def __init__(self,
size,
dropout,
head_count=8,
hidden_size=2048,
context_size=3,
padding_idx=1):
super(HierarchicalContext, self).__init__()
self.context_size = context_size
self.padding_idx = padding_idx
self.layer_norm_query_word = onmt.modules.LayerNorm(size)
self.layer_norm_query_sent = onmt.modules.LayerNorm(size)
self.layer_norm_word = onmt.modules.LayerNorm(size)
self.layer_norm_sent = onmt.modules.LayerNorm(size)
self.dropout = nn.Dropout(dropout)
self.sent_attn = onmt.modules.MultiHeadedAttention(head_count,
size,
dropout=dropout)
self.word_attn = onmt.modules.MultiHeadedAttention(head_count,
size,
dropout=dropout)
self.linear = nn.Linear(2 * size, size)
self.sigmoid = nn.Sigmoid()
self.feed_forward = PositionwiseFeedForward(size, hidden_size, dropout)
def __init__(self, size, dropout, head_count=8, hidden_size=2048):
super(TransformerEncoderLayer, self).__init__()
self.self_attn = onmt.modules.MultiHeadedAttention(head_count,
size,
dropout=dropout)
self.feed_forward = PositionwiseFeedForward(size, hidden_size, dropout)
self.layer_norm = onmt.modules.LayerNorm(size)
self.dropout = nn.Dropout(dropout)
def __init__(self, size, dropout, head_count=8, hidden_size=2048):
super(TransformerDecoderLayer, self).__init__()
self.self_attn = onmt.modules.MultiHeadedAttention(head_count,
size,
dropout=dropout)
self.context_attn = onmt.modules.MultiHeadedAttention(head_count,
size,
dropout=dropout)
self.feed_forward = PositionwiseFeedForward(size, hidden_size, dropout)
self.layer_norm_1 = onmt.modules.LayerNorm(size)
self.layer_norm_2 = onmt.modules.LayerNorm(size)
self.dropout = dropout
self.drop = nn.Dropout(dropout)
mask = self._get_attn_subsequent_mask(MAX_SIZE)
# Register self.mask as a buffer in TransformerDecoderLayer, so
# it gets TransformerDecoderLayer's cuda behavior automatically.
self.register_buffer('mask', mask)
