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import torch.nn as nn
from tencentpretrain.layers.layer_norm import *
from tencentpretrain.layers.position_ffn import PositionwiseFeedForward, GatedFeedForward
from tencentpretrain.layers.multi_headed_attn import MultiHeadedAttention
from tencentpretrain.layers.relative_position_embedding import RelativePositionEmbedding
class TransformerLayer(nn.Module):
"""
Transformer layer mainly consists of two parts:
multi-headed self-attention and feed forward layer.
"""
def __init__(self, args):
super(TransformerLayer, 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)
# 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_2 = nn.Dropout(args.dropout)
if args.layernorm == "t5":
self.layer_norm_1 = T5LayerNorm(args.hidden_size)
self.layer_norm_2 = T5LayerNorm(args.hidden_size)
elif args.layernorm == "rms":
self.layer_norm_1 = RMSNorm(args.hidden_size)
self.layer_norm_2 = RMSNorm(args.hidden_size)
else:
self.layer_norm_1 = LayerNorm(args.hidden_size)
self.layer_norm_2 = LayerNorm(args.hidden_size)
def forward(self, hidden, mask, position_bias=None, has_residual_attention=False, prev_attn=None, freqs_cis=None):
"""
Args:
hidden: [batch_size x seq_length x emb_size]
mask: [batch_size x 1 x seq_length x seq_length]
position_bias: [1 x heads_num x seq_length x seq_length]
Returns:
output: [batch_size x seq_length x hidden_size]
"""
if self.layernorm_positioning == "post":
inter, prev_attn_out = self.self_attn(hidden, hidden, hidden, mask, position_bias, has_residual_attention, prev_attn, freqs_cis)
inter = self.dropout_1(inter)
inter = self.layer_norm_1(inter + hidden)
output = self.dropout_2(self.feed_forward(inter))
output = self.layer_norm_2(output + inter)
else:
inter = self.layer_norm_1(hidden)
inter, prev_attn_out = self.self_attn(inter, inter, inter, mask, position_bias, has_residual_attention, prev_attn, freqs_cis)
inter = self.dropout_1(inter)
hidden = hidden + inter
output = self.layer_norm_2(hidden)
output = self.dropout_2(self.feed_forward(output)) + hidden
return output, prev_attn_out
class TransformerDecoderLayer(nn.Module):
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 forward(self, hidden, encoder_hidden, mask_decoder, mask_encoder, self_position_bias=None, context_position_bias=None):
"""
Args:
hidden: [batch_size x seq_length x emb_size]
encoder_hidden: [batch_size x seq_length x emb_size]
mask_encoder: [batch_size x 1 x seq_length x seq_length]
mask_decoder: [batch_size x 1 x seq_length x seq_length]
self_position_bias: [1 x heads_num x seq_length x seq_length]
context_position_bias: [1 x heads_num x seq_length x seq_length]
Returns:
output: [batch_size x seq_length x hidden_size]
"""
if self.layernorm_positioning == "post":
query, _ = self.self_attn(hidden, hidden, hidden, mask_decoder, self_position_bias)
query = self.dropout_1(query)
query_norm = self.layer_norm_1(query + hidden)
mid, _ = self.context_attn(encoder_hidden, encoder_hidden, query_norm, mask_encoder, context_position_bias)
mid = self.dropout_2(mid)
mid_norm = self.layer_norm_2(mid + query_norm)
output = self.dropout_3(self.feed_forward(mid_norm))
output = self.layer_norm_3(output + mid_norm)
else:
hidden_norm = self.layer_norm_1(hidden)
query, _ = self.self_attn(hidden_norm, hidden_norm, hidden_norm, mask_decoder, self_position_bias)
query = self.dropout_1(query)
query = query + hidden
query_norm = self.layer_norm_2(query)
mid, _ = self.context_attn(encoder_hidden, encoder_hidden, query_norm, mask_encoder, context_position_bias)
mid = self.dropout_2(mid)
mid = mid + query
mid_norm = self.layer_norm_3(mid)
output = self.dropout_3(self.feed_forward(mid_norm)) + mid
return output
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