68 lines
3.3 KiB
Python
68 lines
3.3 KiB
Python
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class AdditiveAttention(nn.Module):
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# Implementing the attention module of Bahdanau et al. 2015 where
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# score(h_j, s_(i-1)) = v . tanh(W_1 h_j + W_2 s_(i-1))
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def __init__(self, encoder_hidden_state_dim, decoder_hidden_state_dim, internal_dim=None):
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super(AdditiveAttention, self).__init__()
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if internal_dim is None:
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internal_dim = int((encoder_hidden_state_dim + decoder_hidden_state_dim) / 2)
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self.w1 = nn.Linear(encoder_hidden_state_dim, internal_dim, bias=False)
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self.w2 = nn.Linear(decoder_hidden_state_dim, internal_dim, bias=False)
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self.v = nn.Linear(internal_dim, 1, bias=False)
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def score(self, encoder_state, decoder_state):
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# encoder_state is of shape (batch, enc_dim)
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# decoder_state is of shape (batch, dec_dim)
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# return value should be of shape (batch, 1)
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return self.v(torch.tanh(self.w1(encoder_state) + self.w2(decoder_state)))
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def forward(self, encoder_states, decoder_state):
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# encoder_states is of shape (batch, num_enc_states, enc_dim)
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# decoder_state is of shape (batch, dec_dim)
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score_vec = torch.cat([self.score(encoder_states[:, i], decoder_state) for i in range(encoder_states.shape[1])],
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dim=1)
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# score_vec is of shape (batch, num_enc_states)
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attention_probs = torch.unsqueeze(F.softmax(score_vec, dim=1), dim=2)
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# attention_probs is of shape (batch, num_enc_states, 1)
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final_context_vec = torch.sum(attention_probs * encoder_states, dim=1)
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# final_context_vec is of shape (batch, enc_dim)
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return final_context_vec, attention_probs
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class TemporallyBatchedAdditiveAttention(AdditiveAttention):
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# Implementing the attention module of Bahdanau et al. 2015 where
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# score(h_j, s_(i-1)) = v . tanh(W_1 h_j + W_2 s_(i-1))
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def __init__(self, encoder_hidden_state_dim, decoder_hidden_state_dim, internal_dim=None):
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super(TemporallyBatchedAdditiveAttention, self).__init__(encoder_hidden_state_dim,
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decoder_hidden_state_dim,
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internal_dim)
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def score(self, encoder_state, decoder_state):
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# encoder_state is of shape (batch, num_enc_states, max_time, enc_dim)
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# decoder_state is of shape (batch, max_time, dec_dim)
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# return value should be of shape (batch, num_enc_states, max_time, 1)
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return self.v(torch.tanh(self.w1(encoder_state) + torch.unsqueeze(self.w2(decoder_state), dim=1)))
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def forward(self, encoder_states, decoder_state):
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# encoder_states is of shape (batch, num_enc_states, max_time, enc_dim)
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# decoder_state is of shape (batch, max_time, dec_dim)
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score_vec = self.score(encoder_states, decoder_state)
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# score_vec is of shape (batch, num_enc_states, max_time, 1)
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attention_probs = F.softmax(score_vec, dim=1)
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# attention_probs is of shape (batch, num_enc_states, max_time, 1)
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final_context_vec = torch.sum(attention_probs * encoder_states, dim=1)
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# final_context_vec is of shape (batch, max_time, enc_dim)
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return final_context_vec, torch.squeeze(torch.transpose(attention_probs, 1, 2), dim=3)
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