File size: 12,930 Bytes
c94c8c9 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 |
from typing import Optional
import einops
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from modules.utils import get_activation_fn
class CrossAttentionLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu",
k_dim=None, v_dim=None, prenorm=True):
super().__init__()
if k_dim is None:
k_dim = d_model
if v_dim is None:
v_dim = d_model
self.prenorm = prenorm
self.multihead_attn = nn.MultiheadAttention(
d_model, nhead, dropout=dropout, batch_first=True, kdim=k_dim, vdim=v_dim
)
# Implementation of Feedforward modules
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.norm3 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.dropout3 = nn.Dropout(dropout)
self.activation = get_activation_fn(activation)
def forward(
self, tgt, memory,
tgt_mask: Optional[Tensor] = None,
memory_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None,
):
tgt2 = tgt
if self.prenorm:
tgt2 = self.norm1(tgt2)
tgt2, cross_attn_matrices = self.multihead_attn(
query=tgt2, key=memory,
value=memory, attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask
)
tgt = tgt + self.dropout2(tgt2)
if not self.prenorm:
tgt = self.norm1(tgt)
if self.prenorm:
tgt2 = self.norm3(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
tgt = tgt + self.dropout3(tgt2)
if not self.prenorm:
tgt = self.norm3(tgt)
return tgt, cross_attn_matrices
class TransformerDecoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu"):
super().__init__()
self.self_attn = nn.MultiheadAttention(
d_model, nhead, dropout=dropout, batch_first=True
)
self.multihead_attn = nn.MultiheadAttention(
d_model, nhead, dropout=dropout, batch_first=True
)
# Implementation of Feedforward modules
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.norm3 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.dropout3 = nn.Dropout(dropout)
self.activation = get_activation_fn(activation)
def forward(
self, tgt, memory,
tgt_mask: Optional[Tensor] = None,
memory_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None,
):
tgt2 = self.norm1(tgt)
tgt2, self_attn_matrices = self.self_attn(
query=tgt2, key=tgt2, value=tgt2, attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask
)
tgt = tgt + self.dropout1(tgt2)
tgt2 = self.norm2(tgt)
tgt2, cross_attn_matrices = self.multihead_attn(
query=tgt2, key=memory,
value=memory, attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask
)
tgt = tgt + self.dropout2(tgt2)
tgt2 = self.norm3(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
tgt = tgt + self.dropout3(tgt2)
return tgt, self_attn_matrices, cross_attn_matrices
class TransformerEncoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, batch_first=True, dropout=0.1, activation="relu", prenorm=False):
super().__init__()
self.self_attn = nn.MultiheadAttention(
d_model, nhead, dropout=dropout, batch_first=batch_first
)
# Implementation of Feedforward modules
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.activation = get_activation_fn(activation)
self.prenorm = prenorm
def forward(
self, tgt, tgt_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
):
tgt2 = tgt
if self.prenorm:
tgt2 = self.norm1(tgt2)
tgt2, self_attn_matrices = self.self_attn(
query=tgt2, key=tgt2, value=tgt2, attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask
)
tgt = tgt + self.dropout1(tgt2)
if not self.prenorm:
tgt = self.norm1(tgt)
if self.prenorm:
tgt = self.norm2(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout2(tgt2)
if not self.prenorm:
tgt = self.norm2(tgt)
return tgt, self_attn_matrices
class MultiHeadAttentionSpatial(nn.Module):
def __init__(
self, d_model, n_head, dropout=0.1, spatial_multihead=True, spatial_dim=5,
spatial_attn_fusion='mul',
):
super().__init__()
assert d_model % n_head == 0, 'd_model: %d, n_head: %d' % (d_model, n_head)
self.n_head = n_head
self.d_model = d_model
self.d_per_head = d_model // n_head
self.spatial_multihead = spatial_multihead
self.spatial_dim = spatial_dim
self.spatial_attn_fusion = spatial_attn_fusion
self.w_qs = nn.Linear(d_model, d_model)
self.w_ks = nn.Linear(d_model, d_model)
self.w_vs = nn.Linear(d_model, d_model)
self.fc = nn.Linear(d_model, d_model)
self.spatial_n_head = n_head if spatial_multihead else 1
if self.spatial_attn_fusion in ['mul', 'bias', 'add']:
self.pairwise_loc_fc = nn.Linear(spatial_dim, self.spatial_n_head)
elif self.spatial_attn_fusion == 'ctx':
self.pairwise_loc_fc = nn.Linear(spatial_dim, d_model)
elif self.spatial_attn_fusion == 'cond':
self.lang_cond_fc = nn.Linear(d_model, self.spatial_n_head * (spatial_dim + 1))
else:
raise NotImplementedError('unsupported spatial_attn_fusion %s' % (self.spatial_attn_fusion))
def forward(self, q, k, v, pairwise_locs, key_padding_mask=None, txt_embeds=None):
residual = q
q = einops.rearrange(self.w_qs(q), 'b l (head k) -> head b l k', head=self.n_head)
k = einops.rearrange(self.w_ks(k), 'b t (head k) -> head b t k', head=self.n_head)
v = einops.rearrange(self.w_vs(v), 'b t (head v) -> head b t v', head=self.n_head)
attn = torch.einsum('hblk,hbtk->hblt', q, k) / np.sqrt(q.shape[-1])
if self.spatial_attn_fusion in ['mul', 'bias', 'add']:
loc_attn = self.pairwise_loc_fc(pairwise_locs)
loc_attn = einops.rearrange(loc_attn, 'b l t h -> h b l t')
if self.spatial_attn_fusion == 'mul':
loc_attn = F.relu(loc_attn)
if not self.spatial_multihead:
loc_attn = einops.repeat(loc_attn, 'h b l t -> (h nh) b l t', nh=self.n_head)
elif self.spatial_attn_fusion == 'ctx':
loc_attn = self.pairwise_loc_fc(pairwise_locs)
loc_attn = einops.rearrange(loc_attn, 'b l t (h k) -> h b l t k', h=self.n_head)
loc_attn = torch.einsum('hblk,hbltk->hblt', q, loc_attn) / np.sqrt(q.shape[-1])
elif self.spatial_attn_fusion == 'cond':
spatial_weights = self.lang_cond_fc(residual)
spatial_weights = einops.rearrange(spatial_weights, 'b l (h d) -> h b l d', h=self.spatial_n_head,
d=self.spatial_dim + 1)
if self.spatial_n_head == 1:
spatial_weights = einops.repeat(spatial_weights, '1 b l d -> h b l d', h=self.n_head)
spatial_bias = spatial_weights[..., :1]
spatial_weights = spatial_weights[..., 1:]
loc_attn = torch.einsum('hbld,bltd->hblt', spatial_weights, pairwise_locs) + spatial_bias
loc_attn = torch.sigmoid(loc_attn)
if key_padding_mask is not None:
mask = einops.repeat(key_padding_mask, 'b t -> h b l t', h=self.n_head, l=q.size(2))
attn = attn.masked_fill(mask, -np.inf)
if self.spatial_attn_fusion in ['mul', 'cond']:
loc_attn = loc_attn.masked_fill(mask, 0)
else:
loc_attn = loc_attn.masked_fill(mask, -np.inf)
if self.spatial_attn_fusion == 'add':
fused_attn = (torch.softmax(attn, 3) + torch.softmax(loc_attn, 3)) / 2
else:
if self.spatial_attn_fusion in ['mul', 'cond']:
fused_attn = torch.log(torch.clamp(loc_attn, min=1e-6)) + attn
else:
fused_attn = loc_attn + attn
fused_attn = torch.softmax(fused_attn, 3)
assert torch.sum(torch.isnan(fused_attn) == 0), print(fused_attn)
output = torch.einsum('hblt,hbtv->hblv', fused_attn, v)
output = einops.rearrange(output, 'head b l v -> b l (head v)')
output = self.fc(output)
return output, fused_attn
class TransformerSpatialDecoderLayer(TransformerDecoderLayer):
def __init__(
self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu",
spatial_multihead=True, spatial_dim=5, spatial_attn_fusion='mul'
):
super().__init__(
d_model, nhead, dim_feedforward=dim_feedforward, dropout=dropout, activation=activation
)
del self.self_attn
self.self_attn = MultiHeadAttentionSpatial(
d_model, nhead, dropout=dropout,
spatial_multihead=spatial_multihead,
spatial_dim=spatial_dim,
spatial_attn_fusion=spatial_attn_fusion,
)
def forward(
self, tgt, memory,
tgt_pairwise_locs: Optional[Tensor] = None,
tgt_mask: Optional[Tensor] = None,
memory_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None,
):
tgt2 = self.norm1(tgt)
tgt2, self_attn_matrices = self.self_attn(
tgt2, tgt2, tgt2, tgt_pairwise_locs,
key_padding_mask=tgt_key_padding_mask
)
tgt = tgt + self.dropout1(tgt2)
tgt2 = self.norm2(tgt)
tgt2, cross_attn_matrices = self.multihead_attn(
query=tgt2, key=memory,
value=memory, attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask
)
tgt = tgt + self.dropout2(tgt2)
tgt2 = self.norm3(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
tgt = tgt + self.dropout3(tgt2)
return tgt, self_attn_matrices, cross_attn_matrices
class TransformerSpatialEncoderLayer(TransformerEncoderLayer):
def __init__(
self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu",
spatial_multihead=True, spatial_dim=5, spatial_attn_fusion='mul'
):
super().__init__(
d_model, nhead, dim_feedforward=dim_feedforward, dropout=dropout, activation=activation
)
del self.self_attn
self.self_attn = MultiHeadAttentionSpatial(
d_model, nhead, dropout=dropout,
spatial_multihead=spatial_multihead,
spatial_dim=spatial_dim,
spatial_attn_fusion=spatial_attn_fusion,
)
def forward(
self, tgt, tgt_pairwise_locs,
tgt_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
):
tgt2 = tgt
tgt2, self_attn_matrices = self.self_attn(
tgt2, tgt2, tgt2, tgt_pairwise_locs,
key_padding_mask=tgt_key_padding_mask
)
tgt = tgt + self.dropout1(tgt2)
tgt = self.norm1(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm2(tgt)
return tgt, self_attn_matrices
|