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import json
import logging
import os
from io import BytesIO
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from transformers import AutoConfig, AutoModel, AutoTokenizer
logger = logging.getLogger(__name__)
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: int = None,
model_args: Dict[str, Any] = None,
tokenizer_args: Dict[str, Any] = None,
config_args: Dict[str, Any] = None,
cache_dir: str = None,
do_lower_case: bool = False,
tokenizer_name_or_path: str = None,
**kwargs,
) -> None:
super().__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 = {}
if kwargs.get("backend", "torch") != "torch":
logger.warning(
f'"jinaai/jina-embeddings-v3" is currently not compatible with the {kwargs["backend"]} backend. '
'Continuing with the "torch" backend.'
)
self.config = AutoConfig.from_pretrained(model_name_or_path, **config_args, cache_dir=cache_dir)
self._lora_adaptations = self.config.lora_adaptations
if (
not isinstance(self._lora_adaptations, list)
or len(self._lora_adaptations) < 1
):
raise ValueError(
f"`lora_adaptations` must be a list and contain at least one element"
)
self._adaptation_map = {
name: idx for idx, name in enumerate(self._lora_adaptations)
}
self.default_task = model_args.pop('default_task', None)
self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=self.config, cache_dir=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__
@property
def default_task(self):
return self._default_task
@default_task.setter
def default_task(self, task: Union[None, str]):
self._validate_task(task)
self._default_task = task
def _validate_task(self, task: str):
if task and task not in self._lora_adaptations:
raise ValueError(
f"Unsupported task '{task}'. "
f"Supported tasks are: {', '.join(self.config.lora_adaptations)}. "
f"Alternatively, don't pass the `task` argument to disable LoRA."
)
def forward(
self, features: Dict[str, torch.Tensor], task: Optional[str] = None
) -> Dict[str, torch.Tensor]:
"""Returns token_embeddings, cls_token"""
self._validate_task(task)
task = task or self.default_task
adapter_mask = None
if task:
task_id = self._adaptation_map[task]
num_examples = features['input_ids'].size(0)
adapter_mask = torch.full(
(num_examples,), task_id, dtype=torch.int32, device=features['input_ids'].device
)
lora_arguments = (
{"adapter_mask": adapter_mask} if adapter_mask is not None else {}
)
output_states = self.auto_model.forward(**features, **lora_arguments, return_dict=False)
output_tokens = output_states[0]
features.update({"token_embeddings": output_tokens, "attention_mask": features["attention_mask"]})
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)
@classmethod
def load(cls, 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 cls(model_name_or_path=input_path, **config)
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