def __init__(self, args):
super(AttnEncoder, self).__init__()
self.layers_num = args.layers_num
self.self_attn = MultiHeadedAttention(args.hidden_size, args.heads_num,
args.dropout)
self.self_attn = nn.ModuleList([
MultiHeadedAttention(args.hidden_size, args.heads_num,
args.dropout) for _ in range(self.layers_num)
])
def __init__(self, args):
super(TransformerDecoderLayer, self).__init__()
self.self_attn = MultiHeadedAttention(args.hidden_size, args.heads_num,
args.dropout)
self.context_attn = MultiHeadedAttention(args.hidden_size,
args.heads_num, args.dropout)
self.layer_norm_1 = LayerNorm(args.hidden_size)
self.layer_norm_2 = LayerNorm(args.hidden_size)
self.layer_norm_3 = LayerNorm(args.hidden_size)
self.feed_forward = PositionwiseFeedForward(args.hidden_size,
args.feedforward_size,
args.hidden_act)
def __init__(self, args):
super(TransformerDecoderLayer, self).__init__()
self.layernorm_positioning = args.layernorm_positioning
if hasattr(args, "attention_head_size"):
attention_head_size = args.attention_head_size
else:
attention_head_size = args.hidden_size // args.heads_num
has_bias = bool(1 - args.remove_transformer_bias)
with_scale = bool(1 - args.remove_attention_scale)
# Multi-headed self-attention.
self.self_attn = MultiHeadedAttention(args.hidden_size,
args.heads_num,
attention_head_size,
args.dropout,
has_bias=has_bias,
with_scale=with_scale)
self.dropout_1 = nn.Dropout(args.dropout)
# Multi-headed context-attention.
self.context_attn = MultiHeadedAttention(args.hidden_size,
args.heads_num,
attention_head_size,
args.dropout,
has_bias=has_bias,
with_scale=with_scale)
self.dropout_2 = nn.Dropout(args.dropout)
# Feed forward layer.
if args.feed_forward == "gated":
self.feed_forward = GatedFeedForward(args.hidden_size,
args.feedforward_size,
args.hidden_act, has_bias)
else:
self.feed_forward = PositionwiseFeedForward(
args.hidden_size, args.feedforward_size, args.hidden_act,
has_bias)
self.dropout_3 = nn.Dropout(args.dropout)
# Layer Normalization
if args.layernorm == "t5":
self.layer_norm_1 = T5LayerNorm(args.hidden_size)
self.layer_norm_2 = T5LayerNorm(args.hidden_size)
self.layer_norm_3 = T5LayerNorm(args.hidden_size)
else:
self.layer_norm_1 = LayerNorm(args.hidden_size)
self.layer_norm_2 = LayerNorm(args.hidden_size)
self.layer_norm_3 = LayerNorm(args.hidden_size)
def __init__(self, args):
super(TransformerDecoderLayer, self).__init__()
self.layernorm_positioning = args.layernorm_positioning
if hasattr(args, "attention_head_size"):
attention_head_size = args.attention_head_size
else:
attention_head_size = args.hidden_size // args.heads_num
has_bias = bool(1 - args.remove_transformer_bias)
# Multi-headed self-attention.
self.self_attn = MultiHeadedAttention(args.hidden_size,
args.heads_num,
attention_head_size,
args.dropout,
has_bias=has_bias)
self.dropout_1 = nn.Dropout(args.dropout)
self.layer_norm_1 = LayerNorm(args.hidden_size, has_bias=has_bias)
# Multi-headed context-attention.
self.context_attn = MultiHeadedAttention(args.hidden_size,
args.heads_num,
attention_head_size,
args.dropout,
has_bias=has_bias)
self.dropout_2 = nn.Dropout(args.dropout)
self.layer_norm_2 = LayerNorm(args.hidden_size, has_bias=has_bias)
# Feed forward layer.
if args.feed_forward == "gated":
self.feed_forward = GatedFeedForward(args.hidden_size,
args.feedforward_size,
args.hidden_act, has_bias)
else:
self.feed_forward = PositionwiseFeedForward(
args.hidden_size, args.feedforward_size, args.hidden_act,
has_bias)
self.dropout_3 = nn.Dropout(args.dropout)
self.layer_norm_3 = LayerNorm(args.hidden_size, has_bias=has_bias)
self.relative_pos_emb = None
if args.relative_position_embedding:
self.relative_pos_emb = RelativePositionEmbedding(
bidirectional=False)
def __init__(self, args, input_size, labels_num):
super(Classifier, self).__init__()
self.input_size = input_size
self.cla_hidden_size = 128
self.cla_heads_num = 2
self.labels_num = labels_num
self.pooling = args.pooling
self.output_layer_0 = nn.Linear(input_size, self.cla_hidden_size)
self.self_atten = MultiHeadedAttention(self.cla_hidden_size, self.cla_heads_num, args.dropout)
self.output_layer_1 = nn.Linear(self.cla_hidden_size, self.cla_hidden_size)
self.output_layer_2 = nn.Linear(self.cla_hidden_size, labels_num)
def __init__(self, args):
super(GptBlock, self).__init__()
# Multi-headed self-attention.
self.self_attn = MultiHeadedAttention(args.hidden_size, args.heads_num,
args.dropout)
self.layer_norm_1 = LayerNorm(args.hidden_size)
# Feed forward layer.
self.feed_forward = PositionwiseFeedForward(args.hidden_size,
args.feedforward_size,
args.hidden_act)
self.layer_norm_2 = LayerNorm(args.hidden_size)
def __init__(self, args):
super(TransformerDecoderLayer, self).__init__()
self.layernorm_positioning = args.layernorm_positioning
# Multi-headed self-attention.
self.self_attn = MultiHeadedAttention(args.hidden_size, args.heads_num,
args.dropout)
self.dropout_1 = nn.Dropout(args.dropout)
self.layer_norm_1 = LayerNorm(args.hidden_size)
# Multi-headed context-attention.
self.context_attn = MultiHeadedAttention(args.hidden_size,
args.heads_num, args.dropout)
self.dropout_2 = nn.Dropout(args.dropout)
self.layer_norm_2 = LayerNorm(args.hidden_size)
# Feed forward layer.
self.feed_forward = PositionwiseFeedForward(args.hidden_size,
args.feedforward_size,
args.hidden_act)
self.dropout_3 = nn.Dropout(args.dropout)
self.layer_norm_3 = LayerNorm(args.hidden_size)
