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embedding_calculator.py
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embedding_calculator.py
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"""_summary
"""
from typing import Optional
import os
import torch
from transformers import AutoModel, AutoTokenizer
import numpy as np
from profiling import Profile
# Paths where the models are stored
MODELS_PATH: str = "models/"
#
class EmbeddingCalculator():
"""_summary_
Raises:
UserWarning: _description_
UserWarning: _description_
UserWarning: _description_
UserWarning: _description_
UserWarning: _description_
Returns:
_type_: _description_
"""
#
def __init__(self, config: dict) -> None:
self.config = config
self.model_name: str = self.config["model_name"]
self.model: AutoModel | None = None
self.att_query: torch.nn.Module | None = None
self.att_key: torch.nn.Module | None = None
self.embeddings: torch.nn.Module | None = None
self.tokenizer: AutoTokenizer | None = None
self.use_cuda: bool = self.config["use_cuda"] == 1
#
if self.use_cuda and not torch.cuda.is_available():
raise UserWarning("Try to use cuda, but cuda is not available")
#
self.device: str = "cpu"
if self.use_cuda:
self.device = "cuda"
#
self.get_model()
#
def model_forward(self, **args):
"""_summary_
Args:
x (_type_): _description_
Raises:
UserWarning: _description_
Returns:
_type_: _description_
"""
if self.model is None:
raise UserWarning("Error, no model used!")
return self.model(**args)
#
def model_forward_skip_last_layers(self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
nb_layers: int = -1) -> torch.Tensor:
"""_summary_
Args:
X (_type_): _description_
nb_layers (int, optional): _description_. Defaults to -1.
Returns:
_type_: _description_
"""
if self.model is None:
raise UserWarning("Error, no model used!")
#
if nb_layers == -1 or nb_layers >= len(self.model.encoder.layer):
return self.model(input_ids=input_ids)
#
x: torch.Tensor = self.model.embeddings(input_ids=input_ids)
#
for i in range(nb_layers):
if attention_mask is not None and False:
x = self.model.encoder.layer[i](x, attention_mask=attention_mask)[0]
else:
x = self.model.encoder.layer[i](x)[0]
#
x = self.model.pooler(x)
#
return x
#
def model_forward_skip_layers(self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
skip_layers: Optional[list[int]] = None) -> torch.Tensor:
"""_summary_
Args:
X (_type_): _description_
skip_layers (list[int], optional): _description_. Defaults to [].
Returns:
_type_: _description_
"""
if self.model is None:
raise UserWarning("Error, no model used!")
#
if skip_layers is None or skip_layers == []:
return self.model(input_ids=input_ids)
#
x = self.model.embeddings(input_ids=input_ids)
#
nb_layers: int = len(self.model.encoder.layer)
for i in range(nb_layers):
if not i in skip_layers:
if attention_mask is not None and False:
x = self.model.encoder.layer[i](x, attention_mask=attention_mask)[0]
else:
x = self.model.encoder.layer[i](x)[0]
#
x = self.model.encoder.pooler(x)
#
return x
#
def model_forward_pass_only_layers(self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
pass_layers: Optional[list[int]] = None) -> torch.Tensor:
"""_summary_
Args:
X (_type_): _description_
pass_layers (list[int], optional): _description_. Defaults to [].
Returns:
_type_: _description_
"""
if self.model is None:
raise UserWarning("Error, no model!")
#
x = self.model.embeddings(input_ids=input_ids)
#
if pass_layers is not None:
#
for i in pass_layers:
if i >= 0 and i < 24:
if attention_mask is not None and False:
x = self.model.encoder.layer[i](x, attention_mask=attention_mask)[0]
else:
x = self.model.encoder.layer[i](x)[0]
#
x = self.model.encoder.pooler(x)
#
return x
#
def get_attention_from_model(self) -> torch.nn.Module | None:
"""_summary_
Returns:
torch.nn.Module | None: _description_
"""
if self.model is None:
return None
#
return self.model.encoder.layer[0].attention
#
def get_model(self) -> None:
"""_summary_
"""
#
if not os.path.exists(f"./{MODELS_PATH}/{self.model_name}"):
self.download_model()
else:
self.load_downloaded_model()
#
print(self.model)
#
if self.model is not None:
self.embeddings = self.model.embeddings
self.att_query = self.model.encoder.layer[0].attention.self.query
self.att_key = self.model.encoder.layer[0].attention.self.key
#
def load_downloaded_model(self, verbose=True) -> None:
"""_summary_
Raises:
UserWarning: _description_
UserWarning: _description_
"""
# Printing infos
if verbose:
print(f"Loading local model {self.model_name}...")
#
self.model = AutoModel.from_pretrained(
f"./{MODELS_PATH}/{self.model_name}")
#
if not self.model:
raise UserWarning("Error while loading the model")
#
self.tokenizer = AutoTokenizer.from_pretrained(
f"./{MODELS_PATH}/{self.model_name}")
#
if not self.tokenizer:
raise UserWarning("Error while loading the tokenizer")
# Printing infos
if verbose:
print(f"{self.model_name} model loaded successfully.")
#
def download_model(self, verbose=True) -> None:
"""_summary_
Raises:
UserWarning: _description_
UserWarning: _description_
"""
#
if verbose:
print(f"Downloading model {self.model_name}...")
#
self.model = AutoModel.from_pretrained(self.model_name)
#
if not self.model:
raise UserWarning("Error while downloading the model")
#
self.tokenizer = AutoTokenizer.from_pretrained(
self.model_name)
#
if not self.tokenizer:
raise UserWarning("Error while downloading the tokenizer")
#
self.model.save_pretrained(f"./{MODELS_PATH}/{self.model_name}")
#
self.tokenizer.save_pretrained(f"./{MODELS_PATH}/{self.model_name}")
#
if verbose:
print(f"{self.model_name} model downloaded"
f" and saved to ./{MODELS_PATH}/{self.model_name}")
# Function to generate sentence embeddings
def get_sentence_embeddings(self,
sentences: list[str],
embedding_skip_layers: dict | None = None) -> list[np.ndarray]:
"""_summary_
Args:
sentences (list[str]): _description_
Returns:
list[np.ndarray]: _description_
"""
if self.tokenizer is None:
raise UserWarning("Error, no tokenizer!")
if self.model is None:
raise UserWarning("Error, no model!")
#
embeddings: list[np.ndarray] = []
# Profiling
if self.config["profiling"] == 1:
p1: Profile = Profile("EmbeddingCalculator.py; Tokenize sentences")
#
inputs = self.tokenizer(sentences,
max_length=512,
truncation=True,
padding='max_length',
return_tensors="pt",
return_attention_mask=True)
# Profiling
if self.config["profiling"] == 1:
p1.finished(f"Nb sentences tokenized: {len(sentences)}")
# Profiling
if self.config["profiling"] == 1:
p2: Profile = Profile("EmbeddingCalculator.py; Calculate embedding")
#
with torch.no_grad():
#
last_hidden_state: torch.Tensor
#
if embedding_skip_layers is not None and \
embedding_skip_layers.get("skip_method") is not None and \
embedding_skip_layers["skip_method"] == "last_layers" and \
embedding_skip_layers.get("nb_layers") is not None:
#
last_hidden_state = self.model_forward_skip_last_layers(
**inputs,
nb_layers=embedding_skip_layers["nb_layers"])
#
elif embedding_skip_layers is not None and \
embedding_skip_layers.get("skip_method") is not None and \
embedding_skip_layers["skip_method"] == "skip_layers" and \
embedding_skip_layers.get("layers_to_skip") is not None:
#
last_hidden_state = self.model_forward_skip_layers(
**inputs,
skip_layers=embedding_skip_layers["layers_to_skip"])
#
elif embedding_skip_layers is not None and \
embedding_skip_layers.get("skip_method") is not None and \
embedding_skip_layers["skip_method"] == "skip_layers" and \
embedding_skip_layers.get("layers_to_pass") is not None:
#
last_hidden_state = self.model_forward_pass_only_layers(
**inputs,
pass_layers=embedding_skip_layers["layers_to_pass"])
#
else:
#
outputs = self.model(**inputs)
last_hidden_state = outputs.last_hidden_state
# Extract the sentence embedding
# (consider averaging or using other methods)
sentence_embedding = torch.mean(last_hidden_state, dim=1)
embeddings += [s.cpu().numpy() for s in sentence_embedding]
#
# Profiling
if self.config["profiling"] == 1:
p2.finished(f"Nb sentence tokenized: {len(sentences)}")
#
return embeddings