)
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|
|
| self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=self.config, cache_dir=cache_dir, **model_args)
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|
|
| if max_seq_length is not None and "model_max_length" not in tokenizer_args:
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| tokenizer_args["model_max_length"] = max_seq_length
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| self.tokenizer = AutoTokenizer.from_pretrained(
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| tokenizer_name_or_path if tokenizer_name_or_path is not None else model_name_or_path,
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| cache_dir=cache_dir,
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| **tokenizer_args,
|
| )
|
|
|
|
|
| if max_seq_length is None:
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| if (
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| hasattr(self.auto_model, "config")
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| and hasattr(self.auto_model.config, "max_position_embeddings")
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| and hasattr(self.tokenizer, "model_max_length")
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| ):
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| max_seq_length = min(self.auto_model.config.max_position_embeddings, self.tokenizer.model_max_length)
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|
|
| self.max_seq_length = max_seq_length
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|
|
| if tokenizer_name_or_path is not None:
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| self.auto_model.config.tokenizer_class = self.tokenizer.__class__.__name__
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|
|
|
|
| @property
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| def default_task(self):
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| return self._default_task
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|
|
| @default_task.setter
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| def default_task(self, task: Union[None, str]):
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| self._validate_task(task)
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| self._default_task = task
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|
|
|
|
| def _validate_task(self, task: str):
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| if task and task not in self._lora_adaptations:
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| raise ValueError(
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| f"Unsupported task '{task}'. "
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| f"Supported tasks are: {', '.join(self.config.lora_adaptations)}. "
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| f"Alternatively, don't pass the `task` argument to disable LoRA."
|
| )
|
|
|
| def forward(
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| self, features: Dict[str, torch.Tensor], task: Optional[str] = None
|
| ) -> Dict[str, torch.Tensor]:
|
| """Returns token_embeddings, cls_token"""
|
| self._validate_task(task)
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| task = task or self.default_task
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| adapter_mask = None
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| if task:
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| task_id = self._adaptation_map[task]
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| num_examples = features['input_ids'].size(0)
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| adapter_mask = torch.full(
|
| (num_examples,), task_id, dtype=torch.int32, device=features['input_ids'].device
|
| )
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|
|
| lora_arguments = (
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| {"adapter_mask": adapter_mask} if adapter_mask is not None else {}
|
| )
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| features.pop('prompt_length', None)
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| output_states = self.auto_model.forward(**features, **lora_arguments, return_dict=False)
|
| output_tokens = output_states[0]
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| features.update({"token_embeddings": output_tokens, "attention_mask": features["attention_mask"]})
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| return features
|
|
|
| def get_word_embedding_dimension(self) -> int:
|
| return self.auto_model.config.hidden_size
|
|
|
| def tokenize(
|
| self,
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| 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):
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| to_tokenize = []
|
| output["text_keys"] = []
|
| for lookup in texts:
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| text_key, text = next(iter(lookup.items()))
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| 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])
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| to_tokenize = [batch1, batch2]
|
|
|
|
|
| to_tokenize = [[str(s).strip() for s in col] for col in to_tokenize]
|
|
|
|
|
| if self.do_lower_case:
|
| to_tokenize = [[s.lower() for s in col] for col in to_tokenize]
|
|
|
| output.update(
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| self.tokenizer(
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| *to_tokenize,
|
| padding=padding,
|
| truncation="longest_first",
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| return_tensors="pt",
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| 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)
|
|
|
|
|
| @classmethod
|
| def load(cls, input_path: str) -> "Transformer":
|
|
|
| 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)
|
|
|
| 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 cls(model_name_or_path=input_path, **config)
|
|
|
