| import torch |
| from torch import Tensor |
| from torch import nn |
| from typing import Union, Tuple, List, Iterable, Dict |
| import os |
| import json |
|
|
|
|
| class WeightedLayerPooling(nn.Module): |
| """ |
| Token embeddings are weighted mean of their different hidden layer representations |
| """ |
| def __init__(self, word_embedding_dimension, num_hidden_layers: int = 12, layer_start: int = 4, layer_weights = None): |
| super(WeightedLayerPooling, self).__init__() |
| self.config_keys = ['word_embedding_dimension', 'layer_start', 'num_hidden_layers'] |
| self.word_embedding_dimension = word_embedding_dimension |
| self.layer_start = layer_start |
| self.num_hidden_layers = num_hidden_layers |
| self.layer_weights = layer_weights if layer_weights is not None else nn.Parameter(torch.tensor([1] * (num_hidden_layers+1 - layer_start), dtype=torch.float)) |
|
|
| def forward(self, features: Dict[str, Tensor]): |
| ft_all_layers = features['all_layer_embeddings'] |
|
|
| all_layer_embedding = torch.stack(ft_all_layers) |
| all_layer_embedding = all_layer_embedding[self.layer_start:, :, :, :] |
|
|
| weight_factor = self.layer_weights.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand(all_layer_embedding.size()) |
| weighted_average = (weight_factor*all_layer_embedding).sum(dim=0) / self.layer_weights.sum() |
|
|
| features.update({'token_embeddings': weighted_average}) |
| return features |
|
|
| def get_word_embedding_dimension(self): |
| return self.word_embedding_dimension |
|
|
| def get_config_dict(self): |
| return {key: self.__dict__[key] for key in self.config_keys} |
|
|
| def save(self, output_path): |
| with open(os.path.join(output_path, 'config.json'), 'w') as fOut: |
| json.dump(self.get_config_dict(), fOut, indent=2) |
|
|
| torch.save(self.state_dict(), os.path.join(output_path, 'pytorch_model.bin')) |
|
|
|
|
| @staticmethod |
| def load(input_path): |
| with open(os.path.join(input_path, 'config.json')) as fIn: |
| config = json.load(fIn) |
|
|
| model = WeightedLayerPooling(**config) |
| model.load_state_dict(torch.load(os.path.join(input_path, 'pytorch_model.bin'), map_location=torch.device('cpu'))) |
| return model |
|
|
