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| # -*- coding: utf-8 -*- | |
| """ Define the Attention Layer of the model. | |
| """ | |
| from __future__ import print_function, division | |
| import torch | |
| from torch.autograd import Variable | |
| from torch.nn import Module | |
| from torch.nn.parameter import Parameter | |
| class Attention(Module): | |
| """ | |
| Computes a weighted average of the different channels across timesteps. | |
| Uses 1 parameter pr. channel to compute the attention value for a single timestep. | |
| """ | |
| def __init__(self, attention_size, return_attention=False): | |
| """ Initialize the attention layer | |
| # Arguments: | |
| attention_size: Size of the attention vector. | |
| return_attention: If true, output will include the weight for each input token | |
| used for the prediction | |
| """ | |
| super(Attention, self).__init__() | |
| self.return_attention = return_attention | |
| self.attention_size = attention_size | |
| self.attention_vector = Parameter(torch.FloatTensor(attention_size)) | |
| self.attention_vector.data.normal_(std=0.05) # Initialize attention vector | |
| def __repr__(self): | |
| s = '{name}({attention_size}, return attention={return_attention})' | |
| return s.format(name=self.__class__.__name__, **self.__dict__) | |
| def forward(self, inputs, input_lengths): | |
| """ Forward pass. | |
| # Arguments: | |
| inputs (Torch.Variable): Tensor of input sequences | |
| input_lengths (torch.LongTensor): Lengths of the sequences | |
| # Return: | |
| Tuple with (representations and attentions if self.return_attention else None). | |
| """ | |
| logits = inputs.matmul(self.attention_vector) | |
| unnorm_ai = (logits - logits.max()).exp() | |
| # Compute a mask for the attention on the padded sequences | |
| # See e.g. https://discuss.pytorch.org/t/self-attention-on-words-and-masking/5671/5 | |
| max_len = unnorm_ai.size(1) | |
| idxes = torch.arange(0, max_len, out=torch.LongTensor(max_len)).unsqueeze(0) | |
| mask = Variable((idxes < input_lengths.unsqueeze(1)).float()) | |
| # apply mask and renormalize attention scores (weights) | |
| if self.attention_vector.device.type == "cuda": | |
| masked_weights = unnorm_ai * mask.cuda() | |
| else: | |
| masked_weights = unnorm_ai * mask | |
| att_sums = masked_weights.sum(dim=1, keepdim=True) # sums per sequence | |
| attentions = masked_weights.div(att_sums) | |
| # apply attention weights | |
| weighted = torch.mul(inputs, attentions.unsqueeze(-1).expand_as(inputs)) | |
| # get the final fixed vector representations of the sentences | |
| representations = weighted.sum(dim=1) | |
| return (representations, attentions if self.return_attention else None) | |