Spaces:
Sleeping
Sleeping
File size: 10,357 Bytes
fcd0a70 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 |
import torch
import torch.nn.functional as F
from torch import nn
def create_projection_layer(hidden_size: int, dropout: float, out_dim: int = None) -> nn.Sequential:
"""
Creates a projection layer with specified configurations.
"""
if out_dim is None:
out_dim = hidden_size
return nn.Sequential(
nn.Linear(hidden_size, out_dim * 4),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(out_dim * 4, out_dim)
)
class SpanQuery(nn.Module):
def __init__(self, hidden_size, max_width, trainable=True):
super().__init__()
self.query_seg = nn.Parameter(torch.randn(hidden_size, max_width))
nn.init.uniform_(self.query_seg, a=-1, b=1)
if not trainable:
self.query_seg.requires_grad = False
self.project = nn.Sequential(
nn.Linear(hidden_size, hidden_size),
nn.ReLU()
)
def forward(self, h, *args):
# h of shape [B, L, D]
# query_seg of shape [D, max_width]
span_rep = torch.einsum('bld, ds->blsd', h, self.query_seg)
return self.project(span_rep)
class SpanMLP(nn.Module):
def __init__(self, hidden_size, max_width):
super().__init__()
self.mlp = nn.Linear(hidden_size, hidden_size * max_width)
def forward(self, h, *args):
# h of shape [B, L, D]
# query_seg of shape [D, max_width]
B, L, D = h.size()
span_rep = self.mlp(h)
span_rep = span_rep.view(B, L, -1, D)
return span_rep.relu()
class SpanCAT(nn.Module):
def __init__(self, hidden_size, max_width):
super().__init__()
self.max_width = max_width
self.query_seg = nn.Parameter(torch.randn(128, max_width))
self.project = nn.Sequential(
nn.Linear(hidden_size + 128, hidden_size),
nn.ReLU()
)
def forward(self, h, *args):
# h of shape [B, L, D]
# query_seg of shape [D, max_width]
B, L, D = h.size()
h = h.view(B, L, 1, D).repeat(1, 1, self.max_width, 1)
q = self.query_seg.view(1, 1, self.max_width, -1).repeat(B, L, 1, 1)
span_rep = torch.cat([h, q], dim=-1)
span_rep = self.project(span_rep)
return span_rep
class SpanConvBlock(nn.Module):
def __init__(self, hidden_size, kernel_size, span_mode='conv_normal'):
super().__init__()
if span_mode == 'conv_conv':
self.conv = nn.Conv1d(hidden_size, hidden_size,
kernel_size=kernel_size)
# initialize the weights
nn.init.kaiming_uniform_(self.conv.weight, nonlinearity='relu')
elif span_mode == 'conv_max':
self.conv = nn.MaxPool1d(kernel_size=kernel_size, stride=1)
elif span_mode == 'conv_mean' or span_mode == 'conv_sum':
self.conv = nn.AvgPool1d(kernel_size=kernel_size, stride=1)
self.span_mode = span_mode
self.pad = kernel_size - 1
def forward(self, x):
x = torch.einsum('bld->bdl', x)
if self.pad > 0:
x = F.pad(x, (0, self.pad), "constant", 0)
x = self.conv(x)
if self.span_mode == "conv_sum":
x = x * (self.pad + 1)
return torch.einsum('bdl->bld', x)
class SpanConv(nn.Module):
def __init__(self, hidden_size, max_width, span_mode):
super().__init__()
kernels = [i + 2 for i in range(max_width - 1)]
self.convs = nn.ModuleList()
for kernel in kernels:
self.convs.append(SpanConvBlock(hidden_size, kernel, span_mode))
self.project = nn.Sequential(
nn.ReLU(),
nn.Linear(hidden_size, hidden_size)
)
def forward(self, x, *args):
span_reps = [x]
for conv in self.convs:
h = conv(x)
span_reps.append(h)
span_reps = torch.stack(span_reps, dim=-2)
return self.project(span_reps)
class SpanEndpointsBlock(nn.Module):
def __init__(self, kernel_size):
super().__init__()
self.kernel_size = kernel_size
def forward(self, x):
B, L, D = x.size()
span_idx = torch.LongTensor(
[[i, i + self.kernel_size - 1] for i in range(L)]).to(x.device)
x = F.pad(x, (0, 0, 0, self.kernel_size - 1), "constant", 0)
# endrep
start_end_rep = torch.index_select(x, dim=1, index=span_idx.view(-1))
start_end_rep = start_end_rep.view(B, L, 2, D)
return start_end_rep
class ConvShare(nn.Module):
def __init__(self, hidden_size, max_width):
super().__init__()
self.max_width = max_width
self.conv_weigth = nn.Parameter(
torch.randn(hidden_size, hidden_size, max_width))
nn.init.kaiming_uniform_(self.conv_weigth, nonlinearity='relu')
self.project = nn.Sequential(
nn.ReLU(),
nn.Linear(hidden_size, hidden_size)
)
def forward(self, x, *args):
span_reps = []
x = torch.einsum('bld->bdl', x)
for i in range(self.max_width):
pad = i
x_i = F.pad(x, (0, pad), "constant", 0)
conv_w = self.conv_weigth[:, :, :i + 1]
out_i = F.conv1d(x_i, conv_w)
span_reps.append(out_i.transpose(-1, -2))
out = torch.stack(span_reps, dim=-2)
return self.project(out)
def extract_elements(sequence, indices):
B, L, D = sequence.shape
K = indices.shape[1]
# Expand indices to [B, K, D]
expanded_indices = indices.unsqueeze(2).expand(-1, -1, D)
# Gather the elements
extracted_elements = torch.gather(sequence, 1, expanded_indices)
return extracted_elements
class SpanMarker(nn.Module):
def __init__(self, hidden_size, max_width, dropout=0.4):
super().__init__()
self.max_width = max_width
self.project_start = nn.Sequential(
nn.Linear(hidden_size, hidden_size * 2, bias=True),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_size * 2, hidden_size, bias=True),
)
self.project_end = nn.Sequential(
nn.Linear(hidden_size, hidden_size * 2, bias=True),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_size * 2, hidden_size, bias=True),
)
self.out_project = nn.Linear(hidden_size * 2, hidden_size, bias=True)
def forward(self, h, span_idx):
# h of shape [B, L, D]
# query_seg of shape [D, max_width]
B, L, D = h.size()
# project start and end
start_rep = self.project_start(h)
end_rep = self.project_end(h)
start_span_rep = extract_elements(start_rep, span_idx[:, :, 0])
end_span_rep = extract_elements(end_rep, span_idx[:, :, 1])
# concat start and end
cat = torch.cat([start_span_rep, end_span_rep], dim=-1).relu()
# project
cat = self.out_project(cat)
# reshape
return cat.view(B, L, self.max_width, D)
class SpanMarkerV0(nn.Module):
"""
Marks and projects span endpoints using an MLP.
"""
def __init__(self, hidden_size: int, max_width: int, dropout: float = 0.4):
super().__init__()
self.max_width = max_width
self.project_start = create_projection_layer(hidden_size, dropout)
self.project_end = create_projection_layer(hidden_size, dropout)
self.out_project = create_projection_layer(hidden_size * 2, dropout, hidden_size)
def forward(self, h: torch.Tensor, span_idx: torch.Tensor) -> torch.Tensor:
B, L, D = h.size()
start_rep = self.project_start(h)
end_rep = self.project_end(h)
start_span_rep = extract_elements(start_rep, span_idx[:, :, 0])
end_span_rep = extract_elements(end_rep, span_idx[:, :, 1])
cat = torch.cat([start_span_rep, end_span_rep], dim=-1).relu()
return self.out_project(cat).view(B, L, self.max_width, D)
class ConvShareV2(nn.Module):
def __init__(self, hidden_size, max_width):
super().__init__()
self.max_width = max_width
self.conv_weigth = nn.Parameter(
torch.randn(hidden_size, hidden_size, max_width)
)
nn.init.xavier_normal_(self.conv_weigth)
def forward(self, x, *args):
span_reps = []
x = torch.einsum('bld->bdl', x)
for i in range(self.max_width):
pad = i
x_i = F.pad(x, (0, pad), "constant", 0)
conv_w = self.conv_weigth[:, :, :i + 1]
out_i = F.conv1d(x_i, conv_w)
span_reps.append(out_i.transpose(-1, -2))
out = torch.stack(span_reps, dim=-2)
return out
class SpanRepLayer(nn.Module):
"""
Various span representation approaches
"""
def __init__(self, hidden_size, max_width, span_mode, **kwargs):
super().__init__()
if span_mode == 'marker':
self.span_rep_layer = SpanMarker(hidden_size, max_width, **kwargs)
elif span_mode == 'markerV0':
self.span_rep_layer = SpanMarkerV0(hidden_size, max_width, **kwargs)
elif span_mode == 'query':
self.span_rep_layer = SpanQuery(
hidden_size, max_width, trainable=True)
elif span_mode == 'mlp':
self.span_rep_layer = SpanMLP(hidden_size, max_width)
elif span_mode == 'cat':
self.span_rep_layer = SpanCAT(hidden_size, max_width)
elif span_mode == 'conv_conv':
self.span_rep_layer = SpanConv(
hidden_size, max_width, span_mode='conv_conv')
elif span_mode == 'conv_max':
self.span_rep_layer = SpanConv(
hidden_size, max_width, span_mode='conv_max')
elif span_mode == 'conv_mean':
self.span_rep_layer = SpanConv(
hidden_size, max_width, span_mode='conv_mean')
elif span_mode == 'conv_sum':
self.span_rep_layer = SpanConv(
hidden_size, max_width, span_mode='conv_sum')
elif span_mode == 'conv_share':
self.span_rep_layer = ConvShare(hidden_size, max_width)
else:
raise ValueError(f'Unknown span mode {span_mode}')
def forward(self, x, *args):
return self.span_rep_layer(x, *args)
|