File size: 18,475 Bytes
2f044c1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 |
import collections
from typing import Any, Dict, Iterator, List, Optional
import torch
from transformers import AutoModel
from transformers.activations import ClippedGELUActivation, GELUActivation
from transformers.modeling_utils import PoolerEndLogits
from relik.reader.data.relik_reader_sample import RelikReaderSample
activation2functions = {
"relu": torch.nn.ReLU(),
"gelu": GELUActivation(),
"gelu_10": ClippedGELUActivation(-10, 10),
}
class RelikReaderCoreModel(torch.nn.Module):
def __init__(
self,
transformer_model: str,
additional_special_symbols: int,
num_layers: Optional[int] = None,
activation: str = "gelu",
linears_hidden_size: Optional[int] = 512,
use_last_k_layers: int = 1,
training: bool = False,
) -> None:
super().__init__()
# Transformer model declaration
self.transformer_model_name = transformer_model
self.transformer_model = (
AutoModel.from_pretrained(transformer_model)
if num_layers is None
else AutoModel.from_pretrained(
transformer_model, num_hidden_layers=num_layers
)
)
# self.transformer_model.resize_token_embeddings(
# self.transformer_model.config.vocab_size + additional_special_symbols
# )
self.activation = activation
self.linears_hidden_size = linears_hidden_size
self.use_last_k_layers = use_last_k_layers
# named entity detection layers
self.ned_start_classifier = self._get_projection_layer(
self.activation, last_hidden=2, layer_norm=False
)
self.ned_end_classifier = PoolerEndLogits(self.transformer_model.config)
# END entity disambiguation layer
self.ed_start_projector = self._get_projection_layer(self.activation)
self.ed_end_projector = self._get_projection_layer(self.activation)
self.training = training
# criterion
self.criterion = torch.nn.CrossEntropyLoss()
def _get_projection_layer(
self,
activation: str,
last_hidden: Optional[int] = None,
input_hidden=None,
layer_norm: bool = True,
) -> torch.nn.Sequential:
head_components = [
torch.nn.Dropout(0.1),
torch.nn.Linear(
self.transformer_model.config.hidden_size * self.use_last_k_layers
if input_hidden is None
else input_hidden,
self.linears_hidden_size,
),
activation2functions[activation],
torch.nn.Dropout(0.1),
torch.nn.Linear(
self.linears_hidden_size,
self.linears_hidden_size if last_hidden is None else last_hidden,
),
]
if layer_norm:
head_components.append(
torch.nn.LayerNorm(
self.linears_hidden_size if last_hidden is None else last_hidden,
self.transformer_model.config.layer_norm_eps,
)
)
return torch.nn.Sequential(*head_components)
def _mask_logits(self, logits: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
mask = mask.unsqueeze(-1)
if next(self.parameters()).dtype == torch.float16:
logits = logits * (1 - mask) - 65500 * mask
else:
logits = logits * (1 - mask) - 1e30 * mask
return logits
def _get_model_features(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
token_type_ids: Optional[torch.Tensor],
):
model_input = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"output_hidden_states": self.use_last_k_layers > 1,
}
if token_type_ids is not None:
model_input["token_type_ids"] = token_type_ids
model_output = self.transformer_model(**model_input)
if self.use_last_k_layers > 1:
model_features = torch.cat(
model_output[1][-self.use_last_k_layers :], dim=-1
)
else:
model_features = model_output[0]
return model_features
def compute_ned_end_logits(
self,
start_predictions,
start_labels,
model_features,
prediction_mask,
batch_size,
) -> Optional[torch.Tensor]:
# todo: maybe when constraining on the spans,
# we should not use a prediction_mask for the end tokens.
# at least we should not during training imo
start_positions = start_labels if self.training else start_predictions
start_positions_indices = (
torch.arange(start_positions.size(1), device=start_positions.device)
.unsqueeze(0)
.expand(batch_size, -1)[start_positions > 0]
).to(start_positions.device)
if len(start_positions_indices) > 0:
expanded_features = torch.cat(
[
model_features[i].unsqueeze(0).expand(x, -1, -1)
for i, x in enumerate(torch.sum(start_positions > 0, dim=-1))
if x > 0
],
dim=0,
).to(start_positions_indices.device)
expanded_prediction_mask = torch.cat(
[
prediction_mask[i].unsqueeze(0).expand(x, -1)
for i, x in enumerate(torch.sum(start_positions > 0, dim=-1))
if x > 0
],
dim=0,
).to(expanded_features.device)
end_logits = self.ned_end_classifier(
hidden_states=expanded_features,
start_positions=start_positions_indices,
p_mask=expanded_prediction_mask,
)
return end_logits
return None
def compute_classification_logits(
self,
model_features,
special_symbols_mask,
prediction_mask,
batch_size,
start_positions=None,
end_positions=None,
) -> torch.Tensor:
if start_positions is None or end_positions is None:
start_positions = torch.zeros_like(prediction_mask)
end_positions = torch.zeros_like(prediction_mask)
model_start_features = self.ed_start_projector(model_features)
model_end_features = self.ed_end_projector(model_features)
model_end_features[start_positions > 0] = model_end_features[end_positions > 0]
model_ed_features = torch.cat(
[model_start_features, model_end_features], dim=-1
)
# computing ed features
classes_representations = torch.sum(special_symbols_mask, dim=1)[0].item()
special_symbols_representation = model_ed_features[special_symbols_mask].view(
batch_size, classes_representations, -1
)
logits = torch.bmm(
model_ed_features,
torch.permute(special_symbols_representation, (0, 2, 1)),
)
logits = self._mask_logits(logits, prediction_mask)
return logits
def forward(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
token_type_ids: Optional[torch.Tensor] = None,
prediction_mask: Optional[torch.Tensor] = None,
special_symbols_mask: Optional[torch.Tensor] = None,
start_labels: Optional[torch.Tensor] = None,
end_labels: Optional[torch.Tensor] = None,
use_predefined_spans: bool = False,
*args,
**kwargs,
) -> Dict[str, Any]:
batch_size, seq_len = input_ids.shape
model_features = self._get_model_features(
input_ids, attention_mask, token_type_ids
)
# named entity detection if required
if use_predefined_spans: # no need to compute spans
ned_start_logits, ned_start_probabilities, ned_start_predictions = (
None,
None,
torch.clone(start_labels)
if start_labels is not None
else torch.zeros_like(input_ids),
)
ned_end_logits, ned_end_probabilities, ned_end_predictions = (
None,
None,
torch.clone(end_labels)
if end_labels is not None
else torch.zeros_like(input_ids),
)
ned_start_predictions[ned_start_predictions > 0] = 1
ned_end_predictions[ned_end_predictions > 0] = 1
else: # compute spans
# start boundary prediction
ned_start_logits = self.ned_start_classifier(model_features)
ned_start_logits = self._mask_logits(ned_start_logits, prediction_mask)
ned_start_probabilities = torch.softmax(ned_start_logits, dim=-1)
ned_start_predictions = ned_start_probabilities.argmax(dim=-1)
# end boundary prediction
ned_start_labels = (
torch.zeros_like(start_labels) if start_labels is not None else None
)
if ned_start_labels is not None:
ned_start_labels[start_labels == -100] = -100
ned_start_labels[start_labels > 0] = 1
ned_end_logits = self.compute_ned_end_logits(
ned_start_predictions,
ned_start_labels,
model_features,
prediction_mask,
batch_size,
)
if ned_end_logits is not None:
ned_end_probabilities = torch.softmax(ned_end_logits, dim=-1)
ned_end_predictions = torch.argmax(ned_end_probabilities, dim=-1)
else:
ned_end_logits, ned_end_probabilities = None, None
ned_end_predictions = ned_start_predictions.new_zeros(batch_size)
# flattening end predictions
# (flattening can happen only if the
# end boundaries were not predicted using the gold labels)
if not self.training:
flattened_end_predictions = torch.clone(ned_start_predictions)
flattened_end_predictions[flattened_end_predictions > 0] = 0
batch_start_predictions = list()
for elem_idx in range(batch_size):
batch_start_predictions.append(
torch.where(ned_start_predictions[elem_idx] > 0)[0].tolist()
)
# check that the total number of start predictions
# is equal to the end predictions
total_start_predictions = sum(map(len, batch_start_predictions))
total_end_predictions = len(ned_end_predictions)
assert (
total_start_predictions == 0
or total_start_predictions == total_end_predictions
), (
f"Total number of start predictions = {total_start_predictions}. "
f"Total number of end predictions = {total_end_predictions}"
)
curr_end_pred_num = 0
for elem_idx, bsp in enumerate(batch_start_predictions):
for sp in bsp:
ep = ned_end_predictions[curr_end_pred_num].item()
if ep < sp:
ep = sp
# if we already set this span throw it (no overlap)
if flattened_end_predictions[elem_idx, ep] == 1:
ned_start_predictions[elem_idx, sp] = 0
else:
flattened_end_predictions[elem_idx, ep] = 1
curr_end_pred_num += 1
ned_end_predictions = flattened_end_predictions
start_position, end_position = (
(start_labels, end_labels)
if self.training
else (ned_start_predictions, ned_end_predictions)
)
# Entity disambiguation
ed_logits = self.compute_classification_logits(
model_features,
special_symbols_mask,
prediction_mask,
batch_size,
start_position,
end_position,
)
ed_probabilities = torch.softmax(ed_logits, dim=-1)
ed_predictions = torch.argmax(ed_probabilities, dim=-1)
# output build
output_dict = dict(
batch_size=batch_size,
ned_start_logits=ned_start_logits,
ned_start_probabilities=ned_start_probabilities,
ned_start_predictions=ned_start_predictions,
ned_end_logits=ned_end_logits,
ned_end_probabilities=ned_end_probabilities,
ned_end_predictions=ned_end_predictions,
ed_logits=ed_logits,
ed_probabilities=ed_probabilities,
ed_predictions=ed_predictions,
)
# compute loss if labels
if start_labels is not None and end_labels is not None and self.training:
# named entity detection loss
# start
if ned_start_logits is not None:
ned_start_loss = self.criterion(
ned_start_logits.view(-1, ned_start_logits.shape[-1]),
ned_start_labels.view(-1),
)
else:
ned_start_loss = 0
# end
if ned_end_logits is not None:
ned_end_labels = torch.zeros_like(end_labels)
ned_end_labels[end_labels == -100] = -100
ned_end_labels[end_labels > 0] = 1
ned_end_loss = self.criterion(
ned_end_logits,
(
torch.arange(
ned_end_labels.size(1), device=ned_end_labels.device
)
.unsqueeze(0)
.expand(batch_size, -1)[ned_end_labels > 0]
).to(ned_end_labels.device),
)
else:
ned_end_loss = 0
# entity disambiguation loss
start_labels[ned_start_labels != 1] = -100
ed_labels = torch.clone(start_labels)
ed_labels[end_labels > 0] = end_labels[end_labels > 0]
ed_loss = self.criterion(
ed_logits.view(-1, ed_logits.shape[-1]),
ed_labels.view(-1),
)
output_dict["ned_start_loss"] = ned_start_loss
output_dict["ned_end_loss"] = ned_end_loss
output_dict["ed_loss"] = ed_loss
output_dict["loss"] = ned_start_loss + ned_end_loss + ed_loss
return output_dict
def batch_predict(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
token_type_ids: Optional[torch.Tensor] = None,
prediction_mask: Optional[torch.Tensor] = None,
special_symbols_mask: Optional[torch.Tensor] = None,
sample: Optional[List[RelikReaderSample]] = None,
top_k: int = 5, # the amount of top-k most probable entities to predict
*args,
**kwargs,
) -> Iterator[RelikReaderSample]:
forward_output = self.forward(
input_ids,
attention_mask,
token_type_ids,
prediction_mask,
special_symbols_mask,
)
ned_start_predictions = forward_output["ned_start_predictions"].cpu().numpy()
ned_end_predictions = forward_output["ned_end_predictions"].cpu().numpy()
ed_predictions = forward_output["ed_predictions"].cpu().numpy()
ed_probabilities = forward_output["ed_probabilities"].cpu().numpy()
batch_predictable_candidates = kwargs["predictable_candidates"]
patch_offset = kwargs["patch_offset"]
for ts, ne_sp, ne_ep, edp, edpr, pred_cands, po in zip(
sample,
ned_start_predictions,
ned_end_predictions,
ed_predictions,
ed_probabilities,
batch_predictable_candidates,
patch_offset,
):
ne_start_indices = [ti for ti, c in enumerate(ne_sp[1:]) if c > 0]
ne_end_indices = [ti for ti, c in enumerate(ne_ep[1:]) if c > 0]
final_class2predicted_spans = collections.defaultdict(list)
spans2predicted_probabilities = dict()
for start_token_index, end_token_index in zip(
ne_start_indices, ne_end_indices
):
# predicted candidate
token_class = edp[start_token_index + 1] - 1
predicted_candidate_title = pred_cands[token_class]
final_class2predicted_spans[predicted_candidate_title].append(
[start_token_index, end_token_index]
)
# candidates probabilities
classes_probabilities = edpr[start_token_index + 1]
classes_probabilities_best_indices = classes_probabilities.argsort()[
::-1
]
titles_2_probs = []
top_k = (
min(
top_k,
len(classes_probabilities_best_indices),
)
if top_k != -1
else len(classes_probabilities_best_indices)
)
for i in range(top_k):
titles_2_probs.append(
(
pred_cands[classes_probabilities_best_indices[i] - 1],
classes_probabilities[
classes_probabilities_best_indices[i]
].item(),
)
)
spans2predicted_probabilities[
(start_token_index, end_token_index)
] = titles_2_probs
if "patches" not in ts._d:
ts._d["patches"] = dict()
ts._d["patches"][po] = dict()
sample_patch = ts._d["patches"][po]
sample_patch["predicted_window_labels"] = final_class2predicted_spans
sample_patch["span_title_probabilities"] = spans2predicted_probabilities
# additional info
sample_patch["predictable_candidates"] = pred_cands
yield ts
|