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| from typing import List, Union | |
| import numpy as np | |
| import torch | |
| from numpy import ndarray | |
| from transformers import (AlbertModel, AlbertTokenizer, BertModel, | |
| BertTokenizer, DistilBertModel, DistilBertTokenizer, | |
| PreTrainedModel, PreTrainedTokenizer, XLMModel, | |
| XLMTokenizer, XLNetModel, XLNetTokenizer) | |
| class BertParent(object): | |
| """ | |
| Base handler for BERT models. | |
| """ | |
| MODELS = { | |
| 'bert-base-uncased': (BertModel, BertTokenizer), | |
| 'bert-large-uncased': (BertModel, BertTokenizer), | |
| 'xlnet-base-cased': (XLNetModel, XLNetTokenizer), | |
| 'xlm-mlm-enfr-1024': (XLMModel, XLMTokenizer), | |
| 'distilbert-base-uncased': (DistilBertModel, DistilBertTokenizer), | |
| 'albert-base-v1': (AlbertModel, AlbertTokenizer), | |
| 'albert-large-v1': (AlbertModel, AlbertTokenizer) | |
| } | |
| def __init__( | |
| self, | |
| model: str, | |
| custom_model: PreTrainedModel = None, | |
| custom_tokenizer: PreTrainedTokenizer = None, | |
| gpu_id: int = 0, | |
| ): | |
| """ | |
| :param model: Model is the string path for the bert weights. If given a keyword, the s3 path will be used. | |
| :param custom_model: This is optional if a custom bert model is used. | |
| :param custom_tokenizer: Place to use custom tokenizer. | |
| """ | |
| base_model, base_tokenizer = self.MODELS.get(model, (None, None)) | |
| self.device = torch.device("cpu") | |
| if torch.cuda.is_available(): | |
| assert ( | |
| isinstance(gpu_id, int) and (0 <= gpu_id and gpu_id < torch.cuda.device_count()) | |
| ), f"`gpu_id` must be an integer between 0 to {torch.cuda.device_count() - 1}. But got: {gpu_id}" | |
| self.device = torch.device(f"cuda:{gpu_id}") | |
| if custom_model: | |
| self.model = custom_model.to(self.device) | |
| else: | |
| self.model = base_model.from_pretrained( | |
| model, output_hidden_states=True).to(self.device) | |
| if custom_tokenizer: | |
| self.tokenizer = custom_tokenizer | |
| else: | |
| self.tokenizer = base_tokenizer.from_pretrained(model) | |
| self.model.eval() | |
| def tokenize_input(self, text: str) -> torch.tensor: | |
| """ | |
| Tokenizes the text input. | |
| :param text: Text to tokenize. | |
| :return: Returns a torch tensor. | |
| """ | |
| tokenized_text = self.tokenizer.tokenize(text) | |
| indexed_tokens = self.tokenizer.convert_tokens_to_ids(tokenized_text) | |
| return torch.tensor([indexed_tokens]).to(self.device) | |
| def _pooled_handler(self, hidden: torch.Tensor, | |
| reduce_option: str) -> torch.Tensor: | |
| """ | |
| Handles torch tensor. | |
| :param hidden: The hidden torch tensor to process. | |
| :param reduce_option: The reduce option to use, such as mean, etc. | |
| :return: Returns a torch tensor. | |
| """ | |
| if reduce_option == 'max': | |
| return hidden.max(dim=1)[0].squeeze() | |
| elif reduce_option == 'median': | |
| return hidden.median(dim=1)[0].squeeze() | |
| return hidden.mean(dim=1).squeeze() | |
| def extract_embeddings( | |
| self, | |
| text: str, | |
| hidden: Union[List[int], int] = -2, | |
| reduce_option: str = 'mean', | |
| hidden_concat: bool = False, | |
| ) -> torch.Tensor: | |
| """ | |
| Extracts the embeddings for the given text. | |
| :param text: The text to extract embeddings for. | |
| :param hidden: The hidden layer(s) to use for a readout handler. | |
| :param squeeze: If we should squeeze the outputs (required for some layers). | |
| :param reduce_option: How we should reduce the items. | |
| :param hidden_concat: Whether or not to concat multiple hidden layers. | |
| :return: A torch vector. | |
| """ | |
| tokens_tensor = self.tokenize_input(text) | |
| pooled, hidden_states = self.model(tokens_tensor)[-2:] | |
| # deprecated temporary keyword functions. | |
| if reduce_option == 'concat_last_4': | |
| last_4 = [hidden_states[i] for i in (-1, -2, -3, -4)] | |
| cat_hidden_states = torch.cat(tuple(last_4), dim=-1) | |
| return torch.mean(cat_hidden_states, dim=1).squeeze() | |
| elif reduce_option == 'reduce_last_4': | |
| last_4 = [hidden_states[i] for i in (-1, -2, -3, -4)] | |
| return torch.cat(tuple(last_4), dim=1).mean(axis=1).squeeze() | |
| elif type(hidden) == int: | |
| hidden_s = hidden_states[hidden] | |
| return self._pooled_handler(hidden_s, reduce_option) | |
| elif hidden_concat: | |
| last_states = [hidden_states[i] for i in hidden] | |
| cat_hidden_states = torch.cat(tuple(last_states), dim=-1) | |
| return torch.mean(cat_hidden_states, dim=1).squeeze() | |
| last_states = [hidden_states[i] for i in hidden] | |
| hidden_s = torch.cat(tuple(last_states), dim=1) | |
| return self._pooled_handler(hidden_s, reduce_option) | |
| def create_matrix( | |
| self, | |
| content: List[str], | |
| hidden: Union[List[int], int] = -2, | |
| reduce_option: str = 'mean', | |
| hidden_concat: bool = False, | |
| ) -> ndarray: | |
| """ | |
| Create matrix from the embeddings. | |
| :param content: The list of sentences. | |
| :param hidden: Which hidden layer to use. | |
| :param reduce_option: The reduce option to run. | |
| :param hidden_concat: Whether or not to concat multiple hidden layers. | |
| :return: A numpy array matrix of the given content. | |
| """ | |
| return np.asarray([ | |
| np.squeeze(self.extract_embeddings( | |
| t, hidden=hidden, reduce_option=reduce_option, hidden_concat=hidden_concat | |
| ).data.cpu().numpy()) for t in content | |
| ]) | |
| def __call__( | |
| self, | |
| content: List[str], | |
| hidden: int = -2, | |
| reduce_option: str = 'mean', | |
| hidden_concat: bool = False, | |
| ) -> ndarray: | |
| """ | |
| Create matrix from the embeddings. | |
| :param content: The list of sentences. | |
| :param hidden: Which hidden layer to use. | |
| :param reduce_option: The reduce option to run. | |
| :param hidden_concat: Whether or not to concat multiple hidden layers. | |
| :return: A numpy array matrix of the given content. | |
| """ | |
| return self.create_matrix(content, hidden, reduce_option, hidden_concat) |