File size: 8,721 Bytes
6065472 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 |
import math
import torch
import torch.nn as nn
from models import BaseDecoder
from utils.model_util import generate_length_mask, PositionalEncoding
from utils.train_util import merge_load_state_dict
class TransformerDecoder(BaseDecoder):
def __init__(self,
emb_dim,
vocab_size,
fc_emb_dim,
attn_emb_dim,
dropout,
freeze=False,
tie_weights=False,
**kwargs):
super().__init__(emb_dim, vocab_size, fc_emb_dim, attn_emb_dim,
dropout=dropout, tie_weights=tie_weights)
self.d_model = emb_dim
self.nhead = kwargs.get("nhead", self.d_model // 64)
self.nlayers = kwargs.get("nlayers", 2)
self.dim_feedforward = kwargs.get("dim_feedforward", self.d_model * 4)
self.pos_encoder = PositionalEncoding(self.d_model, dropout)
layer = nn.TransformerDecoderLayer(d_model=self.d_model,
nhead=self.nhead,
dim_feedforward=self.dim_feedforward,
dropout=dropout)
self.model = nn.TransformerDecoder(layer, self.nlayers)
self.classifier = nn.Linear(self.d_model, vocab_size, bias=False)
if tie_weights:
self.classifier.weight = self.word_embedding.weight
self.attn_proj = nn.Sequential(
nn.Linear(self.attn_emb_dim, self.d_model),
nn.ReLU(),
nn.Dropout(dropout),
nn.LayerNorm(self.d_model)
)
self.init_params()
self.freeze = freeze
if freeze:
for p in self.parameters():
p.requires_grad = False
def init_params(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def load_pretrained(self, pretrained, output_fn):
checkpoint = torch.load(pretrained, map_location="cpu")
if "model" in checkpoint:
checkpoint = checkpoint["model"]
if next(iter(checkpoint)).startswith("decoder."):
state_dict = {}
for k, v in checkpoint.items():
state_dict[k[8:]] = v
loaded_keys = merge_load_state_dict(state_dict, self, output_fn)
if self.freeze:
for name, param in self.named_parameters():
if name in loaded_keys:
param.requires_grad = False
else:
param.requires_grad = True
def generate_square_subsequent_mask(self, max_length):
mask = (torch.triu(torch.ones(max_length, max_length)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
return mask
def forward(self, input_dict):
word = input_dict["word"]
attn_emb = input_dict["attn_emb"]
attn_emb_len = input_dict["attn_emb_len"]
cap_padding_mask = input_dict["cap_padding_mask"]
p_attn_emb = self.attn_proj(attn_emb)
p_attn_emb = p_attn_emb.transpose(0, 1) # [T_src, N, emb_dim]
word = word.to(attn_emb.device)
embed = self.in_dropout(self.word_embedding(word)) * math.sqrt(self.emb_dim) # [N, T, emb_dim]
embed = embed.transpose(0, 1) # [T, N, emb_dim]
embed = self.pos_encoder(embed)
tgt_mask = self.generate_square_subsequent_mask(embed.size(0)).to(attn_emb.device)
memory_key_padding_mask = ~generate_length_mask(attn_emb_len, attn_emb.size(1)).to(attn_emb.device)
output = self.model(embed, p_attn_emb, tgt_mask=tgt_mask,
tgt_key_padding_mask=cap_padding_mask,
memory_key_padding_mask=memory_key_padding_mask)
output = output.transpose(0, 1)
output = {
"embed": output,
"logit": self.classifier(output),
}
return output
class M2TransformerDecoder(BaseDecoder):
def __init__(self, vocab_size, fc_emb_dim, attn_emb_dim, dropout=0.1, **kwargs):
super().__init__(attn_emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout=dropout,)
try:
from m2transformer.models.transformer import MeshedDecoder
except:
raise ImportError("meshed-memory-transformer not installed; please run `pip install git+https://github.com/ruotianluo/meshed-memory-transformer.git`")
del self.word_embedding
del self.in_dropout
self.d_model = attn_emb_dim
self.nhead = kwargs.get("nhead", self.d_model // 64)
self.nlayers = kwargs.get("nlayers", 2)
self.dim_feedforward = kwargs.get("dim_feedforward", self.d_model * 4)
self.model = MeshedDecoder(vocab_size, 100, self.nlayers, 0,
d_model=self.d_model,
h=self.nhead,
d_ff=self.dim_feedforward,
dropout=dropout)
self.init_params()
def init_params(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(self, input_dict):
word = input_dict["word"]
attn_emb = input_dict["attn_emb"]
attn_emb_mask = input_dict["attn_emb_mask"]
word = word.to(attn_emb.device)
embed, logit = self.model(word, attn_emb, attn_emb_mask)
output = {
"embed": embed,
"logit": logit,
}
return output
class EventTransformerDecoder(TransformerDecoder):
def forward(self, input_dict):
word = input_dict["word"] # index of word embeddings
attn_emb = input_dict["attn_emb"]
attn_emb_len = input_dict["attn_emb_len"]
cap_padding_mask = input_dict["cap_padding_mask"]
event_emb = input_dict["event"] # [N, emb_dim]
p_attn_emb = self.attn_proj(attn_emb)
p_attn_emb = p_attn_emb.transpose(0, 1) # [T_src, N, emb_dim]
word = word.to(attn_emb.device)
embed = self.in_dropout(self.word_embedding(word)) * math.sqrt(self.emb_dim) # [N, T, emb_dim]
embed = embed.transpose(0, 1) # [T, N, emb_dim]
embed += event_emb
embed = self.pos_encoder(embed)
tgt_mask = self.generate_square_subsequent_mask(embed.size(0)).to(attn_emb.device)
memory_key_padding_mask = ~generate_length_mask(attn_emb_len, attn_emb.size(1)).to(attn_emb.device)
output = self.model(embed, p_attn_emb, tgt_mask=tgt_mask,
tgt_key_padding_mask=cap_padding_mask,
memory_key_padding_mask=memory_key_padding_mask)
output = output.transpose(0, 1)
output = {
"embed": output,
"logit": self.classifier(output),
}
return output
class KeywordProbTransformerDecoder(TransformerDecoder):
def __init__(self, emb_dim, vocab_size, fc_emb_dim, attn_emb_dim,
dropout, keyword_classes_num, **kwargs):
super().__init__(emb_dim, vocab_size, fc_emb_dim, attn_emb_dim,
dropout, **kwargs)
self.keyword_proj = nn.Linear(keyword_classes_num, self.d_model)
self.word_keyword_norm = nn.LayerNorm(self.d_model)
def forward(self, input_dict):
word = input_dict["word"] # index of word embeddings
attn_emb = input_dict["attn_emb"]
attn_emb_len = input_dict["attn_emb_len"]
cap_padding_mask = input_dict["cap_padding_mask"]
keyword = input_dict["keyword"] # [N, keyword_classes_num]
p_attn_emb = self.attn_proj(attn_emb)
p_attn_emb = p_attn_emb.transpose(0, 1) # [T_src, N, emb_dim]
word = word.to(attn_emb.device)
embed = self.in_dropout(self.word_embedding(word)) * math.sqrt(self.emb_dim) # [N, T, emb_dim]
embed = embed.transpose(0, 1) # [T, N, emb_dim]
embed += self.keyword_proj(keyword)
embed = self.word_keyword_norm(embed)
embed = self.pos_encoder(embed)
tgt_mask = self.generate_square_subsequent_mask(embed.size(0)).to(attn_emb.device)
memory_key_padding_mask = ~generate_length_mask(attn_emb_len, attn_emb.size(1)).to(attn_emb.device)
output = self.model(embed, p_attn_emb, tgt_mask=tgt_mask,
tgt_key_padding_mask=cap_padding_mask,
memory_key_padding_mask=memory_key_padding_mask)
output = output.transpose(0, 1)
output = {
"embed": output,
"logit": self.classifier(output),
}
return output
|