from torch import nn
from transformers import AutoModel, AutoTokenizer, AutoConfig
import json
from typing import List, Dict, Optional
import os
import numpy as np
import logging
class Transformer(nn.Module):
"""Huggingface AutoModel to generate token embeddings.
Loads the correct class, e.g. BERT / RoBERTa etc.
"""
def __init__(self, model_name_or_path: str, max_seq_length: int = 128, model_args: Dict = {}, cache_dir: Optional[str] = None ):
super(Transformer, self).__init__()
self.config_keys = ['max_seq_length']
self.max_seq_length = max_seq_length
config = AutoConfig.from_pretrained(model_name_or_path, **model_args, cache_dir=cache_dir)
self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=config, cache_dir=cache_dir)
self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, cache_dir=cache_dir)
def forward(self, features):
"""Returns token_embeddings, cls_token"""
output_states = self.auto_model(**features)
output_tokens = output_states[0]
cls_tokens = output_tokens[:, 0, :] # CLS token is first token
features.update({'token_embeddings': output_tokens, 'cls_token_embeddings': cls_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, text: str) -> List[int]:
"""
Tokenizes a text and maps tokens to token-ids
"""
return self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(text))
def get_sentence_features(self, tokens: List[int], pad_seq_length: int):
"""
Convert tokenized sentence in its embedding ids, segment ids and mask
:param tokens:
a tokenized sentence
:param pad_seq_length:
the maximal length of the sequence. Cannot be greater than self.sentence_transformer_config.max_seq_length
:return: embedding ids, segment ids and mask for the sentence
"""
pad_seq_length = min(pad_seq_length, self.max_seq_length) + 3 #Add space for special tokens
return self.tokenizer.prepare_for_model(tokens, max_length=pad_seq_length, pad_to_max_length=True, return_tensors='pt')
def get_config_dict(self):
return {key: self.__dict__[key] for key in self.config_keys}
def save(self, output_path: str):
self.auto_model.save_pretrained(output_path)
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):
with open(os.path.join(input_path, 'sentence_bert_config.json')) as fIn:
config = json.load(fIn)
return Transformer(model_name_or_path=input_path, **config)
