Create BertNestedTrainer.py

Nested/trainers/BertNestedTrainer.py

@@ -0,0 +1,203 @@
+import os
+import logging
+import torch
+import numpy as np
+from Nested.trainers import BaseTrainer
+from Nested.utils.metrics import compute_nested_metrics
+
+logger = logging.getLogger(__name__)
+
+
+class BertNestedTrainer(BaseTrainer):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def train(self):
+        best_val_loss, test_loss = np.inf, np.inf
+        num_train_batch = len(self.train_dataloader)
+        num_labels = [len(v) for v in self.train_dataloader.dataset.vocab.tags[1:]]
+        patience = self.patience
+
+        for epoch_index in range(self.max_epochs):
+            self.current_epoch = epoch_index
+            train_loss = 0
+
+            for batch_index, (subwords, gold_tags, tokens, valid_len, logits) in enumerate(self.tag(
+                self.train_dataloader, is_train=True
+            ), 1):
+                self.current_timestep += 1
+
+                # Compute loses for each output
+                # logits = B x T x L x C
+                losses = [self.loss(logits[:, :, i, 0:l].view(-1, logits[:, :, i, 0:l].shape[-1]),
+                                    torch.reshape(gold_tags[:, i, :], (-1,)).long())
+                          for i, l in enumerate(num_labels)]
+
+                torch.autograd.backward(losses)
+
+                # Avoid exploding gradient by doing gradient clipping
+                torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clip)
+
+                self.optimizer.step()
+                self.scheduler.step()
+                batch_loss = sum(l.item() for l in losses)
+                train_loss += batch_loss
+
+                if self.current_timestep % self.log_interval == 0:
+                    logger.info(
+                        "Epoch %d | Batch %d/%d | Timestep %d | LR %.10f | Loss %f",
+                        epoch_index,
+                        batch_index,
+                        num_train_batch,
+                        self.current_timestep,
+                        self.optimizer.param_groups[0]['lr'],
+                        batch_loss
+                    )
+
+            train_loss /= num_train_batch
+
+            logger.info("** Evaluating on validation dataset **")
+            val_preds, segments, valid_len, val_loss = self.eval(self.val_dataloader)
+            val_metrics = compute_nested_metrics(segments, self.val_dataloader.dataset.transform.vocab.tags[1:])
+
+            epoch_summary_loss = {
+                "train_loss": train_loss,
+                "val_loss": val_loss
+            }
+            epoch_summary_metrics = {
+                "val_micro_f1": val_metrics.micro_f1,
+                "val_precision": val_metrics.precision,
+                "val_recall": val_metrics.recall
+            }
+
+            logger.info(
+                "Epoch %d | Timestep %d | Train Loss %f | Val Loss %f | F1 %f",
+                epoch_index,
+                self.current_timestep,
+                train_loss,
+                val_loss,
+                val_metrics.micro_f1
+            )
+
+            if val_loss < best_val_loss:
+                patience = self.patience
+                best_val_loss = val_loss
+                logger.info("** Validation improved, evaluating test data **")
+                test_preds, segments, valid_len, test_loss = self.eval(self.test_dataloader)
+                self.segments_to_file(segments, os.path.join(self.output_path, "predictions.txt"))
+                test_metrics = compute_nested_metrics(segments, self.test_dataloader.dataset.transform.vocab.tags[1:])
+
+                epoch_summary_loss["test_loss"] = test_loss
+                epoch_summary_metrics["test_micro_f1"] = test_metrics.micro_f1
+                epoch_summary_metrics["test_precision"] = test_metrics.precision
+                epoch_summary_metrics["test_recall"] = test_metrics.recall
+
+                logger.info(
+                    f"Epoch %d | Timestep %d | Test Loss %f | F1 %f",
+                    epoch_index,
+                    self.current_timestep,
+                    test_loss,
+                    test_metrics.micro_f1
+                )
+
+                self.save()
+            else:
+                patience -= 1
+
+            # No improvements, terminating early
+            if patience == 0:
+                logger.info("Early termination triggered")
+                break
+
+            self.summary_writer.add_scalars("Loss", epoch_summary_loss, global_step=self.current_timestep)
+            self.summary_writer.add_scalars("Metrics", epoch_summary_metrics, global_step=self.current_timestep)
+
+    def tag(self, dataloader, is_train=True):
+        """
+        Given a dataloader containing segments, predict the tags
+        :param dataloader: torch.utils.data.DataLoader
+        :param is_train: boolean - True for training model, False for evaluation
+        :return: Iterator
+                    subwords (B x T x NUM_LABELS)- torch.Tensor - BERT subword ID
+                    gold_tags (B x T x NUM_LABELS) - torch.Tensor - ground truth tags IDs
+                    tokens - List[Nested.data.dataset.Token] - list of tokens
+                    valid_len (B x 1) - int - valiud length of each sequence
+                    logits (B x T x NUM_LABELS) - logits for each token and each tag
+        """
+        for subwords, gold_tags, tokens, mask, valid_len in dataloader:
+            self.model.train(is_train)
+
+            if torch.cuda.is_available():
+                subwords = subwords.cuda()
+                gold_tags = gold_tags.cuda()
+
+            if is_train:
+                self.optimizer.zero_grad()
+                logits = self.model(subwords)
+            else:
+                with torch.no_grad():
+                    logits = self.model(subwords)
+
+            yield subwords, gold_tags, tokens, valid_len, logits
+
+    def eval(self, dataloader):
+        golds, preds, segments, valid_lens = list(), list(), list(), list()
+        num_labels = [len(v) for v in dataloader.dataset.vocab.tags[1:]]
+        loss = 0
+
+        for _, gold_tags, tokens, valid_len, logits in self.tag(
+            dataloader, is_train=False
+        ):
+            losses = [self.loss(logits[:, :, i, 0:l].view(-1, logits[:, :, i, 0:l].shape[-1]),
+                                torch.reshape(gold_tags[:, i, :], (-1,)).long())
+                      for i, l in enumerate(num_labels)]
+            loss += sum(losses)
+            preds += torch.argmax(logits, dim=3)
+            segments += tokens
+            valid_lens += list(valid_len)
+
+        loss /= len(dataloader)
+
+        # Update segments, attach predicted tags to each token
+        segments = self.to_segments(segments, preds, valid_lens, dataloader.dataset.vocab)
+
+        return preds, segments, valid_lens, loss
+
+    def infer(self, dataloader):
+        golds, preds, segments, valid_lens = list(), list(), list(), list()
+
+        for _, gold_tags, tokens, valid_len, logits in self.tag(
+            dataloader, is_train=False
+        ):
+            preds += torch.argmax(logits, dim=3)
+            segments += tokens
+            valid_lens += list(valid_len)
+
+        segments = self.to_segments(segments, preds, valid_lens, dataloader.dataset.vocab)
+        return segments
+
+    def to_segments(self, segments, preds, valid_lens, vocab):
+        if vocab is None:
+            vocab = self.vocab
+
+        tagged_segments = list()
+        tokens_stoi = vocab.tokens.get_stoi()
+        unk_id = tokens_stoi["UNK"]
+
+        for segment, pred, valid_len in zip(segments, preds, valid_lens):
+            # First, the token at 0th index [CLS] and token at nth index [SEP]
+            # Combine the tokens with their corresponding predictions
+            segment_pred = zip(segment[1:valid_len-1], pred[1:valid_len-1])
+
+            # Ignore the sub-tokens/subwords, which are identified with text being UNK
+            segment_pred = list(filter(lambda t: tokens_stoi[t[0].text] != unk_id, segment_pred))
+
+            # Attach the predicted tags to each token
+            list(map(lambda t: setattr(t[0], 'pred_tag', [{"tag": vocab.get_itos()[tag_id]}
+                                                     for tag_id, vocab in zip(t[1].int().tolist(), vocab.tags[1:])]), segment_pred))
+
+            # We are only interested in the tagged tokens, we do no longer need raw model predictions
+            tagged_segment = [t for t, _ in segment_pred]
+            tagged_segments.append(tagged_segment)
+
+        return tagged_segments