Source code for deeppavlov.models.torch_bert.torch_transformers_classifier

# Copyright 2017 Neural Networks and Deep Learning lab, MIPT
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from logging import getLogger
from pathlib import Path
from typing import List, Dict, Union, Optional, Tuple

import numpy as np
import torch
from torch.nn import BCEWithLogitsLoss
from transformers import AutoModelForSequenceClassification, AutoConfig, AutoModel, AutoTokenizer
from transformers.modeling_outputs import SequenceClassifierOutput

from deeppavlov.core.commands.utils import expand_path
from deeppavlov.core.common.errors import ConfigError
from deeppavlov.core.common.registry import register
from deeppavlov.core.models.torch_model import TorchModel

log = getLogger(__name__)

[docs]@register('torch_transformers_classifier') class TorchTransformersClassifierModel(TorchModel): """Bert-based model for text classification on PyTorch. It uses output from [CLS] token and predicts labels using linear transformation. Args: n_classes: number of classes pretrained_bert: pretrained Bert checkpoint path or key title (e.g. "bert-base-uncased") one_hot_labels: set True if one-hot encoding for labels is used multilabel: set True if it is multi-label classification return_probas: set True if return class probabilites instead of most probable label needed attention_probs_keep_prob: keep_prob for Bert self-attention layers hidden_keep_prob: keep_prob for Bert hidden layers optimizer: optimizer name from `torch.optim` optimizer_parameters: dictionary with optimizer's parameters, e.g. {'lr': 0.1, 'weight_decay': 0.001, 'momentum': 0.9} clip_norm: clip gradients by norm coefficient bert_config_file: path to Bert configuration file (not used if pretrained_bert is key title) is_binary: whether classification task is binary or multi-class num_special_tokens: number of special tokens used by classification model """ def __init__(self, n_classes, pretrained_bert, one_hot_labels: bool = False, multilabel: bool = False, return_probas: bool = False, attention_probs_keep_prob: Optional[float] = None, hidden_keep_prob: Optional[float] = None, optimizer: str = "AdamW", optimizer_parameters: Optional[dict] = None, clip_norm: Optional[float] = None, bert_config_file: Optional[str] = None, is_binary: Optional[bool] = False, num_special_tokens: int = None, **kwargs) -> None: if not optimizer_parameters: optimizer_parameters = {"lr": 1e-3, "weight_decay": 0.01, "betas": (0.9, 0.999), "eps": 1e-6}, self.return_probas = return_probas self.one_hot_labels = one_hot_labels self.multilabel = multilabel self.pretrained_bert = pretrained_bert self.bert_config_file = bert_config_file self.attention_probs_keep_prob = attention_probs_keep_prob self.hidden_keep_prob = hidden_keep_prob self.n_classes = n_classes self.clip_norm = clip_norm self.is_binary = is_binary self.bert_config = None self.num_special_tokens = num_special_tokens if self.multilabel and not self.one_hot_labels: raise RuntimeError('Use one-hot encoded labels for multilabel classification!') if self.multilabel and not self.return_probas: raise RuntimeError('Set return_probas to True for multilabel classification!') if self.return_probas and self.n_classes == 1: raise RuntimeError('Set return_probas to False for regression task!') super().__init__(optimizer=optimizer, optimizer_parameters=optimizer_parameters, **kwargs)
[docs] def train_on_batch(self, features: Dict[str, torch.tensor], y: Union[List[int], List[List[int]]]) -> Dict: """Train model on given batch. This method calls train_op using features and y (labels). Args: features: batch of InputFeatures y: batch of labels (class id or one-hot encoding) Returns: dict with loss and learning_rate values """ _input = {key: for key, value in features.items()} if self.n_classes > 1 and not self.is_binary: _input["labels"] = torch.from_numpy(np.array(y)).to(self.device) # regression else: _input["labels"] = torch.from_numpy(np.array(y, dtype=np.float32)).unsqueeze(1).to(self.device) self.optimizer.zero_grad() tokenized = {key: value for (key, value) in _input.items() if key in self.accepted_keys} loss = self.model(**tokenized).loss if self.is_data_parallel: loss = loss.mean() loss.backward() # Clip the norm of the gradients to 1.0. # This is to help prevent the "exploding gradients" problem. if self.clip_norm: torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clip_norm) self.optimizer.step() if self.lr_scheduler is not None: self.lr_scheduler.step() return {'loss': loss.item()}
[docs] def __call__(self, features: Dict[str, torch.tensor]) -> Union[List[int], List[List[float]]]: """Make prediction for given features (texts). Args: features: batch of InputFeatures Returns: predicted classes or probabilities of each class """ _input = {key: for key, value in features.items()} with torch.no_grad(): tokenized = {key: value for (key, value) in _input.items() if key in self.accepted_keys} # Forward pass, calculate logit predictions logits = self.model(**tokenized) logits = logits[0] if self.return_probas: if self.is_binary: pred = torch.sigmoid(logits).squeeze(1) elif not self.multilabel: pred = torch.nn.functional.softmax(logits, dim=-1) else: pred = torch.nn.functional.sigmoid(logits) pred = pred.detach().cpu().numpy() elif self.n_classes > 1: logits = logits.detach().cpu().numpy() pred = np.argmax(logits, axis=1) # regression else: pred = logits.squeeze(-1).detach().cpu().numpy() return pred
# TODO move to the super class @property def accepted_keys(self) -> Tuple[str]: if self.is_data_parallel: accepted_keys = self.model.module.forward.__code__.co_varnames else: accepted_keys = self.model.forward.__code__.co_varnames return accepted_keys # TODO move to the super class @property def is_data_parallel(self) -> bool: return isinstance(self.model, torch.nn.DataParallel) # TODO this method requires massive refactoring def load(self, fname=None): if fname is not None: self.load_path = fname if self.pretrained_bert: log.debug(f"From pretrained {self.pretrained_bert}.") config = AutoConfig.from_pretrained(self.pretrained_bert, # num_labels=self.n_classes, output_attentions=False, output_hidden_states=False) if self.is_binary: config.add_pooling_layer = False self.model = AutoModelForBinaryClassification(self.pretrained_bert, config) else: self.model = AutoModelForSequenceClassification.from_pretrained(self.pretrained_bert, config=config) # TODO need a better solution here and at # deeppavlov.models.torch_bert.torch_bert_ranker.TorchBertRankerModel.load try: hidden_size = self.model.classifier.out_proj.in_features if self.n_classes != self.model.num_labels: self.model.classifier.out_proj.weight = torch.nn.Parameter(torch.randn(self.n_classes, hidden_size)) self.model.classifier.out_proj.bias = torch.nn.Parameter(torch.randn(self.n_classes)) self.model.classifier.out_proj.out_features = self.n_classes self.model.num_labels = self.n_classes except AttributeError: hidden_size = self.model.classifier.in_features if self.n_classes != self.model.num_labels: self.model.classifier.weight = torch.nn.Parameter(torch.randn(self.n_classes, hidden_size)) self.model.classifier.bias = torch.nn.Parameter(torch.randn(self.n_classes)) self.model.classifier.out_features = self.n_classes self.model.num_labels = self.n_classes elif self.bert_config_file and Path(self.bert_config_file).is_file(): self.bert_config = AutoConfig.from_pretrained(str(expand_path(self.bert_config_file))) if self.attention_probs_keep_prob is not None: self.bert_config.attention_probs_dropout_prob = 1.0 - self.attention_probs_keep_prob if self.hidden_keep_prob is not None: self.bert_config.hidden_dropout_prob = 1.0 - self.hidden_keep_prob self.model = AutoModelForSequenceClassification.from_config(config=self.bert_config) else: raise ConfigError("No pre-trained BERT model is given.") tokenizer = AutoTokenizer.from_pretrained(self.pretrained_bert) if self.num_special_tokens: self.model.resize_token_embeddings(len(tokenizer) + self.num_special_tokens) # TODO that should probably be parametrized in config if self.device.type == "cuda" and torch.cuda.device_count() > 1: self.model = torch.nn.DataParallel(self.model) self.optimizer = getattr(torch.optim, self.optimizer_name)( self.model.parameters(), **self.optimizer_parameters) if self.lr_scheduler_name is not None: self.lr_scheduler = getattr(torch.optim.lr_scheduler, self.lr_scheduler_name)( self.optimizer, **self.lr_scheduler_parameters) super().load()
class AutoModelForBinaryClassification(torch.nn.Module): def __init__(self, pretrained_bert, config): super().__init__() self.pretrained_bert = pretrained_bert self.config = config self.model = AutoModel.from_pretrained(self.pretrained_bert, self.config) self.classifier = BinaryClassificationHead(config) self.classifier.init_weights() def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None): return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.model(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) sequence_output = outputs[0] logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions) class BinaryClassificationHead(torch.nn.Module): def __init__(self, config): super().__init__() self.config = config self.dense = torch.nn.Linear(config.hidden_size, config.hidden_size) self.dropout = torch.nn.Dropout(config.hidden_dropout_prob) self.out_proj = torch.nn.Linear(config.hidden_size, 1) def init_weights(self):, std=self.config.initializer_range) if self.dense.bias is not None: def forward(self, features, **kwargs): x = features[:, 0, :] x = self.dropout(x) x = self.dense(x) x = torch.tanh(x) x = self.dropout(x) x = self.out_proj(x) return x