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bart.py
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# SPDX-FileCopyrightText: 2022 Idiap Research Institute
#
# SPDX-License-Identifier: MIT
""" BART seq2seq model. """
from argparse import ArgumentParser
from pytorch_lightning.core import LightningModule
from torch.optim import Adam
from torch.optim.lr_scheduler import OneCycleLR
from transformers import BartTokenizer, BartForConditionalGeneration, AddedToken
from data_schema import SchemaFactory
from dataloader import BartBatch
from rouge import RougeScorer, RougeAggregator
class BartSummarizer(LightningModule):
def __init__(self, args):
super().__init__()
self.save_hyperparameters(args)
self.annotation_schema = SchemaFactory.get_schema(args.dataset)
special_tokens = self.annotation_schema.get_special_text_tokens()
special_tokens = [AddedToken(t) for t in special_tokens]
self.tokenizer = BartTokenizer.from_pretrained(
args.model_name_or_path, additional_special_tokens=special_tokens,
)
self.model = BartForConditionalGeneration.from_pretrained(args.model_name_or_path)
self.model.resize_token_embeddings(len(self.tokenizer)) # extend embedding matrices for special tokens
@staticmethod
def add_model_specific_args(parent_parser):
parser = ArgumentParser(parents=[parent_parser], add_help=False)
parser.add_argument('--dataset', default='fomc', help='Dataset name')
parser.add_argument('--model_name_or_path', default='facebook/bart-large', help='Path to pretrained model')
# optimization args
parser.add_argument('--max_lr_enc', type=float, default=1e-5, help='Maximum learning rate for encoder params')
parser.add_argument('--max_lr_dec', type=float, default=1e-5, help='Maximum learning rate for decoder params')
parser.add_argument('--max_lr_lm_head', type=float, default=1e-6, help='Maximum learning rate for LM head')
parser.add_argument('--warmup', type=float, default=0.1, help='Fraction of training spent in warmup')
parser.add_argument('--lr_anneal', default='linear', choices=['linear', 'cos'],
help='Annealing of learning rate')
parser.add_argument('--div_warmup', type=float, default=100,
help='Divide max learning rate by this for initial learning rate')
parser.add_argument('--div_final', type=float, default=100,
help='Divide max learning rate by this for final learning rate')
# generation args
parser.add_argument('--max_length', type=int, default=500, help='Max generation length')
parser.add_argument('--min_length', type=int, default=100, help='Min generation length')
parser.add_argument('--length_penalty', type=float, default=0.95, help='Alpha for length penalty')
parser.add_argument('--val_log_summaries', type=int, default=5, help='Number of logged summaries in validation')
return parser
def forward(self, batch: BartBatch):
labels = batch.tgt.clone().detach()
labels = labels[:, 1:].contiguous() # remove decoder start token (EOS token for BART)
labels[labels == self.tokenizer.pad_token_id] = -100 # set padded tokens to -100 to ignore in LM loss
decoder_outputs = self.model(
input_ids=batch.src,
attention_mask=batch.mask_src,
decoder_input_ids=batch.tgt[:, :-1],
decoder_attention_mask=batch.mask_tgt[:, :-1],
labels=labels,
return_dict=True,
)
return decoder_outputs
def configure_optimizers(self):
# remove shared embedding matrix (used in LM head and can't have the same params in different param groups)
encoder_params = (p for n, p in self.model.model.encoder.named_parameters() if n != 'embed_tokens.weight')
decoder_params = (p for n, p in self.model.model.decoder.named_parameters() if n != 'embed_tokens.weight')
optimizer = Adam([
{'params': encoder_params},
{'params': decoder_params},
{'params': self.model.lm_head.parameters()},
])
scheduler = OneCycleLR(
optimizer=optimizer,
max_lr=[self.hparams.max_lr_enc, self.hparams.max_lr_dec, self.hparams.max_lr_lm_head],
total_steps=self.hparams.max_steps,
pct_start=self.hparams.warmup,
anneal_strategy=self.hparams.lr_anneal,
cycle_momentum=False,
div_factor=self.hparams.div_warmup,
final_div_factor=self.hparams.div_final,
last_epoch=-1,
)
return [optimizer], [{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}]
def training_step(self, batch, batch_idx):
seq2seq_lm_output = self(batch)
loss = seq2seq_lm_output.loss
self.log('train_loss', loss)
return loss
def decode(self, output_ids):
""" Decode BPE tokens into text. """
text = self.tokenizer.decode(output_ids, spaces_between_special_tokens=False)
stst_start = self.annotation_schema.mapping['standardized sentence']['text_start']
stst_end = self.annotation_schema.mapping['standardized sentence']['text_end']
text = text.replace(f'{stst_end} {stst_start}', f'{stst_end}<sent>{stst_start}')
text = text.replace(self.tokenizer.bos_token, '')
text = text.replace(self.tokenizer.eos_token, '')
text = ' '.join(text.split())
return text
def compute_rouge(self, beam_output, targets):
scorer = RougeScorer()
scores = []
for output, target in zip(beam_output, targets):
candidate = self.decode(output)
reference = self.decode(target)
scores.append(scorer.compute_rouge_score(candidate, reference, sentence_sep='<sent>'))
return scores
def _shared_eval(self, batch, batch_idx, prefix):
# decode with beam search
beam_output = self.model.generate(
input_ids=batch.src,
attention_mask=batch.mask_src,
max_length=self.hparams.max_length,
min_length=self.hparams.min_length,
no_repeat_ngram_size=3,
length_penalty=self.hparams.length_penalty,
num_beams=5,
)
# if validating, log first N summaries
if prefix == 'val' and hasattr(self.hparams, 'val_log_summaries') and batch_idx < self.hparams.val_log_summaries:
self.logger.experiment.add_text(f'val_summary_{batch_idx}', self.decode(beam_output[0]), self.global_step)
# if testing, write results out
if prefix == 'test':
self.write_output(beam_output, batch.tgt)
# in validation, compute loss
if prefix == 'val':
labels = batch.tgt.clone().detach()
labels = labels[:, 1:].contiguous() # remove decoder start token (EOS token for BART)
labels[labels == self.tokenizer.pad_token_id] = -100 # set padded tokens to -100 to ignore in LM loss
decoder_outputs = self.model(
input_ids=batch.src,
attention_mask=batch.mask_src,
decoder_input_ids=batch.tgt[:, :-1],
decoder_attention_mask=batch.mask_tgt[:, :-1],
labels=labels,
return_dict=True,
)
val_loss = decoder_outputs.loss.item()
self.log('val_loss', val_loss)
# compute ROUGE scores
return self.compute_rouge(beam_output, batch.tgt)
def write_output(self, beam_output, targets):
import os
for output, target in zip(beam_output, targets):
candidate = self.decode(output)
reference = self.decode(target)
with open(os.path.join(self.output_dir, 'candidates.txt'), 'a') as f:
f.write(candidate + '\n')
with open(os.path.join(self.output_dir, 'references.txt'), 'a') as f:
f.write(reference + '\n')
def validation_step(self, batch, batch_idx):
return self._shared_eval(batch, batch_idx, 'val')
def test_step(self, batch, batch_idx):
return self._shared_eval(batch, batch_idx, 'test')
def validation_epoch_end(self, val_outputs):
""" Aggregates ROUGE scores. """
# flatten list of lists
scores = []
for val_output in val_outputs:
scores.extend(val_output)
# aggregate ROUGE scores
aggregator = RougeAggregator()
for score in scores:
aggregator.add_scores(score)
rouge1, rouge2, rougeL = tuple(map(lambda x: x * 100, aggregator.get_rouge_scores()))
self.log('val_rouge1', rouge1)
self.log('val_rouge2', rouge2)
self.log('val_rougeL', rougeL)
self.log('val_rouge', (rouge1 + rouge2 + rougeL) / 3)