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nlpformyself bowendoctor xiedake deanxiao19 yangyyyy kiminh sixy1204 colinsongf neuztb jjjkkyz hitlmt chikin-lau berylv587 mingkin oncejune yuzhang112 bigjeager

def __init__(self, config):
    super(Reduce, self).__init__()
    self.config = config
    self.reduce_h = nn.Linear(config.hidden_dim * 2, config.hidden_dim)
    self.reduce_c = nn.Linear(config.hidden_dim * 2, config.hidden_dim)
    self.dropout = nn.Dropout(p=config.hidden_dropout_prob)

def forward(self, h, c):
    assert self.config.hidden_dim == h.shape[2]
    assert self.config.hidden_dim == c.shape[2]
    h = h.reshape(-1, self.config.hidden_dim * 2)
    c = c.reshape(-1, self.config.hidden_dim * 2)
    h_output = self.dropout(self.reduce_h(h))
    c_output = self.dropout(self.reduce_c(c))
    h_output = F.relu(h_output)
    c_output = F.relu(c_output)
    # h_output.shape = c_output.shape = (batch,hidden)
    return h_output.unsqueeze(0), c_output.unsqueeze(0)