Spaces:
Sleeping
Sleeping
File size: 8,524 Bytes
801501a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 |
"""
Implementation of time conditioned Transformer.
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionalEncoding(nn.Module):
def __init__(self, d_hid, n_position=200):
super(PositionalEncoding, self).__init__()
# Not a parameter
self.register_buffer(
"pos_table", self._get_sinusoid_encoding_table(n_position, d_hid)
)
def _get_sinusoid_encoding_table(self, n_position, d_hid):
"""Sinusoid position encoding table"""
# TODO: make it with torch instead of numpy
def get_position_angle_vec(position):
return [
position / np.power(10000, 2 * (hid_j // 2) / d_hid)
for hid_j in range(d_hid)
]
sinusoid_table = np.array(
[get_position_angle_vec(pos_i) for pos_i in range(n_position)]
)
sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
return torch.FloatTensor(sinusoid_table).unsqueeze(0)
def forward(self, x):
"""
Input:
x: [B,N,D]
"""
return x + self.pos_table[:, : x.size(1)].clone().detach()
class ConcatSquashLinear(nn.Module):
def __init__(self, dim_in, dim_out, dim_ctx):
super(ConcatSquashLinear, self).__init__()
self._layer = nn.Linear(dim_in, dim_out)
self._hyper_bias = nn.Linear(dim_ctx, dim_out, bias=False)
self._hyper_gate = nn.Linear(dim_ctx, dim_out)
def forward(self, ctx, x):
assert ctx.dim() == x.dim()
gate = torch.sigmoid(self._hyper_gate(ctx))
bias = self._hyper_bias(ctx)
ret = self._layer(x) * gate + bias
return ret
class TimeMLP(nn.Module):
def __init__(
self,
dim_in,
dim_h,
dim_out,
dim_ctx=None,
act=F.relu,
dropout=0.0,
use_time=False,
):
super().__init__()
self.act = act
self.use_time = use_time
dim_h = int(dim_h)
if use_time:
self.fc1 = ConcatSquashLinear(dim_in, dim_h, dim_ctx)
self.fc2 = ConcatSquashLinear(dim_h, dim_out, dim_ctx)
else:
self.fc1 = nn.Linear(dim_in, dim_h)
self.fc2 = nn.Linear(dim_h, dim_out)
self.dropout = nn.Dropout(dropout)
def forward(self, x, ctx=None):
if self.use_time:
x = self.fc1(x=x, ctx=ctx)
else:
x = self.fc1(x)
x = self.act(x)
x = self.dropout(x)
if self.use_time:
x = self.fc2(x=x, ctx=ctx)
else:
x = self.fc2(x)
x = self.dropout(x)
return x
class MultiHeadAttention(nn.Module):
def __init__(self, dim_self, dim_ref, num_heads, dropout=0.0):
super().__init__()
self.num_heads = num_heads
head_dim = dim_self // num_heads
self.scale = head_dim**-0.5
self.to_queries = nn.Linear(dim_self, dim_self)
self.to_keys_values = nn.Linear(dim_ref, dim_self * 2)
self.project = nn.Linear(dim_self, dim_self)
self.dropout = nn.Dropout(dropout)
def forward(
self,
x,
y=None,
mask=None,
alpha=None,
):
y = y if y is not None else x
b_a, n, c = x.shape
b, m, d = y.shape
# b n h dh
queries = self.to_queries(x).reshape(
b_a, n, self.num_heads, c // self.num_heads
)
# b m 2 h dh
keys_values = self.to_keys_values(y).reshape(
b, m, 2, self.num_heads, c // self.num_heads
)
keys, values = keys_values[:, :, 0], keys_values[:, :, 1]
if alpha is not None:
out, attention = self.forward_interpolation(
queries, keys, values, alpha, mask
)
else:
attention = torch.einsum("bnhd,bmhd->bnmh", queries, keys) * self.scale
if mask is not None:
if mask.dim() == 2:
mask = mask.unsqueeze(1)
attention = attention.masked_fill(mask.unsqueeze(3), float("-inf"))
attention = attention.softmax(dim=2)
attention = self.dropout(attention)
out = torch.einsum("bnmh,bmhd->bnhd", attention, values).reshape(b, n, c)
out = self.project(out)
return out, attention
class TimeTransformerEncoderLayer(nn.Module):
def __init__(
self,
dim_self,
dim_ctx=None,
num_heads=1,
mlp_ratio=2.0,
act=F.leaky_relu,
dropout=0.0,
use_time=True,
):
super().__init__()
self.use_time = use_time
self.act = act
self.attn = MultiHeadAttention(dim_self, dim_self, num_heads, dropout)
self.attn_norm = nn.LayerNorm(dim_self)
mlp_ratio = int(mlp_ratio)
self.mlp = TimeMLP(
dim_self, dim_self * mlp_ratio, dim_self, dim_ctx, use_time=use_time
)
self.norm = nn.LayerNorm(dim_self)
self.dropout = nn.Dropout(dropout)
def forward(self, x, ctx=None):
res = x
x, attn = self.attn(x)
x = self.attn_norm(x + res)
res = x
x = self.mlp(x, ctx=ctx)
x = self.norm(x + res)
return x, attn
class TimeTransformerDecoderLayer(TimeTransformerEncoderLayer):
def __init__(
self,
dim_self,
dim_ref,
dim_ctx=None,
num_heads=1,
mlp_ratio=2,
act=F.leaky_relu,
dropout=0.0,
use_time=True,
):
super().__init__(
dim_self=dim_self,
dim_ctx=dim_ctx,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
act=act,
dropout=dropout,
use_time=use_time,
)
self.cross_attn = MultiHeadAttention(dim_self, dim_ref, num_heads, dropout)
self.cross_attn_norm = nn.LayerNorm(dim_self)
def forward(self, x, y, ctx=None):
res = x
x, attn = self.attn(x)
x = self.attn_norm(x + res)
res = x
x, attn = self.cross_attn(x, y)
x = self.cross_attn_norm(x + res)
res = x
x = self.mlp(x, ctx=ctx)
x = self.norm(x + res)
return x, attn
class TimeTransformerEncoder(nn.Module):
def __init__(
self,
dim_self,
dim_ctx=None,
num_heads=1,
mlp_ratio=2.0,
act=F.leaky_relu,
dropout=0.0,
use_time=True,
num_layers=3,
last_fc=False,
last_fc_dim_out=None,
):
super().__init__()
self.last_fc = last_fc
if last_fc:
self.fc = nn.Linear(dim_self, last_fc_dim_out)
self.layers = nn.ModuleList(
[
TimeTransformerEncoderLayer(
dim_self,
dim_ctx=dim_ctx,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
act=act,
dropout=dropout,
use_time=use_time,
)
for _ in range(num_layers)
]
)
def forward(self, x, ctx=None):
for i, layer in enumerate(self.layers):
x, attn = layer(x, ctx=ctx)
if self.last_fc:
x = self.fc(x)
return x
class TimeTransformerDecoder(nn.Module):
def __init__(
self,
dim_self,
dim_ref,
dim_ctx=None,
num_heads=1,
mlp_ratio=2.0,
act=F.leaky_relu,
dropout=0.0,
use_time=True,
num_layers=3,
last_fc=True,
last_fc_dim_out=None,
):
super().__init__()
self.last_fc = last_fc
if last_fc:
self.fc = nn.Linear(dim_self, last_fc_dim_out)
self.layers = nn.ModuleList(
[
TimeTransformerDecoderLayer(
dim_self,
dim_ref,
dim_ctx,
num_heads,
mlp_ratio,
act,
dropout,
use_time,
)
for _ in range(num_layers)
]
)
def forward(self, x, y, ctx=None):
for i, layer in enumerate(self.layers):
x, attn = layer(x, y=y, ctx=ctx)
if self.last_fc:
x = self.fc(x)
return x
|