File size: 9,005 Bytes
7cc9175 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 |
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
"""
save_in_root: bool = True
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 {}
)
features.pop('prompt_length', None)
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)
|