Spaces:
Running
Running
File size: 10,275 Bytes
5f028d6 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 |
import math
import numpy as np
import torch
from torch import nn, Tensor
from torch.nn import TransformerEncoder, TransformerEncoderLayer
# from cmib.model.positional_encoding import PositionalEmbedding
class SinPositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=100):
super(SinPositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
# not used in the final model
x = x + self.pe[:x.shape[0], :]
return self.dropout(x)
class MultiHeadedAttention(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout=0.1,
pre_lnorm=True, bias=False):
"""
Multi-headed attention with relative positional encoding and
memory mechanism.
Args:
n_head (int): Number of heads.
d_model (int): Input dimension.
d_head (int): Head dimension.
dropout (float, optional): Dropout value. Defaults to 0.1.
pre_lnorm (bool, optional):
Apply layer norm before rest of calculation. Defaults to True.
In original Transformer paper (pre_lnorm=False):
LayerNorm(x + Sublayer(x))
In tensor2tensor implementation (pre_lnorm=True):
x + Sublayer(LayerNorm(x))
bias (bool, optional):
Add bias to q, k, v and output projections. Defaults to False.
"""
super(MultiHeadedAttention, self).__init__()
self.n_head = n_head
self.d_model = d_model
self.d_head = d_head
self.dropout = dropout
self.pre_lnorm = pre_lnorm
self.bias = bias
self.atten_scale = 1 / math.sqrt(self.d_model)
self.q_linear = nn.Linear(d_model, n_head * d_head, bias=bias)
self.k_linear = nn.Linear(d_model, n_head * d_head, bias=bias)
self.v_linear = nn.Linear(d_model, n_head * d_head, bias=bias)
self.out_linear = nn.Linear(n_head * d_head, d_model, bias=bias)
self.droput_layer = nn.Dropout(dropout)
self.atten_dropout_layer = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(d_model)
def forward(self, hidden, memory=None, mask=None,
extra_atten_score=None):
"""
Args:
hidden (Tensor): Input embedding or hidden state of previous layer.
Shape: (batch, seq, dim)
pos_emb (Tensor): Relative positional embedding lookup table.
Shape: (batch, (seq+mem_len)*2-1, d_head)
pos_emb[:, seq+mem_len]
memory (Tensor): Memory tensor of previous layer.
Shape: (batch, mem_len, dim)
mask (BoolTensor, optional): Attention mask.
Set item value to True if you DO NOT want keep certain
attention score, otherwise False. Defaults to None.
Shape: (seq, seq+mem_len).
"""
combined = hidden
# if memory is None:
# combined = hidden
# mem_len = 0
# else:
# combined = torch.cat([memory, hidden], dim=1)
# mem_len = memory.shape[1]
if self.pre_lnorm:
hidden = self.layer_norm(hidden)
combined = self.layer_norm(combined)
# shape: (batch, q/k/v_len, dim)
q = self.q_linear(hidden)
k = self.k_linear(combined)
v = self.v_linear(combined)
# reshape to (batch, q/k/v_len, n_head, d_head)
q = q.reshape(q.shape[0], q.shape[1], self.n_head, self.d_head)
k = k.reshape(k.shape[0], k.shape[1], self.n_head, self.d_head)
v = v.reshape(v.shape[0], v.shape[1], self.n_head, self.d_head)
# transpose to (batch, n_head, q/k/v_len, d_head)
q = q.transpose(1, 2)
k = k.transpose(1, 2)
v = v.transpose(1, 2)
# add n_head dimension for relative positional embedding lookup table
# (batch, n_head, k/v_len*2-1, d_head)
# pos_emb = pos_emb[:, None]
# (batch, n_head, q_len, k_len)
atten_score = torch.matmul(q, k.transpose(-1, -2))
# qpos = torch.matmul(q, pos_emb.transpose(-1, -2))
# DEBUG
# ones = torch.zeros(q.shape)
# ones[:, :, :, 0] = 1.0
# qpos = torch.matmul(ones, pos_emb.transpose(-1, -2))
# atten_score = atten_score + self.skew(qpos, mem_len)
atten_score = atten_score * self.atten_scale
# if extra_atten_score is not None:
# atten_score = atten_score + extra_atten_score
if mask is not None:
# print(atten_score.shape)
# print(mask.shape)
# apply attention mask
atten_score = atten_score.masked_fill(mask, float("-inf"))
atten_score = atten_score.softmax(dim=-1)
atten_score = self.atten_dropout_layer(atten_score)
# (batch, n_head, q_len, d_head)
atten_vec = torch.matmul(atten_score, v)
# (batch, q_len, n_head*d_head)
atten_vec = atten_vec.transpose(1, 2).flatten(start_dim=-2)
# linear projection
output = self.droput_layer(self.out_linear(atten_vec))
if self.pre_lnorm:
return hidden + output
else:
return self.layer_norm(hidden + output)
class FeedForward(nn.Module):
def __init__(self, d_model, d_inner, dropout=0.1, pre_lnorm=True):
"""
Positionwise feed-forward network.
Args:
d_model(int): Dimension of the input and output.
d_inner (int): Dimension of the middle layer(bottleneck).
dropout (float, optional): Dropout value. Defaults to 0.1.
pre_lnorm (bool, optional):
Apply layer norm before rest of calculation. Defaults to True.
In original Transformer paper (pre_lnorm=False):
LayerNorm(x + Sublayer(x))
In tensor2tensor implementation (pre_lnorm=True):
x + Sublayer(LayerNorm(x))
"""
super(FeedForward, self).__init__()
self.d_model = d_model
self.d_inner = d_inner
self.dropout = dropout
self.pre_lnorm = pre_lnorm
self.layer_norm = nn.LayerNorm(d_model)
self.network = nn.Sequential(
nn.Linear(d_model, d_inner),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(d_inner, d_model),
nn.Dropout(dropout),
)
def forward(self, x):
if self.pre_lnorm:
return x + self.network(self.layer_norm(x))
else:
return self.layer_norm(x + self.network(x))
class TransformerModel(nn.Module):
def __init__(
self,
seq_len: int,
input_dim: int,
d_model: int,
nhead: int,
d_hid: int,
nlayers: int,
dropout: float = 0.5,
out_dim=91,
masked_attention_stage=False,
):
super().__init__()
self.model_type = "Transformer"
self.seq_len = seq_len
self.d_model = d_model
self.nhead = nhead
self.d_hid = d_hid
self.nlayers = nlayers
self.pos_embedding = SinPositionalEncoding(d_model=d_model, dropout=0.1, max_len=seq_len)
if masked_attention_stage:
self.input_layer = nn.Linear(input_dim+1, d_model)
# visible to invisible attention
self.att_layers = nn.ModuleList()
self.pff_layers = nn.ModuleList()
self.pre_lnorm = True
self.layer_norm = nn.LayerNorm(d_model)
for i in range(self.nlayers):
self.att_layers.append(
MultiHeadedAttention(
self.nhead, self.d_model,
self.d_model // self.nhead, dropout=dropout,
pre_lnorm=True,
bias=False
)
)
self.pff_layers.append(
FeedForward(
self.d_model, d_hid,
dropout=dropout,
pre_lnorm=True
)
)
else:
self.att_layers = None
self.input_layer = nn.Linear(input_dim, d_model)
encoder_layers = TransformerEncoderLayer(
d_model, nhead, d_hid, dropout, activation="gelu"
)
self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
self.decoder = nn.Linear(d_model, out_dim)
self.init_weights()
def init_weights(self) -> None:
initrange = 0.1
self.decoder.bias.data.zero_()
self.decoder.weight.data.uniform_(-initrange, initrange)
def forward(self, src: Tensor, src_mask: Tensor, data_mask=None, atten_mask=None) -> Tensor:
"""
Args:
src: Tensor, shape [seq_len, batch_size, embedding_dim]
src_mask: Tensor, shape [seq_len, seq_len]
Returns:
output Tensor of shape [seq_len, batch_size, embedding_dim]
"""
if not data_mask is None:
src = torch.cat([src, data_mask.expand(*src.shape[:-1], data_mask.shape[-1])], dim=-1)
src = self.input_layer(src)
output = self.pos_embedding(src)
# output = src
if self.att_layers:
assert not atten_mask is None
output = output.permute(1, 0, 2)
for i in range(self.nlayers):
output = self.att_layers[i](output, mask=atten_mask)
output = self.pff_layers[i](output)
if self.pre_lnorm:
output = self.layer_norm(output)
output = output.permute(1, 0, 2)
output = self.transformer_encoder(output)
output = self.decoder(output)
return output
|