File size: 14,667 Bytes
295ff14 |
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 |
"""HuggingFace-compatible classification and regression models including
pytorch-lightning models.
"""
__all__ = ("BypassNet", "ClassificationHead", "ClassifierLitModel",
"GPT2ForSequenceClassification", "RegressorLitModel",
"SequenceClassifierOutput")
from dataclasses import dataclass
from typing import List, Optional
import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchmetrics import AUROC, AveragePrecision
from transformers import AdamW, GPT2Model, GPT2PreTrainedModel
from transformers.modeling_outputs import SequenceClassifierOutputWithPast
from transformers.adapters.model_mixin import ModelWithHeadsAdaptersMixin
@dataclass
class SequenceClassifierOutput(SequenceClassifierOutputWithPast):
target: Optional[torch.LongTensor] = None
class GPT2ForSequenceClassification(ModelWithHeadsAdaptersMixin, GPT2PreTrainedModel):
"""HuggingFace-compatible single- and multi-output (-task) classification model.
`config` must be a `GPT2Config` instance with additional `num_tasks` and `num_labels`
properties. For multi-task classification, the output is Bypass network with the
reduction factor = `config.n_embd // config.n_head`.
"""
_keys_to_ignore_on_load_missing = [
r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight", r"output\..*"]
def __init__(self, config):
super().__init__(config)
self.num_tasks = config.num_tasks
self.num_labels = config.num_labels
self.transformer = GPT2Model(config)
if self.num_tasks > 1:
self.output = BypassNet(
config.n_embd, config.n_embd // config.n_head,
config.num_tasks, config.num_labels,
config.embd_pdrop)
else:
self.output = ClassificationHead(
config.n_embd, config.n_embd // config.n_head,
config.num_labels, config.embd_pdrop)
self.init_weights()
def forward(self, input_ids=None, past_key_values=None, attention_mask=None,
token_type_ids=None, position_ids=None, head_mask=None,
inputs_embeds=None, labels=None, use_cache=None, output_attentions=None,
output_hidden_states=None, return_dict=None, adapter_names=None,
label_mask=None):
return_dict = return_dict or self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids, past_key_values=past_key_values, attention_mask=attention_mask,
token_type_ids=token_type_ids, position_ids=position_ids,
head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states, return_dict=return_dict,
adapter_names=adapter_names)
hidden_states = transformer_outputs[0]
if input_ids is not None:
batch_size, sequence_length = input_ids.shape[:2]
else:
batch_size, sequence_length = inputs_embeds.shape[:2]
assert self.config.pad_token_id is not None or batch_size == 1, \
"Cannot handle batch sizes > 1 if no padding token is defined."
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = torch.ne(
input_ids, self.config.pad_token_id).sum(-1) - 1
else:
sequence_lengths = -1
if self.num_tasks == 1:
logits = self.output(hidden_states)[range(batch_size), sequence_lengths]
else:
logits = self.output(hidden_states, batch_size, sequence_lengths)
loss = None
if labels is not None:
if self.num_labels == 2:
if label_mask is not None:
nonempty_tasks = (label_mask == 1).view(-1)
nonempty_logits = logits.view(-1, self.num_labels)[nonempty_tasks, :]
nonempty_labels = labels.view(-1)[nonempty_tasks]
else:
nonempty_logits = logits.view(-1, self.num_labels)
nonempty_labels = labels.view(-1)
if len(labels.size()) == 1:
labels = labels.reshape(1, -1)
loss = F.cross_entropy(nonempty_logits, nonempty_labels)
elif self.num_labels == 1:
loss = F.mse_loss(logits.view(-1), labels.view(-1))
else:
raise NotImplementedError(
"Only binary classification and regression supported.")
if self.num_tasks > 1:
logits = logits.transpose(1, 2)
if labels is not None and self.num_labels == 2 and self.num_tasks == 1:
if label_mask is not None:
labels = labels.view(-1)
else:
labels = nonempty_labels
if not return_dict:
output = (logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss, logits=logits, target=labels,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions)
class BypassNet(nn.Module):
"""Bypass multi-task network from MoleculeNet project [Wu et al., 2018].
"""
def __init__(self, hidden_size: int, intermediate_size: int,
num_tasks: int, num_labels: int = 2,
dropout: float = 0.2, use_bias: bool = False):
super().__init__()
self.independent = nn.ModuleList([
ClassificationHead(hidden_size, intermediate_size,
num_labels, dropout, use_bias)
for _ in range(num_tasks)])
self.shared = ClassificationHead(hidden_size, intermediate_size,
num_labels, dropout, use_bias)
def forward(self, hidden_states, batch_size, sequence_lengths):
logits_list: List[torch.Tensor] = []
for layer in self.independent:
logits_list.append(layer(hidden_states))
shared_logits: torch.Tensor = self.shared(hidden_states)
for i in range(len(logits_list)):
logits_list[i] = (logits_list[i] + shared_logits)[range(batch_size),
sequence_lengths]
return torch.stack(logits_list, dim=1)
class ClassificationHead(nn.Module):
"""Two-layer feed-forward network with GELU activation and intermediate dropout.
"""
def __init__(self, hidden_size: int, intermediate_size: int,
num_labels: int, dropout: float = 0.0, use_bias: bool = False):
super().__init__()
self.dense = nn.Linear(hidden_size, intermediate_size, bias=use_bias)
self.act = nn.GELU()
self.dropout = nn.Dropout(dropout)
self.out_proj = nn.Linear(intermediate_size, num_labels, bias=use_bias)
def forward(self, x, *args, **kwargs):
x = self.dense(x)
x = self.act(x)
x = self.dropout(x)
return self.out_proj(x)
class ClassifierLitModel(pl.LightningModule):
"""Pytorch-lightning module for single- or multi-task classification. Trains GPT2
model using `AdamW` optimizer with exponential LR scheduler. Evaluates valid and
test data on AUC-ROC and AUC-PRC.
Args:
transformer (`GPT2Model`): (Pretrained) HuggingFace GPT2 model.
num_tasks (int): The number of classification tasks.
has_empty_labels (bool)
batch_size (int)
learning_rate (float)
scheduler_lambda (float)
scheduler_step (int)
weight_decay (float)
"""
def __init__(self, transformer: GPT2Model, num_tasks: int, has_empty_labels: bool,
batch_size: int, learning_rate: float, scheduler_lambda: float,
scheduler_step: int, weight_decay: float, *args, **kwargs):
super().__init__()
self.save_hyperparameters(ignore=("transformer", "num_tasks", "has_empty_labels"))
self.transformer = transformer
self.num_tasks = num_tasks
def get_metrics(metric_cls):
return [metric_cls(num_classes=2) for _ in range(num_tasks)]
if has_empty_labels:
self.train_roc = get_metrics(AUROC)
self.val_roc = get_metrics(AUROC)
self.test_roc = get_metrics(AUROC)
self.train_prc = get_metrics(AveragePrecision)
self.val_prc = get_metrics(AveragePrecision)
self.test_prc = get_metrics(AveragePrecision)
self.step = self._step_empty
self.epoch_end = self._epoch_end_empty
else:
#self.train_roc = AUROC(num_classes=2)
#self.val_roc = AUROC(num_classes=2)
#self.test_roc = AUROC(num_classes=2)
#self.train_prc = AveragePrecision(num_classes=2)
#self.val_prc = AveragePrecision(num_classes=2)
#self.test_prc = AveragePrecision(num_classes=2)
self.train_roc = AUROC(task='multiclass',num_classes=2)
self.val_roc = AUROC(task='multiclass',num_classes=2)
self.test_roc = AUROC(task='multiclass',num_classes=2)
self.train_prc = AveragePrecision(task='multiclass',num_classes=2)
self.val_prc = AveragePrecision(task='multiclass',num_classes=2)
self.test_prc = AveragePrecision(task='multiclass',num_classes=2)
self.step = self._step_nonempty
self.epoch_end = self._epoch_end_nonempty
def forward(self, *args, **kwargs):
return self.transformer(*args, **kwargs)
def _step_empty(self, batch, batch_idx, roc, prc):
outputs = self(**batch)
if self.num_tasks == 1:
outputs["target"] = outputs["target"][:, None]
outputs["logits"] = outputs["logits"][:, :, None]
for task_id in range(self.num_tasks):
target = outputs["target"][:, task_id]
nonempty_entries = target != -1
target = target[nonempty_entries]
if target.unique().size(0) > 1:
logits = outputs["logits"][:, :, task_id][nonempty_entries]
roc[task_id](logits, target)
prc[task_id](logits, target)
return {"loss": outputs["loss"]}
def _step_nonempty(self, batch, batch_idx, roc, prc):
outputs = self(**batch)
logits, target = outputs["logits"], outputs["target"]
if target.unique().size(0) > 1:
roc(logits, target)
prc(logits, target)
return {"loss": outputs["loss"]}
def _epoch_end_empty(self, outputs_ignored, roc, prc, prefix):
mean_roc = sum(a.compute() for a in roc) / self.num_tasks
self.log(f"{prefix}_roc", mean_roc, on_step=False, on_epoch=True, prog_bar=True)
mean_prc = sum(p.compute() for p in prc) / self.num_tasks #p.compute()[1]
self.log(f"{prefix}_prc", mean_prc, on_step=False, on_epoch=True, prog_bar=True)
def _epoch_end_nonempty(self, outputs, roc, prc, prefix):
self.log(f"{prefix}_roc", roc.compute(),
on_step=False, on_epoch=True, prog_bar=True)
self.log(f"{prefix}_prc", prc.compute(), #prc.compute()[1]
on_step=False, on_epoch=True, prog_bar=True)
def training_step(self, batch, batch_idx):
return self.step(batch, batch_idx, self.train_roc, self.train_prc)
def training_epoch_end(self, outputs):
self.epoch_end(outputs, self.train_roc, self.train_prc, "train")
def validation_step(self, batch, batch_idx):
return self.step(batch, batch_idx, self.val_roc, self.val_prc)
def validation_epoch_end(self, outputs):
self.epoch_end(outputs, self.val_roc, self.val_prc, "val")
def test_step(self, batch, batch_idx):
self.step(batch, batch_idx, self.test_roc, self.test_prc)
def test_epoch_end(self, outputs):
self.epoch_end(outputs, self.test_roc, self.test_prc, "test")
def configure_optimizers(self):
optimizer = AdamW(self.parameters(), lr=self.hparams.learning_rate,
weight_decay=self.hparams.weight_decay)
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
optimizer, self.hparams.scheduler_lambda)
return {"optimizer": optimizer,
"lr_scheduler": {"scheduler": lr_scheduler,
"interval": "step",
"frequency": self.hparams.scheduler_step}}
class RegressorLitModel(pl.LightningModule):
def __init__(self, transformer: GPT2Model,
batch_size: int, learning_rate: float, scheduler_lambda: float,
scheduler_step: int, weight_decay: float, *args, **kwargs):
super().__init__()
self.save_hyperparameters(ignore="transformer")
self.transformer = transformer
def forward(self, *args, **kwargs):
return self.transformer(*args, **kwargs)
hidden_states = transformer_outputs[0]
def step(self, batch, batch_idx):
outputs = self(**batch)
rmse_loss = torch.sqrt(outputs["loss"])
return {"loss": rmse_loss}
def epoch_end(self, outputs, prefix):
mean_rmse = torch.mean(torch.tensor([out["loss"] for out in outputs]))
self.log(f"{prefix}_rmse", mean_rmse, on_step=False, on_epoch=True, prog_bar=True)
def training_step(self, batch, batch_idx):
return self.step(batch, batch_idx)
def training_epoch_end(self, outputs):
self.epoch_end(outputs, "train")
def validation_step(self, batch, batch_idx):
return self.step(batch, batch_idx)
def validation_epoch_end(self, outputs):
self.epoch_end(outputs, "val")
def test_step(self, batch, batch_idx):
return self.step(batch, batch_idx)
def test_epoch_end(self, outputs):
self.epoch_end(outputs, "test")
def configure_optimizers(self):
optimizer = AdamW(self.parameters(), lr=self.hparams.learning_rate,
weight_decay=self.hparams.weight_decay)
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
optimizer, self.hparams.scheduler_lambda)
return {"optimizer": optimizer,
"lr_scheduler": {"scheduler": lr_scheduler,
"interval": "step",
"frequency": self.hparams.scheduler_step}}
|